diff --git a/.coverage b/.coverage new file mode 100644 index 0000000..6fcd1f9 Binary files /dev/null and b/.coverage differ diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..a21080b --- /dev/null +++ b/.coveragerc @@ -0,0 +1,10 @@ +[run] +include = + src/backtest/results_cache.py + src/data/cache.py + src/utils/http.py + src/reporting/html.py + +[report] +exclude_lines = + pragma: no cover diff --git a/.dockerignore b/.dockerignore index cb66c2e..36a9ab6 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,111 +1,17 @@ -# Git -.git -.gitignore -.gitattributes - -# Docker -Dockerfile* -docker-compose* -.dockerignore - -# Python __pycache__/ *.py[cod] -*$py.class -*.so -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# Virtual environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# IDEs -.vscode/ -.idea/ -*.swp -*.swo -*~ - -# OS -.DS_Store -.DS_Store? -._* -.Spotlight-V100 -.Trashes -ehthumbs.db -Thumbs.db - -# Project specific -cache/ -exports/ -reports_output/ -logs/ -*.log -htmlcov/ -.coverage -coverage.xml .pytest_cache/ .mypy_cache/ .ruff_cache/ - -# Build-context trimming (not needed inside the image) -artifacts/ -quant-strategies/ -tests/ - -# Temporary files +.cache/ +reports/ +.venv/ +.env +http_cache.sqlite +.DS_Store +*.swp *.tmp -*.temp -temp/ -tmp/ - -# Documentation -docs/_build/ -site/ - -# Node.js (if any frontend) -node_modules/ -npm-debug.log* -yarn-debug.log* -yarn-error.log* - -# Jupyter -.ipynb_checkpoints/ -*.ipynb - -# Large data files -*.csv -*.json -*.pkl -*.pickle -*.h5 -*.hdf5 -*.parquet - -# API keys and secrets -.env.local -.env.*.local -secrets/ -*.key -*.pem +.vscode/ +.idea/ +.git +.gitignore diff --git a/.env.example b/.env.example index acc07c1..d786f43 100644 --- a/.env.example +++ b/.env.example @@ -1,106 +1,36 @@ -# Quant Trading System Environment Configuration -# Copy this file to .env and fill in your values +# Copy this file to `.env` and fill in as needed. -# ====================== -# DATA SOURCE API KEYS -# ====================== -# Get free keys from respective providers - -# Alpha Vantage (primary data source) -# Get free key at: https://www.alphavantage.co/support/#api-key -ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key_here - -# Twelve Data (financial data) -# Get free key at: https://twelvedata.com/pricing -TWELVE_DATA_API_KEY=your_twelve_data_key_here - -# Polygon.io (market data) -# Get key at: https://polygon.io/ -POLYGON_API_KEY=your_polygon_key_here - -# Tiingo (financial data) -# Get free key at: https://api.tiingo.com/ -TIINGO_API_KEY=your_tiingo_key_here - -# Finnhub (financial data) -# Get free key at: https://finnhub.io/ -FINNHUB_API_KEY=your_finnhub_key_here - -# Bybit (crypto data - optional) -BYBIT_API_KEY=your_bybit_key_here -BYBIT_API_SECRET=your_bybit_secret_here -BYBIT_TESTNET=false - -# ====================== -# SYSTEM CONFIGURATION -# ====================== -# Basic system settings - -# Environment: development, testing, production -ENVIRONMENT=development - -# Logging level: DEBUG, INFO, WARNING, ERROR +# Common LOG_LEVEL=INFO +DATA_CACHE_DIR=/app/.cache/data +STRATEGIES_PATH=/ext/strategies -# ====================== -# DATABASE CONFIGURATION -# ====================== -# PostgreSQL (primary storage) - -# PostgreSQL Database Settings -POSTGRES_DB=quant_system -POSTGRES_USER=quantuser -POSTGRES_PASSWORD=quantpass - -# PostgreSQL connection (adjust host for local vs Docker) -# For local development: postgresql://quantuser:quantpass@localhost:5432/quant_system -# For Docker containers: postgresql://quantuser:quantpass@postgres:5432/quant_system -DATABASE_URL=postgresql://quantuser:quantpass@postgres:5432/quant_system - -# pgAdmin Configuration -PGADMIN_DEFAULT_EMAIL=admin@quant.local -PGADMIN_DEFAULT_PASSWORD=quantpass -PGADMIN_CONFIG_SERVER_MODE=False - -# Optional Redis for recent overlay cache -# To enable Redis service by default in Compose: export COMPOSE_PROFILES=redis -USE_REDIS_RECENT=false -REDIS_URL=redis://redis:6379/0 +# Set this to your local strategies repo path (absolute path recommended; ~ is not expanded) +# Example (macOS/Linux): /Users/you/code/your-strategies/algorithms/python +# Example (Windows): C:\\Users\\you\\code\\your-strategies\\algorithms\\python +HOST_STRATEGIES_PATH= -# Cache directory (for temporary files) -CACHE_DIR=./cache +# Polygon +POLYGON_API_KEY= -# ====================== -# STRATEGIES (EXTERNAL) -# ====================== -# Absolute host path to your external strategies directory (algorithms/python) -# Example (macOS): /Users//Documents/Websites/Private/quant/quant-strategies/algorithms/python -# This is mounted into the container at /app/external_strategies -STRATEGIES_HOST_PATH=/absolute/path/to/quant-strategies/algorithms/python +# Tiingo +TIINGO_API_KEY= -# ====================== -# DATA SOURCE TUNING -# ====================== -# Prefer Bybit/Twelve for crypto and optionally disable legacy sources to reduce noise -# Set to 'true' to disable these sources for crypto symbols -DISABLE_YAHOO_CRYPTO=false -DISABLE_AV_CRYPTO=false +# Alpaca +ALPACA_API_KEY_ID= +ALPACA_API_SECRET_KEY= -# Twelve Data: prefer a specific exchange for crypto symbols (e.g., BINANCE, COINBASE, BYBIT) -# Example: IMXUSDT -> IMX/USDT:BINANCE -TWELVE_DATA_CRYPTO_EXCHANGE= +# Finnhub +FINNHUB_API_KEY= -# ====================== -# AI/LLM CONFIGURATION -# ====================== -# AI model configuration for investment recommendations +# Twelve Data +TWELVEDATA_API_KEY= -# OpenAI Configuration (primary) -# Get API key at: https://platform.openai.com/api-keys -OPENAI_API_KEY=your_openai_api_key_here -OPENAI_MODEL=gpt-5-mini +# Alpha Vantage +ALPHAVANTAGE_API_KEY= -# Anthropic Configuration (alternative) -# Get API key at: https://console.anthropic.com/ -ANTHROPIC_API_KEY=your_anthropic_api_key_here -ANTHROPIC_MODEL=claude-3-5-sonnet-20241022 +# CCXT (if using authenticated endpoints, optional) +BINANCE_API_KEY= +BINANCE_API_SECRET= +BYBIT_API_KEY= +BYBIT_API_SECRET= diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 8b00e03..6483870 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,20 +1,4 @@ -# Global owners -* @LouisLetcher - -# Core system -/src/core/ @LouisLetcher - -# Strategy algorithms -/src/backtesting_engine/algorithms/ @LouisLetcher - -# CI/CD and workflows -/.github/ @LouisLetcher - -# Documentation -/docs/ @LouisLetcher - -# Configuration -/config/ @LouisLetcher - -# Scripts -/scripts/ @LouisLetcher +# See GOVERNANCE.md for details +* @manuelheck +.github/ @manuelheck +src/ @manuelheck diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md deleted file mode 100644 index 01a2934..0000000 --- a/.github/CONTRIBUTING.md +++ /dev/null @@ -1,65 +0,0 @@ -# Contributing to Quant Trading System - -Thank you for your interest in contributing! This project follows the KISS principle (Keep It Simple, Stupid). - -## Quick Start - -1. Fork the repository -2. Create a feature branch: `git checkout -b feature/your-feature` -3. Make your changes -4. Run tests: `poetry run pytest` -5. Submit a pull request - -## Development Setup - -```bash -# Clone your fork -git clone https://github.com/your-username/quant-system.git -cd quant-system - -# Install dependencies -poetry install --with dev - -# Activate environment -poetry shell - -# Run tests -pytest -``` - -## Code Style - -- Follow PEP 8 -- Use type hints -- Keep functions simple and focused -- Add docstrings for public functions -- Run `black .` and `ruff check .` before committing - -## Testing - -- Write tests for new features -- Maintain test coverage above 80% -- Use descriptive test names -- Mock external dependencies - -## Submitting Changes - -1. Ensure all tests pass -2. Update documentation if needed -3. Follow the pull request template -4. Keep commits focused and atomic -5. Write clear commit messages - -## Questions? - -- Check the documentation first -- Search existing issues -- Start a discussion -- Create an issue with the appropriate template - -## Code of Conduct - -- Be respectful and inclusive -- Focus on the code, not the person -- Help others learn and grow -- Keep discussions constructive diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index ff506f6..2c7c8c1 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,29 +1,25 @@ --- -name: Bug Report -about: Report a bug to help us improve -title: '[BUG] ' -labels: ['bug'] -assignees: '' +name: Bug report +about: Create a report to help us improve +labels: bug --- -## Bug Description -A clear description of what the bug is. +## Describe the bug +A clear and concise description of what the bug is. -## Steps to Reproduce -1. Step 1 -2. Step 2 -3. Step 3 +## To Reproduce +Steps to reproduce the behavior: -## Expected Behavior -What you expected to happen. +1. Command(s) run +2. Config used +3. What happened -## Actual Behavior -What actually happened. +## Expected behavior +What you expected to happen. ## Environment -- Python version: -- OS: -- Portfolio/Strategy: +- Docker or local Python version +- OS/arch -## Additional Context -Any other context about the problem. +## Additional context +Logs, stack traces, screenshots (redact secrets). diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml deleted file mode 100644 index 8c6f3d9..0000000 --- a/.github/ISSUE_TEMPLATE/config.yml +++ /dev/null @@ -1,8 +0,0 @@ -blank_issues_enabled: true -contact_links: - - name: Documentation - url: https://github.com/LouisLetcher/quant-system/tree/main/docs - about: Check the documentation first - - name: Discussions - url: https://github.com/LouisLetcher/quant-system/discussions - about: Ask questions and discuss ideas diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 986dc92..9168220 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,22 +1,17 @@ --- -name: Feature Request -about: Suggest a new feature or improvement -title: '[FEATURE] ' -labels: ['enhancement'] -assignees: '' +name: Feature request +about: Suggest an idea or enhancement +labels: enhancement --- -## Feature Description -A clear description of what you want to happen. +## Summary +What problem does this solve or what value does it add? -## Problem it Solves -What problem does this feature solve? +## Proposed solution +High-level approach (keep it simple if possible). -## Proposed Solution -How do you think this should work? +## Alternatives considered +Any other options you evaluated. -## Alternatives Considered -Any alternative solutions you've considered. - -## Additional Context -Any other context or screenshots about the feature request. +## Additional context +Links, prior art, references. diff --git a/.github/ISSUE_TEMPLATE/strategy_request.md b/.github/ISSUE_TEMPLATE/strategy_request.md deleted file mode 100644 index 0f475be..0000000 --- a/.github/ISSUE_TEMPLATE/strategy_request.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -name: Strategy Request -about: Request a new trading strategy implementation -title: '[STRATEGY] ' -labels: ['strategy', 'enhancement'] -assignees: '' ---- - -## Strategy Name -Name of the trading strategy. - -## Strategy Description -Describe how the strategy works. - -## Entry/Exit Rules -- **Buy signals:** -- **Sell signals:** - -## Parameters -List any configurable parameters. - -## References -Links to papers, articles, or documentation about this strategy. - -## Additional Context -Any other relevant information. diff --git a/.github/SECURITY.md b/.github/SECURITY.md deleted file mode 100644 index f3478a8..0000000 --- a/.github/SECURITY.md +++ /dev/null @@ -1,63 +0,0 @@ -# Security Policy - -## Supported Versions - -We currently support the following versions with security updates: - -| Version | Supported | -| ------- | ------------------ | -| main | :white_check_mark: | -| < 1.0 | :x: | - -## Reporting a Vulnerability - -**Please do not report security vulnerabilities through public GitHub issues.** - -Instead, please report them via: - -1. **Email**: Send details to the repository maintainers -2. **GitHub Security Advisories**: Use the "Security" tab to report privately - -## What to Include - -When reporting a vulnerability, please include: - -- Description of the vulnerability -- Steps to reproduce -- Potential impact -- Suggested fix (if known) - -## Response Timeline - -- **Initial response**: Within 48 hours -- **Status update**: Within 7 days -- **Fix timeline**: Depends on severity - -## Security Best Practices - -### For Contributors -- Never commit API keys or secrets -- Use environment variables for sensitive data -- Follow secure coding practices -- Run security scans before submitting PRs - -### For Users -- Keep dependencies updated -- Use secure API key storage -- Monitor for security advisories -- Report suspicious behavior - -## Dependencies - -We use automated tools to monitor dependencies: - -- **Dependabot**: Automatic dependency updates -- **Safety**: Python security vulnerability scanning -- **Bandit**: Static security analysis - -## Disclosure Policy - -- We follow responsible disclosure -- Security issues are prioritized -- Public disclosure after fix is available -- Credit given to security researchers diff --git a/.github/dependabot.yml b/.github/dependabot.yml index ab95fef..2ce0950 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,13 +1,14 @@ version: 2 updates: - # Python dependencies - - package-ecosystem: "pip" + - package-ecosystem: "github-actions" directory: "/" schedule: interval: "weekly" - - # GitHub Actions - - package-ecosystem: "github-actions" + labels: ["dependencies", "ci"] + - package-ecosystem: "pip" directory: "/" schedule: - interval: "monthly" + interval: "weekly" + allow: + - dependency-type: "all" + labels: ["dependencies", "python"] diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 0603921..e20c0b4 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,31 +1,26 @@ -# Pull Request - -## Description -Brief description of what this PR does. - -## Type of Change -- [ ] Bug fix -- [ ] New feature -- [ ] Breaking change -- [ ] Documentation update -- [ ] Performance improvement -- [ ] Code refactoring - -## Changes Made -- Change 1 -- Change 2 -- Change 3 - -## Testing -- [ ] Tests pass locally -- [ ] New tests added (if applicable) -- [ ] Manual testing completed - -## Checklist -- [ ] Code follows project style guidelines -- [ ] Self-review completed -- [ ] Documentation updated (if needed) -- [ ] No breaking changes (or documented) - -## Related Issues -Fixes #(issue number) +## Summary + +Briefly describe the change and the motivation. + +## Changes + +- What was added/changed? +- Any breaking changes? + +## How to Test + +- Commands or steps to validate locally (Docker/Poetry) +- Relevant config or environment variables + +## Checklist (KISS) + +- [ ] Pre-commit passes locally (`pre-commit run --all-files`) +- [ ] Tests added/updated where it makes sense (80% cov gate) +- [ ] Docs/README updated if needed +- [ ] No secrets committed; `.env` values are excluded +- [ ] Backward compatibility considered (configs, CLI flags) + +## Related Issues/Links + +- Closes # +- References # diff --git a/.github/release.yml b/.github/release.yml new file mode 100644 index 0000000..c40a4d3 --- /dev/null +++ b/.github/release.yml @@ -0,0 +1,19 @@ +changelog: + categories: + - title: Breaking Changes + labels: + - breaking-change + - title: Features + labels: + - feature + - enhancement + - title: Bug Fixes + labels: + - bug + - fix + - title: Maintenance + labels: + - chore + - docs + - ci + - refactor diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6e8df41..960952b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,59 +1,54 @@ name: CI - permissions: contents: read on: push: - branches: [ main ] + branches: [ main, master ] pull_request: - branches: [ main ] + branches: [ main, master ] jobs: - ci: - name: Code Quality & Tests + lint: runs-on: ubuntu-latest - env: - UNIFIED_MODELS_SQLITE: "1" # Force SQLite for unified models during CI - TESTING: "true" # Enable test-mode code paths - DATABASE_URL: "sqlite:///quant_unified_test.db" # Safety: default DB points to SQLite steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Setup Python - uses: actions/setup-python@v5 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: - python-version: "3.12" - + python-version: '3.10' - name: Install Poetry - uses: snok/install-poetry@v1 - - - name: Install dependencies - run: poetry install - - - name: Format check - run: poetry run ruff format --check . - - - name: Lint & Import sort - run: poetry run ruff check . - - - name: Lint Markdown - uses: DavidAnson/markdownlint-cli2-action@v20 - with: - globs: '**/*.md !.venv/**/*.md !node_modules/**/*.md !exports/**/*.md' - - - name: Test with Coverage run: | - poetry run pytest --cov=src --cov-report=xml --cov-report=term-missing --cov-fail-under=10 - echo "Coverage report generated" - - - name: Upload Coverage to Codecov - if: success() - uses: codecov/codecov-action@v4 + pip install poetry==1.8.3 + - name: Install deps + run: | + poetry install --no-root + - name: Ruff lint + run: | + poetry run ruff check . + - name: Pre-commit + run: | + poetry run pre-commit run --all-files --show-diff-on-failure + - name: Markdownlint + run: | + docker run --rm -v "$PWD":/workdir -w /workdir ghcr.io/igorshubovych/markdownlint-cli:latest \ + markdownlint -c .markdownlint.json '**/*.md' + tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: - file: ./coverage.xml - fail_ci_if_error: false - - - name: Build - run: poetry build + python-version: '3.10' + - name: Install Poetry + run: | + pip install poetry==1.8.3 + - name: Install deps + run: | + poetry install --no-root + - name: Run tests with coverage (if present) + run: | + if compgen -G "tests/*.py" > /dev/null; then \ + poetry run pytest -q --maxfail=1 --disable-warnings --cov=src.backtest.results_cache --cov=src.data.cache --cov=src.utils.http --cov=src.reporting.html --cov-report=term-missing --cov-fail-under=80; \ + else \ + echo "No tests present; skipping"; \ + fi diff --git a/.github/workflows/daily-backtest.yml b/.github/workflows/daily-backtest.yml new file mode 100644 index 0000000..d97ec3c --- /dev/null +++ b/.github/workflows/daily-backtest.yml @@ -0,0 +1,52 @@ +name: Daily Backtest +permissions: + contents: read + +on: + schedule: + - cron: '0 5 * * *' # 05:00 UTC daily + workflow_dispatch: + +jobs: + run: + runs-on: ubuntu-latest + env: + STRATEGIES_DIR: strategies + steps: + - name: Checkout quant-system + uses: actions/checkout@v4 + + - name: Checkout strategies repo + if: ${{ secrets.STRATEGIES_REPO != '' && secrets.GH_TOKEN != '' }} + uses: actions/checkout@v4 + with: + repository: ${{ secrets.STRATEGIES_REPO }} + token: ${{ secrets.GH_TOKEN }} + path: strategies + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Install Poetry + run: pip install poetry==1.8.3 + + - name: Install deps + run: poetry install --no-root + + - name: Run backtests (all collections) + env: + POLYGON_API_KEY: ${{ secrets.POLYGON_API_KEY }} + TIINGO_API_KEY: ${{ secrets.TIINGO_API_KEY }} + ALPACA_API_KEY_ID: ${{ secrets.ALPACA_API_KEY_ID }} + ALPACA_API_SECRET_KEY: ${{ secrets.ALPACA_API_SECRET_KEY }} + run: | + mkdir -p reports + poetry run python -m src.main run --config config/example.yaml --strategies-path "${{ env.STRATEGIES_DIR }}" + + - name: Upload reports artifact + uses: actions/upload-artifact@v4 + with: + name: backtest-reports + path: reports/** diff --git a/.github/workflows/gitleaks.yml b/.github/workflows/gitleaks.yml new file mode 100644 index 0000000..339fe81 --- /dev/null +++ b/.github/workflows/gitleaks.yml @@ -0,0 +1,30 @@ +name: Secret Scan (Gitleaks) + +on: + push: + branches: [ main, master ] + pull_request: + branches: [ main, master ] + +jobs: + gitleaks: + runs-on: ubuntu-latest + permissions: + contents: read + security-events: write + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Run Gitleaks + uses: gitleaks/gitleaks-action@v2 + env: + GITLEAKS_LICENSE: ${{ secrets.GITLEAKS_LICENSE }} + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + args: detect --source . --no-banner --redact --report-format sarif --report-path gitleaks.sarif --exit-code 0 + - name: Upload SARIF to GitHub + if: ${{ hashFiles('gitleaks.sarif') != '' }} + uses: github/codeql-action/upload-sarif@v3 + with: + sarif_file: gitleaks.sarif diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 27a3ce0..ddcd52b 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1,8 +1,5 @@ name: Release -permissions: - contents: read - on: push: tags: @@ -10,39 +7,36 @@ on: jobs: release: - name: Create Release runs-on: ubuntu-latest permissions: contents: write + packages: write steps: - name: Checkout uses: actions/checkout@v4 - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - - name: Install Poetry - uses: snok/install-poetry@v1 - - - name: Build package - run: poetry build - - - name: Create Release + - name: Create GitHub Release + id: create_release uses: softprops/action-gh-release@v2 with: - files: dist/* generate_release_notes: true - body: | - ## Quant Trading System Release - **Installation:** - ```bash - pip install ./dist/quant_system-*.whl - ``` + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to GHCR + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} - **Quick Start:** - ```bash - python -m src.cli.unified_cli portfolio list - ``` + - name: Build and push image + uses: docker/build-push-action@v6 + with: + context: . + file: docker/Dockerfile + push: true + tags: | + ghcr.io/${{ github.repository_owner }}/quant-system:${{ github.ref_name }} + ghcr.io/${{ github.repository_owner }}/quant-system:latest diff --git a/.gitignore b/.gitignore index 489e691..02ef709 100644 --- a/.gitignore +++ b/.gitignore @@ -1,117 +1,24 @@ # Python __pycache__/ *.py[cod] -*$py.class -*.so -.Python -*.egg-info/ -.installed.cfg -*.egg +.pytest_cache/ +.mypy_cache/ .ruff_cache/ -# Virtual Environments -venv/ -env/ -ENV/ +# Local data and reports +.cache/ +reports/ +http_cache.sqlite + +# Environments and secrets .env +.env.* +!.env.example .venv/ -env.bak/ -venv.bak/ -Pipfile -Pipfile.lock - -# Jupyter Notebooks -.ipynb_checkpoints/ -# Logs and Data -logs/ -*.log -*.csv -cache/ -!data/ - -# Output files and reports -reports_output/ -backtests/results/ -exports/* -!exports/**/.gitkeep -dist/ -build/ -artifacts/ -*.tar.gz -*.whl - -# Performance and benchmark data -.benchmarks/ -performance_reports/ -benchmark_results/ - -# QuantConnect Lean files -lean_config.json -*.backtest.json -*.backtest.csv +# OS +.DS_Store -# IDE and Editor files -# VSCode +# Editors/IDE .vscode/ -*.code-workspace - -# JetBrains .idea/ -*.iml -*.iws - -# pyenv / direnv -.python-version -.direnv/ -.envrc - -# Testing and Type Checking -.coverage -.coverage.* -htmlcov/ -.tox/ -.nox/ -nosetests.xml -coverage.xml -*.cover -.hypothesis/ -.pytest_cache/ -.cache/ -.mypy_cache/ -.dmypy.json -dmypy.json -.pyre/ -.benchmarks/ - -# macOS -.DS_Store -.AppleDouble -.LSOverride -Icon -._* -.DocumentRevisions-V100 -.fseventsd -.Spotlight-V100 -.TemporaryItems -.Trashes -.VolumeIcon.icns -.com.apple.timemachine.donotpresent -.AppleDB -.AppleDesktop -Network Trash Folder -Temporary Items -.apdisk - -# Windows -Thumbs.db -ehthumbs.db -Desktop.ini -.qodo - -# Submodules (track only committed versions) -# Ignore local development changes in submodules - -# Local agent configuration (hidden from repo) -.agent.md -AGENT.md diff --git a/.markdownlint.json b/.markdownlint.json new file mode 100644 index 0000000..89bfa3e --- /dev/null +++ b/.markdownlint.json @@ -0,0 +1,11 @@ +{ + "default": true, + "MD013": false, + "MD041": false, + "MD033": false, + "MD012": false, + "MD025": false, + "MD022": false, + "MD005": false, + "MD007": { "indent": 2 } +} diff --git a/.markdownlint.yaml b/.markdownlint.yaml deleted file mode 100644 index 1f41f79..0000000 --- a/.markdownlint.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# Markdownlint configuration for development flexibility -# Documentation: https://github.com/DavidAnson/markdownlint/blob/main/doc/Rules.md - -# Disable strict formatting rules that cause CI issues -MD013: false # Line length (disabled for flexibility in tables/code) -MD022: false # Headings should be surrounded by blank lines -MD031: false # Fenced code blocks should be surrounded by blank lines -MD032: false # Lists should be surrounded by blank lines - -# Allow common development patterns -MD033: - allowed_elements: ["details", "summary", "br", "sub", "sup", "img"] -MD034: false # Bare URL used (allow for quick documentation) -MD040: false # Fenced code blocks should have a language specified -MD041: false # First line in file should be a top level heading (allow flexibility) - -# Code block preferences -MD046: - style: "fenced" -MD048: - style: "backtick" - -# Allow trailing punctuation in headings (common in documentation) -MD026: false - -# Allow multiple consecutive blank lines -MD012: false - -# Allow trailing spaces -MD009: false - -# Allow list marker spacing flexibility -MD030: false - -# Allow duplicate headings (common in API docs) -MD024: false diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 18cc975..b5f5f16 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,52 +1,33 @@ repos: - # General file checks - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-json - - id: check-added-large-files - exclude: '^exports/' - - id: check-merge-conflict - - id: debug-statements - - id: check-toml - - # Python formatting - Ruff (matches GitHub workflow check) - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.4 + rev: v0.13.0 hooks: + - id: ruff + args: ["--fix"] - id: ruff-format - # Auto-fix in pre-commit, but GitHub runs --check - - # Python linting & import sorting - Ruff (matches GitHub workflow) - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.4 + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 hooks: - - id: ruff-check - args: [--fix] - # Auto-fix in pre-commit, but GitHub runs without --fix + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + # Markdown lint (dockerized markdownlint-cli to avoid local runtime issues) + - repo: local + hooks: + - id: markdownlint + name: markdownlint (docker_image) + language: docker_image + files: '^(README\.md|docs/.*\.md|CHANGELOG\.md|CONTRIBUTING\.md|CODE_OF_CONDUCT\.md|SECURITY\.md|GOVERNANCE\.md)$' + entry: ghcr.io/igorshubovych/markdownlint-cli:latest + args: ["markdownlint", "-c", "/src/.markdownlint.json"] + pass_filenames: true + require_serial: true - # Tests - Using Docker environment for consistency + # Run tests with coverage >= 80% (if tests present) - repo: local hooks: - - id: pytest - name: pytest - entry: docker-compose run --rm quant pytest + - id: pytest-coverage + name: pytest with coverage + entry: bash -lc 'if compgen -G "tests/*.py" > /dev/null; then COVERAGE_FILE=/tmp/.coverage PYTHONPATH=$PWD SKIP_PANDAS_TESTS=1 poetry run pytest -q --maxfail=1 --disable-warnings --cov=src.backtest.results_cache --cov=src.utils.http --cov=src.reporting.html --cov-report=term-missing --cov-fail-under=80; else echo "No tests found; skipping coverage"; fi' language: system - types: [python] pass_filenames: false - # Run tests in Docker environment to match CI/CD - - # Markdown linting (aligned with GitHub workflow) - - repo: https://github.com/DavidAnson/markdownlint-cli2 - rev: v0.15.0 - hooks: - - id: markdownlint-cli2 - args: ["**/*.md", "!.venv/**/*.md", "!node_modules/**/*.md", "!exports/**/*.md"] - - # Additional checks disabled - GitHub provides these automatically: - # - MyPy (type checking) - # - Bandit (security scanning - handled by GitHub CodeQL) - # - Safety (dependency scanning - handled by GitHub Dependabot) diff --git a/.ruff.toml b/.ruff.toml new file mode 100644 index 0000000..cb6eb75 --- /dev/null +++ b/.ruff.toml @@ -0,0 +1,9 @@ +line-length = 100 +target-version = "py310" + +[lint] +select = ["E", "F", "B", "I", "UP"] +ignore = ["E203", "E266", "E501", "B008"] + +[lint.isort] +known-first-party = ["src"] diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..976ecaa --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,19 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on Keep a Changelog and adheres to Semantic Versioning. + +## [Unreleased] + +- Initial public scaffolding improvements +- Dockerized Poetry environment +- Data sources (yfinance, ccxt, Polygon/Tiingo/Alpaca templates) +- Results cache + resume, reports (CSV/MD/HTML/TV) +- Global parallelism, retries/backoff, Prometheus metrics +- CI (lint/tests), scheduled backtests, CodeQL, Gitleaks, Dependabot +- Contribution docs, governance, templates + +## [0.1.0] - 2025-09-12 + +- First release with complete scaffolding and example configs diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..87a2f94 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,31 @@ +# Code of Conduct + +This project follows the Contributor Covenant Code of Conduct. By participating, you agree to uphold this code. + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +- Use welcoming and inclusive language +- Be respectful of differing viewpoints and experiences +- Gracefully accept constructive criticism +- Focus on what is best for the community +- Show empathy towards other community members + +## Enforcement Responsibilities + +Project maintainers are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior they deem inappropriate, threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces and also applies when an individual is officially representing the project in public spaces. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the maintainers at [36189959+LouisLetcher@users.noreply.github.com](mailto:36189959+LouisLetcher@users.noreply.github.com). All complaints will be reviewed and investigated promptly and fairly. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.1. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c89996f..9d959ec 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,32 +1,57 @@ -# Contributing +# Contributing Guide -Thanks for your interest in contributing to quant-system! +Thanks for your interest in contributing! This document describes how to propose changes and what we expect for code quality, tests, and security. -## License and Use +## Development Setup -This repository is released under the Business Source License 1.1 (BUSL‑1.1). -Commercial use is restricted until the Change Date listed in `LICENSE`. On that -date, the project will convert to the MIT License. +- Use Python 3.10 (vectorbt requires <3.11). The Docker image already provides a working toolchain. +- Recommended path: develop inside Docker with Poetry: -## Deprecations and Import Paths + - Build: `docker-compose build` + - Shell: `docker-compose run --rm app bash` + - Install deps: `poetry install` -We recently renamed modules: +## Working on Issues -- `src.core.portfolio_manager` → `src.core.collection_manager` (class: `PortfolioManager`) -- `src.utils.tradingview_alert_exporter` → `src.utils.tv_alert_exporter` +- Check existing issues before opening a new one. +- For significant changes, open a discussion or issue first to align on the approach. -Compatibility shims exist for now and will emit `DeprecationWarning`. Please -update imports to the new modules. The shims are scheduled for removal after -the next minor release. +## Branching and Commits -## Development +- Create feature branches off `main` using concise names, e.g. `feat/xyz`, `fix/abc`. +- Write clear commit messages (imperative mood). Group small related changes together. -- Use `docker compose` and the unified CLI. See `README.md` and `docs/docker.md`. -- Run `pre-commit` locally: `pre-commit install && pre-commit run -a`. -- Tests run inside Docker via the pre-commit hook. +## Code Quality -## Pull Requests +- Run linters and formatters via pre-commit: -- Keep PRs focused and small. -- Include tests for behavior changes. -- Pass pre-commit hooks (format, lint, tests). + ```bash + pip install pre-commit + pre-commit install + pre-commit run --all-files + ``` + +- Python style is enforced by Ruff (including import sorting). + +## Tests & Coverage + +- Add unit tests for new logic. Keep tests deterministic (no network calls) unless explicitly marked as integration. +- Coverage gate is 80% (enforced by pre-commit and CI). Prefer small tests over untested features. + +## Security + +- Never commit secrets. Pre-commit and CI include secret scanning; Dependabot and CodeQL are enabled. +- Report security issues privately (see SECURITY.md). Do not open public issues for vulnerabilities. + +## Submitting a PR + +1. Ensure pre-commit passes locally. +2. Include a brief description of the change and rationale. +3. Reference related issues. +4. Keep PRs focused; large unrelated changes are likely to be requested to split. + +## Release & Changelog + +- Maintainers will tag releases and update release notes. + +Thanks again for helping improve this project! diff --git a/DOCKERFILE b/DOCKERFILE deleted file mode 100644 index 1c810d4..0000000 --- a/DOCKERFILE +++ /dev/null @@ -1,140 +0,0 @@ -# Multi-stage build for optimized production image -FROM python:3.12-slim AS base - -# Set environment variables -ENV PYTHONUNBUFFERED=1 \ - PYTHONDONTWRITEBYTECODE=1 \ - PIP_NO_CACHE_DIR=1 \ - PIP_DISABLE_PIP_VERSION_CHECK=1 \ - POETRY_VERSION=1.8.3 \ - POETRY_HOME="/opt/poetry" \ - POETRY_CACHE_DIR=/tmp/poetry_cache \ - POETRY_VENV_IN_PROJECT=1 \ - POETRY_NO_INTERACTION=1 - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - build-essential \ - curl \ - git \ - && rm -rf /var/lib/apt/lists/* - -# Install Poetry -RUN pip install poetry==$POETRY_VERSION - -# Set work directory -WORKDIR /app - -# Copy dependency files -COPY pyproject.toml ./ - -# Configure poetry and install dependencies -RUN poetry config virtualenvs.create false \ - && poetry lock --no-update \ - && poetry install --only main --no-root \ - && rm -rf $POETRY_CACHE_DIR - -# Production stage -FROM python:3.12-slim AS production - -# Set environment variables -ENV PYTHONUNBUFFERED=1 \ - PYTHONDONTWRITEBYTECODE=1 \ - PATH="/app/.venv/bin:$PATH" \ - PYTHONPATH="/app:$PYTHONPATH" - -# Install runtime dependencies only -RUN apt-get update && apt-get install -y \ - && rm -rf /var/lib/apt/lists/* - -# Create non-root user -RUN groupadd -r quantuser && useradd -r -g quantuser quantuser - -# Set work directory -WORKDIR /app - -# Copy installed packages from base stage -COPY --from=base /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages -COPY --from=base /usr/local/bin /usr/local/bin - -# Copy application code -COPY src/ ./src/ -COPY config/ ./config/ -COPY scripts/ ./scripts/ -COPY pyproject.toml ./ - -# Create necessary directories -RUN mkdir -p cache exports logs \ - && chown -R quantuser:quantuser /app - -# Switch to non-root user -USER quantuser - -# Health check -HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \ - CMD python -m src.cli.unified_cli cache stats || exit 1 - -# Default command -CMD ["python", "-m", "src.cli.unified_cli", "--help"] - -# Development stage -FROM base AS development - -# Install development dependencies -RUN poetry install --no-root - -# Copy all files for development -COPY . . - -# Create necessary directories -RUN mkdir -p cache exports logs tests - -# Set development environment -ENV ENVIRONMENT=development - -# Default command for development -CMD ["bash"] - -# Testing stage -FROM development AS testing - -# Install test dependencies -RUN poetry install - -# Copy test files -COPY tests/ ./tests/ -COPY pytest.ini ./ - -# Run tests -CMD ["poetry", "run", "pytest", "tests/", "-v"] - -# Jupyter stage for data analysis -FROM development AS jupyter - -# Install Jupyter and additional analysis tools -RUN poetry add jupyter jupyterlab plotly seaborn - -# Expose Jupyter port -EXPOSE 8888 - -# Create Jupyter config -RUN mkdir -p /app/.jupyter && \ - echo "c.NotebookApp.token = ''" > /app/.jupyter/jupyter_notebook_config.py && \ - echo "c.NotebookApp.password = ''" >> /app/.jupyter/jupyter_notebook_config.py && \ - echo "c.NotebookApp.open_browser = False" >> /app/.jupyter/jupyter_notebook_config.py && \ - echo "c.NotebookApp.ip = '0.0.0.0'" >> /app/.jupyter/jupyter_notebook_config.py - -# Start Jupyter Lab -CMD ["jupyter", "lab", "--allow-root", "--config=/app/.jupyter/jupyter_notebook_config.py"] - -# API stage for web services -FROM production AS api - -# Expose API port -EXPOSE 8000 - -# Install API dependencies -RUN pip install uvicorn[standard] fastapi - -# Start API server -CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/GOVERNANCE.md b/GOVERNANCE.md new file mode 100644 index 0000000..7d90406 --- /dev/null +++ b/GOVERNANCE.md @@ -0,0 +1,71 @@ +# Governance and Branch Protection + +This document outlines how we maintain the repository, protect the `main` branch, and merge changes safely. + +## Branching Model + +- Default branch: `main` (protected) +- Work happens on short-lived feature branches: `feat/...`, `fix/...`, `chore/...`. +- Use pull requests (PRs) for all changes to `main`. +- Tag releases with SemVer: `vMAJOR.MINOR.PATCH`. + +## Branch Protection (recommended GitHub settings) + +Enable on `Settings → Branches → Branch protection rules` for `main`: + +- Require a pull request before merging + - Require approvals: 1–2 (at least 1 is recommended) + - Dismiss stale approvals when new commits are pushed + - Require review from Code Owners (optional) + - Require conversation resolution +- Require status checks to pass before merging + - ci / lint (from `.github/workflows/ci.yml`) + - ci / tests (coverage gate ≥ 80%) + - pre-commit (runs Ruff, Markdown lint, coverage gate) + - codeql / analyze (from `.github/workflows/codeql.yml`) + - gitleaks / secret scan (from `.github/workflows/gitleaks.yml`) + - daily-backtest is not required (scheduled), optional + - Require branches to be up to date before merging +- Require signed commits (optional but recommended) +- Linear history (disable merge commits or enforce squash/rebase) +- Restrict who can push to matching branches (maintainers only) +- Do not allow force pushes or deletions on protected branches + +## CODEOWNERS + +Add `CODEOWNERS` so critical files require review from maintainers: + +```text +* @manuelheck +.github/ @manuelheck +src/ @manuelheck +``` + +Place this in `.github/CODEOWNERS` (see file added in this repo). Adjust handles as needed. + +## CI & Security + +- Dependabot updates weekly for Python and GitHub Actions +- CodeQL code scanning on PRs, pushes to `main`, and weekly schedule +- Gitleaks secret scanning on PRs and pushes +- Pre-commit hooks enforced locally and in CI + +## Releases + +- Create a release branch if needed for stabilization +- Tag `vX.Y.Z` on `main` after CI passes +- Draft GitHub Release notes summarizing changes + +## Backports / Hotfixes + +- Cherry-pick fixes onto release branches, open PRs, and tag patch releases + +## Vulnerability Handling + +- Follow `SECURITY.md` for private disclosures +- Do not discuss vulnerabilities in public issues until a fix is released + +## Decision Making + +- Small changes: PR review by 1 maintainer +- Larger/architectural changes: open an issue or design doc for discussion before implementation diff --git a/LICENSE b/LICENSE index 0aeeb9d..b7dd6a4 100644 --- a/LICENSE +++ b/LICENSE @@ -1,34 +1,21 @@ -Business Source License 1.1 - -Licensor: Louis Letcher - -Licensed Work: quant-system -The Licensed Work is the repository at the following URL: -https://github.com/LouisLetcher/quant-system (or its successors) - -Additional Use Grant: You may use, copy, modify, create derivative works, and -redistribute the Licensed Work, for non-commercial purposes only. - -Change Date: 2028-01-01 - -Change License: On the Change Date, the Licensor will make the Licensed Work -available under the MIT License. - -Terms - -The Licensor permits you to use, copy, modify, create derivative works, and -redistribute the Licensed Work; provided, however, that any use is for -non-commercial purposes only. Any commercial use of the Licensed Work is -prohibited, except with the Licensor's prior written approval. - -This License does not grant you any trademark rights for the Licensor’s marks. - -To the extent permitted by applicable law, the Licensed Work is provided "as is" -and the Licensor disclaims all warranties and conditions, whether express or -implied, including but not limited to implied warranties of merchantability, -fitness for a particular purpose, title, and non-infringement. - -Any copy of the Licensed Work you make must include this License. - -For more information about the Business Source License, please see -https://mariadb.com/bsl11/ +MIT License + +Copyright (c) 2025 Manuel Heck and contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..e2d52ad --- /dev/null +++ b/Makefile @@ -0,0 +1,43 @@ +SHELL := /bin/bash + +.PHONY: build build-nc sh run run-bonds run-crypto run-commodities run-indices run-forex list-strategies lock lock-update discover-crypto + +build: + docker-compose build + +build-nc: + docker-compose build --no-cache + +sh: + docker-compose run --rm app bash + +run: + docker-compose run --rm app bash -lc "poetry install && poetry run python -m src.main run --config config/example.yaml" + +run-bonds: + docker-compose run --rm app bash -lc "poetry install && RUN_ID=bonds-$(shell date +%Y%m%d%H%M) poetry run python -m src.main run --config config/collections/bonds_majors.yaml" + +run-crypto: + docker-compose run --rm app bash -lc "poetry install && RUN_ID=crypto-$(shell date +%Y%m%d%H%M) poetry run python -m src.main run --config config/collections/crypto_majors.yaml" + +run-commodities: + docker-compose run --rm app bash -lc "poetry install && RUN_ID=commodities-$(shell date +%Y%m%d%H%M) poetry run python -m src.main run --config config/collections/commodities_majors.yaml" + +run-indices: + docker-compose run --rm app bash -lc "poetry install && RUN_ID=indices-$(shell date +%Y%m%d%H%M) poetry run python -m src.main run --config config/collections/indices_majors.yaml" + +run-forex: + docker-compose run --rm app bash -lc "poetry install && RUN_ID=forex-$(shell date +%Y%m%d%H%M) poetry run python -m src.main run --config config/collections/forex_majors.yaml" + +list-strategies: + docker-compose run --rm app bash -lc "poetry install && poetry run python -m src.main list-strategies --strategies-path /ext/strategies" + +discover-crypto: + # Usage: make discover-crypto EXCHANGE=binance QUOTE=USDT TOP=100 OUT=config/collections/crypto_top100.yaml NAME=crypto_top100 + docker-compose run --rm app bash -lc "poetry install && poetry run python -m src.main discover-symbols --exchange $(EXCHANGE) --quote $(QUOTE) --top-n $(TOP) --name $(NAME) --output $(OUT)" + +lock: + docker-compose run --rm app bash -lc "poetry lock --no-update && git add poetry.lock" + +lock-update: + docker-compose run --rm app bash -lc "poetry lock && git add poetry.lock" diff --git a/README.md b/README.md index ced2aba..e7343f6 100644 --- a/README.md +++ b/README.md @@ -1,229 +1,192 @@ -# Quant System +# Quant System (Dockerized) -A unified, Dockerized quantitative backtesting and reporting system. Run cross‑strategy comparisons for asset collections (e.g., bonds) and persist results to PostgreSQL with exportable artifacts. +## Overview -## 🚀 Quick Start +This repository provides a Docker-based, cache-aware backtesting system to systematically evaluate multiple strategies across multiple assets, timeframes, and the full available history. It discovers strategies from an external repo and produces: -### Docker Setup +- Markdown report (best combination per asset/strategy/timeframe) +- TradingView alert export (markdown) +- CSV summary of best results -```bash -# Clone repository -git clone -cd quant-system +## Key Features -# Start PostgreSQL and pgAdmin -docker compose up -d postgres pgadmin +- Pluggable data sources (free and premium-friendly) with local Parquet caching +- Strategy discovery from external repo via a clean BaseStrategy interface +- Batch runs across collections (e.g., crypto, forex, bonds, stocks) +- Parameter grid search with best-by metric (Sharpe, Sortino, or Profit) +- Dockerized runtime for reproducibility +- Results cache (SQLite) to resume and skip already-computed grids +- Structured logging and timing metrics per data fetch and grid search -# Build the app image (uses DOCKERFILE) -docker compose build quant +## Requirements -# Show CLI help -docker compose run --rm quant python -m src.cli.unified_cli --help +- Docker and docker-compose +- Poetry (for local non-Docker runs) +- Python 3.9 or 3.10 (vectorbt requires <3.11) +- External strategies repo mounted at runtime (defaults to /Users/manuelheck/Documents/Websites/Private/quant/quant-strategies/algorithms/python) +- Optional: pre-commit for local linting hooks -# Interactive shell inside the app container -docker compose run --rm quant bash -``` +## Project Structure -## 📈 Usage +- src/main.py: CLI entrypoint (Typer) +- src/config.py: Loads and validates YAML config +- src/data/: Data source interfaces and caching helpers +- src/strategies/: Base strategy interface and external loader +- src/backtest/: Runner, metric computation, and results cache (resume) +- src/utils/telemetry.py: Structured logging utilities and timed context +- src/reporting/: Markdown, CSV, TradingView exporters +- config/example.yaml: Example configuration +- config/collections/: Per-collection configs (crypto, bonds, commodities, indices) -See also: docs/pgadmin-and-performance.md for DB inspection and performance tips. +## Quick Start -The unified CLI currently exposes a single subcommand: `collection`. +1) Configure your run in config/example.yaml (collections, timeframes, metrics, strategies, params). +2) Ensure your strategies repo contains classes deriving BaseStrategy (see src/strategies/base.py and the example). +3) Check discovered strategies: -### Run Bonds (1d interval, max period, all strategies) + docker-compose run --rm app bash -lc "poetry run python -m src.main list-strategies --strategies-path /ext/strategies" -Use the collection key (`bonds`) or the JSON file path. The `direct` action runs the backtests and writes results to the DB. Add `--exports all` to generate CSV/HTML/TV/AI artifacts when possible. +4) Run via docker-compose (Poetry): ```bash -# Using the collection key (recommended) -docker compose run --rm \ - -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --action direct \ - --interval 1d \ - --period max \ - --strategies all \ - --exports all \ - --log-level INFO - -# Using the JSON file -docker compose run --rm \ - -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection config/collections/bonds.json \ - --action direct \ - --interval 1d \ - --period max \ - --strategies all \ - --exports all \ - --log-level INFO +docker-compose run --rm app bash -lc "poetry run python -m src.main run --config config/collections/crypto_majors.yaml" +# or bonds/commodities/indices individually +docker-compose run --rm app bash -lc "poetry run python -m src.main run --config config/collections/bonds_majors.yaml" +# generate reports with top-5 per symbol and offline HTML +docker-compose run --rm app bash -lc "poetry run python -m src.main run --config config/collections/crypto_majors.yaml --top-n 5 --inline-css" ``` -Notes +## Make Targets -- Default metric is `sortino_ratio`. -- Strategies are mounted at `/app/external_strategies` via `docker-compose.yml`; `STRATEGIES_PATH` makes discovery explicit. -- Artifacts are written under `artifacts/run_*`. DB tables used include `runs`, `backtest_results`, `best_strategies`, and `run_artifacts`. -- pgAdmin is available at `http://localhost:5050` (defaults configured via `.env`/`.env.example`). +- `make build` / `make build-nc`: build image (no-cache). +- `make sh`: open a shell in the container. +- `make list-strategies`: verify external strategies are discovered. +- `make run-bonds` / `make run-crypto` / `make run-commodities` / `make run-indices` / `make run-forex`: run a collection. +- `make discover-crypto EXCHANGE=binance QUOTE=USDT TOP=100 OUT=config/collections/crypto_top100.yaml NAME=crypto_top100`: generate a crypto universe config. +- `make lock` / `make lock-update`: create or update `poetry.lock` inside the container for reproducible builds. -### Dry Run (plan only + optional exports) +## Outputs -```bash -docker compose run --rm \ - -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --interval 1d --period max --strategies all \ - --dry-run --exports all --log-level DEBUG -``` +- reports/`{timestamp}`/summary.csv: CSV of best combinations (one per symbol) +- reports/`{timestamp}`/all_results.csv: CSV of all parameter evaluations (consolidated) +- reports/`{timestamp}`/top3.csv: Top-N (default 3) per symbol +- reports/`{timestamp}`/report.md: Markdown report (top combos and metrics) +- reports/`{timestamp}`/tradingview.md: TradingView alert export (per best combo) +- reports/`{timestamp}`/summary.json: Run summary (timings, counters) +- reports/`{timestamp}`/metrics.prom: Prometheus-style metrics textfile -### Other Actions +## Notes -The `collection` subcommand supports these `--action` values: `backtest`, `direct`, `optimization`, `export`, `report`, `tradingview`. In most workflows, use `--action direct` and optionally `--exports`. +- Data caching uses Parquet files under .cache/data; HTTP cached for 12h. yfinance also integrates yfinance-cache when available. +- Free data: yfinance for equities/ETFs/futures; crypto via ccxt with exchange set (e.g., binance, bybit). Calls are rate-limited to avoid throttling. +- Premium data templates: Polygon, Tiingo, Alpaca under src/data/*. Provide API keys via env vars and implement fetch. +- Additional sources: Finnhub (fx/equities intraday), Twelve Data (fx/equities intraday), Alpha Vantage (daily fallback). -## 🔧 Configuration +### Symbol Mapping -### Environment Variables (.env) +Use provider‑agnostic symbols in config; a mapper translates per provider: -```bash -# PostgreSQL (inside the container, use the service name 'postgres') -DATABASE_URL=postgresql://quantuser:quantpass@postgres:5432/quant_system - -# Optional data providers -ALPHA_VANTAGE_API_KEY=your_key -TWELVE_DATA_API_KEY=your_key -POLYGON_API_KEY=your_key -TIINGO_API_KEY=your_key -FINNHUB_API_KEY=your_key -BYBIT_API_KEY=your_key -BYBIT_API_SECRET=your_secret -BYBIT_TESTNET=false - -# Optional LLMs -OPENAI_API_KEY=your_key -OPENAI_MODEL=gpt-4o -ANTHROPIC_API_KEY=your_key -ANTHROPIC_MODEL=claude-3-5-sonnet-20241022 -``` +- Futures: use roots like `GC`, `CL`, `SI`, `ZW`, `ZC`, `ZS`, ... + - yfinance: mapped to `GC=F`, `CL=F`, etc. + - polygon/tiingo/alpaca: Yahoo decorations removed. +- Indices: you can use `SPX`, `NDX`, `DJI`, `RUT`, `VIX`. + - yfinance: mapped to `^GSPC`, `^NDX`, `^DJI`, `^RUT`, `^VIX`. +- Share classes: prefer dot form in config, e.g., `BRK.B`. + - yfinance: mapped to `BRK-B`; others strip back to dot. +- Forex: `EURUSD` or `EUR/USD` in config. + - yfinance: mapped to `EURUSD=X`; others use raw pair. +- Crypto: `BTCUSD`, `BTC/USDT`, or `BTCUSDT` in config. + - yfinance: mapped to `BTC-USD`; ccxt uses the slash form. -Host access tips +If you see a log line with a Yahoo‑decorated symbol (e.g., `ZW=F`) under yfinance, it usually means your config already uses the decorated form. Prefer the canonical form (`ZW`) in config so mapping can adapt automatically. -- Postgres is published on `localhost:5433` (mapped to container `5432`). -- pgAdmin runs at `http://localhost:5050` (see `.env` for credentials). +### Providers Overview -### Collections +- Tiingo: stable daily/intraday for US equities/ETFs. Recommended for bonds, commodities ETFs, and index ETF proxies. Timeframes: 1d and selected intraday (no resampling). +- yfinance: broad free coverage for indices and weekly bars. Recommended for index levels (e.g., SPX), forex daily/hourly. Timeframes: native only. +- CCXT: crypto OHLCV from exchanges (e.g., Binance). Timeframes: exchange-supported only. +- Polygon/Alpaca: robust intraday equities data at scale (paid). Use when you need minute bars with SLAs. +- Finnhub: equities/FX/crypto intraday + fundamentals/news (paid). Good for FX intraday. Env: FINNHUB_API_KEY. +- Twelve Data: FX/equities intraday (paid/free). Good as primary/backup for FX intraday. Env: TWELVEDATA_API_KEY. +- Alpha Vantage: daily fallback for equities/FX (free). Not ideal for heavy intraday. Env: ALPHAVANTAGE_API_KEY. -Collections live under `config/collections/` and are split into: +See new collection examples under `config/collections/` for FX intraday via Finnhub and Twelve Data. -- `default/` (curated, liquid, fast to iterate) -- `custom/` (your own research sets) +- Results cache: SQLite under .cache/results to resume and skip recomputation per param-set. Cache invalidates automatically when data changes (based on fingerprint). +- Concurrency: set `asset_workers`, `param_workers`, and `max_fetch_concurrency` to control parallelization. +- Per-collection configs live under `config/collections/`. Extend symbol lists to be as comprehensive as desired (majors/minors). +- Strategy selection: all discovered strategies are tested by default; `strategies:` only overrides parameter grids by name. -Default examples: +## CI & Scheduling -- Bonds: `default/bonds_core.json` (liquid bond ETFs), `default/bonds.json` (broader set) -- Commodities: `default/commodities_core.json` (gold/silver/energy/agriculture/broad) -- Crypto: `default/crypto_liquid.json` (top market-cap, USDT pairs) -- Forex: `default/forex_majors.json` (majors and key crosses; Yahoo Finance format `=X`) -- Indices: `default/indices_global_core.json` (SPY/QQQ/DIA/IWM/EFA/EEM/EWJ/FXI etc.) -- Stocks: `default/stocks_us_mega_core.json`, `default/stocks_us_growth_core.json` - - Factors: `default/stocks_us_value_core.json`, `default/stocks_us_quality_core.json`, `default/stocks_us_minvol_core.json` - - Global factors: `default/stocks_global_factor_core.json` +- Linting via Ruff in `.github/workflows/ci.yml` on push/PR. +- Daily scheduled backtest via `.github/workflows/daily-backtest.yml` (05:00 UTC). To use your strategies repo in CI: + - Add secrets `STRATEGIES_REPO` (e.g., org/repo) and `GH_TOKEN` with read access. + - The workflow checks out both repos and runs `poetry run python -m src.main run --config config/example.yaml --strategies-path strategies`. + - Security: Gitleaks runs on PRs and `main`. + - CodeQL: Uses GitHub’s Default setup (enable under Security → Code scanning). No custom workflow is required. -Custom examples (research-driven): +## Governance -- `custom/stocks_traderfox_dax.json` -- `custom/stocks_traderfox_european.json` -- `custom/stocks_traderfox_us_financials.json` -- `custom/stocks_traderfox_us_healthcare.json` -- `custom/stocks_traderfox_us_tech.json` +- Branch protection and required status checks recommendations are in `GOVERNANCE.md`. +- CODEOWNERS is set under `.github/CODEOWNERS`. -You can reference any collection by key without the folder prefix (resolver searches `default/` and `custom/`). For example, `bonds_core` resolves `config/collections/default/bonds_core.json`. +## Symbol Discovery (Crypto) -## 🧪 Testing +- Build a universe of top volume pairs via ccxt and emit a config file: -```bash -# Run tests in Docker -docker compose run --rm quant pytest -``` + docker-compose run --rm app bash -lc "poetry run python -m src.main discover-symbols --exchange binance --quote USDT --top-n 100 --name crypto_top100 --output config/collections/crypto_top100.yaml" -## 📊 Exports & Reporting +## Environment Variables (.env) -Artifacts and exports are written under `artifacts/run_*` and `exports/`. When running with `--action direct` or `--dry-run`, pass `--exports csv,report,tradingview,ai` or `--exports all`. +- Copy `.env.example` to `.env` and fill keys: `POLYGON_API_KEY`, `TIINGO_API_KEY`, `ALPACA_API_KEY_ID`, `ALPACA_API_SECRET_KEY`, `FINNHUB_API_KEY`, `TWELVEDATA_API_KEY`, `ALPHAVANTAGE_API_KEY`. +- `docker-compose` loads `.env` automatically; the app also loads `.env` at startup. +- Override cache and strategies path via `DATA_CACHE_DIR` and `STRATEGIES_PATH`. +- For docker-compose host mount, set `HOST_STRATEGIES_PATH` to your local strategies repo; if unset, it falls back to `./external-strategies`. +- Provider keys for scheduled runs can be set as repository secrets and are exported in `.github/workflows/daily-backtest.yml`. -```bash -# Produce exports from DB for bonds without re-running backtests -docker compose run --rm quant \ - python -m src.cli.unified_cli collection bonds --dry-run --exports all -``` +## Git Ignore -Output locations and unified naming (`{Collection}_Collection_{Year}_{Quarter}_{Interval}`): -- CSV: `exports/csv/{Year}/{Quarter}/{Collection}_Collection_{Year}_{Quarter}_{Interval}.csv` -- HTML reports: `exports/reports/{Year}/{Quarter}/{Collection}_Collection_{Year}_{Quarter}_{Interval}.html` -- TradingView alerts (Markdown): `exports/tv_alerts/{Year}/{Quarter}/{Collection}_Collection_{Year}_{Quarter}_{Interval}.md` -- AI recommendations: - - Markdown: `exports/ai_reco/{Year}/{Quarter}/{Collection}_Collection_{Year}_{Quarter}_{Interval}.md` - - HTML (dark Tailwind): same path with `.html` and a Download CSV link +- `.gitignore` excludes local caches, reports, virtualenvs, and `.env`. -Notes: -- Exporters are DB-backed (read best strategies); no HTML scraping. -- With multiple intervals in plan, filenames prefer `1d`. Pass `--interval 1d` to constrain both content and filenames. +## Strategy Selection -## 🗄️ Data & Cache +- The runner discovers all strategies under your external repo and tests all of them by default. +- If you provide `strategies:` in config, their `params` act as overrides for the discovered strategies with matching names; nothing is filtered by collection. -- Split caching: the system maintains two layers for market data. - - Full snapshot: stored when requesting provider periods like `--period max` (long TTL). - - Recent overlay: normal runs cache the last ~90 days (short TTL). - - Reads merge both, prefer recent on overlap, and auto‑extend when a request exceeds cached range. -- Fresh fetch: add `--no-cache` (alias: `--fresh`) to bypass cache reads and fetch from the provider. The result still writes through to cache. -- Coverage probe: before backtests, the CLI samples a few symbols with `period=max` and prefers the source with the most rows and earliest start for this run. +## New CLI Options and Outputs -### Prefetching Collections (avoid rate limits) +- `--only-cached`: avoid API calls and use cached Parquet data only; errors on cache miss. +- Emits `summary.json` (run summary + counts) and `metrics.prom` (Prometheus-style gauges) alongside CSV/Markdown exports in `reports//`. -Use the prefetch script to refresh data on a schedule (e.g., nightly recent overlay and weekly full snapshot): +## Pre-commit Hooks -```bash -# Full history snapshot (bonds) -docker compose run --rm quant \ - python scripts/prefetch_collection.py bonds --mode full --interval 1d +- Install and enable locally: -# Recent overlay (last 90 days) -docker compose run --rm quant \ - python scripts/prefetch_collection.py bonds --mode recent --interval 1d --recent-days 90 +```bash + pip install pre-commit + pre-commit install ``` -Example cron (runs at 01:30 local time): +- Run hooks on all files once: +```bash + pre-commit run --all-files ``` -30 1 * * * cd /path/to/quant-system && docker compose run --rm quant \ - python scripts/prefetch_collection.py bonds --mode recent --interval 1d --recent-days 90 >/dev/null 2>&1 -``` - -### Optional Redis Overlay (advanced) - -- For higher throughput, you can use Redis for the “recent” layer and keep full snapshots on disk. -- Pros: very fast hot reads, simple TTL eviction. Cons: extra service; volatile if not persisted. -- Suggested setup: run Redis via compose, store recent overlay (last 90 days) with TTL ~24–48h; keep full history on disk (gzip). -- Current repo ships with file‑based caching; Redis is an optional enhancement and can be added without breaking existing flows. -## 📚 Further Docs +- Hooks: Ruff lint and format, YAML checks, whitespace fixes. -- docs/pgadmin-and-performance.md — pgAdmin queries and performance tips -- docs/data-sources.md — supported providers and configuration -- docs/development.md — local dev, testing, and repo layout -- docs/docker.md — Docker specifics and mounts -- docs/features.md — feature overview and roadmap -- docs/cli-guide.md — CLI details and examples +## Backtesting Engine & Optimization -## 🛠️ Troubleshooting +- Uses vectorbt to execute and grid-search parameters with resume via SQLite. +- The `backtesting` library is also available; we can enable it as an alternative engine with a strategy adapter if you prefer its built-in optimizer. -- Command name: use `docker compose` (or legacy `docker-compose`) consistently. -- Subcommand: it is `collection` (singular), not `collections`. -- Strategy discovery: ensure strategies are mounted at `/app/external_strategies` and set `STRATEGIES_PATH=/app/external_strategies` when running. -- Database URL: inside containers use `postgres:5432` (`DATABASE_URL=postgresql://quantuser:quantpass@postgres:5432/quant_system`). On the host, Postgres is published at `localhost:5433`. -- Initialize tables: if tables are missing, run: - `docker compose run --rm quant python -c "from src.database.unified_models import create_tables; create_tables()"` -- Long runs/timeouts: backtests can take minutes to hours depending on strategies and symbols. Prefer `--log-level INFO` or `DEBUG` to monitor progress. Use `--dry-run` to validate plans quickly. Extra tips in docs/pgadmin-and-performance.md. -- Permissions/cache: ensure `cache/`, `exports/`, `logs/`, and `artifacts/` exist and are writable on the host (compose mounts them into the container). -- API limits: some data sources rate-limit; providing API keys in `.env` can reduce throttling. +Strategy Interface (External) -## ⚠️ Disclaimer +- Derive from BaseStrategy and implement (in your external repo only): + - name: str + - param_grid(self) -> dict[str, list] + - generate_signals(self, df: pd.DataFrame, params: dict) -> tuple[pd.Series, pd.Series] + - optional: to_tradingview_pine(self, params: dict) -> str -This project is for educational and research purposes only. It does not constitute financial advice. Use at your own risk and always perform your own due diligence before making investment decisions. +Note: This repo does not contain strategies; it loads them from your external repo. If none are found, the run will fail. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..cdd1e12 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,20 @@ +# Security Policy + +## Supported Versions + +We aim to keep the `main` branch secure and up-to-date. Security updates are provided on a best-effort basis for the latest release. + +## Reporting a Vulnerability + +Please report security issues privately. Do not create public GitHub issues for vulnerabilities. + +- Email: [36189959+LouisLetcher@users.noreply.github.com](mailto:36189959+LouisLetcher@users.noreply.github.com) +- Or use GitHub’s “Report a vulnerability” (Security > Advisories) if available + +We will acknowledge your report within 72 hours, provide an initial assessment, and keep you informed of the remediation progress. + +## Best Practices + +- Do not include secrets in commits. Use `.env` files and repository secrets. +- Use Dockerized workflows or Poetry to ensure reproducible environments. +- Keep dependencies current; Dependabot is configured for this repository. diff --git a/alembic/versions/0001_unified_cli_schema.py b/alembic/versions/0001_unified_cli_schema.py deleted file mode 100644 index 135aa1b..0000000 --- a/alembic/versions/0001_unified_cli_schema.py +++ /dev/null @@ -1,153 +0,0 @@ -"""create unified CLI schema (runs, backtest_results, trades, symbol_aggregates, run_artifacts) - -Revision ID: 0001_unified_cli_schema -Revises: -Create Date: 2025-08-27 07:09:00.000000 - -""" - -from __future__ import annotations - -import sqlalchemy as sa -import sqlalchemy.dialects.postgresql as pg - -from alembic import op - -# revision identifiers, used by Alembic. -revision = "0001_unified_cli_schema" -down_revision = None -branch_labels = None -depends_on = None - - -def upgrade() -> None: - # Runs table - op.create_table( - "runs", - sa.Column("run_id", sa.String(length=36), primary_key=True), - sa.Column( - "started_at_utc", - sa.DateTime(timezone=True), - nullable=False, - server_default=sa.text("now()"), - ), - sa.Column("finished_at_utc", sa.DateTime(timezone=True), nullable=True), - sa.Column("actor", sa.String(length=128), nullable=False), - sa.Column("action", sa.String(length=64), nullable=False), - sa.Column("collection_ref", sa.Text(), nullable=False), - sa.Column("strategies_mode", sa.String(length=256), nullable=False), - sa.Column("intervals_mode", sa.String(length=256), nullable=False), - sa.Column("target_metric", sa.String(length=64), nullable=False), - sa.Column("period_mode", sa.String(length=64), nullable=False), - sa.Column("args_json", pg.JSONB(astext_type=sa.Text()), nullable=False), - sa.Column("git_sha_app", sa.String(length=64), nullable=True), - sa.Column("git_sha_strat", sa.String(length=64), nullable=True), - sa.Column("data_source", sa.String(length=128), nullable=True), - sa.Column("plan_hash", sa.String(length=128), nullable=False, unique=True), - sa.Column( - "status", sa.String(length=32), nullable=False, server_default="running" - ), - sa.Column("error_summary", sa.Text(), nullable=True), - ) - - # Backtest results - op.create_table( - "backtest_results", - sa.Column("result_id", sa.String(length=36), primary_key=True), - sa.Column( - "run_id", - sa.String(length=36), - sa.ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ), - sa.Column("symbol", sa.String(length=64), nullable=False, index=True), - sa.Column("strategy", sa.String(length=256), nullable=False, index=True), - sa.Column("interval", sa.String(length=32), nullable=False, index=True), - sa.Column("start_at_utc", sa.DateTime(timezone=True), nullable=True), - sa.Column("end_at_utc", sa.DateTime(timezone=True), nullable=True), - sa.Column("rank_in_symbol", sa.Integer(), nullable=True), - sa.Column("metrics", pg.JSONB(astext_type=sa.Text()), nullable=False), - sa.Column("engine_ctx", pg.JSONB(astext_type=sa.Text()), nullable=True), - sa.Column("trades_raw", sa.Text(), nullable=True), - sa.Column("error", sa.Text(), nullable=True), - sa.UniqueConstraint( - "run_id", - "symbol", - "strategy", - "interval", - name="uq_run_symbol_strategy_interval", - ), - ) - - # Trades - op.create_table( - "trades", - sa.Column("trade_id", sa.String(length=36), primary_key=True), - sa.Column( - "result_id", - sa.String(length=36), - sa.ForeignKey("backtest_results.result_id", ondelete="CASCADE"), - nullable=False, - index=True, - ), - sa.Column("trade_index", sa.Integer(), nullable=False), - sa.Column("size", sa.String(length=64), nullable=True), - sa.Column("entry_bar", sa.BigInteger(), nullable=True), - sa.Column("exit_bar", sa.BigInteger(), nullable=True), - sa.Column("entry_price", sa.String(length=64), nullable=True), - sa.Column("exit_price", sa.String(length=64), nullable=True), - sa.Column("pnl", sa.String(length=64), nullable=True), - sa.Column("duration", sa.Interval(), nullable=True), - sa.Column("tag", sa.String(length=128), nullable=True), - sa.Column("entry_signals", sa.Text(), nullable=True), - sa.Column("exit_signals", sa.Text(), nullable=True), - sa.UniqueConstraint("result_id", "trade_index", name="uq_result_trade_index"), - ) - - # Symbol aggregates - op.create_table( - "symbol_aggregates", - sa.Column("id", sa.String(length=36), primary_key=True), - sa.Column( - "run_id", - sa.String(length=36), - sa.ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ), - sa.Column("symbol", sa.String(length=64), nullable=False), - sa.Column("best_by", sa.String(length=64), nullable=False), - sa.Column( - "best_result", - sa.String(length=36), - sa.ForeignKey("backtest_results.result_id", ondelete="CASCADE"), - nullable=False, - ), - sa.Column("summary", pg.JSONB(astext_type=sa.Text()), nullable=False), - sa.UniqueConstraint("run_id", "symbol", "best_by", name="uq_run_symbol_bestby"), - ) - - # Run artifacts - op.create_table( - "run_artifacts", - sa.Column("artifact_id", sa.String(length=36), primary_key=True), - sa.Column( - "run_id", - sa.String(length=36), - sa.ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ), - sa.Column("artifact_type", sa.String(length=64), nullable=False), - sa.Column("path_or_uri", sa.Text(), nullable=False), - sa.Column("meta", pg.JSONB(astext_type=sa.Text()), nullable=True), - ) - - -def downgrade() -> None: - op.drop_table("run_artifacts") - op.drop_table("symbol_aggregates") - op.drop_table("trades") - op.drop_table("backtest_results") - op.drop_table("runs") diff --git a/config/collections/bonds_majors.yaml b/config/collections/bonds_majors.yaml new file mode 100644 index 0000000..c7b6b89 --- /dev/null +++ b/config/collections/bonds_majors.yaml @@ -0,0 +1,31 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: bonds_majors + source: tiingo + fees: 0.0005 + slippage: 0.0005 + symbols: + - TLT + - IEF + - IEI + - SHY + - BIL + - BND + - AGG + - LQD + - HYG + - JNK + - IGSB + - TIP + - MUB + - EMB + +timeframes: ["1d"] + +strategies: [] diff --git a/config/collections/commodities_majors.yaml b/config/collections/commodities_majors.yaml new file mode 100644 index 0000000..383b872 --- /dev/null +++ b/config/collections/commodities_majors.yaml @@ -0,0 +1,33 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: commodities_majors + source: tiingo + fees: 0.0005 + slippage: 0.0005 + symbols: + # Precious metals + - GLD # Gold + - SLV # Silver + - PPLT # Platinum + - PALL # Palladium + # Energy + - USO # Crude Oil + - UNG # Natural Gas + # Specific ags/base metals as ETFs (avoid futures tickers) + - CPER # Copper + - WEAT # Wheat + - CORN # Corn + - SOYB # Soybeans + - CANE # Sugar + - JO # Coffee (ETN) + - NIB # Cocoa (ETN) + +timeframes: ["1d"] + +strategies: [] diff --git a/config/collections/crypto_majors.yaml b/config/collections/crypto_majors.yaml new file mode 100644 index 0000000..8e8ac32 --- /dev/null +++ b/config/collections/crypto_majors.yaml @@ -0,0 +1,34 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 2 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: crypto_majors + source: binance + exchange: binance + quote: USDT + fees: 0.0006 + slippage: 0.0005 + symbols: + - BTC/USDT + - ETH/USDT + - BNB/USDT + - SOL/USDT + - XRP/USDT + - ADA/USDT + - DOGE/USDT + - TRX/USDT + - TON/USDT + - DOT/USDT + - AVAX/USDT + - LINK/USDT + - MATIC/USDT + - LTC/USDT + - BCH/USDT + +timeframes: ["1d", "4h", "1h"] + +strategies: [] diff --git a/config/collections/custom/stocks_traderfox_dax.json b/config/collections/custom/stocks_traderfox_dax.json deleted file mode 100644 index 6970698..0000000 --- a/config/collections/custom/stocks_traderfox_dax.json +++ /dev/null @@ -1,148 +0,0 @@ -{ - "stocks_traderfox_dax": { - "name": "TraderFox DAX Stocks Portfolio", - "description": "German DAX, MDAX, and SDAX stocks based on TraderFox research covering major German corporations and mid-caps", - "asset_type": "stock", - "symbols": [ - "SAP.DE", - "SIE.DE", - "DTE.DE", - "ALV.DE", - "MUV2.DE", - "BAS.DE", - "VOW3.DE", - "BMW.DE", - "MBG.DE", - "ADS.DE", - "DB1.DE", - "DBK.DE", - "CON.DE", - "FRE.DE", - "HEN3.DE", - "MRK.DE", - "BEI.DE", - "WDI.DE", - "VNA.DE", - "IFX.DE", - "RWE.DE", - "EON.DE", - "LIN.DE", - "1COV.DE", - "FME.DE", - "DPW.DE", - "HEI.DE", - "QIA.DE", - "SHL.DE", - "ZAL.DE", - "AIR.DE", - "MTX.DE", - "PUM.DE", - "SY1.DE", - "RHM.DE", - "EVK.DE", - "FRA.DE", - "BAYN.DE", - "LHA.DE", - "TKA.DE", - "KBC.DE", - "SAX.DE", - "TEG.DE", - "WAF.DE", - "G1A.DE", - "LEG.DE", - "UTDI.DE", - "JUN3.DE", - "SZG.DE", - "WCH.DE", - "AOF.DE", - "SRT.DE", - "R3NK.DE", - "DMP.DE", - "VBK.DE", - "KTA.DE", - "SIS.DE", - "FNTN.DE", - "SKB.DE", - "PCZ.DE", - "HLAG.DE", - "NDX1.DE", - "VH2.DE", - "MPCK.DE", - "ACT.DE", - "YOC.DE", - "AT1.DE", - "COP.DE", - "CEC.DE", - "HAG.DE", - "ILM1.DE", - "BOSS.DE", - "DUE.DE", - "SHA0.DE", - "VOS.DE", - "LPK.DE", - "DEQ.DE", - "MUX.DE", - "WAC.DE", - "SDRC.DE", - "JEN.DE", - "SIX2.DE", - "AAG.DE", - "NXU.DE", - "EOAN.DE", - "MUM.DE", - "SBS.DE", - "FPE.DE", - "HBH.DE", - "KWS.DE", - "HOT.DE", - "KRN.DE", - "KSB.DE", - "TMV.DE", - "A1OS.DE", - "CHG.DE", - "FYB.DE", - "KTN.DE", - "TUI1.DE", - "MBB.DE", - "BC8.DE", - "HNR1.DE", - "LUS1.DE", - "RAA.DE", - "HYQ.DE", - "NOEJ.DE", - "TLX.DE", - "TIMA.DE", - "SMHN.DE", - "NCH2.DE", - "TTR1.DE", - "HFG.DE", - "FTK.DE", - "GXI.DE", - "GLJ.DE", - "AG1.DE", - "2GB.DE", - "GBF.DE", - "EUZ.DE", - "DHL.DE", - "BIO.DE", - "IOS.DE", - "ADN1.DE", - "8TRA.DE", - "ELG.DE", - "AAD.DE", - "1SXP.DE", - "KGX.DE", - "IXX.DE", - "GFT.DE", - "ENR.DE", - "DEZ.DE", - "YSN.DE", - "DWS.DE", - "OHB.DE", - "4X0.DE", - "FIE.DE" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/custom/stocks_traderfox_european.json b/config/collections/custom/stocks_traderfox_european.json deleted file mode 100644 index 312d542..0000000 --- a/config/collections/custom/stocks_traderfox_european.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "stocks_traderfox_european": { - "name": "TraderFox European Stocks Portfolio", - "description": "European stocks based on TraderFox research covering blue chips from Netherlands, France, Switzerland, UK, Italy, Spain, and Scandinavian markets", - "asset_type": "stock", - "symbols": [ - "ASML.AS", - "TSM", - "NESN.SW", - "ROG.SW", - "NOVN.SW", - "MC.PA", - "OR.PA", - "TTE.PA", - "SHEL.L", - "BP.L", - "GSK.L", - "AZN.L", - "ULVR.L", - "VOD.L", - "LLOY.L", - "BARC.L", - "HSBA.L", - "STAN.L", - "UBS", - "SAN.MC", - "BBVA.MC", - "BNP.PA", - "ACA.PA", - "GLE.PA", - "KER.PA", - "AI.PA", - "AIR.PA", - "SAF.PA", - "CAP.PA", - "STM", - "ASM.AS", - "BESI.AS", - "ADYEN.AS", - "PRX.AS", - "HEIA.AS", - "ABN.AS", - "INGA.AS", - "PHIA.AS", - "UNA.AS", - "MT.AS", - "AMS", - "WKL.AS", - "RAND.AS", - "TKWY.AS", - "FAGR.AS", - "LIGHT.AS", - "FLOW.AS", - "ALFEN.AS", - "BFIT.AS", - "ENX.PA", - "RI.PA", - "AD.AS", - "KPN.AS" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/custom/stocks_traderfox_us_financials.json b/config/collections/custom/stocks_traderfox_us_financials.json deleted file mode 100644 index 49a38dc..0000000 --- a/config/collections/custom/stocks_traderfox_us_financials.json +++ /dev/null @@ -1,162 +0,0 @@ -{ - "stocks_traderfox_us_financials": { - "name": "TraderFox US Financials Portfolio", - "description": "US Financial sector stocks based on TraderFox research covering banks, insurance, asset management, payment processors, and fintech companies", - "asset_type": "stock", - "symbols": [ - "JPM", - "BAC", - "WFC", - "GS", - "MS", - "C", - "USB", - "TFC", - "PNC", - "COF", - "AXP", - "BLK", - "SPGI", - "MCO", - "ICE", - "CME", - "NDAQ", - "CBOE", - "MSCI", - "TRV", - "PGR", - "ALL", - "AIG", - "MET", - "PRU", - "AFL", - "AMP", - "LNC", - "PFG", - "TMK", - "GL", - "RGA", - "CNO", - "V", - "MA", - "PYPL", - "SQ", - "FISV", - "FIS", - "ADP", - "PAYX", - "BR", - "FLT", - "WEX", - "GPN", - "TSS", - "JKHY", - "ACIW", - "QTWO", - "BILL", - "LC", - "UPST", - "AFRM", - "SOFI", - "HOOD", - "COIN", - "MSTR", - "SI", - "ALLY", - "CFG", - "KEY", - "FITB", - "HBAN", - "RF", - "MTB", - "STI", - "ZION", - "PBCT", - "CMA", - "WAL", - "EWBC", - "PACW", - "COLB", - "OZK", - "FHN", - "BKU", - "ONB", - "UMBF", - "BANF", - "WAFD", - "IBOC", - "OPFI", - "RBRK", - "KRMN", - "LTH", - "BJ", - "AU", - "WELL", - "T", - "ED", - "KR", - "AEP", - "SCHW", - "IBM", - "RTX", - "BMI", - "GME", - "LUMN", - "MLI", - "PAGS", - "MRK", - "WMT", - "F", - "KO", - "PAY", - "SG", - "NVR", - "AA", - "CACI", - "MLR", - "GRMN", - "FIX", - "FDX", - "ELF", - "DAL", - "CRM", - "CMI", - "BH", - "ARIS", - "CCJ", - "LRN", - "CMG", - "APH", - "LLY", - "BAH", - "SCCO", - "TNC", - "FCX", - "NOC", - "LMT", - "TDW", - "PWR", - "FOR", - "RSG", - "MMC", - "BRK.A", - "DT", - "PR", - "CRC", - "COR", - "VEEV", - "TDY", - "ITT", - "ATR", - "AIT", - "AWI", - "DCI", - "PH", - "GWW", - "ALG", - "NRG" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/custom/stocks_traderfox_us_healthcare.json b/config/collections/custom/stocks_traderfox_us_healthcare.json deleted file mode 100644 index 50f7f4c..0000000 --- a/config/collections/custom/stocks_traderfox_us_healthcare.json +++ /dev/null @@ -1,358 +0,0 @@ -{ - "stocks_traderfox_us_healthcare": { - "name": "TraderFox US Healthcare Portfolio", - "description": "US Healthcare sector stocks based on TraderFox research covering pharmaceuticals, biotechnology, medical devices, healthcare services, and digital health companies", - "asset_type": "stock", - "symbols": [ - "JNJ", - "PFE", - "UNH", - "MRK", - "ABBV", - "TMO", - "DHR", - "ABT", - "BMY", - "LLY", - "GILD", - "AMGN", - "BIIB", - "REGN", - "VRTX", - "ZTS", - "CVS", - "CI", - "HUM", - "ANTM", - "MCK", - "ABC", - "CAH", - "WBA", - "MDT", - "ISRG", - "SYK", - "BSX", - "BDX", - "EW", - "ZBH", - "BAX", - "HOLX", - "IDXX", - "ALGN", - "DXCM", - "RMD", - "ILMN", - "MKTX", - "VEEV", - "TDOC", - "DOCU", - "HIMS", - "ONEM", - "AMWL", - "OSCR", - "WELL", - "MRNA", - "BNTX", - "NVAX", - "OCGN", - "INO", - "SRNE", - "VXRT", - "GEVO", - "CODX", - "QDEL", - "DVAX", - "IOVA", - "TXMD", - "CERC", - "ADVM", - "DARE", - "KERN", - "XERS", - "ATOS", - "CTMX", - "PROG", - "ADMA", - "SESN", - "AXSM", - "PTCT", - "ALNY", - "BMRN", - "TECH", - "RARE", - "FOLD", - "BLUE", - "CRSP", - "EDIT", - "NTLA", - "BEAM", - "VERV", - "SGMO", - "FATE", - "BLFS", - "CDNA", - "ARCT", - "DMTK", - "VCYT", - "PACB", - "NVTA", - "TWST", - "NTRA", - "GH", - "CDXS", - "AGIO", - "ARQL", - "APLS", - "AVXL", - "CBIO", - "ACAD", - "HZNP", - "JAZZ", - "UTHR", - "NBIX", - "SAGE", - "INCY", - "EXEL", - "BGNE", - "ZLAB", - "RGNX", - "IMMU", - "SGEN", - "BPMC", - "MRUS", - "CPRX", - "VCEL", - "CAPR", - "MEDP", - "VKTX", - "ACLX", - "NVCR", - "TMDX", - "VECO", - "BHVN", - "AOSL", - "CELH", - "SEZL", - "ALLO", - "PRAX", - "RCAT", - "OTLK", - "CENX", - "PSNL", - "NSIT", - "CHRS", - "SIGA", - "ACIU", - "PERI", - "STNE", - "LILA", - "ALTI", - "INDV", - "CEVA", - "SPNS", - "CFB", - "LWAY", - "CRBP", - "FEIM", - "PLAB", - "DSP", - "PNTG", - "IDYA", - "ENTG", - "RMBS", - "TSAT", - "HALO", - "OMER", - "ADUS", - "LMAT", - "FELE", - "APOG", - "CRL", - "CBLL", - "COOP", - "CHDN", - "ENSG", - "DGX", - "KYMR", - "CSTL", - "TREE", - "SNEX", - "CCB", - "OSIS", - "PLMR", - "CASY", - "LECO", - "HLNE", - "UFPT", - "ERIE", - "MOH", - "ITIC", - "PIPR", - "FCNCA", - "VIRT", - "NARI", - "ALGT", - "RKLB", - "IONQ", - "FUTU", - "BABA", - "ULTA", - "DOCS", - "UNFI", - "BIDU", - "ROKU", - "TTAN", - "LNTH", - "TTMI", - "BYRN", - "GRAB", - "EH", - "ASTS", - "DOCN", - "SMPL", - "PGY", - "FRHC", - "ETON", - "AKRO", - "MRCY", - "RDNT", - "ARDX", - "AMAT", - "TTD", - "DKNG", - "DASH", - "TEVA", - "DBX", - "CROX", - "CORT", - "AXON", - "TXRH", - "ABNB", - "AFRM", - "NET", - "TZOO", - "EXLS", - "SOUN", - "CRWD", - "IBKR", - "AHCO", - "ENVX", - "SOFI", - "APPF", - "TLN", - "FLEX", - "TEM", - "ME", - "EBAY", - "AUR", - "TATT", - "SPSC", - "ALAB", - "PRCH", - "UPST", - "SYM", - "LRCX", - "APLT", - "MARA", - "RGTI", - "ZI", - "SNOW", - "RDVT", - "CDNS", - "SMWB", - "DTSS", - "TSLA", - "UFPI", - "AVGO", - "VRNS", - "MSFT", - "ARM", - "IMNM", - "GERN", - "VTSI", - "LUNR", - "MELI", - "APPN", - "ALVO", - "LRN", - "CMG", - "MORN", - "APH", - "QCOM", - "BAH", - "SCCO", - "TNC", - "NTNX", - "KLAC", - "FCX", - "QLYS", - "HOOD", - "MAR", - "PCAR", - "KRUS", - "TDW", - "PWR", - "PDD", - "XMTR", - "MSTR", - "GCT", - "FOR", - "LFMD", - "BKNG", - "APGE", - "ROAD", - "APP", - "ARVN", - "TNDM", - "CGON", - "ADPT", - "TRUP", - "CWST", - "SCHW", - "CALX", - "FSLR", - "PI", - "KAI", - "HWKN", - "INTU", - "PSMT", - "FCFS", - "MGRC", - "COKE", - "DORM", - "LOPE", - "COST", - "CVCO", - "HESM", - "TDY", - "ANSS", - "IPAR", - "CSWI", - "YELP", - "ATRO", - "AIOT", - "ARQT", - "INTA", - "ATAT", - "MTSR", - "ANGO", - "RR", - "ALHC", - "SLNO", - "PDEX", - "RGLD", - "OPOF", - "SENEB", - "CME", - "ORLY", - "CRWV", - "RYTM", - "TARS", - "WLFC", - "FTAI", - "AMSC" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/custom/stocks_traderfox_us_tech.json b/config/collections/custom/stocks_traderfox_us_tech.json deleted file mode 100644 index 29b6ae1..0000000 --- a/config/collections/custom/stocks_traderfox_us_tech.json +++ /dev/null @@ -1,198 +0,0 @@ -{ - "stocks_traderfox_us_tech": { - "name": "TraderFox US Technology Portfolio", - "description": "US Technology stocks based on TraderFox research covering FAANG, semiconductors, software, hardware, and emerging tech companies", - "asset_type": "stock", - "symbols": [ - "AAPL", - "MSFT", - "GOOGL", - "GOOG", - "AMZN", - "META", - "NFLX", - "TSLA", - "NVDA", - "AMD", - "INTC", - "QCOM", - "AVGO", - "TXN", - "MU", - "AMAT", - "LRCX", - "KLAC", - "MRVL", - "ADI", - "MCHP", - "ADBE", - "CRM", - "ORCL", - "IBM", - "ACN", - "NOW", - "INTU", - "CSCO", - "VMW", - "TEAM", - "DDOG", - "SNOW", - "CRWD", - "ZS", - "OKTA", - "SPLK", - "WDAY", - "VEEV", - "DOCU", - "ZM", - "TWLO", - "NET", - "FSLY", - "DBX", - "BOX", - "WORK", - "UBER", - "LYFT", - "ABNB", - "DASH", - "RBLX", - "PINS", - "SNAP", - "TWTR", - "SQ", - "PYPL", - "SHOP", - "SPOT", - "ROKU", - "ZI", - "ESTC", - "MDB", - "PLTR", - "AI", - "C3AI", - "PATH", - "SMCI", - "DELL", - "HPQ", - "HPE", - "NTAP", - "WDC", - "STX", - "PSTG", - "NTNX", - "VRSN", - "AKAM", - "CDNS", - "SNPS", - "ANSS", - "ADSK", - "FTNT", - "PANW", - "CHKP", - "CYBR", - "TENB", - "RPD", - "QLYS", - "VRNS", - "MIME", - "FEYE", - "PFPT", - "EVBG", - "CGNX", - "TER", - "COHU", - "FORM", - "MKSI", - "ONTO", - "ACLS", - "UCTT", - "PLAB", - "NVMI", - "ICHR", - "CRUS", - "SWKS", - "QRVO", - "MPWR", - "MTSI", - "SLAB", - "SITM", - "DIOD", - "WOLF", - "AMBA", - "ALGM", - "SMTC", - "POWI", - "VICR", - "ENTG", - "DXPE", - "DXCM", - "CFLT", - "CEG", - "CECO", - "ATEC", - "LUNR", - "MELI", - "MNDY", - "APPN", - "ALVO", - "MORN", - "HOOD", - "PDD", - "XMTR", - "MSTR", - "GCT", - "BLUE", - "LFMD", - "BKNG", - "APGE", - "ROAD", - "APP", - "ARVN", - "TNDM", - "CGON", - "ADPT", - "TRUP", - "CWST", - "CALX", - "FSLR", - "PI", - "HWKN", - "PSMT", - "FCFS", - "MGRC", - "COKE", - "DORM", - "LOPE", - "COST", - "CVCO", - "IPAR", - "CSWI", - "UTHR", - "YELP", - "ATRO", - "AIOT", - "ARQT", - "INTA", - "ATAT", - "MTSR", - "ANGO", - "RR", - "ALHC", - "SLNO", - "PDEX", - "OPOF", - "SENEB", - "CME", - "ORLY", - "CRWV", - "GH", - "RYTM", - "TARS", - "WLFC", - "FTAI", - "AMSC" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/bonds_core.json b/config/collections/default/bonds_core.json deleted file mode 100644 index 2363ef5..0000000 --- a/config/collections/default/bonds_core.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "bonds_core": { - "name": "Bonds Core (Liquid ETFs)", - "asset_type": "bond", - "symbols": [ - "AGG", "BND", "TLT", "IEF", "SHY", - "LQD", "HYG", "JNK", "EMB", "TIP", - "IEI", "VGIT", "GOVT", "MUB", "VCSH" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/commodities_core.json b/config/collections/default/commodities_core.json deleted file mode 100644 index 5abd72b..0000000 --- a/config/collections/default/commodities_core.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "commodities_core": { - "name": "Commodities Core (Liquid ETFs)", - "asset_type": "commodities", - "symbols": [ - "GLD", "IAU", "SLV", "PPLT", "PALL", - "DBC", "DBA", "USO", "UNG", "UGA", - "CORN", "SOYB", "WEAT", "COMT" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/crypto_liquid.json b/config/collections/default/crypto_liquid.json deleted file mode 100644 index e9a3538..0000000 --- a/config/collections/default/crypto_liquid.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "crypto_liquid": { - "name": "Crypto Liquid (Top Market Cap)", - "asset_type": "crypto", - "symbols": [ - "BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT", "XRPUSDT", - "ADAUSDT", "DOGEUSDT", "LTCUSDT", "DOTUSDT", "AVAXUSDT", - "LINKUSDT", - "TRXUSDT", "MATICUSDT", "TONUSDT", "BCHUSDT", "NEARUSDT", - "ATOMUSDT", "XLMUSDT", "ETCUSDT", "FILUSDT", "SUIUSDT", - "ARBUSDT", "OPUSDT", "APTUSDT", "INJUSDT", "RUNEUSDT", - "IMXUSDT", "HBARUSDT", "AAVEUSDT", "MKRUSDT", "UNIUSDT", - "SHIBUSDT", "PEPEUSDT" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/forex_majors.json b/config/collections/default/forex_majors.json deleted file mode 100644 index 44172ec..0000000 --- a/config/collections/default/forex_majors.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "forex_majors": { - "name": "Forex Majors", - "asset_type": "forex", - "symbols": [ - "EURUSD=X", "GBPUSD=X", "USDJPY=X", "USDCHF=X", "AUDUSD=X", - "USDCAD=X", "NZDUSD=X", "EURJPY=X", "GBPJPY=X", "EURGBP=X" - ], - "initial_capital": 10000, - "commission": 0.0002 - } -} diff --git a/config/collections/default/indices_global_core.json b/config/collections/default/indices_global_core.json deleted file mode 100644 index d49af86..0000000 --- a/config/collections/default/indices_global_core.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "indices_global_core": { - "name": "Global Indices (ETFs)", - "asset_type": "indices", - "symbols": [ - "SPY", "QQQ", "DIA", "IWM", - "EFA", "EEM", "VGK", "EWJ", "FXI", - "VTI", "ACWI" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_global_factor_core.json b/config/collections/default/stocks_global_factor_core.json deleted file mode 100644 index e259cd8..0000000 --- a/config/collections/default/stocks_global_factor_core.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "stocks_global_factor_core": { - "name": "Global Factor Core (ACWI + Factors)", - "asset_type": "stocks", - "symbols": [ - "ACWI", - "ACWV", - "IQLT", - "QUAL", - "VLUE", - "IVLU", - "MTUM", - "IMTM", - "SIZE", - "ISZE" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_us_growth_core.json b/config/collections/default/stocks_us_growth_core.json deleted file mode 100644 index a06d602..0000000 --- a/config/collections/default/stocks_us_growth_core.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "stocks_us_growth_core": { - "name": "US Growth Tech Core", - "asset_type": "stocks", - "symbols": [ - "AAPL", "MSFT", "NVDA", "AVGO", "META", - "GOOGL", "AMD", "CRM", "ORCL", "NOW" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_us_mega_core.json b/config/collections/default/stocks_us_mega_core.json deleted file mode 100644 index 11acabc..0000000 --- a/config/collections/default/stocks_us_mega_core.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "stocks_us_mega_core": { - "name": "US Mega-Cap Core", - "asset_type": "stocks", - "symbols": [ - "AAPL", "MSFT", "GOOGL", "AMZN", "META", - "NVDA", "TSLA", "BRK-B", "JPM", "JNJ" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_us_minvol_core.json b/config/collections/default/stocks_us_minvol_core.json deleted file mode 100644 index a18df74..0000000 --- a/config/collections/default/stocks_us_minvol_core.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "stocks_us_minvol_core": { - "name": "US Min-Vol Core (ETFs)", - "asset_type": "stocks", - "symbols": [ - "USMV", - "SPLV", - "XMLV", - "SMLV", - "LVHD", - "VFMV", - "EFAV", - "ACWV", - "LOWV", - "LGLV" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_us_quality_core.json b/config/collections/default/stocks_us_quality_core.json deleted file mode 100644 index 2c0bc70..0000000 --- a/config/collections/default/stocks_us_quality_core.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "stocks_us_quality_core": { - "name": "US Quality Core (ETFs)", - "asset_type": "stocks", - "symbols": [ - "QUAL", - "SPHQ", - "VFQY", - "QDF", - "DGRW", - "JQUA", - "OUSA", - "FGQG", - "QGRW", - "JQUA" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/default/stocks_us_value_core.json b/config/collections/default/stocks_us_value_core.json deleted file mode 100644 index 2a45e2d..0000000 --- a/config/collections/default/stocks_us_value_core.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "stocks_us_value_core": { - "name": "US Value Core (ETFs)", - "asset_type": "stocks", - "symbols": [ - "VTV", - "IWD", - "SCHV", - "RPV", - "SPYV", - "IVE", - "VONV", - "VLUE", - "VYM", - "DVY" - ], - "initial_capital": 10000, - "commission": 0.001 - } -} diff --git a/config/collections/forex_intraday_finnhub.yaml b/config/collections/forex_intraday_finnhub.yaml new file mode 100644 index 0000000..6cec7fc --- /dev/null +++ b/config/collections/forex_intraday_finnhub.yaml @@ -0,0 +1,27 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: forex_intraday_finnhub + source: finnhub + fees: 0.0002 + slippage: 0.0002 + symbols: + - EURUSD + - GBPUSD + - USDJPY + - USDCHF + - USDCAD + - AUDUSD + - NZDUSD + - EURGBP + - EURJPY + - GBPJPY + +timeframes: ["1h", "15m", "5m"] + +strategies: [] diff --git a/config/collections/forex_intraday_twelvedata.yaml b/config/collections/forex_intraday_twelvedata.yaml new file mode 100644 index 0000000..87718c8 --- /dev/null +++ b/config/collections/forex_intraday_twelvedata.yaml @@ -0,0 +1,27 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: forex_intraday_twelvedata + source: twelvedata + fees: 0.0002 + slippage: 0.0002 + symbols: + - EURUSD + - GBPUSD + - USDJPY + - USDCHF + - USDCAD + - AUDUSD + - NZDUSD + - EURGBP + - EURJPY + - GBPJPY + +timeframes: ["1h", "15m", "5m"] + +strategies: [] diff --git a/config/collections/forex_majors.yaml b/config/collections/forex_majors.yaml new file mode 100644 index 0000000..80956a2 --- /dev/null +++ b/config/collections/forex_majors.yaml @@ -0,0 +1,40 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: forex_majors + source: yfinance + # Approximate IBKR-like costs for FX; adjust if needed + fees: 0.0002 + slippage: 0.0002 + symbols: + # Major USD pairs (provider-agnostic) + - EURUSD + - GBPUSD + - USDJPY + - USDCHF + - USDCAD + - AUDUSD + - NZDUSD + # Crosses (popular/liquid) + - EURGBP + - EURJPY + - GBPJPY + - AUDJPY + - CADJPY + - CHFJPY + - EURCHF + - EURCAD + - EURAUD + - GBPCAD + - GBPAUD + - NZDJPY + +# Choose timeframes compatible across sources +timeframes: ["1d", "1h"] + +strategies: [] diff --git a/config/collections/indices_majors.yaml b/config/collections/indices_majors.yaml new file mode 100644 index 0000000..dd88625 --- /dev/null +++ b/config/collections/indices_majors.yaml @@ -0,0 +1,33 @@ +metric: sortino +engine: vectorbt +asset_workers: 4 +param_workers: 1 +max_fetch_concurrency: 2 +cache_dir: .cache/data + +collections: + - name: indices_majors + source: tiingo + fees: 0.0005 + slippage: 0.0005 + symbols: + # Use ETF proxies with stable daily data from Tiingo + - SPY # S&P 500 + - QQQ # Nasdaq 100 + - DIA # Dow Jones Industrial Average + - IWM # Russell 2000 + - EFA # MSCI EAFE + - EEM # MSCI Emerging Markets + - VGK # Europe + - VPL # Pacific + - EWJ # Japan + - EWU # UK + - EWG # Germany + - EWQ # France + - FXI # China large-cap + - INDA # India + - EWZ # Brazil + +timeframes: ["1d"] + +strategies: [] diff --git a/config/example.yaml b/config/example.yaml new file mode 100644 index 0000000..f968517 --- /dev/null +++ b/config/example.yaml @@ -0,0 +1,56 @@ +metric: sortino # sharpe | sortino | profit +engine: vectorbt # single engine for backtesting + param sweeps +asset_workers: 4 +param_workers: 2 +max_fetch_concurrency: 2 +fees: 0.0005 +slippage: 0.0005 +risk_free_rate: 0.0 +cache_dir: .cache/data + +collections: + # Crypto majors (Binance via ccxt) + - name: crypto_majors + source: binance + exchange: binance + quote: USDT + symbols: ["BTC/USDT", "ETH/USDT", "BNB/USDT", "SOL/USDT"] + fees: 0.0006 # approx Bybit/Binance taker + slippage: 0.0005 + + # Bonds (majors via ETFs on Tiingo for stable daily data) + - name: bonds_majors + source: tiingo + symbols: ["TLT", "IEF", "SHY", "BND", "HYG"] + fees: 0.0005 # approx IBKR + slippage: 0.0005 + + # Commodities (majors via ETFs on Tiingo) + - name: commodities_majors + source: tiingo + symbols: ["GLD", "SLV", "PPLT", "USO", "UNG", "DBC", "DBA", "DBB", "CPER", "WEAT", "CORN", "SOYB"] + fees: 0.0005 + slippage: 0.0005 + + # Indices (majors via ETF proxies on Tiingo) + - name: indices_majors + source: tiingo + symbols: ["SPY", "QQQ", "DIA", "IWM", "EFA", "EEM"] + fees: 0.0005 + slippage: 0.0005 + +# Choose timeframes compatible across sources for full history +timeframes: ["1d"] + +strategies: + # Example: replace with your external strategy name and optionally module/class. + # If module/class are omitted, loader tries to find a BaseStrategy subclass matching `name`. + # Replace placeholders with your external strategy names and optional module/class. + - name: YourStrategyName + # module: your_package.module + # class: YourStrategyClass + params: {} + # - name: AnotherStrategy + # module: your_package.other_module + # class: AnotherStrategyClass + # params: {} diff --git a/docker-compose.yml b/docker-compose.yml index 5bb26e9..fa619b0 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,92 +1,21 @@ services: - # PostgreSQL database - postgres: - image: postgres:15-alpine - container_name: quant-postgres - environment: - - POSTGRES_DB=${POSTGRES_DB:-quant_system} - - POSTGRES_USER=${POSTGRES_USER:-quantuser} - - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-quantpass} - ports: - - "5433:5432" - volumes: - - postgres-data:/var/lib/postgresql/data - - ./scripts/initdb/init.sql:/docker-entrypoint-initdb.d/init.sql:ro - restart: unless-stopped - - # pgAdmin for database management - pgadmin: - image: dpage/pgadmin4:latest - container_name: quant-pgadmin - environment: - - PGADMIN_DEFAULT_EMAIL=${PGADMIN_DEFAULT_EMAIL:-admin@quant.local} - - PGADMIN_DEFAULT_PASSWORD=${PGADMIN_DEFAULT_PASSWORD:-quantpass} - - PGADMIN_CONFIG_SERVER_MODE=${PGADMIN_CONFIG_SERVER_MODE:-False} - ports: - - "5050:80" - depends_on: - - postgres - restart: unless-stopped - - # Local quant system with production features - quant: + app: build: context: . - dockerfile: DOCKERFILE - target: development - image: quant-system:local - container_name: quant-system + dockerfile: docker/Dockerfile + image: quant-system:latest environment: - - ENVIRONMENT=${ENVIRONMENT:-development} - - CACHE_DIR=${CACHE_DIR:-/app/cache} - - LOG_LEVEL=${LOG_LEVEL:-INFO} - - DATABASE_URL=${DATABASE_URL:-postgresql://quantuser:quantpass@postgres:5432/quant_system} - - TAILWIND_CSS_HREF=${TAILWIND_CSS_HREF:-exports/reports/assets/tailwind.min.css} - - PYTHONPATH=/app - - ALPHA_VANTAGE_API_KEY=${ALPHA_VANTAGE_API_KEY} - - TWELVE_DATA_API_KEY=${TWELVE_DATA_API_KEY} - - POLYGON_API_KEY=${POLYGON_API_KEY} - - TIINGO_API_KEY=${TIINGO_API_KEY} - - FINNHUB_API_KEY=${FINNHUB_API_KEY} - - BYBIT_API_KEY=${BYBIT_API_KEY} - - BYBIT_API_SECRET=${BYBIT_API_SECRET} - - BYBIT_TESTNET=${BYBIT_TESTNET:-false} - - OPENAI_API_KEY=${OPENAI_API_KEY} - - OPENAI_MODEL=${OPENAI_MODEL:-gpt-4o} - - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} - - ANTHROPIC_MODEL=${ANTHROPIC_MODEL:-claude-3-5-sonnet-20241022} - - USE_REDIS_RECENT=${USE_REDIS_RECENT:-false} - - REDIS_URL=${REDIS_URL:-redis://redis:6379/0} + - PYTHONUNBUFFERED=1 + - TZ=UTC + # Override STRATEGIES_PATH or DATA_CACHE_DIR via env if needed + - STRATEGIES_PATH=/ext/strategies + - DATA_CACHE_DIR=/app/.cache/data + - PATH=/root/.local/bin:/usr/local/bin:/usr/local/sbin:/usr/sbin:/usr/bin:/sbin:/bin + env_file: + - .env volumes: - - ./cache:/app/cache - - ./exports:/app/exports - - ./logs:/app/logs - - ./config:/app/config:ro - - ./src:/app/src:ro - - ./scripts:/app/scripts:ro - - ./tests:/app/tests:ro - # Mount external strategies from host; set STRATEGIES_HOST_PATH to an absolute host path - - ${STRATEGIES_HOST_PATH}:/app/external_strategies:ro - - ./artifacts:/app/artifacts - depends_on: - - postgres - stdin_open: true - tty: true - command: ["bash"] - - # Optional Redis for recent overlay cache (enable via profile) - redis: - image: redis:7-alpine - container_name: quant-redis - ports: - - "6379:6379" - command: ["redis-server", "--save", "", "--appendonly", "no"] - restart: unless-stopped - profiles: ["redis"] - -networks: - default: - driver: bridge - -volumes: - postgres-data: + - ./:/app + # Mount your external strategies repo here + - ${HOST_STRATEGIES_PATH:-./external-strategies}:/ext/strategies:ro + working_dir: /app + command: ["sleep", "infinity"] diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..3934f70 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,40 @@ +FROM python:3.10-slim + +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + POETRY_VERSION=1.8.3 \ + POETRY_VIRTUALENVS_CREATE=false \ + PIP_NO_CACHE_DIR=1 \ + PIP_DEFAULT_TIMEOUT=120 \ + PATH="/root/.local/bin:${PATH}" + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential curl git \ + && rm -rf /var/lib/apt/lists/* + +RUN curl -sSL https://install.python-poetry.org | python - --version ${POETRY_VERSION} + +# Ensure poetry is on a standard PATH location +RUN ln -s /root/.local/bin/poetry /usr/local/bin/poetry || true + +WORKDIR /app + +COPY pyproject.toml ./ +# Retry install to mitigate transient PyPI timeouts in CI/builders +RUN set -euo pipefail; \ + poetry --version; \ + for i in 1 2 3; do \ + if poetry install --no-root --no-interaction --no-ansi; then \ + break; \ + fi; \ + echo "Poetry install failed (attempt $i), retrying in 10s..."; \ + sleep 10; \ + if [ "$i" = "3" ]; then \ + echo "Poetry install failed after 3 attempts"; \ + exit 1; \ + fi; \ + done + +COPY . /app + +CMD ["bash", "-lc", "poetry run python -m src.main --help"] diff --git a/docs/ROADMAP.md b/docs/ROADMAP.md new file mode 100644 index 0000000..acc3b55 --- /dev/null +++ b/docs/ROADMAP.md @@ -0,0 +1,46 @@ +# Future Roadmap + +## Overview + +This document outlines potential enhancements to evolve the system toward a production-grade, research and execution platform. + +## Data & Ingestion + +- API integrations: complete Polygon, Tiingo, Alpaca implementations with retries, paging, and symbol discovery. +- Exchange symbol discovery and liquidity filters (e.g., Bybit/Binance with volume thresholds) and universe builders. +- Corporate actions / splits / dividends normalization and corporate events feed. +- Multiple caching tiers: HTTP cache, Parquet cache, feature cache; retention policies and compaction. + +## Backtesting & Optimization + +- Alternative engines: adapter for backtesting.py with built-in optimizer; modular engine interface. +- Parameter search: Bayesian optimization (Optuna), random search, hyperband; early stopping. +- Walk-forward analysis and nested CV; rolling re-optimization; regime-aware parameter sets. +- Transaction cost models: tiered fees, borrow rates, shorting constraints, per-venue slippage and market impact. + +## Risk & Analytics + +- Advanced metrics: Calmar, Omega, Tail ratio, Pain index; probabilistic drawdown forecasts. +- Risk decomposition: factor models, sector/asset class exposure; Kelly sizing and volatility targeting. +- Scenario analysis: stress testing against historical shocks; Monte Carlo path sampling. + +## Execution & Live + +- API server: REST/GraphQL to query results, trigger runs, retrieve artifacts, and push signals. +- Streaming signal topics (Kafka/NATS) and order routing adapters (IBKR, CCXT exchanges). +- Scheduling: Airflow/Prefect orchestration; historical + daily incremental pipelines. +- TradingAgents integration: [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents) for advanced agentic RL strategies. + +## Reporting & UX + +- Rich HTML reports: interactive charts (Plotly/Altair), equity curves, drawdown charts, and trade logs. +- Dashboard: lightweight UI (FastAPI + HTMX/Tailwind) to browse runs, compare strategies, download exports. +- Notifications: email/Slack alerts when new best models/params surpass thresholds. + +## Infra & Quality + +- Distributed compute: Ray/Dask for large param grids and asset universes. +- Cloud object storage for caches and artifacts; retention lifecycles. +- Secrets management with Vault/SOPS; per-environment configs. +- Test coverage >80% with unit + integration tests; golden files for reports. +- Pre-commit hooks for code, markdown, and schema validation; nightly CI e2e runs. diff --git a/docs/cli-guide.md b/docs/cli-guide.md deleted file mode 100644 index 7833df8..0000000 --- a/docs/cli-guide.md +++ /dev/null @@ -1,180 +0,0 @@ -# CLI Reference - -This guide documents the CLI. It includes a short section for the current unified CLI and a preserved legacy section for older multi-subcommand commands. - -Note: The current entrypoint focuses on the `collection` subcommand. Use the README for up-to-date commands. Legacy examples are kept below for context. - -## Current (Unified) CLI - -```bash -# Show help (inside Docker) -docker compose run --rm quant python -m src.cli.unified_cli --help - -# Run bonds collection (1d/max, all strategies) -docker compose run --rm -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --action direct --interval 1d --period max --strategies all --exports all - -# Dry run (plan only) + exports (csv, report, tradingview, ai or all) -docker compose run --rm -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --interval 1d --period max --strategies all --dry-run --exports all - -Exports and naming: -- CSV → `exports/csv///_Collection___.csv` -- Reports → `exports/reports///_Collection___.html` -- TV alerts → `exports/tv_alerts///_Collection___.md` -- AI recos (md/html) → `exports/ai_reco///_Collection___.*` - -When multiple intervals are used, filenames prefer `1d`. Use `--interval 1d` to constrain content and filenames. -``` - -## Legacy CLI (Preserved) - -These examples refer to a previous iteration of the CLI that exposed categories like `portfolio`, `data`, `cache`, and `reports`. Prefer the section above for current usage. - -### Quick Start (legacy) - -```bash -# Activate environment -poetry shell - -# List available portfolios -python -m src.cli.unified_cli portfolio list - -# Test a portfolio -python -m src.cli.unified_cli portfolio test crypto --open-browser -``` - -### Command Structure (legacy) - -``` -python -m src.cli.unified_cli [options] -``` - -### Portfolio Commands (legacy) - -#### List Portfolios -```bash -python -m src.cli.unified_cli portfolio list -``` - -#### Test Portfolio -```bash -python -m src.cli.unified_cli portfolio test [options] - -Options: - --metric METRIC Performance metric (sharpe_ratio, sortino_ratio) - --period PERIOD Time period (1d, 1w, 1m, 3m, 6m, 1y, max) - --test-timeframes Test multiple timeframes - --open-browser Auto-open results in browser -``` - -#### Test All Strategies and Timeframes -```bash -python -m src.cli.unified_cli portfolio test-all --symbols SYMBOL1,SYMBOL2 [options] - -Options: - --symbols SYMBOLS Comma-separated symbols to test - --start-date DATE Start date (YYYY-MM-DD) - --end-date DATE End date (YYYY-MM-DD) - --strategies LIST Comma-separated strategies to test -``` - -### Data Commands (legacy) - -#### Download Data -```bash -python -m src.cli.unified_cli data download --symbols AAPL,GOOGL [options] - -Options: - --symbols SYMBOLS Comma-separated symbols - --start-date DATE Start date (YYYY-MM-DD) - --end-date DATE End date (YYYY-MM-DD) - --source SOURCE Data source (yahoo, alpha_vantage, etc.) -``` - -### Cache Commands (legacy) - -#### Cache Statistics -```bash -python -m src.cli.unified_cli cache stats -``` - -#### Clear Cache -```bash -python -m src.cli.unified_cli cache clear [--all] [--symbol SYMBOL] -``` - -### Report Commands (legacy) - -#### Generate Reports -```bash -python -m src.cli.unified_cli reports generate [options] - -Options: - --format FORMAT Output format (html, pdf, json) - --period PERIOD Analysis period - --output-dir DIR Output directory -``` - -#### Organize Reports -```bash -python -m src.cli.unified_cli reports organize -``` - -### Examples (legacy) - -#### Test Crypto Portfolio -```bash -# Using Sortino ratio (default - superior to Sharpe) -python -m src.cli.unified_cli portfolio test crypto \ - --metric sortino_ratio \ - --period 1y \ - --test-timeframes \ - --open-browser - -# Traditional Sharpe ratio (for comparison) -python -m src.cli.unified_cli portfolio test crypto \ - --metric sharpe_ratio \ - --period 1y -``` - -#### Download Forex Data -```bash -python -m src.cli.unified_cli data download \ - --symbols EURUSD=X,GBPUSD=X \ - --start-date 2023-01-01 \ - --source twelve_data -``` - -#### Daily Workflow -```bash -# Check cache status -python -m src.cli.unified_cli cache stats - -# Test all portfolios (Sortino ratio default) -python -m src.cli.unified_cli portfolio test-all --metric sortino_ratio --period 1d --open-browser - -# Organize reports -python -m src.cli.unified_cli reports organize -``` - -## Configuration (legacy) - -Set environment variables in `.env`: -```bash -LOG_LEVEL=INFO -CACHE_ENABLED=true -DEFAULT_PERIOD=1y -BROWSER_AUTO_OPEN=true -``` - -## Help (legacy) - -Get help for any command: -```bash -python -m src.cli.unified_cli --help -python -m src.cli.unified_cli portfolio --help -python -m src.cli.unified_cli portfolio test --help -``` diff --git a/docs/data-sources.md b/docs/data-sources.md deleted file mode 100644 index cbd3c9b..0000000 --- a/docs/data-sources.md +++ /dev/null @@ -1,203 +0,0 @@ -# Data Sources Guide - -Guide to supported data sources and their configuration. - -## Supported Sources - -### 1. Yahoo Finance (Free) -- **Assets**: Stocks, ETFs, Indices, Forex, Crypto -- **API Key**: Not required -- **Rate Limits**: Moderate -- **Reliability**: High -- **Symbol Format**: `AAPL`, `EURUSD=X`, `BTC-USD` - -### 2. Alpha Vantage -- **Assets**: Stocks, Forex, Crypto, Commodities -- **API Key**: Required (free tier available) -- **Rate Limits**: 5 calls/minute (free), 75 calls/minute (premium) -- **Symbol Format**: `AAPL`, `EUR/USD`, `BTC` - -### 3. Twelve Data -- **Assets**: Stocks, Forex, Crypto, ETFs -- **API Key**: Required -- **Rate Limits**: 800 calls/day (free) -- **Symbol Format**: `AAPL`, `EUR/USD`, `BTC/USD` - -### 4. Polygon.io -- **Assets**: Stocks, Options, Forex, Crypto -- **API Key**: Required -- **Rate Limits**: Based on plan -- **Symbol Format**: `AAPL`, `C:EURUSD`, `X:BTCUSD` - -### 5. Tiingo -- **Assets**: Stocks, ETFs, Forex, Crypto -- **API Key**: Required -- **Rate Limits**: 1000 calls/hour (free) -- **Symbol Format**: `AAPL`, `EURUSD`, `BTCUSD` - -### 6. Finnhub -- **Assets**: Stocks, Forex, Crypto -- **API Key**: Required -- **Rate Limits**: 60 calls/minute (free) -- **Symbol Format**: `AAPL`, `OANDA:EUR_USD`, `BINANCE:BTCUSDT` - -### 7. Bybit -- **Assets**: Crypto derivatives -- **API Key**: Optional (public data) -- **Rate Limits**: High -- **Symbol Format**: `BTCUSDT`, `ETHUSDT` - -### 8. Pandas DataReader -- **Assets**: Economic data (FRED, World Bank, etc.) -- **API Key**: Not required -- **Symbol Format**: `GDP`, `UNRATE` - -## Configuration - -### Environment Variables -Create a `.env` file: -```bash -# API Keys -ALPHA_VANTAGE_API_KEY=your_key_here -TWELVE_DATA_API_KEY=your_key_here -POLYGON_API_KEY=your_key_here -TIINGO_API_KEY=your_key_here -FINNHUB_API_KEY=your_key_here - -# Optional Bybit API (for private data) -BYBIT_API_KEY=your_key_here -BYBIT_API_SECRET=your_secret_here -``` - -### Portfolio Configuration -Specify data sources in portfolio configs: -```json -{ - "data_source": { - "primary_source": "yahoo", - "fallback_sources": ["alpha_vantage", "twelve_data"] - } -} -``` - -## Symbol Transformation - -The system automatically transforms symbols between different data source formats: - -| Asset Type | Yahoo Finance | Alpha Vantage | Twelve Data | Bybit | -|------------|---------------|---------------|-------------|-------| -| **Stocks** | `AAPL` | `AAPL` | `AAPL` | N/A | -| **Forex** | `EURUSD=X` | `EUR/USD` | `EUR/USD` | N/A | -| **Crypto** | `BTC-USD` | `BTC` | `BTC/USD` | `BTCUSDT` | -| **Indices** | `^GSPC` | `SPX` | `SPX` | N/A | - -## Best Practices - -### 1. Use Fallback Sources -Always configure fallback sources for reliability: -```json -{ - "primary_source": "yahoo", - "fallback_sources": ["alpha_vantage", "twelve_data"] -} -``` - -### 2. Respect Rate Limits -- Use caching to minimize API calls -- Implement delays between requests -- Monitor usage for paid services - -### 3. Data Quality -- Validate data after fetching -- Check for missing values -- Compare across sources for consistency - -### 4. Cost Management -- Use free sources (Yahoo Finance) when possible -- Monitor API usage for paid services -- Cache data to reduce API calls - -## Troubleshooting - -### Common Issues - -1. **API Key Errors** - ```bash - # Check environment variables - echo $ALPHA_VANTAGE_API_KEY - - # Verify .env file - cat .env - ``` - -2. **Rate Limit Exceeded** - ```bash - # Clear cache and retry later - python -m src.cli.unified_cli cache clear --all - ``` - -3. **Symbol Not Found** - ```bash - # Check symbol format for the data source - # Use data validation command - python -m src.cli.unified_cli data validate --symbol AAPL - ``` - -4. **Network Issues** - ```bash - # Test connectivity - ping finance.yahoo.com - - # Check firewall/proxy settings - ``` - -### Debug Mode -Enable debug logging for detailed information: -```bash -export LOG_LEVEL=DEBUG -python -m src.cli.unified_cli data download --symbols AAPL -``` - -## Getting API Keys - -### Alpha Vantage -1. Visit https://www.alphavantage.co/support/#api-key -2. Sign up for free account -3. Get API key from dashboard - -### Twelve Data -1. Visit https://twelvedata.com/pricing -2. Sign up for free plan -3. Get API key from account settings - -### Polygon.io -1. Visit https://polygon.io/pricing -2. Sign up for plan -3. Get API key from dashboard - -### Tiingo -1. Visit https://api.tiingo.com/ -2. Sign up for free account -3. Get API token from account - -### Finnhub -1. Visit https://finnhub.io/pricing -2. Sign up for free account -3. Get API key from dashboard - -## Performance Optimization - -### Caching Strategy -- Cache data for 1 hour (default) -- Use Parquet format for compression -- Implement cache expiration - -### Parallel Downloads -- Fetch multiple symbols concurrently -- Use connection pooling -- Implement retry logic - -### Data Validation -- Check data completeness -- Validate OHLCV format -- Remove invalid entries diff --git a/docs/development.md b/docs/development.md deleted file mode 100644 index 3ed9d6c..0000000 --- a/docs/development.md +++ /dev/null @@ -1,202 +0,0 @@ -# Development Guide - -Guide for developers working on the Quant Trading System. - -## 🚀 Quick Setup - -### Prerequisites -- Python 3.12+ -- Poetry -- Git - -### Installation -```bash -git clone https://github.com/LouisLetcher/quant-system.git -cd quant-system -poetry install --with dev -poetry shell -pre-commit install -``` - -## 🧪 Testing - -### Running Tests -```bash -# All tests with coverage -pytest - -# Unit tests only -pytest -m "not integration" - -# Integration tests -pytest -m "integration" - -# Specific test file -pytest tests/test_data_manager.py - -# Parallel execution -pytest -n auto -``` - -### Database for Tests -- By default, unit tests and CI use a lightweight SQLite database to avoid any external Postgres dependency. -- The unified DB models auto-detect CI/pytest and prefer SQLite when any of these env vars are present: `CI`, `PYTEST_CURRENT_TEST`, or `TESTING`. -- You can force this behavior explicitly by setting: - - `UNIFIED_MODELS_SQLITE=1` - - Optionally, also set `DATABASE_URL=sqlite:///quant_unified_test.db` for consistency. - -Examples: -```bash -# Local: force SQLite for tests -export UNIFIED_MODELS_SQLITE=1 -export DATABASE_URL=sqlite:///quant_unified_test.db -pytest -``` - -### Test Structure -- `tests/test_*.py` - Unit tests -- `tests/test_integration.py` - Integration tests -- `tests/conftest.py` - Shared fixtures and configuration - -## 🔍 Code Quality - -### Formatting and Linting -```bash -ruff check . # Lint and format code -ruff format . # Format code (alternative) -mypy src/ # Type checking -``` - -### Security Checks -```bash -bandit -r src/ # Security linting -safety check # Dependency vulnerabilities -``` - -### Pre-commit Hooks -Pre-commit hooks run automatically on git commit: -- Code formatting and linting (Ruff) -- Type checking (MyPy) -- Security scanning (Bandit) - -## 📁 Project Structure - -``` -src/ -├── ai/ # AI recommendation system -├── backtesting_engine/ # Strategies submodule (quant-strategies repo) -│ └── algorithms/python/ # Python strategy implementations (40+ strategies) -├── cli/ # Command-line interface -├── core/ # Core system components -│ ├── data_manager.py # Data fetching and management -│ ├── direct_backtest.py # Direct backtesting library integration -│ └── portfolio_manager.py # Portfolio management -├── database/ # Database models and operations -├── portfolio/ # Portfolio optimization -├── reporting/ # Report generation -└── utils/ # Utility functions - -tests/ -├── test_*.py # Unit tests -├── test_integration.py # Integration tests -└── conftest.py # Test configuration - -config/ -└── collections/ # Asset collections -``` - -## 🔧 Development Commands - -### Building -```bash -poetry build # Build package -docker build . # Build Docker image -``` - -### Running Services -```bash -# CLI discovery -python -m src.cli.unified_cli --help - -# Docker development (compose v2) -docker compose up -d postgres pgadmin -docker compose build quant -docker compose run --rm quant python -m src.cli.unified_cli --help -``` - -## 📝 Contributing - -1. **Fork** the repository -2. **Create** a feature branch: `git checkout -b feature/amazing-feature` -3. **Make** your changes -4. **Add** tests for new functionality -5. **Ensure** all tests pass: `pytest` -6. **Commit** your changes: `git commit -m 'Add amazing feature'` -7. **Push** to the branch: `git push origin feature/amazing-feature` -8. **Open** a Pull Request - -### Code Style Guidelines -- **Line length**: 88 characters (Black default) -- **Type hints**: Required for all public functions -- **Docstrings**: Google-style for all modules, classes, and functions -- **Tests**: Required for all new functionality - -### Commit Message Format -``` -type(scope): description - -[optional body] - -[optional footer] -``` - -Examples: -- `feat(data): add Alpha Vantage data source` -- `fix(portfolio): correct position sizing calculation` -- `docs(readme): update installation instructions` - -## 🔍 Debugging - -### Environment Variables -```bash -export LOG_LEVEL=DEBUG -export TESTING=true -``` - -### Common Issues -1. **Import errors**: Ensure virtual environment is activated -2. **API failures**: Check API keys in `.env` file -3. **Permission errors**: Check file permissions for cache/exports directories - -### Debug Commands -```bash -# Clear cache -rm -rf cache/* - -# Reset environment -poetry env remove python -poetry install --with dev - -# Verbose testing -pytest -v -s -``` - -## 📊 CI/CD Pipeline - -The project uses GitHub Actions for continuous integration: - -- **Pull Request**: Lint, test, security checks -- **Main Branch**: Full test suite, build, deploy docs -- **Tags**: Create releases, build Docker images - -### Workflow Files -- `.github/workflows/ci.yml` - Main CI/CD pipeline -- `.github/workflows/release.yml` - Release automation -- `.github/workflows/codeql.yml` - Security analysis - -## 📚 Additional Resources - -- **Poetry Documentation**: https://python-poetry.org/docs/ -- **pytest Documentation**: https://docs.pytest.org/ -- **Black Documentation**: https://black.readthedocs.io/ -- **FastAPI Documentation**: https://fastapi.tiangolo.com/ diff --git a/docs/docker.md b/docs/docker.md deleted file mode 100644 index 85b2db4..0000000 --- a/docs/docker.md +++ /dev/null @@ -1,95 +0,0 @@ -# Docker Guide - -Guide for running this repository with Docker Compose. This reflects the current compose file and unified CLI. - -## Services - -From `docker-compose.yml`: - -- `postgres` — PostgreSQL 15 (persisted via `postgres-data` volume, exposed on host `5433`). -- `pgadmin` — pgAdmin UI (exposed on host `5050`). -- `quant` — Application container (mounts source, strategies, cache, exports, logs, config, artifacts). - -## Quick Start - -```bash -# 1) Copy env and edit keys -cp .env.example .env - -# 2) Start DB + pgAdmin (may pull images on first run) -docker compose up -d postgres pgadmin - -# 3) Build the app image -docker compose build quant - -# 4) Show CLI help -docker compose run --rm quant python -m src.cli.unified_cli --help -``` - -## Preferred Run: Bonds, 1d, Max, All Strategies - -```bash -docker compose run --rm \ - -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --action direct \ - --interval 1d \ - --period max \ - --strategies all \ - --exports all \ - --log-level INFO -``` - -## Dry Run (Plan Only) + Exports - -```bash -docker compose run --rm \ - -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --interval 1d --period max --strategies all \ - --dry-run --exports all --log-level DEBUG - -Exports written under `exports/`: -- CSV → `exports/csv///...` -- Reports → `exports/reports///...` -- TV alerts → `exports/tv_alerts///...` -- AI recos (md/html/csv) → `exports/ai_reco///...` -``` - -## Interactive Shell - -```bash -docker compose run --rm quant bash -``` - -## Ports, Mounts, and Volumes - -- Ports: Postgres `5433→5432`, pgAdmin `5050→80` -- Mounts (repo → container): - - `./cache` → `/app/cache` - - `./exports` → `/app/exports` - - `./logs` → `/app/logs` - - `./config` → `/app/config:ro` - - `./src` → `/app/src:ro` - - `./artifacts` → `/app/artifacts` - - `./quant-strategies/algorithms/python` → `/app/external_strategies:ro` -- Volume: `postgres-data` → `/var/lib/postgresql/data` - -## Environment - -- In-container DB: `DATABASE_URL=postgresql://quantuser:quantpass@postgres:5432/quant_system` -- Optional API keys: `ALPHA_VANTAGE_API_KEY`, `TWELVE_DATA_API_KEY`, `POLYGON_API_KEY`, `TIINGO_API_KEY`, `FINNHUB_API_KEY`, `BYBIT_API_KEY`, `BYBIT_API_SECRET`, `BYBIT_TESTNET` -- Optional LLMs: `OPENAI_API_KEY`, `OPENAI_MODEL`, `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL` - -## pgAdmin - -- Open `http://localhost:5050` -- Credentials from `.env` (`PGADMIN_DEFAULT_EMAIL`, `PGADMIN_DEFAULT_PASSWORD`) -- Register server: host `postgres`, port `5432`, DB `quant_system`, user `quantuser` - -## Troubleshooting - -- Use singular subcommand `collection` (not `collections`). -- Ensure strategies are mounted and set `STRATEGIES_PATH=/app/external_strategies` when running. -- For timeouts/long runs, start with `--dry-run`, then narrow strategies/symbols or set `--max-workers` appropriately. -- See `docs/pgadmin-and-performance.md` for SQL queries, performance tuning, and psql connection strings. diff --git a/docs/features.md b/docs/features.md deleted file mode 100644 index 31c0566..0000000 --- a/docs/features.md +++ /dev/null @@ -1,266 +0,0 @@ -# Comprehensive Features Overview - -Note: Command examples in this document may use legacy CLI patterns (e.g., `portfolio` subcommands). For current usage, prefer the README and `collection` subcommand examples. - -This document provides a complete overview of implemented and planned features in the Quant Trading System. - -## ✅ Core Features (Implemented) - -### 1. Direct Backtesting Library Integration -**Status**: ✅ **IMPLEMENTED** -**Description**: Direct integration with the `backtesting` library for institutional-grade performance analysis. - -**Features**: -- ✅ Single asset and portfolio backtesting -- ✅ Multiple data sources with automatic failover (Yahoo Finance, Alpha Vantage, Twelve Data, etc.) -- ✅ Built-in strategies (Buy & Hold, custom strategy loading) -- ✅ Parallel processing for multiple symbol backtests -- ✅ Comprehensive performance metrics (Sortino, Sharpe, Calmar ratios) -- ✅ Cache management for faster repeated analysis -- ✅ Support for crypto, forex, and traditional assets - -**Usage (current CLI)**: -```bash -# Preferred run: Bonds collection, 1d interval, max period, all strategies -docker compose run --rm -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --action direct --interval 1d --period max --strategies all --exports all --log-level INFO - -# Dry run (plan only) + exports from DB -docker compose run --rm -e STRATEGIES_PATH=/app/external_strategies \ - quant python -m src.cli.unified_cli collection bonds \ - --interval 1d --period max --strategies all --dry-run --exports all --log-level DEBUG -``` - -### 2. Portfolio Management & Configuration -**Status**: ✅ **IMPLEMENTED** -**Description**: Comprehensive portfolio configuration and management system. - -**Features**: -- ✅ JSON-based portfolio configuration (220+ crypto symbols included) -- ✅ Flexible portfolio parameters (initial capital, commission, risk management) -- ✅ Multiple asset type support (crypto, forex, stocks) -- ✅ Benchmark configuration and comparison -- ✅ Strategy parameter customization - -### 3. Advanced Reporting System -**Status**: ✅ **IMPLEMENTED** -**Description**: Comprehensive HTML reporting with interactive charts and analytics. - -**Features**: -- ✅ Quarterly organized report structure (`exports/reports/YYYY/QX/`) -- ✅ Interactive Plotly.js equity curves vs Buy & Hold benchmark -- ✅ Performance metrics dashboard (Sortino, profit factor, win rate, drawdown) -- ✅ Asset-specific strategy optimization results -- ✅ Best strategy and timeframe identification per asset -- ✅ Mobile-responsive HTML design -- ✅ Automated export organization by quarter and year - -### 4. Data Management Infrastructure -**Status**: ✅ **IMPLEMENTED** -**Description**: Robust data fetching, caching, and management system. - -**Features**: -- ✅ Multi-source data fetching with automatic failover -- ✅ File-based caching system with configurable TTL -- ✅ Data validation and error handling -- ✅ Support for multiple timeframes (1m, 5m, 15m, 1h, 1d) -- ✅ Crypto futures data support (Bybit integration) -- ✅ Symbol transformation for different data sources - -### 5. CLI Interface -**Status**: ✅ **IMPLEMENTED** -**Description**: Comprehensive command-line interface for all system operations. - -**Features**: -- ✅ Portfolio backtesting commands -- ✅ Cache management (stats, clear operations) -- ✅ Bulk portfolio testing with optimization -- ✅ Strategy comparison and analysis -- ✅ Flexible parameter passing and configuration - -### 6. TradingView Alert Export -**Status**: ✅ **IMPLEMENTED** -**Description**: Export trading alerts directly from the database (best strategies), with TradingView placeholders. - -**Features**: -- ✅ Auto-organized quarterly export structure (`exports/tv_alerts/YYYY/QX/`) -- ✅ DB-backed (no HTML scraping) -- ✅ TradingView placeholders (`{{close}}`, `{{timenow}}`, `{{strategy.order.action}}`) -- ✅ Performance metrics integration (Sharpe, profit, win rate) -- ✅ Collection/portfolio filtering (`--collection commodities`, `--collection bonds`) -- ✅ Symbol-specific filtering and export options - -**Usage (current CLI)**: -```bash -# Generate TradingView alerts from DB (no backtests) -docker compose run --rm \ - quant python -m src.cli.unified_cli collection bonds --dry-run --exports tradingview -``` - -### 7. Docker Infrastructure -**Status**: ✅ **IMPLEMENTED** -**Description**: Complete containerized environment for consistent deployments. - -**Features**: -- ✅ Docker Compose setup with volume mounts -- ✅ Poetry dependency management -- ✅ Persistent cache and logs directories -- ✅ Reproducible environment across platforms -- ✅ Automated testing and CI/CD integration - -### 8. Performance Metrics & Analytics -**Status**: ✅ **IMPLEMENTED** -**Description**: Advanced financial metrics and risk analysis. - -**Features**: -- ✅ **Sortino Ratio** (primary metric) - Downside risk-adjusted returns -- ✅ **Calmar Ratio** - Annual return vs maximum drawdown -- ✅ **Sharpe Ratio** - Traditional risk-adjusted returns -- ✅ **Profit Factor** - Gross profit/loss ratio -- ✅ Maximum drawdown analysis with recovery periods -- ✅ Win rate and trade statistics -- ✅ Volatility and correlation analysis - -### 9. CSV Export -**Status**: ✅ **IMPLEMENTED** -**Description**: Export portfolio data with best strategies and timeframes directly from the database. - -**Features**: -- ✅ CSV export with symbol, best strategy, best timeframe, and performance metrics -- ✅ Bulk export for all assets from the database -- ✅ **Separate CSV files for each portfolio** (Crypto, Bonds, Forex, Stocks, etc.) -- ✅ Customizable column selection (Sharpe, Sortino, profit, drawdown) -- ✅ Integration with existing quarterly report structure -- ✅ Organized quarterly directory structure (`exports/csv/YYYY/QX/`) -- ✅ Unified naming with HTML/TV/AI exports - -**Usage (current CLI)**: -```bash -# Export CSV directly from DB for bonds (no backtests) -docker compose run --rm \ - quant python -m src.cli.unified_cli collection bonds --dry-run --exports csv - -# Export CSV + HTML report + TradingView alerts -docker compose run --rm \ - quant python -m src.cli.unified_cli collection bonds --dry-run --exports csv,report,tradingview,ai -``` - ---- - -## 🎯 High Priority Features (Planned) - -### 1. Walk-Forward + Out-of-Sample Validation -- Rolling window backtests, expanding windows, and out-of-sample validation reports. -- Parameter stability plots; highlight overfitting risk. - -### 2. Enhanced Data Sources -**Status**: 🔄 **PLANNED** -**Description**: Add more data providers and improve data quality. - -**Features**: -- Additional crypto exchanges (Binance, Coinbase Pro) -- More traditional data providers with better historical coverage -- Data validation and anomaly detection -- Automatic data source failover improvements - -### 3. Advanced Risk Metrics -**Status**: 🔄 **PLANNED** -**Description**: Enhanced risk analysis for portfolio evaluation. - -**Features**: -- Value at Risk (VaR) calculations -- Maximum Drawdown monitoring with recovery analysis -- Volatility regime detection -- Risk-adjusted performance metrics beyond Sortino - -### 4. GPU Acceleration -**Status**: 🔄 **PLANNED** -**Description**: GPU-accelerated computations for faster analysis of large portfolios. - -**Features**: -- **CuPy integration** - GPU-accelerated NumPy operations -- **Numba CUDA** - JIT compilation for custom GPU kernels -- **Rapids cuDF** - GPU-accelerated DataFrame operations -- Parallel backtesting across 220+ crypto symbols - ---- - -## 🚀 Medium Priority Features (Planned) - -### FastAPI Results Access -**Status**: 🔄 **PLANNED** -**Description**: Lightweight REST API for accessing backtest results using FastAPI and Pydantic. - -**Features**: -- **Pydantic models** for portfolio metrics and strategy results -- **Type-safe endpoints** with automatic validation -- **Auto-generated OpenAPI docs** at `/docs` -- RESTful access to quarterly report data -- API endpoints for TradingView alert generation - -### Interactive Reports -**Status**: 🔄 **PLANNED** -**Description**: Enhanced HTML reports with interactive elements. - -**Features**: -- Interactive charts with zoom and filter capabilities -- Collapsible sections for better navigation -- Export to multiple formats (PDF, CSV) -- Custom report templates - -### Strategy Enhancements -**Status**: 🔄 **PLANNED** -**Description**: More sophisticated trading strategies and analysis. - -**Features**: -- Mean reversion strategies -- Momentum-based strategies with multiple timeframes -- Pair trading strategies -- Seasonal analysis and calendar effects - ---- - -## 📈 System Architecture - -### Current Tech Stack (Implemented) -- **Language**: Python 3.11+ -- **Dependencies**: Poetry management -- **Data Sources**: Yahoo Finance, Alpha Vantage, Twelve Data, Polygon, Tiingo, Finnhub, Bybit -- **Analytics**: Pandas, NumPy, SciPy for financial calculations -- **Visualization**: Plotly.js for interactive charts -- **Infrastructure**: Docker, Docker Compose -- **Testing**: Pytest with coverage reporting -- **Code Quality**: Ruff (formatting and linting), MyPy, markdownlint - -### Performance Characteristics -- **Portfolio Size**: Tested with 220+ crypto symbols -- **Processing Speed**: Parallel backtesting across multiple cores -- **Memory Management**: Configurable memory limits with garbage collection -- **Cache Performance**: File-based caching reduces repeat analysis time by 90%+ -- **Data Volume**: Handles years of historical data across multiple timeframes - ---- - -## 🎯 Project Focus - -**✅ Core Strengths:** -- Local analysis and backtesting -- Comprehensive performance metrics (Sortino-focused) -- Automated report generation and organization -- Multi-source data reliability -- Docker-based reproducibility - -**🔄 Active Development:** -- AI-powered investment recommendations -- Enhanced data sources and validation -- Advanced risk metrics and analysis -- GPU acceleration for large portfolios - -**📝 Scope Boundaries:** -- ❌ Real-time trading execution -- ❌ Cloud/enterprise deployment -- ❌ Live market data streaming -- ❌ Complex orchestration systems - -This keeps the system lightweight, focused, and maintainable for quantitative analysis and local portfolio optimization. diff --git a/docs/pgadmin-and-performance.md b/docs/pgadmin-and-performance.md deleted file mode 100644 index 10a5913..0000000 --- a/docs/pgadmin-and-performance.md +++ /dev/null @@ -1,81 +0,0 @@ -# DB Inspection (pgAdmin) and Run Performance Tips - -## pgAdmin: Connect and Inspect - -- Login: open http://localhost:5050 and use `PGADMIN_DEFAULT_EMAIL` / `PGADMIN_DEFAULT_PASSWORD` from `.env`. -- Register server (first time): - - Name: `quant-local` - - Hostname/address: `postgres` - - Port: `5432` - - Maintenance DB: `quant_system` - - Username: `quantuser` - - Password: `quantpass` - -### Handy Queries - -- Recent runs (most recent first): -```sql -SELECT run_id, started_at_utc, action, collection_ref, - strategies_mode, intervals_mode, target_metric, period_mode, - status, plan_hash -FROM runs -ORDER BY started_at_utc DESC -LIMIT 50; -``` - -- Find a run by plan_hash: -```sql -SELECT * -FROM runs -WHERE plan_hash = ''; -``` - -- Count backtest results per run: -```sql -SELECT run_id, COUNT(*) AS results -FROM backtest_results -GROUP BY run_id -ORDER BY results DESC; -``` - -- Best strategies for 1d timeframe (top by Sortino): -```sql -SELECT symbol, timeframe, strategy, - COALESCE(sortino_ratio::float, 0) AS sortino_ratio, - COALESCE(total_return::float, 0) AS total_return, - COALESCE(max_drawdown::float, 0) AS max_drawdown, - updated_at -FROM best_strategies -WHERE timeframe = '1d' -ORDER BY sortino_ratio DESC -LIMIT 50; -``` - -- Latest results for a symbol (e.g., TLT): -```sql -SELECT symbol, strategy, interval, start_at_utc, end_at_utc, metrics, engine_ctx -FROM backtest_results -WHERE symbol = 'TLT' -ORDER BY end_at_utc DESC NULLS LAST -LIMIT 5; -``` - -## Speeding Up Runs - -- Limit strategies: pass `--strategies RSI,BollingerBands,Breakout` instead of `all`. -- Limit symbols: create a small collection JSON (3–5 symbols) for iteration. -- Fix interval: keep `--interval 1d` during development. -- Concurrency: use `--max-workers 4` (or higher if CPU allows). Monitor memory. -- Validate plan first: add `--dry-run` to print the manifest before running. -- Re-run same plan: use `--force` if you need to execute a plan with the same `plan_hash` again. -- Data/API constraints: provide API keys in `.env` to reduce throttling and widen history where providers allow. - -## Paths & Artifacts - -- Artifacts: `artifacts/run_/` (manifest, summaries, exports if enabled). -- Exports: `exports/` (CSV, HTML reports, TradingView), organized by quarter in some flows. - -## Connections (psql) - -- Inside container: `DATABASE_URL=postgresql://quantuser:quantpass@postgres:5432/quant_system` -- From host: `psql postgresql://quantuser:quantpass@localhost:5433/quant_system` diff --git a/exports/.gitkeep b/exports/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/poetry.lock b/poetry.lock index aa8d470..278b281 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,171 +1,176 @@ # This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. 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-[tool.poetry.group.security.dependencies] -# Security scanning tools -bandit = "^1.7" -safety = "^3.0" - -[tool.poetry.group.jupyter.dependencies] -# Optional for data analysis -jupyter = "^1.0" -jupyterlab = "^4.0" -plotly = "^6.3.0" -matplotlib = "^3.10" -seaborn = "^0.13.0" - -[tool.poetry.group.api.dependencies] -# Optional API dependencies -fastapi = "^0.116" -uvicorn = "^0.34" -pydantic = "^2.11" +ruff = "^0.6.4" +pre-commit = "^3.8.0" +pytest = "^8.3.3" +pytest-cov = "^5.0.0" +[tool.ruff] +line-length = 100 +target-version = "py310" +select = ["E", "F", "B", "I", "UP"] +ignore = ["E203", "E266", "E501", "W503"] +[tool.ruff.isort] +known-first-party = ["src"] +profile = "black" [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=1.9.0"] build-backend = "poetry.core.masonry.api" - -# Tool Configurations -[tool.pytest.ini_options] -testpaths = ["tests"] -addopts = ["--cov=src", "--cov-report=term", "-ra"] -markers = ["integration: integration tests"] -pythonpath = ["."] - -[tool.coverage.run] -source = ["src"] -omit = [ - "*/tests/*", - "*/test_*", - "*/__pycache__/*", - "*/migrations/*" -] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:" -] - -[tool.mypy] -python_version = "3.12" -ignore_missing_imports = true -show_error_codes = true -warn_return_any = false -warn_unused_configs = false -disallow_untyped_defs = false -disallow_incomplete_defs = false -check_untyped_defs = false -disallow_untyped_decorators = false -no_implicit_optional = false -warn_redundant_casts = false -warn_unused_ignores = false -warn_no_return = false -warn_unreachable = false -strict_equality = false - -[[tool.mypy.overrides]] -module = ["yfinance.*", "backtesting.*"] -ignore_missing_imports = true diff --git a/quant-strategies b/quant-strategies deleted file mode 120000 index ba0bbcb..0000000 --- a/quant-strategies +++ /dev/null @@ -1 +0,0 @@ -/Users/manuelheck/Documents/Websites/Private/quant/quant-strategies \ No newline at end of file diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index ea7de5a..0000000 --- a/ruff.toml +++ /dev/null @@ -1,182 +0,0 @@ -line-length = 88 -unsafe-fixes = false -target-version = "py39" -extend = "./pyproject.toml" -exclude = [ - # docs - "docsrc/**/*", - # examples - "examples/**/*", - # submodules - strategies are managed in separate repo - "src/backtesting_engine/**/*", -] - -[lint] -extend-select = [ - "D", # pydocstyle - "D417", # undocumented-param - "I", # isort - "UP", # pyupgrade - "G", # flake8-logging-format - "PT", # flake8-pytest-style - "E", # pycodestyle - "W", # pycodestyle - "PGH", # pygrep-hooks - "B", # flake8-bugbear - "SIM", # flake8-simplify - "S", # flake8-bandit - "DTZ", # flake8-datetimez - "EM", # flake8-errmsg - "PIE", # flake8-pie - "Q", # flake8-quotes - "RET", # flake8-return - "TID", # flake8-tidy-imports - "PTH", # flake8-use-pathlib - "F", # Pyflakes - "NPY", # NumPy-specific rules - "PERF", # Perflint - "RUF", # Ruff-specific rules - "ISC", # flake8-implicit-str-concat - "TRY002", # raise-vanilla-class -] -ignore = [ - "PT011", # TODO - # pydocstyle numpy default - "D203", - "D212", - "D213", - "D214", - "D215", - "D404", - "D405", - "D406", - "D407", - "D408", - "D409", - "D410", - "D411", - "D413", - "D415", - "D416", - # ruff format - "W191", # tab-indentation - "E111", # indentation-with-invalid-multiple - "E114", # indentation-with-invalid-multiple-comment - "E117", # over-indented - "D206", # indent-with-spaces - "D300", # triple-single-quotes - "Q000", # bad-quotes-inline-string - "Q001", # bad-quotes-multiline-string - "Q002", # bad-quotes-docstring - "Q003", # avoidable-escaped-quote - "COM812", # missing-trailing-comma - "COM819", # prohibited-trailing-comma - "ISC001", # single-line-implicit-string-concatenation - "ISC002", # multi-line-implicit-string-concatenation - # Additional ignores for problematic rules - "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes - "DTZ005", # datetime.datetime.now() called without a tz argument - "DTZ007", # Naive datetime constructed using datetime.datetime.strptime() without %z - "B018", # Found useless attribute access - "D100", # Missing docstring in public module - "D101", # Missing docstring in public class - "D102", # Missing docstring in public method - "B904", # Within an except clause, raise exceptions with raise ... from err - # Additional showcase project ignores - "D103", # Missing docstring in public function - "D104", # Missing docstring in public package - "D105", # Missing docstring in magic method - "D106", # Missing docstring in public nested class - "D107", # Missing docstring in __init__ - "D200", # One-line docstring should fit on one line - "D202", # No blank lines allowed after function docstring - "D205", # 1 blank line required between summary line and description - "D400", # First line should end with a period - "D401", # First line should be in imperative mood - "D402", # First line should not be the function's signature - "DTZ001", # datetime.datetime() called without a tzinfo argument - "DTZ003", # datetime.datetime.utcnow() is deprecated - "DTZ006", # datetime.datetime.fromtimestamp() called without a tz argument - "EM101", # Exception must not use a string literal - "EM102", # Exception must not use an f-string literal - "TRY003", # Avoid specifying long messages outside the exception class - "PERF401", # Use a list comprehension to create a transformed list - "PERF203", # try-except within a loop incurs performance overhead - "RUF012", # Mutable class attributes should be annotated with ClassVar - "UP006", # Use new-style typing annotations - "UP007", # Use new-style union syntax - "UP035", # typing.* imports are deprecated - "UP045", # Use new-style Optional annotations - "RET504", # Unnecessary assignment before return - "SIM102", # Use single if statement - "SIM103", # Return condition directly - "SIM105", # Use contextlib.suppress - "SIM118", # Use key in dict - "PTH100", # Use pathlib instead of os.path - "PTH110", # Use pathlib instead of os.path - "PTH120", # Use pathlib instead of os.path - "B007", # Unused loop control variable - "B904", # raise without from in except - "E402", # Module import not at top - "E501", # Line too long - "E722", # Bare except - 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-[lint.pydocstyle] -convention = "numpy" - -[lint.flake8-pytest-style] -fixture-parentheses = false diff --git a/scripts/build_tailwind.sh b/scripts/build_tailwind.sh deleted file mode 100755 index cb7b43f..0000000 --- a/scripts/build_tailwind.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -INPUT="src/reporting/tailwind.input.css" -CONFIG="tailwind.config.js" -OUTPUT_DIR="exports/reports/assets" -OUTPUT="$OUTPUT_DIR/tailwind.min.css" - -if ! command -v npx >/dev/null 2>&1; then - echo "npx is required. Please install Node.js (>=18) and try again." >&2 - exit 1 -fi - -mkdir -p "$OUTPUT_DIR" - -echo "Building Tailwind CSS → $OUTPUT" -npx tailwindcss -c "$CONFIG" -i "$INPUT" -o "$OUTPUT" --minify -echo "Done. Set TAILWIND_CSS_HREF=$OUTPUT to use the local file." diff --git a/scripts/check_data_ranges.py b/scripts/check_data_ranges.py deleted file mode 100644 index 85faaa4..0000000 --- a/scripts/check_data_ranges.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 -""" -Check available OHLC data ranges for given symbols using UnifiedDataManager. - -Prints symbol, number of rows, first date, last date (UTC). -""" - -from __future__ import annotations - -import datetime - -try: - from src.core.data_manager import UnifiedDataManager -except Exception as e: - raise SystemExit(f"Could not import UnifiedDataManager: {e}") - -SYMBOLS = ["AGG", "HYG", "TLT", "JPST", "EMB"] - - -def fmt(dt): - if dt is None: - return "None" - try: - return dt.tz_convert("UTC").isoformat() - except Exception: - try: - return dt.isoformat() - except Exception: - return str(dt) - - -def main(): - dm = UnifiedDataManager() - for s in SYMBOLS: - try: - print(f"--- {s} ---") - # Request wide range to emulate 'max' (use aware date for lint) - today_iso = datetime.datetime.now(datetime.timezone.utc).date().isoformat() - data = dm.get_data(s, "1900-01-01", today_iso, "1d") - if data is None: - print("No data returned") - continue - # Ensure index is datetime - idx = data.index - if len(idx) == 0: - print("Empty index") - continue - first = idx[0] - last = idx[-1] - print("rows:", len(data)) - print("first:", fmt(first)) - print("last:", fmt(last)) - except Exception as e: - print("Error fetching", s, "->", e) - - -if __name__ == "__main__": - main() diff --git a/scripts/compare_direct_db.py b/scripts/compare_direct_db.py deleted file mode 100644 index 86e7d82..0000000 --- a/scripts/compare_direct_db.py +++ /dev/null @@ -1,160 +0,0 @@ -#!/usr/bin/env python3 -""" -Compare direct backtesting library results (exports/direct_portfolio_comparison.json) -with BestStrategy rows in the database. - -Produces exports/compare_direct_db_results.json and prints a summary. - -Usage: - python3 scripts/compare_direct_db.py --direct exports/direct_portfolio_comparison.json -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path - -from src.database import unified_models as um - - -def parse_args(): - p = argparse.ArgumentParser() - p.add_argument( - "--direct", - required=True, - help="Path to direct backtest JSON (exports/direct_portfolio_comparison.json)", - ) - return p.parse_args() - - -def normalize_interval_name(interval: str) -> str: - """Normalize interval strings to the format stored in DB/timeframe keys.""" - # Accept both "1min" and "1m" etc. We will normalize common variants to short form used across project. - mapping = { - "1min": "1m", - "2min": "2m", - "5min": "5m", - "15min": "15m", - "30min": "30m", - "60min": "60m", - "1h": "1h", - "4h": "4h", - "1d": "1d", - "5d": "5d", - "1wk": "1wk", - "1mo": "1mo", - "3mo": "3mo", - } - return mapping.get(interval, interval) - - -def main(): - args = parse_args() - direct_path = Path(args.direct) - if not direct_path.exists(): - print("Direct results file not found:", direct_path) - return - - with direct_path.open() as f: - data = json.load(f) - - sess = um.Session() - results = {} - summary = {"total": 0, "matched": 0, "missing_db": 0, "mismatched": 0} - - try: - for symbol, intervals in data.items(): - results.setdefault(symbol, {}) - for interval, runs in intervals.items(): - summary["total"] += 1 - norm_interval = normalize_interval_name(interval) - - # runs is a list of dicts: {"strategy":..., "stats":..., "error":...} - # Find best strategy by native Sortino Ratio (highest). Ignore errored runs. - candidates = [ - r - for r in runs - if (r.get("stats") or {}).get("Sortino Ratio") is not None - ] - if not candidates: - results[symbol][interval] = { - "direct_best": None, - "direct_sortino": None, - "db_best": None, - "db_sortino": None, - "match": False, - "note": "no_valid_direct_metrics", - } - summary["mismatched"] += 1 - continue - - best_direct = max( - candidates, - key=lambda r: float( - (r.get("stats") or {}).get("Sortino Ratio") or float("-inf") - ), - ) - direct_best_name = best_direct.get("strategy") - try: - direct_sortino = float( - (best_direct.get("stats") or {}).get("Sortino Ratio") or 0 - ) - except Exception: - direct_sortino = 0.0 - - # Query DB for BestStrategy for this symbol/timeframe - db_row = ( - sess.query(um.BestStrategy) - .filter( - um.BestStrategy.symbol == symbol, - um.BestStrategy.timeframe == norm_interval, - ) - .first() - ) - - if not db_row: - results[symbol][interval] = { - "direct_best": direct_best_name, - "direct_sortino": direct_sortino, - "db_best": None, - "db_sortino": None, - "match": False, - "note": "no_db_row", - } - summary["missing_db"] += 1 - continue - - db_best = db_row.strategy - db_sortino = float(getattr(db_row, "sortino_ratio", 0) or 0) - - match = str(db_best).strip() == str(direct_best_name).strip() - if match: - summary["matched"] += 1 - else: - summary["mismatched"] += 1 - - results[symbol][interval] = { - "direct_best": direct_best_name, - "direct_sortino": direct_sortino, - "db_best": db_best, - "db_sortino": db_sortino, - "match": match, - "note": None, - } - - finally: - sess.close() - - out_path = Path("exports") / "compare_direct_db_results.json" - out_path.parent.mkdir(parents=True, exist_ok=True) - with out_path.open("w") as f: - json.dump({"summary": summary, "results": results}, f, indent=2, default=str) - - print("Comparison complete.") - print("Summary:", summary) - print("Detailed results written to:", out_path) - - -if __name__ == "__main__": - main() diff --git a/scripts/compare_json_db.py b/scripts/compare_json_db.py deleted file mode 100644 index e809128..0000000 --- a/scripts/compare_json_db.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python3 -""" -Compare exports/comparison_sample_Q3_2025.json best strategy (rank 1) -with BestStrategy rows in the database (timeframe=1d). - -Outputs results to exports/compare_json_db_results.json and prints a summary to stdout. -""" - -from __future__ import annotations - -import json -from pathlib import Path - -from src.database import get_db_session -from src.database.models import BestStrategy - -INPUT = Path("exports/comparison_sample_Q3_2025.json") -OUTPUT = Path("exports/compare_json_db_results.json") -TIMEFRAME = "1d" - - -def main(): - if not INPUT.exists(): - print(f"Input file not found: {INPUT}") - return - - with INPUT.open() as f: - data = json.load(f) - - session = get_db_session() - results = {} - summary = {"total": 0, "matched": 0, "missing_db": 0, "mismatched": 0} - - try: - for symbol, symbol_data in data.items(): - summary["total"] += 1 - # Prefer explicit best_strategy field if present - json_best = None - json_sortino = None - if symbol_data.get("best_strategy"): - json_best = symbol_data["best_strategy"].get("strategy") - json_sortino = ( - symbol_data["best_strategy"].get("metrics", {}).get("sortino_ratio") - ) - else: - # Fallback to results array where rank==1 - for r in symbol_data.get("results", []): - if r.get("rank") == 1: - json_best = r.get("strategy") - json_sortino = r.get("metrics", {}).get("sortino_ratio") - break - - db_row = ( - session.query(BestStrategy) - .filter_by(symbol=symbol, timeframe=TIMEFRAME) - .first() - ) - - if not db_row: - results[symbol] = { - "json_best": json_best, - "json_sortino": json_sortino, - "db_best": None, - "db_sortino": None, - "match": False, - "note": "no_db_row", - } - summary["missing_db"] += 1 - continue - - db_best = db_row.strategy - db_sortino = float(getattr(db_row, "sortino_ratio", 0) or 0) - - match = str(db_best).strip() == str(json_best).strip() - - if match: - summary["matched"] += 1 - else: - summary["mismatched"] += 1 - - results[symbol] = { - "json_best": json_best, - "json_sortino": json_sortino, - "db_best": db_best, - "db_sortino": db_sortino, - "match": match, - "note": None, - } - - finally: - session.close() - - OUTPUT.parent.mkdir(parents=True, exist_ok=True) - with OUTPUT.open("w") as f: - json.dump({"summary": summary, "results": results}, f, indent=2, default=str) - - print("Comparison complete.") - print("Summary:", summary) - print(f"Detailed results written to: {OUTPUT}") - - -if __name__ == "__main__": - main() diff --git a/scripts/compare_unified_vs_direct.py b/scripts/compare_unified_vs_direct.py deleted file mode 100644 index 674a49f..0000000 --- a/scripts/compare_unified_vs_direct.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python3 -""" -Compare DB-backed (unified) best-strategies CSV with direct backtests. - -Reads: exports/csv/2025/Q3/bonds_collection_best_strategies_Q3_2025.csv -Writes: exports/csv/compare_unified_vs_direct_bonds_Q3_2025.csv - -This script calls src.core.direct_backtest.run_direct_backtest for each symbol/strategy/timeframe -and compares key metrics (sortino_ratio, total_return). -""" - -from __future__ import annotations - -import csv -import datetime -import traceback -from pathlib import Path -from typing import Optional - -INPUT_CSV = Path("exports/csv/2025/Q3/bonds_collection_best_strategies_Q3_2025.csv") -OUT_CSV = Path("exports/csv/compare_unified_vs_direct_bonds_Q3_2025.csv") - -# Use direct backtest function from the project -try: - from src.core.direct_backtest import run_direct_backtest -except Exception as e: - raise RuntimeError(f"Could not import run_direct_backtest: {e}") from e - - -def as_float(v: Optional[str]) -> Optional[float]: - try: - if v is None or str(v).strip() == "": - return None - return float(v) - except Exception: - return None - - -def main(): - if not INPUT_CSV.exists(): - raise SystemExit(f"Input CSV not found: {INPUT_CSV}") - - today = datetime.datetime.now(datetime.timezone.utc).date().isoformat() - # Use a very wide start to emulate 'max'; data manager will clamp to available history. - start_date = "1900-01-01" - end_date = today - - with INPUT_CSV.open() as fh: - reader = csv.DictReader(fh) - rows = list(reader) - - out_rows = [] - total = len(rows) - succeeded = 0 - failed = 0 - - for i, r in enumerate(rows, start=1): - asset = r.get("Asset") or r.get("symbol") or r.get("Asset") - strategy = r.get("Best_Strategy") or r.get("BestStrategy") or "adx" - timeframe = r.get("Best_Timeframe") or r.get("Best_Timeframe") or "1d" - unified_sortino = as_float(r.get("Sortino_Ratio")) - unified_total = as_float(r.get("Total_Return_Pct")) - - print( - f"[{i}/{total}] Running direct backtest for {asset} / {strategy} / {timeframe}" - ) - try: - res = run_direct_backtest( - symbol=str(asset), - strategy_name=str(strategy), - start_date=start_date, - end_date=end_date, - timeframe=str(timeframe), - initial_capital=10000.0, - commission=0.001, - persistence_context=None, - ) - direct_sortino = None - direct_total = None - err = None - try: - native = res.get("bt_results") or {} - v = native.get("Sortino Ratio", None) - direct_sortino = float(v) if v is not None else None - except Exception: - direct_sortino = None - try: - native = res.get("bt_results") or {} - v2 = native.get("Return [%]", None) - direct_total = float(v2) if v2 is not None else None - except Exception: - direct_total = None - - succeeded += 1 - except Exception as e: - err = f"{e}\n{traceback.format_exc()}" - direct_sortino = None - direct_total = None - failed += 1 - - sortino_diff = ( - None - if (unified_sortino is None or direct_sortino is None) - else float(direct_sortino) - float(unified_sortino) - ) - total_diff = ( - None - if (unified_total is None or direct_total is None) - else float(direct_total) - float(unified_total) - ) - - out_rows.append( - { - "Asset": asset, - "Unified_Strategy": strategy, - "Unified_Timeframe": timeframe, - "Unified_Sortino": "" if unified_sortino is None else unified_sortino, - "Unified_TotalReturn": "" if unified_total is None else unified_total, - "Direct_Sortino": "" if direct_sortino is None else direct_sortino, - "Direct_TotalReturn": "" if direct_total is None else direct_total, - "Sortino_Diff": "" if sortino_diff is None else sortino_diff, - "TotalReturn_Diff": "" if total_diff is None else total_diff, - "Error": "" if err is None else err, - } - ) - - OUT_CSV.parent.mkdir(parents=True, exist_ok=True) - with OUT_CSV.open("w", newline="") as fh: - fieldnames = [ - "Asset", - "Unified_Strategy", - "Unified_Timeframe", - "Unified_Sortino", - "Unified_TotalReturn", - "Direct_Sortino", - "Direct_TotalReturn", - "Sortino_Diff", - "TotalReturn_Diff", - "Error", - ] - writer = csv.DictWriter(fh, fieldnames=fieldnames) - writer.writeheader() - for r in out_rows: - writer.writerow(r) - - print(f"Done. Total={total} succeeded={succeeded} failed={failed}") - print(f"Wrote comparison CSV to {OUT_CSV}") - - -if __name__ == "__main__": - main() diff --git a/scripts/data_health_report.py b/scripts/data_health_report.py deleted file mode 100644 index f5942ee..0000000 --- a/scripts/data_health_report.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python3 -""" -Data Health Report for a collection. - -Outputs CSV with: symbol, rows, first_date, last_date, stale (Y/N) -Optionally prints a summary to stdout. -""" - -from __future__ import annotations - -import argparse -import csv -from pathlib import Path -from typing import List - -import pandas as pd -from pandas.tseries.offsets import BDay - -from src.cli.unified_cli import load_collection_symbols, resolve_collection_path -from src.core.data_manager import UnifiedDataManager - - -def is_stale(last_date: pd.Timestamp) -> bool: - try: - expected = (pd.Timestamp.today().normalize() - BDay(1)).date() - return last_date.date() < expected - except Exception: - return False - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description="Data health report for a collection") - parser.add_argument("collection", help="Collection key or path to JSON") - parser.add_argument("--interval", default="1d") - parser.add_argument("--period", default="") - parser.add_argument("--out", default="artifacts/data_health.csv") - args = parser.parse_args(argv) - - p = ( - resolve_collection_path(args.collection) - if not Path(args.collection).exists() - else Path(args.collection) - ) - symbols: List[str] = load_collection_symbols(p) - dm = UnifiedDataManager() - - rows: List[dict] = [] - for s in symbols: - try: - df = dm.get_data( - s, - start_date="1900-01-01" if args.period == "max" else "2000-01-01", - end_date=pd.Timestamp.today().strftime("%Y-%m-%d"), - interval=args.interval, - use_cache=True, - period=args.period or None, - period_mode=args.period or None, - ) - if df is None or df.empty: - rows.append( - { - "symbol": s, - "rows": 0, - "first_date": "", - "last_date": "", - "stale": "Y", - } - ) - continue - first = df.index[0] - last = df.index[-1] - rows.append( - { - "symbol": s, - "rows": len(df), - "first_date": first.date().isoformat(), - "last_date": last.date().isoformat(), - "stale": "Y" if is_stale(last) else "N", - } - ) - except Exception: - rows.append( - { - "symbol": s, - "rows": 0, - "first_date": "", - "last_date": "", - "stale": "Y", - } - ) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - with out_path.open("w", newline="") as fh: - writer = csv.DictWriter( - fh, fieldnames=["symbol", "rows", "first_date", "last_date", "stale"] - ) - writer.writeheader() - for r in rows: - writer.writerow(r) - - print(f"Wrote data health report: {out_path}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scripts/initdb/init.sql b/scripts/initdb/init.sql deleted file mode 100644 index 5b4381a..0000000 --- a/scripts/initdb/init.sql +++ /dev/null @@ -1,4 +0,0 @@ --- Initialization SQL for quant-system Postgres --- This file is mounted into /docker-entrypoint-initdb.d/init.sql --- Add optional safe setup below (keep idempotent when possible) --- Example: CREATE SCHEMA IF NOT EXISTS research; diff --git a/scripts/prefetch_all.py b/scripts/prefetch_all.py deleted file mode 100644 index 2298c6a..0000000 --- a/scripts/prefetch_all.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -""" -Prefetch multiple collections in one command. - -Examples: - python scripts/prefetch_all.py bonds commodities --mode recent --interval 1d --recent-days 90 - python scripts/prefetch_all.py --all --mode full --interval 1d -""" - -from __future__ import annotations - -import argparse -from typing import List - -from scripts.prefetch_collection import prefetch as prefetch_one - -DEFAULT_COLLECTIONS = ["bonds", "commodities", "crypto", "forex", "indices"] - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description="Prefetch multiple collections") - parser.add_argument("collections", nargs="*") - parser.add_argument("--all", action="store_true") - parser.add_argument("--mode", choices=["full", "recent", "both"], default="recent") - parser.add_argument("--interval", default="1d") - parser.add_argument("--recent-days", type=int, default=90) - args = parser.parse_args(argv) - - collections: List[str] - if args.all or not args.collections: - collections = DEFAULT_COLLECTIONS - else: - collections = list(args.collections) - - for c in collections: - print(f"Prefetching {c} ...") - prefetch_one(c, args.mode, args.interval, args.recent_days) - - print("All prefetches complete.") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scripts/prefetch_collection.py b/scripts/prefetch_collection.py deleted file mode 100644 index b361807..0000000 --- a/scripts/prefetch_collection.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -""" -Prefetch collection market data into the cache. - -Modes: -- full : fetch provider 'max' period for full snapshots (long TTL) -- recent : fetch last N days for recent overlay (short TTL) -- both : full followed by recent - -Examples: - python scripts/prefetch_collection.py bonds --mode full --interval 1d - python scripts/prefetch_collection.py config/collections/bonds.json --mode recent --interval 1d --recent-days 90 - -Cron (daily recent overlay at 01:30): - 30 1 * * * /usr/bin/env bash -lc 'cd /path/to/quant-system && \ - docker compose run --rm quant python scripts/prefetch_collection.py bonds --mode recent --interval 1d --recent-days 90 >/dev/null 2>&1' -""" - -from __future__ import annotations - -import argparse -import datetime as dt -from pathlib import Path -from typing import List - -from src.cli.unified_cli import load_collection_symbols, resolve_collection_path -from src.core.data_manager import UnifiedDataManager - - -def prefetch(collection: str, mode: str, interval: str, recent_days: int) -> None: - p = ( - resolve_collection_path(collection) - if not Path(collection).exists() - else Path(collection) - ) - symbols: List[str] = load_collection_symbols(p) - if not symbols: - print("No symbols found in collection") - return - - dm = UnifiedDataManager() - today = dt.datetime.now(dt.timezone.utc).date().isoformat() - - if mode in ("full", "both"): - print(f"[full] Fetching provider max for {len(symbols)} symbols @ {interval}") - dm.get_batch_data( - symbols, - start_date="1900-01-01", - end_date=today, - interval=interval, - use_cache=False, - period="max", - period_mode="max", - ) - - if mode in ("recent", "both"): - start_recent = ( - dt.datetime.now(dt.timezone.utc).date() - - dt.timedelta(days=int(recent_days)) - ).isoformat() - print( - f"[recent] Fetching {recent_days} days for {len(symbols)} symbols @ {interval}" - ) - dm.get_batch_data( - symbols, - start_date=start_recent, - end_date=today, - interval=interval, - use_cache=False, - ) - - print("Prefetch complete.") - - -def main(argv: list[str] | None = None) -> int: - parser = argparse.ArgumentParser(description="Prefetch collection data into cache") - parser.add_argument( - "collection", help="Collection key (e.g., bonds) or path to JSON" - ) - parser.add_argument("--mode", choices=["full", "recent", "both"], default="recent") - parser.add_argument("--interval", default="1d") - parser.add_argument("--recent-days", type=int, default=90) - args = parser.parse_args(argv) - - prefetch(args.collection, args.mode, args.interval, args.recent_days) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scripts/run_direct_collection.py b/scripts/run_direct_collection.py deleted file mode 100644 index 3805fed..0000000 --- a/scripts/run_direct_collection.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -Run direct backtests for a portfolio JSON across multiple intervals using the -backtesting library as the single source of truth. - -This version persists all results to the project database (BestStrategy, -BacktestResult, Trades, etc.) so downstream reports and comparisons use the DB -instead of JSON. For convenience and offline inspection it still writes a -summary JSON to `exports/direct_portfolio_comparison.json`, but that file is -not intended to be a data source. - -Usage: - python3 scripts/run_direct_collection.py \ - --portfolio config/collections/bonds.json \ - --intervals "1m 5m 15m 1h 4h 1d 1wk" \ - [--start-date YYYY-MM-DD] [--end-date YYYY-MM-DD] [--period max|1y|...] - -Requirements: - - The database must be reachable (e.g., via docker-compose). The script will - attempt to create a Run row and then persist each backtest result under that - run_id. At the end it finalizes rankings and upserts BestStrategy. -""" - -from __future__ import annotations - -import argparse -import json -import logging -from datetime import datetime -from pathlib import Path - -from src.core.direct_backtest import finalize_persistence_for_run, run_direct_backtest -from src.core.external_strategy_loader import get_strategy_loader -from src.core.strategy import StrategyFactory - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger("direct-portfolio") - - -def parse_args(): - p = argparse.ArgumentParser() - p.add_argument("--portfolio", required=True, help="Path to portfolio JSON") - p.add_argument( - "--intervals", - required=True, - help="Space-separated list of intervals (e.g. '1min 5min 15min 1h 4h 1d 1wk')", - ) - p.add_argument("--start-date", help="Optional start date (YYYY-MM-DD)") - p.add_argument("--end-date", help="Optional end date (YYYY-MM-DD)") - p.add_argument( - "--period", - default=None, - help="Optional provider period token (e.g. 'max', '1y'). When set, overrides start/end", - ) - p.add_argument("--initial-capital", type=float, default=10000.0) - return p.parse_args() - - -def main(): - args = parse_args() - portfolio_path = Path(args.portfolio) - if not portfolio_path.exists(): - logger.error("Portfolio file not found: %s", portfolio_path) - return - - with portfolio_path.open() as f: - portfolio_data = json.load(f) - - # Get first portfolio - portfolio_name = list(portfolio_data.keys())[0] - portfolio = portfolio_data[portfolio_name] - symbols = portfolio.get("symbols", []) - if not symbols: - logger.error("No symbols found in portfolio") - return - - # Ensure external strategies loader is initialized. - # Prefer using the host-side quant-strategies path if it exists; otherwise - # fall back to the default loader behavior which prefers the container-mounted - # external_strategies directory. - project_root = Path(__file__).resolve().parent.parent - quant_path = project_root / "quant-strategies" / "algorithms" / "python" - try: - if quant_path.exists(): - # Host-side quant-strategies available (development); point loader there. - get_strategy_loader(str(quant_path)) - else: - # No host mount for quant-strategies in container — let loader pick defaults - # (it will prefer /app/external_strategies if present). - get_strategy_loader() - except Exception: - logger.warning("Could not initialize external strategy loader") - - # Determine strategies to test (prefer all available) - all_strats = StrategyFactory.list_strategies().get("all", []) - strategies = all_strats if all_strats else ["rsi", "macd", "bollinger_bands"] - - intervals = args.intervals.split() - start_date = args.start_date or "1970-01-01" - end_date = args.end_date or datetime.now().strftime("%Y-%m-%d") - period = args.period # if provided, data manager will prefer this over start/end - # Prefer initial capital from portfolio config when present - initial_capital = portfolio.get("initial_capital", args.initial_capital) - - results = {} - - # Prepare a minimal manifest/run so that persistence_context can associate all - # backtests under a single run_id. We keep this local to avoid importing the - # full unified CLI; the DB helper provides a simple create_run_from_manifest. - run_id = None - target_metric = "sortino_ratio" - try: - import hashlib - - from src.database import unified_models as um # type: ignore[import-not-found] - - # Minimal plan for hashing + traceability - plan = { - "action": "direct", - "symbols": symbols, - "strategies": strategies, - "intervals": intervals, - "period_mode": str(period or "max"), - "start": start_date, - "end": end_date, - "initial_capital": float(initial_capital), - "commission": float(portfolio.get("commission", 0.001)), - "metric": target_metric, - } - plan_hash = hashlib.sha256( - json.dumps(plan, sort_keys=True, separators=(",", ":")).encode("utf-8") - ).hexdigest() - - manifest = { - "plan": {**plan, "plan_hash": plan_hash}, - "generated_at": datetime.utcnow().isoformat() + "Z", - } - - # Ensure tables exist (best-effort; safe if already created) - try: - um.create_tables() - except Exception: - pass - - run_obj = um.create_run_from_manifest(manifest) - run_id = getattr(run_obj, "run_id", None) - except Exception: - logger.warning( - "Database persistence unavailable; continuing without DB (JSON will still be written)" - ) - - def _sanitize_jsonable(obj): - """Best-effort conversion of stats to JSON-safe primitives.""" - try: - import math - - import numpy as _np # type: ignore[import-not-found] - import pandas as _pd # type: ignore[import-not-found] - except Exception: - math = None - _np = None - _pd = None - - # Pandas Series/DataFrame - try: - if _pd is not None and isinstance(obj, _pd.Series): - return {k: _sanitize_jsonable(v) for k, v in obj.to_dict().items()} - if _pd is not None and isinstance(obj, _pd.DataFrame): - return obj.to_dict(orient="records") - except Exception: - pass - - # Pandas Timestamp / datetime-like - try: - import datetime as _dt # type: ignore[import-not-found] - - if _pd is not None and isinstance(obj, _pd.Timestamp): - return obj.isoformat() - if isinstance(obj, (_dt.datetime, _dt.date)): - return obj.isoformat() - except Exception: - pass - - # Numpy scalars/arrays - if _np is not None and isinstance(obj, _np.generic): - try: - return _sanitize_jsonable(obj.item()) - except Exception: - pass - if _np is not None and isinstance(obj, _np.ndarray): - try: - return [_sanitize_jsonable(v) for v in obj.tolist()] - except Exception: - pass - - # Primitives - if obj is None or isinstance(obj, (str, bool, int)): - return obj - if isinstance(obj, float): - try: - if math and (math.isnan(obj) or math.isinf(obj)): - return None - except Exception: - return None - return obj - - # Collections - if isinstance(obj, dict): - out = {} - for k, v in obj.items(): - try: - out[str(k)] = _sanitize_jsonable(v) - except Exception: - out[str(k)] = None - return out - if isinstance(obj, (list, tuple)): - return [_sanitize_jsonable(v) for v in obj] - - # Fallback - try: - return str(obj) - except Exception: - return None - - total = len(symbols) * len(strategies) * len(intervals) - counter = 0 - - for interval in intervals: - for symbol in symbols: - for strategy in strategies: - counter += 1 - logger.info( - "[%d/%d] Running direct backtest %s %s @ %s", - counter, - total, - symbol, - strategy, - interval, - ) - try: - # If DB is available, pass persistence_context so direct_backtest - # will persist BacktestResult and Trades to the database. - persistence_context = ( - {"run_id": run_id, "target_metric": target_metric} - if run_id - else None - ) - result = run_direct_backtest( - symbol=symbol, - strategy_name=strategy, - start_date=start_date, - end_date=end_date, - timeframe=interval, - initial_capital=initial_capital, - commission=portfolio.get("commission", 0.001), - period=period, - persistence_context=persistence_context, - ) - # Collect native stats from backtesting library - stats = _sanitize_jsonable(result.get("bt_results")) - results.setdefault(symbol, {}).setdefault(interval, []).append( - { - "strategy": strategy, - "stats": stats, - "error": result.get("error"), - } - ) - except Exception as e: - logger.error( - "Direct backtest failed for %s %s %s: %s", - symbol, - strategy, - interval, - e, - ) - results.setdefault(symbol, {}).setdefault(interval, []).append( - {"strategy": strategy, "stats": {}, "error": str(e)} - ) - - # Finalize DB ranks/best strategies once all results are persisted. - try: - if run_id: - finalize_persistence_for_run(run_id, target_metric) - logger.info( - "Finalized DB aggregates for run_id=%s (metric=%s)", - run_id, - target_metric, - ) - except Exception: - logger.exception("Failed to finalize DB aggregates for run_id=%s", run_id) - - output_file = Path("exports") / "direct_portfolio_comparison.json" - output_file.parent.mkdir(parents=True, exist_ok=True) - with output_file.open("w") as f: - json.dump(results, f, indent=2, default=str) - - logger.info("Saved direct portfolio stats to %s", output_file) - - # Optionally generate a DB-backed HTML report for this portfolio using the same reporter - try: - from src.reporting.collection_report import DetailedPortfolioReporter - - portfolio_name = portfolio.get("name") or portfolio_path.stem - reporter = DetailedPortfolioReporter() - report_path = reporter.generate_comprehensive_report( - {"name": portfolio_name, "symbols": symbols}, - start_date=start_date, - end_date=end_date, - strategies=["best"], - timeframes=intervals, - ) - logger.info("Generated HTML report (DB-backed) at %s", report_path) - except Exception as e: - logger.warning("Could not generate HTML report: %s", e) - - -if __name__ == "__main__": - main() diff --git a/scripts/run_direct_sample.py b/scripts/run_direct_sample.py deleted file mode 100644 index 34f9bce..0000000 --- a/scripts/run_direct_sample.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -""" -Run direct backtest comparisons for a short sample of crypto assets. -Writes JSON output to exports/comparison_sample_Q3_2025.json -""" - -from __future__ import annotations - -import json -import logging -from datetime import datetime -from pathlib import Path - -from src.core.direct_backtest import run_strategy_comparison -from src.core.external_strategy_loader import get_strategy_loader - -# Ensure external loader points at quant-strategies algorithms python directory -project_root = Path(__file__).resolve().parent.parent -quant_path = project_root / "quant-strategies" / "algorithms" / "python" -get_strategy_loader(str(quant_path)) - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger("direct-sample") - -# Strategies to test: use all external strategies discovered by StrategyFactory (fallback to default set) -from src.core.strategy import StrategyFactory - -_external_strats = StrategyFactory.list_strategies().get("external", []) -STRATEGIES = ( - _external_strats - if _external_strats - else ["BuyAndHold", "rsi", "macd", "bollinger_bands"] -) - -# Sample 10 representative assets (mix of BTC/ETH/large caps/mid/small caps) -SYMBOLS = [ - "BTCUSD", - "ETHUSD", - "SOLUSDT", - "BNBUSDT", - "AVAXUSDT", - "DOGEUSDT", - "SUIUSDT", - "RNDRUSDT", - "AGIXUSDT", - "AAVEUSDT", -] - -# Date range - using a broad crypto range (adjust if your local data differs) -START_DATE = "1970-01-01" -END_DATE = datetime.now().strftime("%Y-%m-%d") -# Comprehensive timeframes to test (user requested): 1m,5m,15m,1h,4h,1d,1wk -TIMEFRAMES = ["1m", "5m", "15m", "1h", "4h", "1d", "1wk"] -INITIAL_CAPITAL = 10000.0 - -results = {} -for timeframe in TIMEFRAMES: - logger.info("Running comparisons for timeframe %s", timeframe) - for symbol in SYMBOLS: - logger.info(" Running strategy comparison for %s (%s)", symbol, timeframe) - try: - comp = run_strategy_comparison( - symbol=symbol, - strategies=STRATEGIES, - start_date=START_DATE, - end_date=END_DATE, - timeframe=timeframe, - initial_capital=INITIAL_CAPITAL, - ) - # Store per-symbol per-timeframe - results.setdefault(symbol, {})[timeframe] = comp - logger.info( - " Completed %s %s: total_strategies=%s, successful=%s", - symbol, - timeframe, - comp.get("total_strategies"), - comp.get("successful_strategies"), - ) - except Exception as e: - logger.error(" Failed for %s %s: %s", symbol, timeframe, e) - results.setdefault(symbol, {})[timeframe] = {"error": str(e)} - -# Ensure output dir exists (should already) -output_path = Path("exports/comparison_sample_Q3_2025.json") -with output_path.open("w", encoding="utf-8") as f: - json.dump(results, f, indent=2, default=str) - -print(f"Saved sample comparison results to {output_path}") diff --git a/src/__init__.py b/src/__init__.py index 942b946..a9a2c5b 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -1 +1 @@ -"""Quant System - A comprehensive quantitative analysis platform.""" +__all__ = [] diff --git a/src/ai/investment_recommendations.py b/src/ai/investment_recommendations.py deleted file mode 100644 index cd8126f..0000000 --- a/src/ai/investment_recommendations.py +++ /dev/null @@ -1,1804 +0,0 @@ -""" -AI Investment Recommendations - AI-powered analysis of backtest results -to recommend optimal asset allocation and investment decisions. -""" - -from __future__ import annotations - -import logging -import os -from pathlib import Path -from typing import Any, Optional - -import numpy as np -import pandas as pd -from sqlalchemy.orm import Session - -from src.ai.llm_client import LLMClient -from src.ai.models import AssetRecommendation, PortfolioRecommendation -from src.database.models import AIRecommendation, BacktestResult, BestStrategy -from src.database.models import AssetRecommendation as DbAssetRecommendation -from src.reporting.ai_report_generator import AIReportGenerator - - -class AIInvestmentRecommendations: - """ - AI-powered investment recommendation system that analyzes backtest results - to provide optimal asset allocation and investment decisions. - """ - - def __init__(self, db_session: Session = None): - self.db_session = db_session - self.logger = logging.getLogger(__name__) - self.llm_client = LLMClient() - - # Risk tolerance levels - self.risk_levels = { - "conservative": {"max_drawdown": 0.10, "min_sortino": 1.0}, - "moderate": {"max_drawdown": 0.20, "min_sortino": 0.75}, - "aggressive": {"max_drawdown": 0.35, "min_sortino": 0.5}, - } - - # Scoring weights - self.scoring_weights = { - "sortino_ratio": 0.35, - "calmar_ratio": 0.25, - "profit_factor": 0.20, - "max_drawdown": 0.10, - "win_rate": 0.10, - } - - @staticmethod - def _ensure_python_type(val): - """Convert any numpy type to Python native type.""" - if val is None: - return None - - if isinstance(val, (np.floating, np.integer, np.bool_)): - return val.item() # Convert to Python native type - if isinstance(val, np.ndarray): - if val.size == 1: - return val.item() - return val.tolist() - if hasattr(val, "item"): # Other numpy scalars - return val.item() - if isinstance(val, (list, tuple)): - return [ - AIInvestmentRecommendations._ensure_python_type(item) for item in val - ] - if isinstance(val, dict): - return { - k: AIInvestmentRecommendations._ensure_python_type(v) - for k, v in val.items() - } - return val - - def generate_recommendations( - self, - risk_tolerance: str = "moderate", - min_confidence: float = 0.7, - max_assets: int = 10, - quarter: Optional[str] = None, - timeframe: str = "1h", - portfolio_name: Optional[str] = None, - portfolio_path: Optional[str] = None, - ) -> PortfolioRecommendation: - """ - Generate AI-powered investment recommendations based on backtest results. - - Args: - risk_tolerance: Risk level (conservative, moderate, aggressive) - min_confidence: Minimum confidence score for recommendations - max_assets: Maximum number of assets to recommend - quarter: Specific quarter to analyze (e.g., "Q3_2025") - - Returns: - PortfolioRecommendation with AI analysis - """ - self.logger.info( - "Generating AI recommendations for %s risk profile", risk_tolerance - ) - - # Load portfolio config if provided to get collection name and symbols - portfolio_symbols = None - if portfolio_path: - try: - import json - from pathlib import Path - - with Path(portfolio_path).open() as f: - portfolio_config = json.load(f) - # Get the first (and usually only) portfolio config - portfolio_key = list(portfolio_config.keys())[0] - portfolio_symbols = portfolio_config[portfolio_key].get( - "symbols", [] - ) - portfolio_name = portfolio_key # Use actual collection name - self.logger.info( - "Using %s symbols from %s collection", - len(portfolio_symbols), - portfolio_name, - ) - except Exception as e: - self.logger.warning("Could not load portfolio config: %s", e) - - # Load backtest results with optional symbol filtering - backtest_data = self._load_backtest_results(quarter, portfolio_symbols) - if not backtest_data: - raise ValueError("No backtest results found") - - # Performance-based scoring - scored_assets = self._calculate_performance_scores(backtest_data) - - # Risk-adjusted filtering - filtered_assets = self._apply_risk_filters(scored_assets, risk_tolerance) - - # Portfolio correlation analysis - correlation_data = self._analyze_correlations(filtered_assets) - - # Strategy-asset matching - optimized_assets = self._optimize_strategy_asset_matching(filtered_assets) - - # Generate allocation suggestions - allocations = self._suggest_allocations( - optimized_assets, risk_tolerance, max_assets - ) - - # Red flag detection - flagged_assets = self._detect_red_flags(allocations) - - # Calculate confidence scores - confidence_scores = self._calculate_confidence_scores( - flagged_assets, backtest_data - ) - - # Create asset recommendations for ALL assets (no filtering by confidence) - recommendations = [] - for asset_data in confidence_scores: - # Determine investment recommendation - base_reasoning = asset_data.get( - "reasoning", - f"Sortino: {asset_data['sortino_ratio']:.2f}, Max DD: {asset_data['max_drawdown']:.1%}", - ) - - if asset_data["confidence"] >= 0.6 and asset_data["score"] >= 0.3: - invest_decision = "INVEST_WITH_RISK_MANAGEMENT" - reasoning = f"Moderate performance metrics suggest cautious investment. {base_reasoning}" - elif asset_data["confidence"] >= 0.4 and asset_data["score"] >= 0.2: - invest_decision = "CONSIDER_WITH_HIGH_CAUTION" - reasoning = f"Below-average performance requires extreme caution. {base_reasoning}" - else: - invest_decision = "DO_NOT_INVEST" - reasoning = ( - f"Poor performance metrics indicate high risk. {base_reasoning}" - ) - - # Calculate trading parameters - trading_params = self._calculate_trading_parameters(asset_data, timeframe) - - recommendation = AssetRecommendation( - symbol=asset_data["symbol"], - strategy=asset_data["strategy"], - score=self._ensure_python_type(asset_data["score"]), - confidence=self._ensure_python_type(asset_data["confidence"]), - allocation_percentage=self._ensure_python_type( - asset_data["allocation"] - ), # Always show suggested allocation - risk_level=self._classify_risk_level(asset_data), - reasoning=reasoning, - red_flags=[*asset_data.get("red_flags", []), invest_decision], - sortino_ratio=asset_data["sortino_ratio"], - calmar_ratio=asset_data["calmar_ratio"], - max_drawdown=asset_data["max_drawdown"], - win_rate=0.0, # Not available in database - profit_factor=1.0, # Not available in database - total_return=asset_data["total_return"], - # Trading parameters - trading_style=trading_params["trading_style"], - timeframe=trading_params["timeframe"], - risk_per_trade=trading_params["risk_per_trade"], - stop_loss=trading_params["stop_loss_points"], - take_profit=trading_params["take_profit_points"], - position_size=trading_params["position_size_percent"], - ) - recommendations.append(recommendation) - - # Generate AI analysis - ai_analysis = self._generate_ai_analysis( - recommendations, correlation_data, risk_tolerance - ) - - portfolio_rec = PortfolioRecommendation( - recommendations=recommendations, - total_score=self._ensure_python_type( - np.mean([r.score for r in recommendations]) - ), - risk_profile=risk_tolerance, - diversification_score=self._ensure_python_type( - correlation_data["diversification_score"] - ), - correlation_analysis=correlation_data["correlations"], - overall_reasoning=ai_analysis["reasoning"], - warnings=ai_analysis["warnings"], - confidence=self._ensure_python_type( - np.mean([r.confidence for r in recommendations]) - ), - ) - - # Save to database and exports - self._save_to_database(portfolio_rec, quarter, portfolio_name) - self._save_to_exports( - recommendations, risk_tolerance, quarter, portfolio_name, timeframe - ) - - return portfolio_rec - - def _generate_portfolio_filtered_recommendations( - self, - symbols: list[str], - risk_tolerance: str = "moderate", - min_confidence: float = 0.6, - max_assets: int = 10, - quarter: Optional[str] = None, - timeframe: str = "1h", - portfolio_name: Optional[str] = None, - ) -> PortfolioRecommendation: - """Generate recommendations specifically for portfolio symbols only.""" - self.logger.info( - "Generating AI recommendations for %s risk profile", risk_tolerance - ) - - # Load backtest results and filter by portfolio symbols immediately - backtest_data = self._load_backtest_results(quarter, None) - if not backtest_data: - raise ValueError("No backtest results found") - - # Filter to only include portfolio symbols and get best strategy per asset - portfolio_backtest_data = [] - symbol_best_strategies = {} - - # Group by symbol and find best strategy for each - for asset in backtest_data: - if asset["symbol"] in symbols: - symbol = asset["symbol"] - - # Keep track of best strategy per symbol (highest sortino ratio) - if symbol not in symbol_best_strategies or ( - asset["sortino_ratio"] - > symbol_best_strategies[symbol]["sortino_ratio"] - ): - symbol_best_strategies[symbol] = asset - - # Convert to list format (only best strategy per asset) - portfolio_backtest_data = list(symbol_best_strategies.values()) - - if not portfolio_backtest_data: - # Return empty portfolio recommendation - return PortfolioRecommendation( - recommendations=[], - total_score=0.0, - risk_profile=risk_tolerance, - diversification_score=0.0, - correlation_analysis={}, - overall_reasoning="No backtested assets found in portfolio. Only assets with backtest or optimization data are analyzed.", - warnings=["Portfolio contains no backtested assets"], - confidence=0.0, - ) - - self.logger.info( - "Found %d backtested assets from portfolio symbols", - len(portfolio_backtest_data), - ) - - # Performance-based scoring (no filtering, just scoring) - scored_assets = self._calculate_performance_scores(portfolio_backtest_data) - - # Skip most filtering - just use scored assets directly - # Portfolio correlation analysis - correlation_data = self._analyze_correlations(scored_assets) - - # Calculate equal allocation for portfolio assets (like typical bond portfolios) - num_assets = len(scored_assets) - if num_assets > 0: - base_allocation = 100.0 / num_assets # Equal weight allocation - for asset in scored_assets: - # Adjust allocation slightly based on performance score - score_multiplier = 0.8 + ( - asset["score"] * 0.4 - ) # 0.8x to 1.2x based on score - asset["allocation"] = self._ensure_python_type( - min(20.0, max(2.0, base_allocation * score_multiplier)) - ) - - # Use scored assets directly (no filtering) - confidence_scores = self._calculate_confidence_scores( - scored_assets, portfolio_backtest_data - ) - - # Create asset recommendations for ALL portfolio assets (no filtering by confidence) - recommendations = [] - for asset_data in confidence_scores: - # Determine investment recommendation - base_reasoning = asset_data.get( - "reasoning", - f"Sortino: {asset_data['sortino_ratio']:.2f}, Max DD: {asset_data['max_drawdown']:.1%}", - ) - - if asset_data["confidence"] >= 0.6 and asset_data["score"] >= 0.3: - invest_decision = "INVEST_WITH_RISK_MANAGEMENT" - reasoning = f"Moderate performance metrics suggest cautious investment. {base_reasoning}" - elif asset_data["confidence"] >= 0.4 and asset_data["score"] >= 0.2: - invest_decision = "CONSIDER_WITH_HIGH_CAUTION" - reasoning = f"Below-average performance requires extreme caution. {base_reasoning}" - else: - invest_decision = "DO_NOT_INVEST" - reasoning = ( - f"Poor performance metrics indicate high risk. {base_reasoning}" - ) - - # Calculate trading parameters - trading_params = self._calculate_trading_parameters(asset_data, timeframe) - - recommendation = AssetRecommendation( - symbol=asset_data["symbol"], - strategy=asset_data["strategy"], - score=self._ensure_python_type(asset_data["score"]), - confidence=self._ensure_python_type(asset_data["confidence"]), - allocation_percentage=self._ensure_python_type( - asset_data["allocation"] - ), # Always show suggested allocation - risk_level=self._classify_risk_level(asset_data), - reasoning=reasoning, - red_flags=[*asset_data.get("red_flags", []), invest_decision], - sortino_ratio=asset_data["sortino_ratio"], - calmar_ratio=asset_data["calmar_ratio"], - max_drawdown=asset_data["max_drawdown"], - win_rate=0.0, # Not available in database - profit_factor=1.0, # Not available in database - total_return=asset_data["total_return"], - # Trading parameters - trading_style=trading_params["trading_style"], - timeframe=trading_params["timeframe"], - risk_per_trade=trading_params["risk_per_trade"], - stop_loss=trading_params["stop_loss_points"], - take_profit=trading_params["take_profit_points"], - position_size=trading_params["position_size_percent"], - ) - recommendations.append(recommendation) - - # Generate AI analysis - ai_analysis = self._generate_ai_analysis( - recommendations, correlation_data, risk_tolerance - ) - - portfolio_rec = PortfolioRecommendation( - recommendations=recommendations, - total_score=self._ensure_python_type( - np.mean([r.score for r in recommendations]) - ), - risk_profile=risk_tolerance, - diversification_score=self._ensure_python_type( - correlation_data["diversification_score"] - ), - correlation_analysis=correlation_data["correlations"], - overall_reasoning=ai_analysis["reasoning"], - warnings=ai_analysis["warnings"], - confidence=self._ensure_python_type( - np.mean([r.confidence for r in recommendations]) - ), - ) - - return portfolio_rec - - def generate_portfolio_recommendations( - self, - portfolio_config_path: str, - risk_tolerance: str = "moderate", - min_confidence: float = 0.6, - max_assets: int = 10, - quarter: Optional[str] = None, - timeframe: str = "1h", - filename_interval: Optional[str] = None, - generate_html: bool = True, - ) -> tuple[PortfolioRecommendation, str]: - """Generate AI recommendations for a specific portfolio with HTML report.""" - import json - from pathlib import Path - - # Load portfolio configuration - portfolio_path = Path(portfolio_config_path) - with portfolio_path.open() as f: - portfolio_config = json.load(f) - - # Handle nested portfolio configuration - if len(portfolio_config) == 1: - # Single key, assume it's the portfolio config - portfolio_key = list(portfolio_config.keys())[0] - portfolio_data = portfolio_config[portfolio_key] - else: - # Direct configuration - portfolio_data = portfolio_config - - portfolio_name = portfolio_data.get( - "name", portfolio_path.stem.replace("_", " ").title() - ) - symbols = portfolio_data.get("symbols", []) - - self.logger.info( - "Generating AI recommendations for %s portfolio (%d symbols)", - portfolio_name, - len(symbols), - ) - - # Generate recommendations for only the portfolio symbols - # (filter backtest data first before generating recommendations) - portfolio_filtered_recommendations = ( - self._generate_portfolio_filtered_recommendations( - symbols=symbols, - risk_tolerance=risk_tolerance, - min_confidence=min_confidence, - max_assets=max_assets, - quarter=quarter, - timeframe=timeframe, - portfolio_name=portfolio_name, - ) - ) - - portfolio_recommendations = portfolio_filtered_recommendations.recommendations - - self.logger.info( - "Generated recommendations for %d backtested assets from %d portfolio symbols", - len(portfolio_recommendations), - len(symbols), - ) - - # Use the filtered portfolio recommendations - filtered_portfolio = portfolio_filtered_recommendations - - # Save to markdown exports (skip database save due to model mismatch) - self._save_to_exports( - filtered_portfolio.recommendations, - risk_tolerance, - quarter, - portfolio_name, - filename_interval or timeframe, - ) - - # Try to save to database (may fail due to model mismatch) - try: - self._save_to_database(filtered_portfolio, quarter, portfolio_name) - except Exception as e: - self.logger.warning( - "Database save failed: %s - continuing with markdown export", e - ) - - html_path = "" - if generate_html: - # Generate HTML report - report_generator = AIReportGenerator() - # Determine year/quarter parts from quarter token or now - from datetime import datetime as _dt - - if quarter and "_" in (quarter or ""): - quarter_part, year_part = quarter.split("_") - else: - now = _dt.now() - quarter_part = quarter or f"Q{(now.month - 1) // 3 + 1}" - year_part = str(now.year) - html_path = report_generator.generate_html_report( - recommendation=filtered_portfolio, - portfolio_name=portfolio_name, - year=year_part, - quarter=quarter_part, - interval=filename_interval or timeframe, - ) - - return filtered_portfolio, html_path - - def _load_backtest_results( - self, - quarter: Optional[str] = None, - portfolio_symbols: Optional[list[str]] = None, - ) -> list[dict]: - """Load backtest results, preferring primary DB but falling back to unified_models.""" - results: list[dict] = [] - used_source = None - - # Try primary DB if a session is available - if self.db_session is not None: - try: - results = self._load_from_database(quarter, portfolio_symbols) - if results: - used_source = "primary_db" - except Exception as e: - self.logger.warning("Primary DB load failed: %s", e) - - # Fallback to unified_models BestStrategy if no primary data - if not results: - try: - results = self._load_from_unified_models(quarter, portfolio_symbols) - if results: - used_source = "unified_models" - except Exception as e: - self.logger.warning("Unified models load failed: %s", e) - - if not results: - self.logger.warning( - "No results found for AI recommendations after all fallbacks" - ) - # Last-resort fallback: parse CSV exports produced in the quarterly folder - try: - csv_results = self._load_from_csv_exports(quarter, portfolio_symbols) - if csv_results: - self.logger.info( - "Using CSV exports for AI recommendations (%d rows)", - len(csv_results), - ) - return csv_results - except Exception as e: - self.logger.debug("CSV fallback failed: %s", e) - return [] - - self.logger.info( - "Using %s data for AI recommendations (%d rows)", used_source, len(results) - ) - return results - - def _load_from_unified_models( - self, - quarter: Optional[str] = None, - portfolio_symbols: Optional[list[str]] = None, - ) -> list[dict]: - from datetime import datetime - - try: - from src.database import unified_models as um - except Exception: - return [] - - sess = um.Session() - try: - q = sess.query(um.BestStrategy) - if portfolio_symbols: - q = q.filter(um.BestStrategy.symbol.in_(portfolio_symbols)) - - if quarter: - year, qstr = quarter.split("_") - qnum = int(qstr[1]) - start_month = (qnum - 1) * 3 + 1 - end_month = qnum * 3 - start_date = datetime(int(year), start_month, 1) - end_date = ( - datetime(int(year) + 1, 1, 1) - if qnum == 4 - else datetime(int(year), end_month + 1, 1) - ) - q = q.filter( - um.BestStrategy.updated_at >= start_date, - um.BestStrategy.updated_at < end_date, - ) - - q = q.order_by(um.BestStrategy.sortino_ratio.desc()) - rows = q.all() - out = [ - { - "symbol": r.symbol, - "strategy": r.strategy, - "sortino_ratio": float(r.sortino_ratio or 0), - "calmar_ratio": float(r.calmar_ratio or 0), - "sharpe_ratio": float(r.sharpe_ratio or 0), - "total_return": float(r.total_return or 0), - "max_drawdown": float(r.max_drawdown or 0), - "created_at": r.updated_at.isoformat() if r.updated_at else None, - } - for r in rows - ] - return out - finally: - try: - sess.close() - except Exception: - pass - - def _load_from_csv_exports( - self, - quarter: Optional[str] = None, - portfolio_symbols: Optional[list[str]] = None, - ) -> list[dict]: - """Load best-per-asset rows from CSV exports under exports/csv//.""" - from pathlib import Path - - # Determine year and quarter folder - year_part = None - quarter_part = None - if quarter and "_" in quarter: - q, y = quarter.split("_") - quarter_part = q - year_part = y - else: - from datetime import datetime as _dt - - now = _dt.utcnow() - year_part = str(now.year) - quarter_part = f"Q{((now.month - 1) // 3) + 1}" - - base = Path("exports/csv") / str(year_part) / str(quarter_part) - if not base.exists(): - return [] - - rows: list[dict] = [] - # Load all CSVs for the quarter; we'll filter to portfolio symbols - for csv_path in base.glob("*.csv"): - try: - df = pd.read_csv(str(csv_path)) - except Exception as e: - self.logger.debug("Failed reading CSV %s: %s", csv_path, e) - continue - # Normalize expected columns - cols = {c.lower(): c for c in df.columns} - # Prefer 'Asset' else 'Symbol' - asset_col = cols.get("asset") or cols.get("symbol") - strat_col = cols.get("best_strategy") or cols.get("strategy") - tf_col = cols.get("best_timeframe") or cols.get("timeframe") - if not asset_col or not strat_col: - continue - # Filter symbols if provided - if portfolio_symbols: - df = df[df[asset_col].isin(portfolio_symbols)] - if df.empty: - continue - - # Build numeric metrics with safe defaults - def _num(colname: str, _cols=cols, _df=df) -> pd.Series: - c = _cols.get(colname.lower()) - if not c or c not in _df.columns: - return pd.Series([np.nan] * len(_df)) - try: - return pd.to_numeric(_df[c], errors="coerce") - except Exception: - return pd.Series([np.nan] * len(_df)) - - srt = _num("Sortino_Ratio") - cal = _num("Calmar_Ratio") - shp = _num("Sharpe_Ratio") - trn = _num("Total_Return_Pct") - if trn.isna().all(): - trn = _num("Total_Return") - mdd = _num("Max_Drawdown_Pct") - if mdd.isna().all(): - mdd = _num("Max_Drawdown") - - tmp = pd.DataFrame( - { - "symbol": df[asset_col].astype(str), - "strategy": df[strat_col].astype(str), - "timeframe": df[tf_col].astype(str) if tf_col else "1d", - "sortino_ratio": srt.fillna(0.0), - "calmar_ratio": cal.fillna(0.0), - "sharpe_ratio": shp.fillna(0.0), - "total_return": trn.fillna(0.0), - "max_drawdown": mdd.fillna(0.0), - } - ) - rows.extend(tmp.to_dict("records")) - - if not rows: - return [] - - # Reduce to best per symbol by Sortino - by_symbol = {} - for r in rows: - sym = r.get("symbol") - if not sym: - continue - if sym not in by_symbol or float(r.get("sortino_ratio") or 0) > float( - by_symbol[sym].get("sortino_ratio") or 0 - ): - by_symbol[sym] = r - - return list(by_symbol.values()) - - def _load_from_database( - self, - quarter: Optional[str] = None, - portfolio_symbols: Optional[list[str]] = None, - ) -> list[dict]: - """Load best strategies from database for faster and cleaner recommendations.""" - from datetime import datetime - - # Query best_strategies table directly - much more efficient - query = self.db_session.query(BestStrategy) - - # Filter by portfolio symbols if provided - if portfolio_symbols: - query = query.filter(BestStrategy.symbol.in_(portfolio_symbols)) - self.logger.info( - "Filtering by %s portfolio symbols", len(portfolio_symbols) - ) - - if quarter: - # Filter by quarter if specified - year, q = quarter.split("_") - quarter_num = int(q[1]) - start_month = (quarter_num - 1) * 3 + 1 - end_month = quarter_num * 3 - - start_date = datetime(int(year), start_month, 1) - if quarter_num == 4: - end_date = datetime(int(year) + 1, 1, 1) - else: - end_date = datetime(int(year), end_month + 1, 1) - - query = query.filter( - BestStrategy.updated_at >= start_date, - BestStrategy.updated_at < end_date, - ) - - # Order by primary metric (Sortino ratio) descending - query = query.order_by(BestStrategy.sortino_ratio.desc()) - results = query.all() - - self.logger.info("Loaded %d best strategies from database", len(results)) - - return [ - { - "symbol": result.symbol, - "strategy": result.strategy, - "sortino_ratio": float(result.sortino_ratio or 0), - "calmar_ratio": float(result.calmar_ratio or 0), - "sharpe_ratio": float(result.sharpe_ratio or 0), - "total_return": float(result.total_return or 0), - "max_drawdown": float(result.max_drawdown or 0), - "created_at": result.updated_at.isoformat() - if result.updated_at - else None, - } - for result in results - ] - - def _load_from_reports(self, quarter: Optional[str] = None) -> list[dict]: - """Load backtest results from HTML reports.""" - reports_dir = Path("exports/reports") - - if quarter: - year, q = quarter.split("_") - reports_path = reports_dir / year / q - else: - # Get latest quarter - reports_path = reports_dir / "2025" / "Q3" - - if not reports_path.exists(): - self.logger.warning("Reports directory %s not found", reports_path) - return [] - - # Parse HTML reports to extract metrics - return self._parse_html_reports(reports_path) - - def _parse_html_reports(self, reports_path: Path) -> list[dict]: - """Parse HTML reports to extract backtest metrics.""" - - from bs4 import BeautifulSoup - - parsed_data = [] - - # Find HTML reports in the directory - html_files = list(reports_path.glob("*.html")) - - for html_file in html_files: - try: - with Path(html_file).open(encoding="utf-8") as f: - content = f.read() - - soup = BeautifulSoup(content, "html.parser") - - # Find asset sections - asset_sections = soup.find_all("div", class_="asset-section") - - for section in asset_sections: - # Extract asset symbol from the title - asset_title = section.find("h2", class_="asset-title") - if not asset_title: - continue - - symbol = asset_title.text.strip() - - # Extract best strategy from the badge - strategy_badge = section.find("span", class_="strategy-badge") - if not strategy_badge: - continue - - # Parse "Best: Strategy Name" - strategy_text = strategy_badge.text.strip() - if strategy_text.startswith("Best: "): - strategy = strategy_text[6:].strip() # Remove "Best: " - else: - continue - - # Extract metrics from metric cards - metrics_data = { - "symbol": symbol, - "strategy": strategy, - "sortino_ratio": 0.0, - "calmar_ratio": 0.0, - "sharpe_ratio": 0.0, - "profit_factor": 0.0, - "max_drawdown": 0.0, - "volatility": 0.0, - "win_rate": 0.0, - "total_return": 0.0, - "num_trades": 0, - "created_at": "2025-08-14", - "initial_capital": 10000, - "final_value": 10000, - } - - # Find metric cards and extract values - metric_cards = section.find_all("div", class_="metric-card") - for card in metric_cards: - label_elem = card.find("div", class_="metric-label") - value_elem = card.find("div", class_="metric-value") - - if not label_elem or not value_elem: - continue - - label = label_elem.text.strip().lower() - value_text = value_elem.text.strip() - - # Parse metric values - try: - # Remove % and convert to float - if "%" in value_text: - value = float(value_text.replace("%", "")) / 100 - else: - value = float(value_text) - - # Map labels to our metric keys - if "sortino" in label: - metrics_data["sortino_ratio"] = value - elif "calmar" in label: - metrics_data["calmar_ratio"] = value - elif "sharpe" in label: - metrics_data["sharpe_ratio"] = value - elif "profit factor" in label: - metrics_data["profit_factor"] = value - elif "max drawdown" in label or "maximum drawdown" in label: - metrics_data["max_drawdown"] = value - elif "volatility" in label: - metrics_data["volatility"] = value - elif "win rate" in label: - metrics_data["win_rate"] = value - elif "total return" in label: - metrics_data["total_return"] = value - except ValueError: - continue - - parsed_data.append(metrics_data) - - except Exception as e: - self.logger.warning("Error parsing HTML report %s: %s", html_file, e) - continue - - self.logger.info("Parsed %d asset metrics from HTML reports", len(parsed_data)) - return parsed_data - - def _calculate_performance_scores(self, backtest_data: list[dict]) -> list[dict]: - """Calculate performance scores for each asset based on metrics.""" - scored_assets = [] - - for asset in backtest_data: - # Normalize metrics for scoring - sortino_score = min(max(asset["sortino_ratio"], 0), 5) / 5.0 - calmar_score = min(max(asset["calmar_ratio"], 0), 5) / 5.0 - drawdown_score = max(0, 1 - abs(asset["max_drawdown"]) / 100) - return_score = min(max(asset["total_return"] / 100, 0), 1.0) - - # Calculate weighted score using available metrics - score = ( - sortino_score * 0.4 # Primary metric - + calmar_score * 0.3 # Secondary metric - + return_score * 0.2 # Return component - + drawdown_score * 0.1 # Risk component - ) - - asset["score"] = self._ensure_python_type(score) - scored_assets.append(asset) - - return sorted(scored_assets, key=lambda x: x["score"], reverse=True) - - def _apply_risk_filters( - self, assets: list[dict], risk_tolerance: str - ) -> list[dict]: - """Filter assets based on risk tolerance.""" - risk_criteria = self.risk_levels[risk_tolerance] - - filtered = [] - for asset in assets: - max_dd = abs(asset["max_drawdown"]) - sortino = asset["sortino_ratio"] - - if ( - max_dd <= risk_criteria["max_drawdown"] - and sortino >= risk_criteria["min_sortino"] - ): - filtered.append(asset) - - return filtered - - def _analyze_correlations(self, assets: list[dict]) -> dict: - """Analyze portfolio correlations for diversification.""" - # This would calculate actual correlations using price data - # TODO: Implement actual correlation calculation using price data - symbols = [asset["symbol"] for asset in assets] - - # Placeholder correlation matrix - to be implemented - correlations = {} - # Calculate a basic diversification score based on number of assets - # More assets generally means better diversification - diversification_score = min(0.9, 0.3 + (len(symbols) * 0.1)) - _ = symbols # Unused for now - - return { - "correlations": correlations, - "diversification_score": diversification_score, - } - - def _optimize_strategy_asset_matching(self, assets: list[dict]) -> list[dict]: - """Find optimal strategy-asset combinations.""" - # Group by symbol and find best strategy for each - symbol_strategies = {} - - for asset in assets: - symbol = asset["symbol"] - if symbol not in symbol_strategies: - symbol_strategies[symbol] = [] - symbol_strategies[symbol].append(asset) - - # Select best strategy per symbol - optimized = [] - for symbol, strategies in symbol_strategies.items(): - best_strategy = max(strategies, key=lambda x: x["score"]) - optimized.append(best_strategy) - - return optimized - - def _suggest_allocations( - self, assets: list[dict], _: str, max_assets: int - ) -> list[dict]: - """Suggest portfolio allocations based on scores and risk.""" - # Take top assets - top_assets = assets[:max_assets] - - if not top_assets: - return [] - - # Calculate allocations based on scores - total_score = sum(asset["score"] for asset in top_assets) - - for asset in top_assets: - if total_score > 0: - allocation = (asset["score"] / total_score) * 100 - else: - allocation = 100 / len(top_assets) - - asset["allocation"] = self._ensure_python_type(allocation) - - return top_assets - - def _detect_red_flags(self, assets: list[dict]) -> list[dict]: - """Detect potential issues with recommended assets.""" - for asset in assets: - red_flags = [] - - # High drawdown warning - if abs(asset["max_drawdown"]) > 0.3: - red_flags.append("High maximum drawdown risk") - - # Low Sortino ratio - if asset["sortino_ratio"] < 0.5: - red_flags.append("Low risk-adjusted returns") - - # High drawdown (using max_drawdown as risk indicator) - if abs(asset["max_drawdown"]) > 40: - red_flags.append("High drawdown risk") - - # Low total return - if asset["total_return"] < 5: - red_flags.append("Low returns") - - # Poor risk-adjusted return (low Sharpe ratio) - if asset["sharpe_ratio"] < 0.5: - red_flags.append("Poor risk-adjusted returns") - - asset["red_flags"] = red_flags - - return assets - - def _calculate_confidence_scores( - self, assets: list[dict], _: list[dict] - ) -> list[dict]: - """Calculate confidence scores based on data quality and consistency.""" - for asset in assets: - confidence_factors = [] - - # Performance stability (Sortino ratio) - use actual database metric - sortino_factor = float(min(max(asset["sortino_ratio"], 0) / 2, 1.0)) - confidence_factors.append(sortino_factor) - - # Risk management (drawdown control) - use actual database metric - drawdown_factor = float(max(0, 1 - abs(asset["max_drawdown"]) / 50)) - confidence_factors.append(drawdown_factor) - - # Return consistency (Calmar ratio) - use actual database metric - calmar_factor = float(min(max(asset["calmar_ratio"], 0) / 2, 1.0)) - confidence_factors.append(calmar_factor) - - # Risk-adjusted performance (Sharpe ratio) - use actual database metric - sharpe_factor = float(min(max(asset["sharpe_ratio"], 0) / 2, 1.0)) - confidence_factors.append(sharpe_factor) - - # Calculate weighted confidence and ensure it's a Python float - asset["confidence"] = self._ensure_python_type(np.mean(confidence_factors)) - - return assets - - def _classify_risk_level(self, asset_data: dict) -> str: - """Classify asset risk level based on database metrics only.""" - max_dd = abs(asset_data["max_drawdown"]) - sortino_ratio = asset_data["sortino_ratio"] - - # Use drawdown and Sortino ratio for risk classification - if max_dd <= 10 and sortino_ratio >= 1.0: - return "Low" - if max_dd <= 25 and sortino_ratio >= 0.5: - return "Medium" - return "High" - - def _calculate_trading_parameters( - self, asset_data: dict, timeframe: str = "1h" - ) -> dict: - """Calculate trading parameters based on timeframe and asset characteristics.""" - - # Determine trading style based on timeframe - timeframe_minutes = self._timeframe_to_minutes(timeframe) - is_scalping = timeframe_minutes < 60 # Less than 1 hour = scalping - - trading_style = "scalp" if is_scalping else "swing" - - # Get asset volatility for parameter adjustment - volatility = asset_data.get("volatility", 0.02) # Default 2% volatility - max_drawdown = abs(asset_data.get("max_drawdown", 0.05)) - - if is_scalping: - # Scalping parameters (tighter, more frequent trades) - base_risk = 0.5 # 0.5% base risk per trade for scalping - base_sl_points = max( - 5, volatility * 1000 - ) # Minimum 5 points, volatility-adjusted - base_tp_points = base_sl_points * 2 # 1:2 risk-reward for scalping - position_size = 5.0 # Smaller position sizes for scalping - - # Adjust based on volatility - if volatility > 0.05: # High volatility assets - base_risk *= 0.7 # Reduce risk - base_sl_points *= 1.5 - base_tp_points *= 1.5 - else: - # Swing trading parameters (wider, longer-term trades) - base_risk = 2.0 # 2% base risk per trade for swing - base_sl_points = max( - 20, volatility * 3000 - ) # Minimum 20 points, volatility-adjusted - base_tp_points = base_sl_points * 3 # 1:3 risk-reward for swing - position_size = 10.0 # Larger position sizes for swing - - # Adjust based on volatility and drawdown - if volatility > 0.03: # High volatility assets - base_risk *= 0.8 - base_sl_points *= 1.2 - base_tp_points *= 1.2 - - if max_drawdown > 0.2: # High drawdown history - base_risk *= 0.6 - position_size *= 0.8 - - # Risk level adjustments - risk_level = self._classify_risk_level(asset_data) - if risk_level == "High": - base_risk *= 0.5 - position_size *= 0.7 - elif risk_level == "Low": - base_risk *= 1.2 - position_size *= 1.1 - - return { - "trading_style": trading_style, - "timeframe": timeframe, - "risk_per_trade": round(base_risk, 1), - "stop_loss_points": round(base_sl_points, 0), - "take_profit_points": round(base_tp_points, 0), - "position_size_percent": round(position_size, 1), - } - - def _timeframe_to_minutes(self, timeframe: str) -> int: - """Convert timeframe string to minutes.""" - timeframe = timeframe.lower() - - if "m" in timeframe: - return int(timeframe.replace("m", "")) - if "h" in timeframe: - return int(timeframe.replace("h", "")) * 60 - if "d" in timeframe: - return int(timeframe.replace("d", "")) * 24 * 60 - if "w" in timeframe: - return int(timeframe.replace("w", "")) * 7 * 24 * 60 - return 60 # Default to 1 hour - - def _generate_ai_analysis( - self, - recommendations: list[AssetRecommendation], - correlation_data: dict, - risk_tolerance: str, - ) -> dict[str, Any]: - """Generate AI-powered analysis and reasoning.""" - if not recommendations: - return { - "reasoning": "No backtested assets found in portfolio. Only assets with backtest or optimization data are analyzed.", - "warnings": ["Portfolio contains no backtested assets"], - } - - # Prepare data for AI analysis - analysis_data = { - "risk_tolerance": risk_tolerance, - "num_recommendations": len(recommendations), - "avg_sortino": np.mean([r.sortino_ratio for r in recommendations]), - "avg_calmar": np.mean([r.calmar_ratio for r in recommendations]), - "max_drawdown_range": [r.max_drawdown for r in recommendations], - "diversification_score": correlation_data["diversification_score"], - "total_allocation": sum(r.allocation_percentage for r in recommendations), - "red_flags_count": sum(len(r.red_flags) for r in recommendations), - } - - # Generate AI reasoning - try: - ai_response = self.llm_client.analyze_portfolio( - analysis_data, recommendations - ) - return { - "reasoning": ai_response.get( - "reasoning", "Analysis completed successfully" - ), - "warnings": ai_response.get("warnings", []), - } - except Exception as e: - self.logger.error("AI analysis failed: %s", e) - return { - "reasoning": f"Quantitative analysis complete. {len(recommendations)} assets recommended with average Sortino ratio of {analysis_data['avg_sortino']:.2f}", - "warnings": [ - "AI analysis unavailable - using quantitative metrics only" - ], - } - - def get_asset_comparison( - self, symbols: list[str], strategy: Optional[str] = None - ) -> pd.DataFrame: - """Compare assets side by side with key metrics.""" - if self.db_session: - from sqlalchemy import or_ - - # Filter for results that contain any of the requested symbols - symbol_filters = [BacktestResult.symbols.any(symbol) for symbol in symbols] - query = self.db_session.query(BacktestResult).filter(or_(*symbol_filters)) - if strategy: - query = query.filter(BacktestResult.strategy == strategy) - - results = query.all() - - comparison_data = [] - for result in results: - comparison_data.append( - { - "Symbol": result.symbols[0] if result.symbols else "UNKNOWN", - "Strategy": result.strategy, - "Sortino Ratio": float(result.sortino_ratio or 0), - "Calmar Ratio": float(result.calmar_ratio or 0), - "Max Drawdown": float(result.max_drawdown or 0), - "Total Return": float(result.total_return or 0), - "Win Rate": float(result.win_rate or 0), - "Profit Factor": float(result.profit_factor or 0), - } - ) - - return pd.DataFrame(comparison_data) - - return pd.DataFrame() - - def explain_recommendation(self, symbol: str, strategy: str) -> dict[str, Any]: - """Get detailed explanation for a specific recommendation.""" - # Load specific asset data - asset_data = self._get_asset_data(symbol, strategy) - - if not asset_data: - return {"error": "Asset data not found"} - - # Generate detailed AI explanation - try: - explanation = self.llm_client.explain_asset_recommendation(asset_data) - return explanation - except Exception as e: - self.logger.error("Failed to generate explanation: %s", e) - return { - "summary": f"Asset {symbol} with {strategy} strategy shows Sortino ratio of {asset_data.get('sortino_ratio', 0):.2f}", - "strengths": ["Quantitative metrics available"], - "concerns": ["AI explanation unavailable"], - "recommendation": "Review metrics manually", - } - - def generate_practical_recommendations_from_html( - self, html_report_path: str, risk_tolerance: str = "moderate" - ) -> str: - """Generate practical trading recommendations from HTML report using database data.""" - from pathlib import Path - - from bs4 import BeautifulSoup - - # Parse HTML to get collection assets - html_path = Path(html_report_path) - if not html_path.exists(): - raise ValueError(f"HTML report not found: {html_report_path}") - - with html_path.open(encoding="utf-8") as f: - soup = BeautifulSoup(f.read(), "html.parser") - - # Extract asset symbols from HTML - asset_sections = soup.find_all("div", class_="asset-section") - symbols = [] - for section in asset_sections: - title = section.find("h2", class_="asset-title") - if title: - symbols.append(title.get_text().strip()) - - if not symbols: - raise ValueError("No assets found in HTML report") - - # Get database data for these symbols - if not self.db_session: - raise ValueError("Database session required") - - from src.database.models import BestStrategy - - # Query database for actual performance data - strategies = ( - self.db_session.query(BestStrategy) - .filter(BestStrategy.symbol.in_(symbols)) - .order_by(BestStrategy.sortino_ratio.desc()) - .all() - ) - - if not strategies: - raise ValueError("No strategy data found for collection assets") - - # Generate practical trading recommendations - collection_name = html_path.stem.replace("_Q3_2025", "").replace("_", " ") - - recommendations = self._create_practical_trading_guide( - strategies, collection_name, risk_tolerance - ) - - return recommendations - - def _create_practical_trading_guide( - self, strategies: list, collection_name: str, risk_tolerance: str - ) -> str: - """Create practical trading guide with entry/exit rules.""" - - # Sort by Sortino ratio and categorize by performance tiers (bond-appropriate thresholds) - top_tier = [s for s in strategies if float(s.sortino_ratio) > 1.0] - mid_tier = [s for s in strategies if 0.5 <= float(s.sortino_ratio) <= 1.0] - - guide = f"""# {collection_name} - Practical Trading Strategy Guide -**Model:** GPT-5-mini | **Generated:** Q3 2025 -**Risk Profile:** {risk_tolerance.title()} -**Assets Analyzed:** {len(strategies)} - -## 🎯 Actionable Investment Recommendations - -### **🥇 TOP TIER STRATEGIES** (Sortino > 1.0) -""" - - for i, strategy in enumerate(top_tier[:5], 1): - # Calculate practical levels based on historical performance - take_profit = min( - float(strategy.total_return) * 0.3, 10.0 - ) # 30% of total return, max 10% - stop_loss = min( - float(strategy.max_drawdown) * 0.5, 5.0 - ) # 50% of max drawdown, max 5% - - guide += f""" -#### {i}. **{strategy.symbol} - {strategy.strategy.upper()}** (Sortino: {strategy.sortino_ratio:.3f}) -- **Entry Signal**: {self._get_entry_signal(strategy.strategy)} -- **Take Profit**: +{take_profit:.1f}% or technical reversal -- **Stop Loss**: -{stop_loss:.1f}% strict -- **Position Size**: {self._get_position_size(strategy.sortino_ratio, risk_tolerance)}% allocation -- **Max Drawdown Risk**: {strategy.max_drawdown:.1f}% -- **Historical Return**: {float(strategy.total_return):.1f}% over backtest period -""" - - # Individual recommendations for ALL assets - guide += f""" -## 📋 **INDIVIDUAL ASSET RECOMMENDATIONS** -*Complete analysis of all {len(strategies)} assets in collection* - -""" - - for strategy in strategies: - sortino = float(strategy.sortino_ratio) - total_return = float(strategy.total_return) - max_dd = float(strategy.max_drawdown) - - # Calculate levels for each asset - take_profit = min(total_return * 0.3, 10.0) if total_return > 0 else 3.0 - stop_loss = min(max_dd * 0.5, 5.0) if max_dd > 0 else 2.0 - - # Determine recommendation level - if sortino > 1.0: - recommendation = "🟢 **BUY**" - allocation = self._get_position_size(sortino, risk_tolerance) - elif sortino > 0.5: - recommendation = "🟡 **HOLD**" - allocation = max( - self._get_position_size(sortino, risk_tolerance) - 5, 5 - ) - else: - recommendation = "🔴 **AVOID**" - allocation = 0 - - guide += f""" -### **{strategy.symbol}** - {strategy.strategy.upper()} -- **Rating**: {recommendation} | **Sortino**: {sortino:.3f} -- **Entry**: {self._get_entry_signal(strategy.strategy)} -- **Take Profit**: +{take_profit:.1f}% | **Stop Loss**: -{stop_loss:.1f}% -- **Allocation**: {allocation}% | **Return**: {total_return:.1f}% | **Max DD**: {max_dd:.1f}% -""" - - guide += f""" -## 📊 **Portfolio Construction** - -### **{risk_tolerance.title()} Risk Allocation:** -``` -{self._create_allocation_table(top_tier, mid_tier, risk_tolerance)} -``` - -## 🚨 **Risk Management Rules** -- **Maximum single position**: {30 if risk_tolerance == "aggressive" else 25 if risk_tolerance == "moderate" else 20}% -- **Portfolio max drawdown**: {15 if risk_tolerance == "aggressive" else 10 if risk_tolerance == "moderate" else 6}% -- **Rebalance trigger**: 20% deviation from target weights -- **Emergency exit**: Strategy technical breakdown - -## 🎯 **Entry/Exit Decision Framework** - -### **Universal BUY Conditions:** -1. Technical signal confirmed (strategy-specific) -2. Risk-reward ratio > 2:1 -3. No major economic events in next 48h -4. Portfolio correlation < 0.8 - -### **Universal SELL Conditions:** -1. Take profit target reached -2. Stop loss triggered -3. Technical momentum reversal -4. Risk management override - -**This guide provides actionable trading rules based on {len(strategies)} backtested strategies using real database metrics.** -""" - - return guide - - def _get_entry_signal(self, strategy: str) -> str: - """Get entry signal description for strategy.""" - strategy_lower = strategy.lower() - if "bollinger" in strategy_lower: - return "Price touches lower Bollinger Band" - if "rsi" in strategy_lower: - return "RSI < 30 (oversold)" - if "macd" in strategy_lower: - return "MACD bullish crossover" - return "Buy signal confirmed" - - def _get_position_size(self, sortino_ratio: float, risk_tolerance: str) -> int: - """Calculate position size based on performance and risk tolerance.""" - base_size = ( - 20 - if risk_tolerance == "aggressive" - else 15 - if risk_tolerance == "moderate" - else 10 - ) - - if sortino_ratio > 3.0: - return min(base_size + 10, 30) - if sortino_ratio > 2.0: - return min(base_size + 5, 25) - if sortino_ratio > 1.0: - return base_size - return max(base_size - 5, 5) - - def _create_allocation_table( - self, top_tier: list, mid_tier: list, risk_tolerance: str - ) -> str: - """Create allocation percentage table.""" - total_allocation = ( - 80 - if risk_tolerance == "aggressive" - else 70 - if risk_tolerance == "moderate" - else 60 - ) - - if not top_tier: - return "No suitable strategies found for allocation" - - # Distribute allocation among top performers - top_3 = top_tier[:3] - if len(top_3) == 1: - allocations = [total_allocation] - elif len(top_3) == 2: - allocations = [total_allocation * 0.6, total_allocation * 0.4] - else: - allocations = [ - total_allocation * 0.4, - total_allocation * 0.35, - total_allocation * 0.25, - ] - - table = "" - for i, (strategy, alloc) in enumerate(zip(top_3, allocations)): - table += f"{alloc:.0f}% {strategy.symbol} ({strategy.strategy.upper()}) - Sortino {strategy.sortino_ratio:.2f}\n" - - cash_reserve = 100 - total_allocation - table += f"{cash_reserve}% Cash Reserve" - - return table - - def _get_asset_data(self, symbol: str, strategy: str) -> dict: - """Get specific asset backtest data.""" - if self.db_session: - # Use PostgreSQL-specific array operations to avoid ARRAY.contains() error - from sqlalchemy import func - - result = ( - self.db_session.query(BacktestResult) - .filter( - func.array_to_string(BacktestResult.symbols, ",").contains(symbol), - BacktestResult.strategy == strategy, - ) - .first() - ) - - if result: - return { - "symbol": symbol, - "strategy": strategy, - "sortino_ratio": float(result.sortino_ratio or 0), - "calmar_ratio": float(result.calmar_ratio or 0), - "max_drawdown": float(result.max_drawdown or 0), - "total_return": float(result.total_return or 0), - "win_rate": float(result.win_rate or 0), - "profit_factor": float(result.profit_factor or 0), - "volatility": float(result.volatility or 0), - } - - return {} - - def _save_to_exports( - self, - recommendations: list[AssetRecommendation], - risk_tolerance: str, - quarter: str, - portfolio_name: Optional[str] = None, - interval: Optional[str] = None, - ): - """Save recommendations to exports/ai_reco using unified filename convention.""" - from datetime import datetime - from pathlib import Path - - # Parse quarter and year or use current - if quarter and "_" in quarter: - # quarter might be like "Q3_2025" - quarter_part, year_part = quarter.split("_") - else: - current_date = datetime.now() - quarter_part = quarter or f"Q{(current_date.month - 1) // 3 + 1}" - year_part = str(current_date.year) - - # Create organized exports directory - exports_dir = Path("exports/ai_reco") / year_part / quarter_part - exports_dir.mkdir(parents=True, exist_ok=True) - - # Build unified filename: _Collection___.md - collection_name = portfolio_name or "All_Collections" - sanitized = ( - collection_name.replace(" ", "_").replace("/", "_").strip("_") - or "All_Collections" - ) - safe_interval = (interval or "multi").replace("/", "-") - filename = ( - f"{sanitized}_Collection_{year_part}_{quarter_part}_{safe_interval}.md" - ) - - # Generate markdown content - markdown_content = self._generate_markdown_report( - recommendations, risk_tolerance, quarter_part, year_part, collection_name - ) - - # Save to markdown file - output_path = exports_dir / filename - with output_path.open("w", encoding="utf-8") as f: - f.write(markdown_content) - - # Also provide a CSV export for analysts - try: - import pandas as _pd - - rows = [] - for rec in recommendations: - rows.append( - { - "Symbol": rec.symbol, - "Strategy": rec.strategy, - "Timeframe": rec.timeframe, - "Allocation_Pct": rec.allocation_percentage, - "Risk_Level": rec.risk_level, - "Confidence": rec.confidence, - "Sortino": rec.sortino_ratio, - "Calmar": rec.calmar_ratio, - "Max_Drawdown_Pct": rec.max_drawdown, - "Sharpe(approx)": rec.sharpe_ratio, - "Total_Return_Pct": rec.total_return, - "Trading_Style": rec.trading_style, - "Risk_Per_Trade_Pct": rec.risk_per_trade, - "Position_Size_Pct": rec.position_size, - "Stop_Loss_Points": rec.stop_loss, - "Take_Profit_Points": rec.take_profit, - } - ) - df = _pd.DataFrame(rows) - csv_filename = filename.replace(".md", ".csv") - df.to_csv(exports_dir / csv_filename, index=False) - self.logger.info( - "AI recommendations CSV saved to %s", exports_dir / csv_filename - ) - except Exception as _e: - self.logger.debug("Could not write AI CSV export: %s", _e) - - self.logger.info("AI recommendations saved to %s", output_path) - - def _generate_markdown_report( - self, - recommendations: list[AssetRecommendation], - risk_tolerance: str, - quarter: str, - year: str, - collection_name: str, - ) -> str: - """Generate markdown report for AI recommendations.""" - from datetime import datetime - - # Header - markdown = f"""# AI Investment Recommendations: {collection_name.title()} Collection - -## Summary -- **Collection**: {collection_name.title()} -- **Quarter**: {quarter} {year} -- **Risk Tolerance**: {risk_tolerance.title()} -- **Total Recommendations**: {len(recommendations)} -- **Generated**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} - ---- - -""" - - if not recommendations: - markdown += """## No Recommendations Available - -No backtested assets found in the portfolio. Only assets with backtest or optimization data are analyzed. - -### Warnings: -- Portfolio contains no backtested assets - -""" - return markdown - - # Recommendations section - markdown += "## Top Recommendations\n\n" - - for i, rec in enumerate(recommendations, 1): - # Format risk level with appropriate emoji - risk_emoji = {"Low": "🟢", "Medium": "🟡", "High": "🔴"}.get( - rec.risk_level, "⚪" - ) - - markdown += f"""### {i}. {rec.symbol} - {rec.strategy} - -**Allocation**: {rec.allocation_percentage:.1f}% | **Risk Level**: {risk_emoji} {rec.risk_level} | **Confidence**: {rec.confidence_score:.1f}% - -#### Performance Metrics -| Metric | Value | -|--------|-------| -| Sortino Ratio | {rec.sortino_ratio:.3f} | -| Calmar Ratio | {rec.calmar_ratio:.3f} | -| Max Drawdown | {rec.max_drawdown:.2f}% | -| Win Rate | {rec.win_rate:.1f}% | -| Profit Factor | {rec.profit_factor:.2f} | - -#### Analysis -{rec.reasoning} - -""" - if rec.red_flags: - markdown += f"""#### ⚠️ Risk Factors -{chr(10).join(f"- {flag}" for flag in rec.red_flags)} - -""" - - markdown += "---\n\n" - - # Footer - markdown += f"""## Disclaimer - -This analysis is for educational purposes only and should not be considered as financial advice. -Past performance does not guarantee future results. Always conduct your own research and consider -your risk tolerance before making investment decisions. - -**Generated by**: Quant System AI Recommendations -**Report Date**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} -""" - - return markdown - - def _save_to_database( - self, - portfolio_rec: PortfolioRecommendation, - quarter: str, - portfolio_name: Optional[str] = None, - ): - """Save AI recommendations to PostgreSQL database using normalized structure.""" - if not self.db_session: - self.logger.warning("No database session - skipping database save") - return - - from datetime import datetime - - # Determine which LLM model was used - llm_model = "unknown" - if os.getenv("OPENAI_API_KEY"): - llm_model = os.getenv("OPENAI_MODEL", "gpt-4o") - elif os.getenv("ANTHROPIC_API_KEY"): - llm_model = os.getenv("ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022") - - # Parse quarter and year - quarter_str = ( - quarter or f"Q{(datetime.now().month - 1) // 3 + 1}_{datetime.now().year}" - ) - if "_" in quarter_str: - q_part, year_part = quarter_str.split("_") - year = int(year_part) - quarter_only = q_part - else: - year = datetime.now().year - quarter_only = quarter_str - - # Calculate portfolio-level metrics with type conversion - total_return = self._ensure_python_type( - sum( - rec.allocation_percentage * rec.total_return - for rec in portfolio_rec.recommendations - ) - / 100 - ) - portfolio_risk = self._ensure_python_type( - sum( - rec.allocation_percentage * rec.max_drawdown - for rec in portfolio_rec.recommendations - ) - / 100 - ) - - try: - # Check if recommendation already exists (unique constraint check) - existing_rec = ( - self.db_session.query(AIRecommendation) - .filter_by( - portfolio_name=portfolio_name or "default", - quarter=quarter_only, - year=year, - risk_tolerance=portfolio_rec.risk_profile, - ) - .first() - ) - - if existing_rec: - # Update existing record - existing_rec.total_score = self._ensure_python_type( - portfolio_rec.total_score - ) - existing_rec.confidence = self._ensure_python_type( - portfolio_rec.confidence - ) - existing_rec.diversification_score = self._ensure_python_type( - portfolio_rec.diversification_score - ) - existing_rec.total_assets = len(portfolio_rec.recommendations) - existing_rec.expected_return = total_return - existing_rec.portfolio_risk = portfolio_risk - existing_rec.overall_reasoning = portfolio_rec.overall_reasoning - existing_rec.warnings = self._ensure_python_type(portfolio_rec.warnings) - existing_rec.correlation_analysis = self._ensure_python_type( - portfolio_rec.correlation_analysis - ) - existing_rec.llm_model = llm_model - ai_rec = existing_rec - self.logger.info("Updated existing AI recommendation record") - else: - # Create new AI recommendation record matching database schema - ai_rec = AIRecommendation( - portfolio_name=portfolio_name or "default", - quarter=quarter_only, - year=year, - risk_tolerance=portfolio_rec.risk_profile, - total_score=self._ensure_python_type(portfolio_rec.total_score), - confidence=self._ensure_python_type(portfolio_rec.confidence), - diversification_score=self._ensure_python_type( - portfolio_rec.diversification_score - ), - total_assets=len(portfolio_rec.recommendations), - expected_return=total_return, - portfolio_risk=portfolio_risk, - overall_reasoning=portfolio_rec.overall_reasoning, - warnings=self._ensure_python_type(portfolio_rec.warnings), - correlation_analysis=self._ensure_python_type( - portfolio_rec.correlation_analysis - ), - llm_model=llm_model, - ) - - self.db_session.add(ai_rec) - self.logger.info("Created new AI recommendation record") - - self.db_session.flush() # Get/Update the ID - - # Create individual asset recommendation records using manual conversion - for rec in portfolio_rec.recommendations: - # Convert to plain dict to avoid dataclass numpy issues - # Ultimate safety conversion - manually check each field - def force_native_type(val): - """Forcefully convert to native Python type.""" - if val is None: - return None - val_str = str(type(val)) - if "numpy" in val_str: - return float(val) - return val - - # Create asset recommendation with only fields that exist in database model - asset_rec = DbAssetRecommendation( - ai_recommendation_id=ai_rec.id, - symbol=rec.symbol, - recommendation_type=rec.recommendation_type, # BUY/SELL/HOLD - confidence_score=force_native_type(rec.confidence), - reasoning=rec.reasoning, - ) - self.db_session.add(asset_rec) - - self.db_session.commit() - self.logger.info( - "AI recommendations saved to database: %s_%s, %s", - quarter_only, - year, - portfolio_rec.risk_profile, - ) - - except Exception as e: - self.db_session.rollback() - self.logger.error("Failed to save AI recommendations to database: %s", e) - raise diff --git a/src/ai/llm_client.py b/src/ai/llm_client.py deleted file mode 100644 index e916055..0000000 --- a/src/ai/llm_client.py +++ /dev/null @@ -1,68 +0,0 @@ -"""LLM Client for AI Investment Recommendations.""" - -from __future__ import annotations - -import logging -from typing import Any - - -class LLMClient: - """Simple LLM client for generating investment recommendations.""" - - def __init__(self): - self.logger = logging.getLogger(__name__) - - def generate_portfolio_analysis( - self, backtest_data: list[dict], risk_profile: str - ) -> dict[str, Any]: - """Generate portfolio analysis using backtest data.""" - - # Simple rule-based analysis since we don't have actual LLM - # Filter by risk profile - if risk_profile == "conservative": - filtered_data = [d for d in backtest_data if d.get("max_drawdown", 0) > -20] - elif risk_profile == "moderate": - filtered_data = [d for d in backtest_data if d.get("max_drawdown", 0) > -40] - else: # aggressive - filtered_data = backtest_data - - # Select top performers by Sortino ratio - top_performers = sorted( - filtered_data, key=lambda x: x.get("sortino_ratio", 0), reverse=True - )[:10] - - return { - "reasoning": f"Based on {risk_profile} risk profile, selected top {len(top_performers)} strategies with appropriate risk levels.", - "confidence_score": 0.85, - "expected_return": sum(d.get("total_return", 0) for d in top_performers) - / len(top_performers) - if top_performers - else 0, - "expected_risk": sum(abs(d.get("max_drawdown", 0)) for d in top_performers) - / len(top_performers) - if top_performers - else 0, - "recommendations": top_performers, - } - - def explain_asset_recommendation(self, asset_data: dict) -> dict[str, Any]: - """Explain a specific asset recommendation.""" - - symbol = asset_data.get("symbol", "Unknown") - strategy = asset_data.get("strategy", "Unknown") - sortino = asset_data.get("sortino_ratio", 0) - - reasoning = f"Asset {symbol} with {strategy} strategy shows strong performance with Sortino ratio of {sortino:.3f}." - - if sortino > 1.0: - reasoning += " This indicates excellent risk-adjusted returns." - elif sortino > 0.5: - reasoning += " This shows good risk-adjusted performance." - else: - reasoning += " Performance may need improvement." - - return { - "reasoning": reasoning, - "confidence_score": min(0.95, max(0.3, sortino / 2.0)), - "recommendation": "BUY" if sortino > 0.5 else "HOLD", - } diff --git a/src/ai/models.py b/src/ai/models.py deleted file mode 100644 index 18184c9..0000000 --- a/src/ai/models.py +++ /dev/null @@ -1,90 +0,0 @@ -"""AI Models for Investment Recommendations.""" - -from __future__ import annotations - -from dataclasses import dataclass - - -@dataclass -class AssetRecommendation: - """AI recommendation for a single asset.""" - - symbol: str - strategy: str - score: float - confidence: float - allocation_percentage: float - risk_level: str - reasoning: str - red_flags: list[str] - sortino_ratio: float - calmar_ratio: float - max_drawdown: float - win_rate: float - profit_factor: float - total_return: float - trading_style: str - timeframe: str - position_size: float - risk_per_trade: float - stop_loss: float - take_profit: float - - @property - def confidence_score(self) -> float: - return self.confidence - - @property - def sharpe_ratio(self) -> float: - # Calculate approximate Sharpe from available data - return self.sortino_ratio * 0.8 # Rough approximation - - @property - def recommendation_type(self) -> str: - if self.confidence > 0.7 and self.sortino_ratio > 1.0: - return "BUY" - if self.confidence > 0.5: - return "HOLD" - return "SELL" - - @property - def risk_score(self) -> float: - return abs(self.max_drawdown) / 100 - - -@dataclass -class PortfolioRecommendation: - """AI recommendation for a portfolio.""" - - recommendations: list[AssetRecommendation] - total_score: float - risk_profile: str - diversification_score: float - correlation_analysis: dict - overall_reasoning: str - warnings: list[str] - confidence: float - - @property - def total_assets(self) -> int: - return len(self.recommendations) - - @property - def expected_return(self) -> float: - if not self.recommendations: - return 0.0 - return sum(r.total_return for r in self.recommendations) / len( - self.recommendations - ) - - @property - def asset_recommendations(self) -> list[AssetRecommendation]: - return self.recommendations - - @property - def reasoning(self) -> str: - return self.overall_reasoning - - @property - def confidence_score(self) -> float: - return self.confidence diff --git a/src/backtest/__init__.py b/src/backtest/__init__.py new file mode 100644 index 0000000..a9a2c5b --- /dev/null +++ b/src/backtest/__init__.py @@ -0,0 +1 @@ +__all__ = [] diff --git a/src/backtest/metrics.py b/src/backtest/metrics.py new file mode 100644 index 0000000..83cf6fc --- /dev/null +++ b/src/backtest/metrics.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import numpy as np +import pandas as pd + + +def sharpe_ratio( + returns: pd.Series, risk_free_rate: float = 0.0, periods_per_year: int = 252 +) -> float: + er = returns.mean() * periods_per_year - risk_free_rate + sd = returns.std(ddof=0) * np.sqrt(periods_per_year) + if sd == 0 or np.isnan(sd): + return float("nan") + return float(er / sd) + + +def sortino_ratio( + returns: pd.Series, risk_free_rate: float = 0.0, periods_per_year: int = 252 +) -> float: + downside = returns.copy() + downside[downside > 0] = 0 + dd = downside.std(ddof=0) * np.sqrt(periods_per_year) + er = returns.mean() * periods_per_year - risk_free_rate + if dd == 0 or np.isnan(dd): + return float("nan") + return float(er / dd) + + +def total_return(equity: pd.Series) -> float: + if len(equity) == 0: + return 0.0 + return float(equity.iloc[-1] / equity.iloc[0] - 1.0) diff --git a/src/backtest/results_cache.py b/src/backtest/results_cache.py new file mode 100644 index 0000000..a185dd2 --- /dev/null +++ b/src/backtest/results_cache.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +import json +import sqlite3 +from pathlib import Path +from typing import Any + +ENGINE_VERSION = "1" + + +class ResultsCache: + def __init__(self, root: Path): + self.root = Path(root) + self.root.mkdir(parents=True, exist_ok=True) + self.db_path = self.root / "results.sqlite" + self._ensure() + + def _ensure(self): + con = sqlite3.connect(self.db_path) + try: + con.execute( + """ + CREATE TABLE IF NOT EXISTS results ( + collection TEXT, + symbol TEXT, + timeframe TEXT, + strategy TEXT, + params_json TEXT, + metric_name TEXT, + metric_value REAL, + stats_json TEXT, + data_fingerprint TEXT, + fees REAL, + slippage REAL, + run_id TEXT, + engine_version TEXT, + created_at DATETIME DEFAULT CURRENT_TIMESTAMP, + PRIMARY KEY(collection, symbol, timeframe, strategy, params_json, metric_name, data_fingerprint, fees, slippage, engine_version) + ) + """ + ) + # Backward-compat: ensure run_id column exists + try: + con.execute("ALTER TABLE results ADD COLUMN run_id TEXT") + except Exception: + pass + con.commit() + finally: + con.close() + + def get( + self, + *, + collection: str, + symbol: str, + timeframe: str, + strategy: str, + params: dict[str, Any], + metric_name: str, + data_fingerprint: str, + fees: float, + slippage: float, + run_id: str | None = None, + ) -> dict[str, Any] | None: + params_json = json.dumps(params, sort_keys=True) + con = sqlite3.connect(self.db_path) + try: + cur = con.execute( + """ + SELECT metric_value, stats_json FROM results + WHERE collection=? AND symbol=? AND timeframe=? AND strategy=? + AND params_json=? AND metric_name=? AND data_fingerprint=? + AND fees=? AND slippage=? AND engine_version=? + """, + ( + collection, + symbol, + timeframe, + strategy, + params_json, + metric_name, + data_fingerprint, + fees, + slippage, + ENGINE_VERSION, + ), + ) + row = cur.fetchone() + if not row: + return None + metric_value, stats_json = row + return { + "metric_value": float(metric_value), + "stats": json.loads(stats_json), + } + finally: + con.close() + + def set( + self, + *, + collection: str, + symbol: str, + timeframe: str, + strategy: str, + params: dict[str, Any], + metric_name: str, + metric_value: float, + stats: dict[str, Any], + data_fingerprint: str, + fees: float, + slippage: float, + run_id: str | None = None, + ) -> None: + params_json = json.dumps(params, sort_keys=True) + con = sqlite3.connect(self.db_path) + try: + con.execute( + """ + INSERT OR REPLACE INTO results + (collection, symbol, timeframe, strategy, params_json, metric_name, metric_value, stats_json, data_fingerprint, fees, slippage, run_id, engine_version) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + collection, + symbol, + timeframe, + strategy, + params_json, + metric_name, + float(metric_value), + json.dumps(stats, sort_keys=True), + data_fingerprint, + fees, + slippage, + run_id, + ENGINE_VERSION, + ), + ) + con.commit() + finally: + con.close() + + def list_by_run(self, run_id: str) -> list[dict[str, Any]]: + con = sqlite3.connect(self.db_path) + try: + cur = con.execute( + """ + SELECT collection, symbol, timeframe, strategy, params_json, metric_name, metric_value, stats_json + FROM results WHERE run_id = ? ORDER BY collection, symbol, timeframe, strategy + """, + (run_id,), + ) + rows = [] + for r in cur.fetchall(): + ( + collection, + symbol, + timeframe, + strategy, + params_json, + metric_name, + metric_value, + stats_json, + ) = r + rows.append( + { + "collection": collection, + "symbol": symbol, + "timeframe": timeframe, + "strategy": strategy, + "params": json.loads(params_json), + "metric": metric_name, + "metric_value": float(metric_value), + "stats": json.loads(stats_json), + } + ) + return rows + finally: + con.close() diff --git a/src/backtest/runner.py b/src/backtest/runner.py new file mode 100644 index 0000000..7ffe039 --- /dev/null +++ b/src/backtest/runner.py @@ -0,0 +1,350 @@ +from __future__ import annotations + +import itertools +import threading +import warnings +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +try: # Silence numba deprecation noise from vectorbt on Py3.10 stack + from numba.core.errors import ( + NumbaDeprecationWarning, + NumbaPendingDeprecationWarning, + ) + + warnings.filterwarnings("ignore", category=NumbaDeprecationWarning) + warnings.filterwarnings("ignore", category=NumbaPendingDeprecationWarning) + warnings.filterwarnings("ignore", message=".*generated_jit is deprecated.*", category=Warning) +except Exception: + pass +import vectorbt as vbt + +from ..config import CollectionConfig, Config +from ..data.alpaca_source import AlpacaSource +from ..data.alphavantage_source import AlphaVantageSource +from ..data.base import DataSource +from ..data.ccxt_source import CCXTSource +from ..data.finnhub_source import FinnhubSource +from ..data.polygon_source import PolygonSource +from ..data.tiingo_source import TiingoSource +from ..data.twelvedata_source import TwelveDataSource +from ..data.yfinance_source import YFinanceSource +from ..strategies.base import BaseStrategy +from ..strategies.registry import discover_external_strategies +from ..utils.telemetry import get_logger, time_block +from .metrics import sharpe_ratio, sortino_ratio, total_return +from .results_cache import ResultsCache + + +@dataclass +class BestResult: + collection: str + symbol: str + timeframe: str + strategy: str + params: dict[str, Any] + metric_name: str + metric_value: float + stats: dict[str, Any] + + +class BacktestRunner: + def __init__(self, cfg: Config, strategies_root: Path, run_id: str | None = None): + self.cfg = cfg + self.strategies_root = strategies_root + self.external_index = discover_external_strategies(strategies_root) + self.results_cache = ResultsCache(Path(self.cfg.cache_dir).parent / "results") + self.run_id = run_id + self.logger = get_logger() + + def _make_source(self, col: CollectionConfig) -> DataSource: + cache_dir = Path(self.cfg.cache_dir) + src = col.source.lower() + if src == "yfinance": + return YFinanceSource(cache_dir) + if src in ("ccxt", "binance", "bybit"): + if not col.exchange: + # Allow shorthand where source is the exchange name + exchange = src if src != "ccxt" else None + if not exchange: + raise ValueError("exchange is required for ccxt collection") + return CCXTSource(exchange, cache_dir) + return CCXTSource(col.exchange, cache_dir) + if src == "polygon": + return PolygonSource(cache_dir) + if src == "tiingo": + return TiingoSource(cache_dir) + if src == "alpaca": + return AlpacaSource(cache_dir) + if src == "finnhub": + return FinnhubSource(cache_dir) + if src == "twelvedata": + return TwelveDataSource(cache_dir) + if src == "alphavantage": + return AlphaVantageSource(cache_dir) + raise ValueError(f"Unsupported data source: {col.source}") + + def _fees_slippage_for(self, col: CollectionConfig) -> tuple[float, float]: + # Defaults: IBKR-like for traditional markets, Bybit-like for crypto + if col.fees is not None or col.slippage is not None: + return ( + col.fees if col.fees is not None else self.cfg.fees, + col.slippage if col.slippage is not None else self.cfg.slippage, + ) + src = col.source.lower() + if src in ("binance", "bybit", "ccxt"): + return (0.0006, 0.0005) # approx taker + small slippage + # yfinance/polygon/tiingo/alpaca stocks/etfs + return (0.0005, 0.0005) + + def _grid(self, grid: dict[str, list[Any]]): + if not grid: + yield {} + return + keys = list(grid.keys()) + for values in itertools.product(*(grid[k] for k in keys)): + yield dict(zip(keys, values, strict=False)) + + def _evaluate_metric(self, metric: str, returns: pd.Series, equity: pd.Series) -> float: + metric = metric.lower() + if metric == "sharpe": + return sharpe_ratio(returns, risk_free_rate=self.cfg.risk_free_rate) + if metric == "sortino": + return sortino_ratio(returns, risk_free_rate=self.cfg.risk_free_rate) + if metric == "profit": + return total_return(equity) + raise ValueError(f"Unknown metric: {metric}") + + def run_all(self, only_cached: bool = False) -> list[BestResult]: + best_results: list[BestResult] = [] + self.metrics = { + "result_cache_hits": 0, + "result_cache_misses": 0, + "param_evals": 0, + "symbols_tested": 0, + "strategies_used": set(), + } + # Collect per-symbol failures (e.g., data fetch issues) + self.failures: list[dict[str, Any]] = [] + + overrides = {s.name: s.params for s in self.cfg.strategies} if self.cfg.strategies else {} + + # Global fetch concurrency control + fetch_sema = threading.Semaphore(max(1, getattr(self.cfg, "max_fetch_concurrency", 2))) + metrics_lock = threading.Lock() + results_lock = threading.Lock() + + jobs: list[tuple[CollectionConfig, str, str, str]] = [] + for col in self.cfg.collections: + for symbol in col.symbols: + for timeframe in self.cfg.timeframes: + for name in self.external_index.keys(): + jobs.append((col, symbol, timeframe, name)) + + def run_job(job: tuple[CollectionConfig, str, str, str]): + col, symbol, timeframe, strat_name = job + StrategyClass = self.external_index[strat_name] + strat: BaseStrategy = StrategyClass() + base_params = overrides.get(strat_name, {}) if overrides else {} + grid_override = base_params.get("grid") if isinstance(base_params, dict) else None + if isinstance(grid_override, dict): + grid = grid_override + static_params = {k: v for k, v in base_params.items() if k != "grid"} + else: + grid = strat.param_grid() | base_params + static_params = {} + source = self._make_source(col) + # Fetch with global semaphore + timing + with fetch_sema: + with time_block( + self.logger, + "data_fetch", + collection=col.name, + symbol=symbol, + timeframe=timeframe, + source=col.source, + ): + try: + df = source.fetch(symbol, timeframe, only_cached=only_cached) + except Exception as e: + # Record failure and skip this (collection, symbol, timeframe) + with metrics_lock: + self.failures.append( + { + "collection": col.name, + "symbol": symbol, + "timeframe": timeframe, + "source": col.source, + "error": str(e), + } + ) + return + price = df["Close"].astype(float) + data_fingerprint = f"{len(df)}:{df.index[-1].isoformat()}:{float(price.iloc[-1])}" + fees_use, slippage_use = self._fees_slippage_for(col) + + best_val = -np.inf + best: tuple[dict[str, Any], dict[str, Any]] | None = None + with metrics_lock: + self.metrics["symbols_tested"] += 1 + self.metrics["strategies_used"].add(strat.name) + + def eval_params(params: dict[str, Any]): + call_params = {**static_params, **params} + entries, exits = strat.generate_signals(df, call_params) + entries = entries.reindex(df.index).fillna(False) + exits = exits.reindex(df.index).fillna(False) + try: + pf = vbt.Portfolio.from_signals( + price, + entries, + exits, + fees=fees_use, + slippage=slippage_use, + init_cash=10000.0, + ) + except Exception: + return params, None, None + returns = pf.returns() + equity = (1 + returns).cumprod() + return params, returns, equity + + with time_block( + self.logger, + "grid_search", + collection=col.name, + symbol=symbol, + timeframe=timeframe, + strategy=strat.name, + ): + futures = [] + with ThreadPoolExecutor( + max_workers=max(1, getattr(self.cfg, "param_workers", 1)) + ) as ex: + for params in self._grid(grid): + cached = self.results_cache.get( + collection=col.name, + symbol=symbol, + timeframe=timeframe, + strategy=strat.name, + params=params, + metric_name=self.cfg.metric, + data_fingerprint=data_fingerprint, + fees=fees_use, + slippage=slippage_use, + ) + if cached is not None: + with metrics_lock: + self.metrics["result_cache_hits"] += 1 + val = float(cached["metric_value"]) + # Record this cached evaluation under current run_id + try: + self.results_cache.set( + collection=col.name, + symbol=symbol, + timeframe=timeframe, + strategy=strat.name, + params=params, + metric_name=self.cfg.metric, + metric_value=val, + stats=cached["stats"], + data_fingerprint=data_fingerprint, + fees=fees_use, + slippage=slippage_use, + run_id=self.run_id, + ) + except Exception: + pass + if val > best_val: + best_val = val + best = (params, cached["stats"]) + continue + with metrics_lock: + self.metrics["result_cache_misses"] += 1 + futures.append(ex.submit(eval_params, params)) + + for fut in as_completed(futures): + params, returns, equity = fut.result() + if returns is None or equity is None: + continue + with metrics_lock: + self.metrics["param_evals"] += 1 + val = self._evaluate_metric(self.cfg.metric, returns, equity) + if np.isnan(val): + continue + if val > best_val: + best_val = val + roll_max = equity.cummax() + dd = (equity / roll_max) - 1.0 + max_dd = float(dd.min()) + trades = int((returns != 0).sum()) + # Duration and CAGR for Calmar + try: + days = max(1, (equity.index[-1] - equity.index[0]).days) + years = max(1e-9, days / 365.25) + ending = float(equity.iloc[-1]) + cagr = ( + (ending ** (1.0 / years)) - 1.0 if ending > 0 else float("nan") + ) + except Exception: + cagr = float("nan") + calmar = float("nan") + if max_dd < 0: + try: + calmar = float(cagr / abs(max_dd)) + except Exception: + calmar = float("nan") + stats = { + "sharpe": float(sharpe_ratio(returns)), + "sortino": float(sortino_ratio(returns)), + "profit": float(total_return(equity)), + "trades": trades, + "max_drawdown": max_dd, + "cagr": float(cagr), + "calmar": float(calmar), + } + self.results_cache.set( + collection=col.name, + symbol=symbol, + timeframe=timeframe, + strategy=strat.name, + params=params, + metric_name=self.cfg.metric, + metric_value=float(best_val), + stats=stats, + data_fingerprint=data_fingerprint, + fees=fees_use, + slippage=slippage_use, + run_id=self.run_id, + ) + best = (params, stats) + + if best is not None: + with results_lock: + params_best, stats_best = best + best_results.append( + BestResult( + collection=col.name, + symbol=symbol, + timeframe=timeframe, + strategy=strat.name, + params=params_best, + metric_name=self.cfg.metric, + metric_value=float(best_val), + stats=stats_best, + ) + ) + + # Execute jobs with global executor + with ThreadPoolExecutor(max_workers=max(1, getattr(self.cfg, "asset_workers", 1))) as ex: + list(as_completed([ex.submit(run_job, job) for job in jobs])) + + if isinstance(self.metrics.get("strategies_used"), set): + self.metrics["strategies_count"] = len(self.metrics["strategies_used"]) # type: ignore + self.metrics.pop("strategies_used", None) + return best_results diff --git a/src/cli/__init__.py b/src/cli/__init__.py deleted file mode 100644 index 3ae4425..0000000 --- a/src/cli/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Command line interface modules.""" diff --git a/src/cli/config/__init__.py b/src/cli/config/__init__.py deleted file mode 100644 index 6b4b7ef..0000000 --- a/src/cli/config/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Configuration management for CLI commands.""" - -from __future__ import annotations - -from .config_loader import ( - get_asset_config, - get_default_parameters, - get_portfolio_config, - is_portfolio, - load_assets_config, -) - -__all__ = [ - "get_asset_config", - "get_default_parameters", - "get_portfolio_config", - "is_portfolio", - "load_assets_config", -] diff --git a/src/cli/config/config_loader.py b/src/cli/config/config_loader.py deleted file mode 100644 index 4018e6a..0000000 --- a/src/cli/config/config_loader.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Configuration loader for CLI commands.""" - -from __future__ import annotations - -import json -from pathlib import Path - -from src.utils.config_manager import ConfigManager - - -def load_assets_config(): - """Load the assets configuration from config/assets_config.json.""" - config_path = Path("config") / "assets_config.json" - if config_path.exists(): - with config_path.open() as f: - return json.load(f) - return {"portfolios": {}} - - -def is_portfolio(ticker): - """Check if the given ticker is a portfolio name in assets_config.json.""" - assets_config = load_assets_config() - return ticker in assets_config.get("portfolios", {}) - - -def get_portfolio_config(portfolio_name): - """Get configuration for a specific portfolio.""" - assets_config = load_assets_config() - return assets_config.get("portfolios", {}).get(portfolio_name, None) - - -def get_asset_config(ticker): - """Get asset-specific config if available in any portfolio.""" - assets_config = load_assets_config() - - # Search through all portfolios for the ticker - for portfolio in assets_config.get("portfolios", {}).values(): - for asset in portfolio.get("assets", []): - if asset["ticker"] == ticker: - return asset - - return None - - -def get_default_parameters(): - """Get default backtest parameters from config.""" - config = ConfigManager() - return { - "commission": config.get("backtest.default_commission", 0.001), - "initial_capital": config.get("backtest.initial_capital", 10000), - "period": config.get("backtest.default_period", "max"), - "intervals": config.get("backtest.default_intervals", ["1d", "1wk"]), - "iterations": config.get("optimizer.iterations", 50), - } diff --git a/src/cli/direct_backtest_cli.py b/src/cli/direct_backtest_cli.py deleted file mode 100644 index 341216b..0000000 --- a/src/cli/direct_backtest_cli.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -Direct Backtesting CLI Functions -Uses backtesting library directly for ground truth results. -""" - -from __future__ import annotations - -import logging -from datetime import datetime - -from src.database import get_db_session -from src.database.models import BacktestResult as DBBacktestResult -from src.database.models import BestStrategy, Trade - - -def save_direct_backtest_to_database(result_dict: dict, metric: str = "sortino_ratio"): - """Save direct backtesting library results to database.""" - logger = logging.getLogger(__name__) - - if result_dict["error"]: - logger.warning("Cannot save failed backtest: %s", result_dict["error"]) - return - - symbol = result_dict["symbol"] - strategy = result_dict["strategy"] - timeframe = result_dict["timeframe"] - metrics = result_dict["metrics"] - - session = get_db_session() - - try: - # Create BacktestResult entry - db_result = DBBacktestResult( - name=f"direct_{strategy}_{symbol}_{timeframe}", - symbols=[symbol], - strategy=strategy, - timeframe=timeframe, - start_date=datetime.strptime( - "2023-01-01", "%Y-%m-%d" - ).date(), # Use from result_dict if available - end_date=datetime.strptime("2023-12-31", "%Y-%m-%d").date(), - initial_capital=metrics.get("start_value", 10000.0), - final_value=metrics.get("end_value", 10000.0), - total_return=metrics.get("total_return", 0.0), - sortino_ratio=metrics.get("sortino_ratio", 0.0), - calmar_ratio=metrics.get("calmar_ratio", 0.0), - sharpe_ratio=metrics.get("sharpe_ratio", 0.0), - profit_factor=metrics.get("profit_factor", 1.0), - max_drawdown=metrics.get("max_drawdown", 0.0), - volatility=metrics.get("volatility", 0.0), - downside_deviation=0.0, # Not available from backtesting library directly - win_rate=metrics.get("win_rate", 0.0), - average_win=0.0, # Could be calculated from trades if needed - average_loss=0.0, - parameters={}, - ) - - session.add(db_result) - session.flush() # Get the ID - - # Save trades from backtesting library as entry/exit pairs (consistent with unified_cli.py) - if result_dict["trades"] is not None and not result_dict["trades"].empty: - trades_df = result_dict["trades"] - current_equity = 10000.0 # Initial capital - - for _, trade_row in trades_df.iterrows(): - # Each backtesting library trade is a round-trip, create entry and exit - entry_value = float(trade_row["Size"]) * float(trade_row["EntryPrice"]) - exit_value = float(trade_row["Size"]) * float(trade_row["ExitPrice"]) - - # Create ENTRY trade record - entry_trade = Trade( - backtest_result_id=db_result.id, - symbol=symbol, - strategy=result_dict["strategy"], - timeframe=result_dict["timeframe"], - trade_datetime=trade_row["EntryTime"], - side="BUY", - size=float(trade_row["Size"]), - price=float(trade_row["EntryPrice"]), - equity_before=current_equity, - equity_after=current_equity - entry_value, - ) - session.add(entry_trade) - current_equity -= entry_value - - # Create EXIT trade record - exit_trade = Trade( - backtest_result_id=db_result.id, - symbol=symbol, - strategy=result_dict["strategy"], - timeframe=result_dict["timeframe"], - trade_datetime=trade_row["ExitTime"], - side="SELL", - size=float(trade_row["Size"]), - price=float(trade_row["ExitPrice"]), - equity_before=current_equity, - equity_after=current_equity + exit_value, - ) - session.add(exit_trade) - current_equity += exit_value - - # Update BestStrategy table - update_best_strategy_direct(session, result_dict, metric) - - session.commit() - logger.info("Saved %s/%s results to database", symbol, strategy) - - except Exception as e: - session.rollback() - logger.error("Failed to save %s/%s: %s", symbol, strategy, e) - raise e - finally: - session.close() - - -def update_best_strategy_direct( - session, result_dict: dict, metric: str = "sortino_ratio" -): - """Update best strategy table with direct backtesting results.""" - logger = logging.getLogger(__name__) - symbol = result_dict["symbol"] - strategy = result_dict["strategy"] - timeframe = result_dict["timeframe"] - metrics = result_dict["metrics"] - - # Check existing best strategy - existing = ( - session.query(BestStrategy) - .filter_by(symbol=symbol, timeframe=timeframe) - .first() - ) - - current_metric_value = metrics.get(metric, 0) - - # Determine if this is better - is_better = False - if not existing: - is_better = True - else: - existing_metric_value = getattr(existing, metric, 0) or 0 - - # Compare by metric (higher is better for most metrics) - if metric == "max_drawdown": - is_better = current_metric_value < existing_metric_value - else: - is_better = current_metric_value > existing_metric_value - - if is_better: - if existing: - # Update existing record - existing.strategy = strategy - existing.sortino_ratio = metrics.get("sortino_ratio", 0) - existing.calmar_ratio = metrics.get("calmar_ratio", 0) - existing.sharpe_ratio = metrics.get("sharpe_ratio", 0) - existing.total_return = metrics.get("total_return", 0) - existing.max_drawdown = metrics.get("max_drawdown", 0) - else: - # Create new record - new_best = BestStrategy( - symbol=symbol, - timeframe=timeframe, - strategy=strategy, - sortino_ratio=metrics.get("sortino_ratio", 0), - calmar_ratio=metrics.get("calmar_ratio", 0), - sharpe_ratio=metrics.get("sharpe_ratio", 0), - total_return=metrics.get("total_return", 0), - max_drawdown=metrics.get("max_drawdown", 0), - ) - session.add(new_best) - - logger.info( - "Updated best strategy for %s/%s: %s (Sortino: %.3f)", - symbol, - timeframe, - strategy, - current_metric_value, - ) diff --git a/src/cli/main.py b/src/cli/main.py deleted file mode 100644 index 711c68c..0000000 --- a/src/cli/main.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Main entry point for the CLI system.""" - -from __future__ import annotations - -import codecs -import sys -import warnings - -# Suppress warnings for cleaner output -warnings.filterwarnings("ignore") - -# Import unified CLI -from src.cli.unified_cli import main as unified_main - -# Set console output encoding to UTF-8 -if sys.stdout.encoding != "utf-8": - sys.stdout = codecs.getwriter("utf-8")(sys.stdout.buffer, "strict") -if sys.stderr.encoding != "utf-8": - sys.stderr = codecs.getwriter("utf-8")(sys.stderr.buffer, "strict") - - -def main() -> None: - """Redirect to unified CLI system.""" - unified_main() - - -if __name__ == "__main__": - main() diff --git a/src/cli/unified_cli.py b/src/cli/unified_cli.py deleted file mode 100644 index 9ebe5fa..0000000 --- a/src/cli/unified_cli.py +++ /dev/null @@ -1,1164 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified CLI entrypoint: src.cli.unified_cli - -This module implements the `collection` subcommand which builds a deterministic -plan (plan_hash), writes a manifest, supports --dry-run, and delegates work to -the project's backtest engine and DB/persistence layers if available. - -This is intentionally conservative: it validates inputs, expands strategies and -intervals where possible, and provides clear hooks for the engine and DB code. -All optional integrations are guarded to avoid import-time failures. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import logging -import shutil -import sys -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence - -# Constants -DEFAULT_METRIC = "sortino_ratio" -SUPPORTED_INTERVALS = ["1m", "5m", "15m", "1h", "1d", "1wk", "1mo", "3mo"] -INTRADAY_MAX_DAYS = 60 -ONE_MINUTE_MAX_DAYS = 7 - -log = logging.getLogger("unified_cli") - - -# Install a global excepthook that will log uncaught exceptions with a full traceback. -# This is useful when running inside Docker where stderr/telnet output may be suppressed. -def _unified_excepthook(exc_type, exc_value, tb): - import traceback as _traceback - - try: - log.exception("Uncaught exception", exc_info=(exc_type, exc_value, tb)) - except Exception: - # If logging fails for any reason, still print the traceback to stderr. - _traceback.print_exception(exc_type, exc_value, tb) - else: - _traceback.print_exception(exc_type, exc_value, tb) - - -import sys as _sys - -_sys.excepthook = _unified_excepthook - - -def _setup_logging(level: str) -> None: - numeric = getattr(logging, level.upper(), logging.INFO) - logging.basicConfig(level=numeric, format="%(asctime)s %(levelname)s %(message)s") - - -def resolve_collection_path(collection_arg: str) -> Path: - p = Path(collection_arg) - if p.exists(): - return p.resolve() - # try config/collections/.json - base = Path("config") / "collections" - # Aliases for curated defaults - alias_map = { - # Curated defaults - "bonds": "bonds_core", - "commodities": "commodities_core", - "crypto": "crypto_liquid", - "forex": "forex_majors", - "indices": "indices_global_core", - # Convenience aliases - "tech_growth": "stocks_us_growth_core", - "us_mega": "stocks_us_mega_core", - "value": "stocks_us_value_core", - "quality": "stocks_us_quality_core", - "minvol": "stocks_us_minvol_core", - "global_factors": "stocks_global_factor_core", - } - key = alias_map.get(collection_arg, collection_arg) - candidates = [ - base / f"{key}.json", - base / "default" / f"{key}.json", - base / "custom" / f"{key}.json", - ] - for alt in candidates: - if alt.exists(): - return alt.resolve() - raise FileNotFoundError(f"Collection file not found: {collection_arg}") - - -def compute_plan_hash(plan: Dict[str, Any]) -> str: - # Deterministic serialization: sort keys - payload = json.dumps( - plan, sort_keys=True, separators=(",", ":"), ensure_ascii=False - ) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def load_collection_symbols(collection_path: Path) -> List[str]: - """ - Load symbols from a collection JSON. - - Supported formats: - - Plain list: ["AAPL", "MSFT", ...] - - Dict with top-level "symbols" (or "assets"/"symbols_list"): - {"symbols": ["AAPL", ...], ...} - - Named collection object (common in config/collections/*.json): - {"bonds": {"symbols": [...], "name": "...", ...}} - - Dict of multiple named collections: returns symbols for the first matching - collection that contains a 'symbols' list (best-effort). - """ - try: - with collection_path.open() as f: - data = json.load(f) - except Exception as exc: - raise RuntimeError( - f"Failed to read collection file {collection_path}: {exc}" - ) from exc - - # If the file itself is a plain list of symbols - if isinstance(data, list): - return [str(s).upper() for s in data] - - # If the file is a dict, try common keys first - if isinstance(data, dict): - # Direct keys that point to a symbols list - for key in ("symbols", "assets", "symbols_list"): - if key in data and isinstance(data[key], list): - return [str(s).upper() for s in data[key]] - - # If the file wraps one or more named collections (e.g., {"bonds": {...}}) - # find the first value that itself contains a 'symbols' list - for val in data.values(): - if isinstance(val, dict): - for key in ("symbols", "assets", "symbols_list"): - if key in val and isinstance(val[key], list): - return [str(s).upper() for s in val[key]] - - raise RuntimeError( - f"Collection JSON at {collection_path} missing 'symbols' list or unsupported format" - ) - - -def expand_strategies(strategies_arg: str) -> List[str]: - # strategies_arg can be comma-separated or 'all' - parts = [p.strip() for p in strategies_arg.split(",") if p.strip()] - if len(parts) == 1 and parts[0].lower() == "all": - # Prefer explicit environment variable or container-mounted path when running inside Docker. - # This avoids trying to read host paths from within the container. - try: - import os - - candidates = [] - env_path = os.getenv("STRATEGIES_PATH") - if env_path: - candidates.append(env_path) - - # Common container mount used in docker-compose - candidates.append("/app/external_strategies") - - # Host-local fallback (works when running on host) - candidates.append(str(Path("quant-strategies").resolve())) - candidates.append(str(Path("external_strategies").resolve())) - - from src.core.external_strategy_loader import get_strategy_loader - from src.core.strategy import StrategyFactory - - strategies = [] - for cand in candidates: - try: - if not cand: - continue - p = Path(cand) - if not p.exists(): - continue - loader = get_strategy_loader(str(cand)) - try: - strategies = StrategyFactory.list_strategies(loader=loader) - if isinstance(strategies, dict): - strategies = ( - strategies.get("all") - or strategies.get("external") - or [] - ) - except Exception: - strategies = [] - # If we found any, return them (deduplicated & sorted) - if strategies: - return sorted(set(strategies)) - # If loader supports listing candidates without importing, try that - try: - candidates_list = loader.list_strategy_candidates() - if candidates_list: - return sorted(set(candidates_list)) - except Exception: - pass - except Exception as exc: - # try next candidate, but log for diagnostics - log.debug("Strategy discovery failed for %s: %s", cand, exc) - continue - - # Last fallback: try the local algorithms/python dir if present - alt_dir = Path("quant-strategies") / "algorithms" / "python" - if alt_dir.exists(): - cand = [p.stem for p in alt_dir.glob("*.py") if p.is_file()] - if cand: - return sorted(set(cand)) - - # If nothing found, proceed with an empty list (safe default for dry-run/tests) - log.warning( - "Could not expand 'all' strategies: no strategy repository found; proceeding with none" - ) - return [] - except Exception as exc: - log.warning( - "Could not expand 'all' strategies: %s; proceeding with none", exc - ) - return [] - - # explicit list - expanded: List[str] = [] - for part in parts: - expanded.extend([s.strip() for s in part.split("+") if s.strip()]) - return sorted(set(expanded)) - - -def expand_intervals(interval_arg: str) -> List[str]: - parts = [p.strip() for p in interval_arg.split(",") if p.strip()] - if len(parts) == 1 and parts[0].lower() == "all": - return SUPPORTED_INTERVALS.copy() - # validate - invalid = [p for p in parts if p not in SUPPORTED_INTERVALS] - if invalid: - raise RuntimeError( - f"Unknown intervals requested: {invalid}. Supported: {SUPPORTED_INTERVALS}" - ) - return parts - - -def clamp_interval_period( - interval: str, start: Optional[str], end: Optional[str], period_mode: str -) -> Dict[str, Optional[str]]: - """ - Enforce provider constraints: - - 1m allowed only for last ONE_MINUTE_MAX_DAYS days - - <1d intraday intervals allowed only for last INTRADAY_MAX_DAYS days - Returns dict with possibly modified 'start'/'end' and 'period_mode' (may remain 'max') - """ - # This function returns the passed args unchanged by default. Real clamping requires querying provider - # for available date ranges; here we provide warnings and leave exact clamping to data manager. - if interval == "1m": - # warn user if period_mode == 'max' - if period_mode == "max": - log.warning( - "Interval '1m' may be limited to the last %d days by the data provider", - ONE_MINUTE_MAX_DAYS, - ) - elif interval in ("5m", "15m", "1h"): - if period_mode == "max": - log.warning( - "Intraday interval '%s' may be limited to the last %d days by the data provider", - interval, - INTRADAY_MAX_DAYS, - ) - return {"start": start, "end": end, "period_mode": period_mode} - - -def write_manifest(outdir: Path, manifest: Dict[str, Any]) -> Path: - outdir.mkdir(parents=True, exist_ok=True) - manifest_path = outdir / "run_manifest.json" - with manifest_path.open("w", encoding="utf-8") as fh: - json.dump(manifest, fh, indent=2, sort_keys=True, ensure_ascii=False) - return manifest_path - - -def try_get_git_sha(path: Path) -> Optional[str]: - # Try to read git sha for the given path if it's a git repo - git_exe = shutil.which("git") - if git_exe is None: - return None - if not (path / ".git").exists(): - return None - try: - import subprocess - - out = subprocess.check_output( - [git_exe, "-C", str(path.resolve()), "rev-parse", "HEAD"], - stderr=subprocess.DEVNULL, - ) - return out.decode().strip() - except Exception: - return None - - -def persist_run_row_placeholder(manifest: Dict[str, Any]) -> None: - # Hook: try to persist the initial run row to DB using unified_models if available. - try: - from src.database import unified_models - - # unified_models should expose create_run_from_manifest(manifest) or similar. - if hasattr(unified_models, "create_run_from_manifest"): - unified_models.create_run_from_manifest(manifest) - log.info( - "Persisted run row to DB via unified_models.create_run_from_manifest" - ) - else: - log.debug( - "unified_models module found but create_run_from_manifest not present" - ) - except Exception: - log.debug( - "DB persistence not available (unified_models missing or failed). Continuing without DB." - ) - - -def run_plan(manifest: Dict[str, Any], outdir: Path, dry_run: bool = False) -> int: - """ - Execute the resolved plan. - - This implementation delegates to src.core.direct_backtest.UnifiedBacktestEngine.run if available. - If unavailable, it will write a placeholder summary and return 0 on success. - """ - if dry_run: - print(json.dumps(manifest, indent=2, sort_keys=True, ensure_ascii=False)) - return 0 - - # Persist a run row (best-effort) - persist_run_row_placeholder(manifest) - - # If action is 'direct', use the direct backtester with DB persistence - try: - plan_action = manifest.get("plan", {}).get("action") - except Exception: - plan_action = None - - if plan_action == "direct": - try: - from src.core.data_manager import UnifiedDataManager - from src.core.direct_backtest import ( - finalize_persistence_for_run, - run_direct_backtest, - ) - except Exception: - log.exception("Direct backtester not available") - return 12 - - plan = manifest.get("plan", {}) - symbols = plan.get("symbols", []) - strategies = plan.get("strategies", []) - intervals = plan.get("intervals", ["1d"]) # usually one - period_mode = plan.get("period_mode", "max") - start = plan.get("start") or "" - end = plan.get("end") or "" - initial_capital = plan.get("initial_capital", 10000) - commission = plan.get("commission", 0.001) - target_metric = plan.get("metric", DEFAULT_METRIC) - plan_hash = plan.get("plan_hash") - - # Initialize external strategies loader when a path is available (container-safe) - try: - from src.core.external_strategy_loader import get_strategy_loader - - spath = plan.get("strategies_path") - if spath: - get_strategy_loader(str(spath)) - except Exception: - # best-effort; loader may already be initialized elsewhere - pass - - # Ensure a run row exists - run_id = None - try: - from src.database import unified_models - - run_obj = None - if hasattr(unified_models, "ensure_run_for_manifest"): - run_obj = unified_models.ensure_run_for_manifest(manifest) - else: - run_obj = unified_models.create_run_from_manifest(manifest) - run_id = getattr(run_obj, "run_id", None) - except Exception: - run_id = None - - persistence_context = ( - {"run_id": run_id, "target_metric": target_metric, "plan_hash": plan_hash} - if run_id - else None - ) - - # Optional: probe sources for best coverage and set ordering overrides - try: - dm_probe = UnifiedDataManager() - # Detect asset type from the first symbol; fall back to 'stocks' - asset_type_probe = "stocks" - try: - if symbols: - asset_type_probe = dm_probe._detect_asset_type(symbols[0]) - except Exception: - pass - sample_syms = symbols[: min(5, len(symbols))] - if sample_syms: - ordered = dm_probe.probe_and_set_order( - asset_type_probe, - sample_syms, - interval=intervals[0] if intervals else "1d", - ) - if ordered: - log.info( - "Source order override for %s: %s", asset_type_probe, ordered - ) - except Exception: - log.debug("Coverage probe failed; continuing with default ordering") - - for interval in intervals: - for symbol in symbols: - for strat in strategies: - try: - _ = run_direct_backtest( - symbol=symbol, - strategy_name=strat, - start_date=start, - end_date=end, - timeframe=interval, - initial_capital=float(initial_capital), - commission=float(commission), - period=(period_mode if period_mode else None), - use_cache=bool(plan.get("use_cache", True)), - persistence_context=persistence_context, - ) - except Exception: - log.exception( - "Direct backtest failed for %s %s %s", - symbol, - strat, - interval, - ) - continue - - # Finalize DB ranks/best strategy - try: - if persistence_context: - finalize_persistence_for_run( - persistence_context.get("run_id"), target_metric - ) - except Exception: - log.exception( - "Finalization failed for run %s", - (persistence_context or {}).get("run_id"), - ) - - return 0 - - # Delegate to engine if available (use the unified backtest engine implementation) - try: - from src.core.backtest_engine import UnifiedBacktestEngine - - # The Backtest Engine class expects different init args; instantiate and run batch if available. - engine = UnifiedBacktestEngine() - # If engine exposes a run() method accepting manifest/outdir, prefer that; otherwise, run a batch run. - if hasattr(engine, "run"): - try: - res = engine.run(manifest=manifest, outdir=outdir) # type: ignore[attr-defined] - log.info( - "Engine run finished with result: %s", - getattr(res, "status", "unknown"), - ) - # Best-effort: if engine returned a summary dict, persist it to the outdir - try: - import json as _json # local import - - summary_path = Path(outdir) / "engine_run_summary.json" - if isinstance(res, dict): - try: - summary_path.parent.mkdir(parents=True, exist_ok=True) - with summary_path.open("w", encoding="utf-8") as fh: - _json.dump( - res, - fh, - indent=2, - sort_keys=True, - ensure_ascii=False, - ) - log.info("Wrote engine summary to %s", summary_path) - except Exception: - log.exception( - "Failed to write engine summary to %s", summary_path - ) - except Exception: - log.debug( - "Engine returned non-dict or failed to write summary (continuing)" - ) - return 0 - except Exception: - # fall back to batch behavior below - pass - - # Fall back: attempt to run batch backtests using run_batch_backtests if manifest is compatible - try: - plan = manifest.get("plan", {}) - config_kwargs = { - "symbols": plan.get("symbols", []), - "strategies": plan.get("strategies", []), - "start_date": plan.get("start"), - "end_date": plan.get("end"), - "initial_capital": plan.get("initial_capital", 10000), - "interval": plan.get("intervals", ["1d"])[0] - if plan.get("intervals") - else "1d", - "max_workers": plan.get("max_workers", 4), - } - # Use BacktestConfig dataclass if available - try: - from src.core.backtest_engine import ( - BacktestConfig, # type: ignore[import-not-found] - ) - - cfg = BacktestConfig(**config_kwargs) - results = engine.run_batch_backtests(cfg) - log.info( - "Engine run_batch_backtests finished with %d results", len(results) - ) - return 0 - except Exception: - log.debug( - "Could not construct BacktestConfig; skipping engine batch run" - ) - except Exception: - log.debug("Engine fallback path failed") - # If we reach here, engine couldn't be driven programmatically - raise RuntimeError("Engine found but could not be executed with manifest") - except Exception as exc: - log.exception("Backtest engine not available or failed: %s", exc) - - # Fallback: write a minimal summaries JSON - summary = { - "manifest": manifest, - "status": "fallback_no_engine", - "timestamp": datetime.utcnow().isoformat() + "Z", - } - fallback_path = outdir / "run_summary_fallback.json" - with fallback_path.open("w", encoding="utf-8") as fh: - json.dump(summary, fh, indent=2, sort_keys=True, ensure_ascii=False) - log.warning("Wrote fallback summary to %s", fallback_path) - return 0 - - -def _run_requested_exports( - resolved_plan: Dict[str, Any], collection_path: Path, symbols: List[str] -) -> None: - """Run requested exports (report, csv, ai, tradingview) best-effort. - - - Avoids hard DB connectivity failures; individual exporters handle fallbacks. - - CSV exporter falls back to unified_models or quarterly reports when needed. - - AI recommendations fall back to unified_models when primary DB is unavailable. - """ - exports_val = resolved_plan.get("exports", "") or "" - try: - exports_list = [ - e.strip().lower() for e in str(exports_val).split(",") if e.strip() - ] - except Exception: - exports_list = [] - if not exports_list: - return - - log = logging.getLogger("unified_cli") - - # Prepare portfolio context - portfolio_name = collection_path.stem - try: - import json as _json - - with collection_path.open() as _fh: - _cdata = _json.load(_fh) - if isinstance(_cdata, dict): - if isinstance(_cdata.get("name"), str): - portfolio_name = _cdata.get("name") or portfolio_name - else: - first = next(iter(_cdata.values())) if _cdata else None - if isinstance(first, dict) and isinstance(first.get("name"), str): - portfolio_name = first.get("name") or portfolio_name - except Exception: - pass - - portfolio_config = {"name": portfolio_name, "symbols": sorted(symbols)} - - do_report = ("report" in exports_list) or ("all" in exports_list) - do_csv = ("csv" in exports_list) or ("all" in exports_list) - do_tradingview = ("tradingview" in exports_list) or ("all" in exports_list) - do_ai = ("ai" in exports_list) or ("all" in exports_list) - - # Determine quarter/year/interval context - y_now = datetime.utcnow().year - m = datetime.utcnow().month - q_now = (m - 1) // 3 + 1 - quarter = f"Q{q_now}" - year = str(y_now) - try: - _intervals = list(resolved_plan.get("intervals") or []) - # Single-file export policy: - # - Use '1d' for filenames when present, else first interval - # - If multiple intervals were requested, do not filter by interval in exporters (pass None) - interval_for_filename = ( - "1d" if "1d" in _intervals else (_intervals[0] if _intervals else "1d") - ) - multiple_intervals = len(_intervals) > 1 - interval_filter = None if multiple_intervals else interval_for_filename - except Exception: - interval_for_filename = "1d" - interval_filter = interval_for_filename - - # Report - if do_report: - try: - from src.reporting.collection_report import DetailedPortfolioReporter - - reporter = DetailedPortfolioReporter() - start_date = resolved_plan.get("start") or "" - end_date = resolved_plan.get("end") or "" - try: - report_path = reporter.generate_comprehensive_report( - portfolio_config, - start_date or datetime.utcnow().strftime("%Y-%m-%d"), - end_date or datetime.utcnow().strftime("%Y-%m-%d"), - resolved_plan.get("strategies", []), - timeframes=[interval_for_filename] - if interval_for_filename - else None, - filename_interval=( - "multi" - if (len(resolved_plan.get("intervals") or []) > 1) - else interval_for_filename - ), - ) - except TypeError: - # Backward-compat: reporter without filename_interval arg - report_path = reporter.generate_comprehensive_report( - portfolio_config, - start_date or datetime.utcnow().strftime("%Y-%m-%d"), - end_date or datetime.utcnow().strftime("%Y-%m-%d"), - resolved_plan.get("strategies", []), - timeframes=[interval_for_filename] - if interval_for_filename - else None, - ) - log.info("Generated HTML report at %s", report_path) - except Exception: - log.exception("DetailedPortfolioReporter failed (continuing)") - - # CSV (DB-backed with fallback to unified_models or quarterly reports) - if do_csv: - try: - from src.utils.csv_exporter import RawDataCSVExporter - - csv_exporter = RawDataCSVExporter() - # Prefer calendar from plan start/end if present - try: - if resolved_plan.get("start"): - sd = datetime.fromisoformat(resolved_plan.get("start")) - else: - sd = datetime.utcnow() - except Exception: - sd = datetime.utcnow() - quarter = f"Q{((sd.month - 1) // 3) + 1}" - year = str(sd.year) - - csv_files = csv_exporter.export_from_database_primary( - quarter, - year, - output_filename=None, - export_format="best-strategies", - portfolio_name=portfolio_config.get("name") or "", - portfolio_path=str(collection_path), - interval=interval_filter, - ) - if not csv_files: - csv_files = csv_exporter.export_from_quarterly_reports( - quarter, - year, - export_format="best-strategies", - collection_name=portfolio_config.get("name"), - interval=interval_filter, - ) - log.info("Generated CSV exports: %s", csv_files) - except Exception: - log.exception("CSV export failed (continuing)") - - # AI recommendations (DB primary with unified_models fallback inside class) - if do_ai: - try: - from src.ai.investment_recommendations import AIInvestmentRecommendations - from src.database.db_connection import get_db_session - - db_sess = None - try: - db_sess = get_db_session() - except Exception: - db_sess = None - ai = AIInvestmentRecommendations(db_session=db_sess) - _rec, ai_html_path = ai.generate_portfolio_recommendations( - portfolio_config_path=str(collection_path), - risk_tolerance="moderate", - min_confidence=0.6, - max_assets=10, - quarter=f"{quarter}_{year}", - timeframe=interval_for_filename, # concrete for trading params - filename_interval=( - "multi" - if (len(resolved_plan.get("intervals") or []) > 1) - else interval_for_filename - ), - generate_html=True, - ) - log.info("Generated AI recommendations at %s", ai_html_path) - except Exception: - log.exception("AI recommendations export failed (continuing)") - - # TradingView alerts - if do_tradingview: - try: - from src.utils.tv_alert_exporter import TradingViewAlertExporter - - tv_exporter = TradingViewAlertExporter(reports_dir="exports/reports") - alerts = tv_exporter.export_alerts( - output_file=None, - collection_filter=portfolio_config.get("name"), - interval=interval_filter, - symbols=portfolio_config.get("symbols") or [], - ) - log.info("Generated TradingView alerts for %d assets", len(alerts)) - except Exception: - log.exception("TradingView alerts export failed (continuing)") - - -def handle_collection_run(argv: Sequence[str]) -> int: - parser = argparse.ArgumentParser( - prog="unified_cli collection", - description="Run unified backtests for a collection", - ) - parser.add_argument( - "collection", - help="Path to collection JSON file or collection key under config/collections", - ) - parser.add_argument( - "--action", - default="direct", - choices=[ - "backtest", - "direct", - "optimization", - "export", - "report", - "tradingview", - ], - help="Action to perform", - ) - parser.add_argument( - "--metric", - default=DEFAULT_METRIC, - help=f"Primary metric used for ranking (default: {DEFAULT_METRIC})", - ) - parser.add_argument( - "--strategies", - default="all", - help="Comma-separated strategies or 'all' (default: all)", - ) - period_group = parser.add_mutually_exclusive_group() - period_group.add_argument( - "--period", - default="max", - help="Named period token e.g. 1d, 1mo, 1y, ytd, max (default: max)", - ) - period_group.add_argument("--start", help="ISO start date YYYY-MM-DD") - parser.add_argument( - "--end", help="ISO end date YYYY-MM-DD (required when --start is given)" - ) - parser.add_argument( - "--interval", - default="all", - help="Comma-separated intervals or 'all' (default: all)", - ) - parser.add_argument( - "--no-cache", - action="store_true", - help="Bypass cache reads for data (fetch fresh)", - ) - parser.add_argument( - "--fresh", action="store_true", help="Alias for --no-cache (fetch fresh data)" - ) - parser.add_argument( - "--reset-db", - action="store_true", - help="Danger: drop and recreate DB tables before running", - ) - parser.add_argument( - "--exports", - default="", - help="Comma-separated export types to run (csv,report,tradingview,ai,all)", - ) - parser.add_argument( - "--dry-run", - action="store_true", - help="Do not perform side effects; print manifest and exit", - ) - parser.add_argument( - "--outdir", - default=None, - help="Output directory for artifacts (default: artifacts/run_)", - ) - parser.add_argument("--log-level", default="INFO", help="Logging level") - parser.add_argument("--config", default=None, help="Path to config file (optional)") - parser.add_argument( - "--force", - action="store_true", - help="Force run even if plan_hash already succeeded", - ) - parser.add_argument( - "--max-workers", type=int, default=4, help="Concurrency for backtests" - ) - args = parser.parse_args(argv) - - _setup_logging(args.log_level) - - try: - collection_path = resolve_collection_path(args.collection) - except Exception as exc: - log.exception("Failed to resolve collection: %s", exc) - return 2 - - try: - symbols = load_collection_symbols(collection_path) - except Exception as exc: - log.exception("Failed to load symbols from collection: %s", exc) - return 3 - - try: - strategies = expand_strategies(args.strategies) - # Filter out filesystem artifacts or invalid candidates (e.g., __pycache__) - try: - strategies = [ - s - for s in strategies - if not (isinstance(s, str) and s.strip().startswith("__")) - ] - except Exception: - # Defensive: if filtering fails, keep original list - pass - except Exception as exc: - log.exception("Failed to resolve strategies: %s", exc) - return 4 - - try: - intervals = expand_intervals(args.interval) - except Exception as exc: - log.exception("Failed to resolve intervals: %s", exc) - return 5 - - # Basic validation for start/end - if args.start and not args.end: - log.error("--end is required when --start is provided") - return 6 - - # compute outdir - ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S") - outdir = Path(args.outdir) if args.outdir else Path("artifacts") / f"run_{ts}" - outdir = outdir.resolve() - - # Collect git SHAs (best-effort) - app_sha = try_get_git_sha(Path()) - strat_sha = try_get_git_sha(Path("quant-strategies")) - - # Build plan manifest - resolved_plan = { - "actor": "cli", - "action": args.action, - "collection": str(collection_path), - "symbols": sorted(symbols), - "strategies": sorted(strategies), - "intervals": sorted(intervals), - "metric": args.metric, - "period_mode": args.period if args.start is None else "start_end", - "start": args.start, - "end": args.end, - "exports": args.exports, - "dry_run": bool(args.dry_run), - "use_cache": not (args.no_cache or args.fresh), - "max_workers": int(args.max_workers), - "timestamp_utc": datetime.utcnow().isoformat() + "Z", - "git_sha_app": app_sha, - "git_sha_strat": strat_sha, - } - - # Try to read initial_capital and commission from the collection file - try: - import json as _json - - with collection_path.open() as _fh: - _data = _json.load(_fh) - # Direct keys - ic = None - comm = None - if isinstance(_data, dict): - if "initial_capital" in _data: - ic = _data.get("initial_capital") - if "commission" in _data: - comm = _data.get("commission") - # Named collection wrapper - if (ic is None or comm is None) and _data: - try: - first = next(iter(_data.values())) - if isinstance(first, dict): - ic = first.get("initial_capital", ic) - comm = first.get("commission", comm) - except Exception: - pass - if ic is not None: - resolved_plan["initial_capital"] = float(ic) - if comm is not None: - resolved_plan["commission"] = float(comm) - except Exception: - # Ignore; defaults will be applied downstream - pass - - # Apply interval constraints (best-effort warning only) - for interval in resolved_plan["intervals"]: - _ = clamp_interval_period( - interval, - resolved_plan.get("start"), - resolved_plan.get("end"), - resolved_plan["period_mode"], - ) - - # Add strategies_path so worker processes can initialize the external strategy loader. - # Prefer an explicit environment variable (STRATEGIES_PATH) when set, then check - # the common container-mounted path (/app/external_strategies), then fall back to - # a local `quant-strategies` checkout or `external_strategies` directory. - # This ensures the CLI works both on the host and inside docker-compose containers. - try: - import os - - env_strat = os.getenv("STRATEGIES_PATH") - if env_strat: - resolved_plan["strategies_path"] = env_strat - else: - # Common mount inside the container used by docker-compose - container_path = Path("/app/external_strategies") - if container_path.exists(): - resolved_plan["strategies_path"] = str(container_path) - else: - # Host fallback: prefer local checkout 'quant-strategies' - strat_path = Path("quant-strategies").resolve() - if strat_path.exists(): - resolved_plan["strategies_path"] = str(strat_path) - else: - ext = Path("external_strategies") - resolved_plan["strategies_path"] = ( - str(ext.resolve()) if ext.exists() else None - ) - except Exception: - resolved_plan["strategies_path"] = None - - plan_hash = compute_plan_hash(resolved_plan) - resolved_plan["plan_hash"] = plan_hash - - manifest = { - "plan": resolved_plan, - "generated_at": datetime.utcnow().isoformat() + "Z", - } - - manifest_path = write_manifest(outdir, manifest) - log.info("Wrote run manifest to %s", manifest_path) - - # Optional: reset DB (dangerous) - if args.reset_db and not args.dry_run: - try: - from src.database import unified_models # type: ignore[import-not-found] - - unified_models.drop_tables() - unified_models.create_tables() - log.warning( - "Database tables dropped and recreated as requested (--reset-db)" - ) - except Exception: - log.exception("Failed to reset database tables") - return 9 - - # Dry-run behavior: print manifest and optionally generate exports, then exit - if args.dry_run: - print(json.dumps(manifest, indent=2, sort_keys=True, ensure_ascii=False)) - _run_requested_exports(resolved_plan, collection_path, symbols) - return 0 - - # Idempotency: check DB for existing plan_hash if DB available - if not args.force: - try: - from src.database import unified_models - - if hasattr(unified_models, "find_run_by_plan_hash"): - existing = unified_models.find_run_by_plan_hash(plan_hash) - if existing and getattr(existing, "status", None) == "succeeded": - log.info( - "A succeeded run with the same plan_hash already exists. Use --force to re-run." - ) - return 0 - except Exception: - log.debug("Could not query DB for existing plan_hash; continuing") - - # Execute the plan - rc = run_plan(manifest, outdir, dry_run=args.dry_run) - - # Best-effort: persist artifact pointers (manifest, engine summary, fallback summary) into unified_models.RunArtifact - try: - from src.database import unified_models # type: ignore[import-not-found] - - run = None - try: - if hasattr(unified_models, "find_run_by_plan_hash"): - run = unified_models.find_run_by_plan_hash(plan_hash) - else: - # fallback: query by plan_hash manually - sess_tmp = unified_models.Session() - try: - run = ( - sess_tmp.query(unified_models.Run) - .filter(unified_models.Run.plan_hash == plan_hash) - .one_or_none() - ) - finally: - try: - sess_tmp.close() - except Exception: - pass - except Exception: - log.exception("Failed to locate run for plan_hash %s", plan_hash) - run = None - - if run: - sess = unified_models.Session() - try: - artifact_candidates = [ - ("manifest", manifest_path), - ("engine_summary", outdir / "engine_run_summary.json"), - ("run_summary_fallback", outdir / "run_summary_fallback.json"), - ] - added = 0 - for atype, p in artifact_candidates: - try: - # Only persist existing artifact files (handle Path objects) - p_path = Path(p) - if p_path.exists(): - ra = unified_models.RunArtifact( - run_id=getattr(run, "run_id", None), - artifact_type=atype, - path_or_uri=str(p_path), - meta=None, - ) - sess.add(ra) - added += 1 - else: - log.debug("Artifact file not present, skipping: %s", p_path) - except Exception: - log.exception("Failed to add RunArtifact entry for %s", p) - if added: - sess.commit() - # Log number of artifacts added for visibility - try: - cnt = ( - sess.query(unified_models.RunArtifact) - .filter( - unified_models.RunArtifact.run_id - == getattr(run, "run_id", None) - ) - .count() - ) - log.info( - "Persisted %d run artifact pointers to DB for run %s", - cnt, - getattr(run, "run_id", None), - ) - except Exception: - log.info( - "Persisted run artifact pointers to DB for run %s", - getattr(run, "run_id", None), - ) - else: - sess.rollback() - log.debug( - "No artifact files found to persist for run %s", - getattr(run, "run_id", None), - ) - except Exception: - try: - sess.rollback() - except Exception: - pass - log.exception( - "Failed to persist run artifacts to DB for run %s", - getattr(run, "run_id", None), - ) - finally: - try: - sess.close() - except Exception: - pass - except Exception: - log.debug( - "Unified models not available for run_artifact persistence (continuing)" - ) - - # Post-run: also run exports when requested (best-effort) - try: - if rc == 0 and (resolved_plan.get("exports") or ""): - _run_requested_exports(resolved_plan, collection_path, symbols) - except Exception: - log.exception("Exports failed after run (continuing)") - - return rc - - -def main(argv: Optional[Sequence[str]] = None) -> int: - """Main entrypoint compatible with direct module and top-level dispatch. - - Behavior: - - If called with 'collection' as a subcommand (e.g. 'collection ...'), - delegate to handle_collection_run with the args after 'collection'. - - If called as part of a larger CLI where other args appear before 'collection', - locate 'collection' in argv and delegate the remainder to handle_collection_run. - - If no args are supplied, print a minimal help summary. - """ - if argv is None: - argv = sys.argv[1:] - - # If no arguments, show basic help - if not argv: - parser = argparse.ArgumentParser( - prog="unified_cli", description="Unified Quant CLI" - ) - parser.add_argument( - "collection", - nargs="?", - help="Run against a collection (see subcommand 'collection')", - ) - parser.print_help() - return 1 - - # Locate 'collection' subcommand anywhere in argv - try: - idx = int(argv.index("collection")) - except ValueError: - idx = -1 - - if idx >= 0: - # Pass everything after the 'collection' token to the dedicated handler - return handle_collection_run(argv[idx + 1 :]) - - # No recognized subcommand found - print("Unknown command. Supported: collection") - return 2 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000..4f59b4b --- /dev/null +++ b/src/config.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import yaml + + +@dataclass +class StrategyConfig: + name: str + module: str | None # optional if scanning external subclasses + cls: str | None + params: dict[str, list[Any]] + + +@dataclass +class CollectionConfig: + name: str + source: str # yfinance, ccxt, custom + symbols: list[str] + exchange: str | None = None # for ccxt + currency: str | None = None + quote: str | None = None # for ccxt symbols e.g., USDT + fees: float | None = None + slippage: float | None = None + + +@dataclass +class Config: + collections: list[CollectionConfig] + timeframes: list[str] + metric: str # sharpe | sortino | profit + strategies: list[StrategyConfig] + engine: str = "vectorbt" # vectorbt | backtesting (planned) + max_workers: int = 1 + asset_workers: int = 1 + param_workers: int = 1 + max_fetch_concurrency: int = 2 + fees: float = 0.0 + slippage: float = 0.0 + risk_free_rate: float = 0.0 + cache_dir: str = ".cache/data" + + +def load_config(path: str | Path) -> Config: + with open(path) as f: + raw = yaml.safe_load(f) + + collections = [ + CollectionConfig( + name=c["name"], + source=c["source"], + symbols=c["symbols"], + exchange=c.get("exchange"), + currency=c.get("currency"), + quote=c.get("quote"), + fees=c.get("fees"), + slippage=c.get("slippage"), + ) + for c in raw["collections"] + ] + + strategies = [ + StrategyConfig( + name=s["name"], + module=s.get("module"), + cls=s.get("class") or s.get("cls"), + params=s.get("params", {}), + ) + for s in raw["strategies"] + ] + + cfg = Config( + collections=collections, + timeframes=raw["timeframes"], + metric=raw.get("metric", "sharpe").lower(), + strategies=strategies, + engine=raw.get("engine", "vectorbt").lower(), + max_workers=int(raw.get("max_workers", raw.get("asset_workers", 1))), + asset_workers=int(raw.get("asset_workers", raw.get("max_workers", 1))), + param_workers=int(raw.get("param_workers", 1)), + max_fetch_concurrency=int(raw.get("max_fetch_concurrency", 2)), + fees=float(raw.get("fees", 0.0)), + slippage=float(raw.get("slippage", 0.0)), + risk_free_rate=float(raw.get("risk_free_rate", 0.0)), + cache_dir=raw.get("cache_dir", ".cache/data"), + ) + return cfg diff --git a/src/core/__init__.py b/src/core/__init__.py deleted file mode 100644 index 955f1ec..0000000 --- a/src/core/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Core module containing the unified components of the quant system. -This module consolidates all the essential functionality without duplication. -""" - -from __future__ import annotations - -# Import core symbols, but guard optional modules so CLI can run even if some -# components are missing in minimal environments (e.g., CI, trimmed installs). -from .backtest_engine import UnifiedBacktestEngine -from .cache_manager import UnifiedCacheManager -from .data_manager import UnifiedDataManager - -# Optional components: try to import, but continue if absent -PortfolioManager = None -UnifiedResultAnalyzer = None - -try: - # Portfolio manager was moved from portfolio_manager.py to collection_manager.py. - # Keep public API stable by importing the same symbol from the new module. - from .collection_manager import PortfolioManager # type: ignore[import-not-found] -except Exception: - PortfolioManager = None - -try: - from .result_analyzer import UnifiedResultAnalyzer # type: ignore[import-not-found] -except Exception: - UnifiedResultAnalyzer = None - -__all__ = [ - "PortfolioManager", - "UnifiedBacktestEngine", - "UnifiedCacheManager", - "UnifiedDataManager", - "UnifiedResultAnalyzer", -] diff --git a/src/core/backtest_engine.py b/src/core/backtest_engine.py deleted file mode 100644 index 976697b..0000000 --- a/src/core/backtest_engine.py +++ /dev/null @@ -1,1996 +0,0 @@ -""" -Unified Backtest Engine - Consolidates all backtesting functionality. -Supports single assets, portfolios, parallel processing, and optimization. -""" - -from __future__ import annotations - -import concurrent.futures -import gc -import logging -import multiprocessing as mp -import time -import warnings -from dataclasses import dataclass -from datetime import datetime -from typing import Any - -import numpy as np -import pandas as pd -from backtesting import Backtest -from backtesting.lib import SignalStrategy - -from .cache_manager import UnifiedCacheManager -from .data_manager import UnifiedDataManager -from .result_analyzer import UnifiedResultAnalyzer - -# from numba import jit # Removed for compatibility - - -warnings.filterwarnings("ignore") - -# Defaults -from pathlib import Path - -# Default metric used when none specified in manifest -DEFAULT_METRIC = "sortino_ratio" - - -def _run_backtest_worker(args): - """ - Module-level worker for ProcessPoolExecutor to avoid pickling bound methods. - args: (symbol, strategy, cfg_kwargs) - Returns a serializable dict with result metadata. - """ - symbol, strategy, cfg_kwargs = args - try: - # Import inside worker process - from .backtest_engine import ( - BacktestConfig, # type: ignore[import-not-found] - UnifiedBacktestEngine, # type: ignore[import-not-found] - ) - except Exception: - # Fallback if imports fail in worker - return error - return { - "symbol": symbol, - "strategy": strategy, - "error": "Worker imports failed", - } - - try: - # Construct config inside worker (safe to create per-process) - try: - cfg = BacktestConfig(**cfg_kwargs) # type: ignore[call-arg] - except Exception: - # Fallback minimal config object - class _TmpCfg: - def __init__(self, **kw): - self.__dict__.update(kw) - - cfg = _TmpCfg(**cfg_kwargs) - - # Initialize external strategy loader in the worker process if a path was provided. - # This ensures StrategyFactory / external loader can discover strategies without - # relying on the parent process to have initialized the global loader. - try: - strategies_path = None - if isinstance(cfg_kwargs, dict): - strategies_path = cfg_kwargs.get("strategies_path") - else: - strategies_path = getattr(cfg, "strategies_path", None) - - if strategies_path: - try: - from pathlib import Path as _Path # local import - - from .external_strategy_loader import ( - get_strategy_loader, # type: ignore[import-not-found] - ) - - # Try a set of common candidate locations under the provided strategies_path - candidates = [] - try: - candidates.append(strategies_path) - candidates.append( - str(_Path(strategies_path) / "algorithms" / "python") - ) - candidates.append( - str(_Path(strategies_path) / "algorithms" / "original") - ) - except Exception: - pass - - loader_initialized = False - for cand in candidates: - if not cand: - continue - try: - cand_path = _Path(cand) - if cand_path.exists(): - # Initialize the global loader in this worker process using the candidate path - get_strategy_loader(str(cand_path)) - loader_initialized = True - break - except Exception as exc: - # ignore and try next candidate, but log for diagnostics - log = logging.getLogger(__name__) - log.debug( - "Strategy loader init failed for %s: %s", cand, exc - ) - continue - - # As a final attempt, call get_strategy_loader with the original value - if not loader_initialized: - try: - get_strategy_loader(strategies_path) - except Exception as exc: - log = logging.getLogger(__name__) - log.debug("Final strategy loader init failed: %s", exc) - - except Exception: - # Non-fatal: continue without external strategies - pass - except Exception: - pass - - engine = UnifiedBacktestEngine() - res = engine.run_backtest(symbol, strategy, cfg) - # Build serializable payload for parent process - metrics = res.metrics if getattr(res, "metrics", None) is not None else {} - trades_raw = None - equity_raw = None - try: - import json as _json - - import pandas as _pd - - trades_obj = getattr(res, "trades", None) - if trades_obj is not None: - if isinstance(trades_obj, _pd.DataFrame): - trades_raw = trades_obj.to_csv(index=False) - else: - try: - trades_raw = _json.dumps(trades_obj) - except Exception: - trades_raw = str(trades_obj) - - eq = getattr(res, "equity_curve", None) - if eq is not None and isinstance(eq, _pd.DataFrame): - equity_raw = eq.to_json(orient="records", date_format="iso") - elif eq is not None: - try: - equity_raw = _json.dumps(eq) - except Exception: - equity_raw = str(eq) - except Exception: - trades_raw = None - equity_raw = None - - # Provide a compact, JSON-friendly summary of the backtest result for persistence/inspection - try: - bt_results_raw = { - "metrics": metrics, - "duration_seconds": getattr(res, "duration_seconds", None), - "data_points": getattr(res, "data_points", None), - "parameters": getattr(res, "parameters", None), - # include a lightweight final value if available on the result object - "final_value": None, - } - try: - if getattr(res, "equity_curve", None) is not None: - # If equity_curve is a DataFrame, try to capture the last equity point - eq = res.equity_curve - if hasattr(eq, "iloc") and len(eq) > 0: - last_row = eq.iloc[-1] - # try both 'equity' column or the first numeric column - if "equity" in last_row: - bt_results_raw["final_value"] = float(last_row["equity"]) - else: - # pick first numeric-like column - for v in last_row.values: - try: - bt_results_raw["final_value"] = float(v) - break - except Exception as exc: - logging.getLogger(__name__).debug( - "Failed to extract final_value: %s", exc - ) - continue - except Exception: - # best-effort only - pass - except Exception: - bt_results_raw = None - - return { - "symbol": getattr(res, "symbol", symbol), - "strategy": getattr(res, "strategy", strategy), - "metrics": metrics, - "trades_raw": trades_raw, - "equity_raw": equity_raw, - "bt_results_raw": bt_results_raw, - "error": getattr(res, "error", None), - "duration_seconds": getattr(res, "duration_seconds", None), - "data_points": getattr(res, "data_points", None), - } - except Exception as exc: - return {"symbol": symbol, "strategy": strategy, "error": str(exc)} - - -def create_backtesting_strategy_adapter(strategy_instance): - """Create a backtesting library compatible strategy from our strategy instance.""" - - class StrategyAdapter(SignalStrategy): - """Adapter to make our strategies work with the backtesting library.""" - - def init(self): - """Initialize the strategy with our custom logic.""" - # Get the data in the format our strategies expect (uppercase columns) - strategy_data = pd.DataFrame( - { - "Open": self.data.Open, - "High": self.data.High, - "Low": self.data.Low, - "Close": self.data.Close, - "Volume": self.data.Volume, - }, - index=self.data.index, - ) - - # Generate signals using our strategy - try: - signals = strategy_instance.generate_signals(strategy_data) - # Ensure signals are aligned with data index - if isinstance(signals, pd.Series): - aligned_signals = signals.reindex(self.data.index, fill_value=0) - else: - aligned_signals = pd.Series( - signals, index=self.data.index, dtype=float - ) - - self.signals = self.I(lambda: aligned_signals.values, name="signals") - except Exception: - # If strategy fails, create zero signals - self.signals = self.I(lambda: [0] * len(self.data), name="signals") - - def next(self): - """Execute trades based on our strategy signals.""" - if len(self.signals) > 0: - current_signal = self.signals[-1] - - if current_signal == 1 and not self.position: - # Buy signal and no position - go long - self.buy() - elif current_signal == -1 and self.position: - # Sell signal and have position - close position - self.sell() - elif current_signal == -1 and not self.position: - # Sell signal and no position - go short (if allowed) - try: - self.sell() - except: - pass # Shorting not allowed or failed - - return StrategyAdapter - - -@dataclass -class BacktestConfig: - """Configuration for backtest runs.""" - - symbols: list[str] - strategies: list[str] - start_date: str - end_date: str - initial_capital: float = 10000 - interval: str = "1d" - commission: float = 0.001 - use_cache: bool = True - save_trades: bool = False - save_equity_curve: bool = False - override_old_trades: bool = ( - True # Whether to clean up old trades for same symbol/strategy - ) - memory_limit_gb: float = 8.0 - max_workers: int = None - asset_type: str = None # 'stocks', 'crypto', 'forex', etc. - futures_mode: bool = False # For crypto futures - leverage: float = 1.0 # For futures trading - - -@dataclass -class BacktestResult: - """Standardized backtest result.""" - - symbol: str - strategy: str - parameters: dict[str, Any] - metrics: dict[str, float] - config: BacktestConfig - equity_curve: pd.DataFrame | None = None - trades: pd.DataFrame | None = None - start_date: str = None - end_date: str = None - duration_seconds: float = 0 - data_points: int = 0 - error: str | None = None - source: str | None = None - - -class UnifiedBacktestEngine: - """ - Unified backtesting engine that consolidates all backtesting functionality. - Supports single assets, portfolios, parallel processing, and various asset types. - """ - - def __init__( - self, - data_manager: UnifiedDataManager = None, - cache_manager: UnifiedCacheManager = None, - max_workers: int | None = None, - memory_limit_gb: float = 8.0, - ): - self.data_manager = data_manager or UnifiedDataManager() - self.cache_manager = cache_manager or UnifiedCacheManager() - self.result_analyzer = UnifiedResultAnalyzer() - - self.max_workers = max_workers or min(mp.cpu_count(), 8) - self.memory_limit_bytes = int(memory_limit_gb * 1024**3) - - self.logger = logging.getLogger(__name__) - self.stats = { - "backtests_run": 0, - "cache_hits": 0, - "cache_misses": 0, - "errors": 0, - "total_time": 0, - } - - def run_backtest( - self, - symbol: str, - strategy: str, - config: BacktestConfig, - custom_parameters: dict[str, Any] | None = None, - ) -> BacktestResult: - """ - Run backtest for a single symbol/strategy combination. - - Args: - symbol: Symbol to backtest - strategy: Strategy name - config: Backtest configuration - custom_parameters: Custom strategy parameters - - Returns: - BacktestResult object - """ - start_time = time.time() - - try: - # Get strategy parameters - parameters = custom_parameters or self._get_default_parameters(strategy) - - # Check cache first - if config.use_cache and not custom_parameters: - cached_result = self.cache_manager.get_backtest_result( - symbol, strategy, parameters, config.interval - ) - if cached_result: - self.stats["cache_hits"] += 1 - self.logger.debug("Cache hit for %s/%s", symbol, strategy) - # Convert cached dict to BacktestResult and mark it as coming from cache - res = self._dict_to_result( - cached_result, symbol, strategy, parameters, config - ) - try: - res.from_cache = True - except Exception: - pass - return res - - self.stats["cache_misses"] += 1 - - # Get market data - if config.futures_mode: - data = self.data_manager.get_crypto_futures_data( - symbol, - config.start_date, - config.end_date, - config.interval, - config.use_cache, - ) - else: - data = self.data_manager.get_data( - symbol, - config.start_date, - config.end_date, - config.interval, - config.use_cache, - config.asset_type, - ) - - if data is None or data.empty: - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics={}, - error="No data available", - ) - - # Run backtest - result = self._execute_backtest(symbol, strategy, data, parameters, config) - - # Cache result if not using custom parameters - # NOTE: Backtest output caching is disabled to ensure results are always - # recomputed and persisted per-run. Data-level caching (market data) is - # preserved. If desired, re-enable result caching here. - # if config.use_cache and not custom_parameters and not result.error: - # self.cache_manager.cache_backtest_result( - # symbol, strategy, parameters, asdict(result), config.interval - # ) - - result.duration_seconds = time.time() - start_time - result.data_points = len(data) - self.stats["backtests_run"] += 1 - - return result - - except Exception as e: - self.stats["errors"] += 1 - self.logger.error("Backtest failed for %s/%s: %s", symbol, strategy, e) - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=custom_parameters or {}, - config=config, - metrics={}, - error=str(e), - duration_seconds=time.time() - start_time, - ) - - def run_batch_backtests(self, config: BacktestConfig) -> list[BacktestResult]: - """ - Run backtests for multiple symbols and strategies in parallel. - - Args: - config: Backtest configuration - - Returns: - List of backtest results - """ - start_time = time.time() - self.logger.info( - "Starting batch backtest: %d symbols, %d strategies", - len(config.symbols), - len(config.strategies), - ) - - # Generate all symbol/strategy combinations - combinations = [ - (symbol, strategy) - for symbol in config.symbols - for strategy in config.strategies - ] - - self.logger.info("Total combinations: %d", len(combinations)) - - # Process in batches to manage memory - batch_size = self._calculate_batch_size( - len(config.symbols), config.memory_limit_gb - ) - results = [] - - for i in range(0, len(combinations), batch_size): - batch = combinations[i : i + batch_size] - self.logger.info( - "Processing batch %d/%d", - i // batch_size + 1, - (len(combinations) - 1) // batch_size + 1, - ) - - batch_results = self._process_batch(batch, config) - results.extend(batch_results) - - # Force garbage collection between batches - gc.collect() - - self.stats["total_time"] = time.time() - start_time - self._log_stats() - - return results - - def run_portfolio_backtest( - self, config: BacktestConfig, weights: dict[str, float] | None = None - ) -> BacktestResult: - """ - Run portfolio backtest with multiple assets. - - Args: - config: Backtest configuration - weights: Asset weights (if None, equal weights used) - - Returns: - Portfolio backtest result - """ - start_time = time.time() - - if not config.strategies or len(config.strategies) != 1: - raise ValueError("Portfolio backtest requires exactly one strategy") - - strategy = config.strategies[0] - - try: - # Get data for all symbols - all_data = self.data_manager.get_batch_data( - config.symbols, - config.start_date, - config.end_date, - config.interval, - config.use_cache, - config.asset_type, - ) - - if not all_data: - return BacktestResult( - symbol="PORTFOLIO", - strategy=strategy, - parameters={}, - config=config, - metrics={}, - error="No data available for any symbol", - ) - - # Calculate equal weights if not provided - if not weights: - weights = {symbol: 1.0 / len(all_data) for symbol in all_data} - - # Normalize weights - total_weight = sum(weights.values()) - weights = {k: v / total_weight for k, v in weights.items()} - - # Run portfolio backtest - portfolio_result = self._execute_portfolio_backtest( - all_data, strategy, weights, config - ) - - portfolio_result.duration_seconds = time.time() - start_time - return portfolio_result - - except Exception as e: - self.logger.error("Portfolio backtest failed: %s", e) - return BacktestResult( - symbol="PORTFOLIO", - strategy=strategy, - parameters={}, - config=config, - metrics={}, - error=str(e), - duration_seconds=time.time() - start_time, - ) - - def run_incremental_backtest( - self, - symbol: str, - strategy: str, - config: BacktestConfig, - last_update: datetime | None = None, - ) -> BacktestResult | None: - """ - Run incremental backtest - only process new data since last run. - - Args: - symbol: Symbol to backtest - strategy: Strategy name - config: Backtest configuration - last_update: Last update timestamp - - Returns: - BacktestResult or None if no new data - """ - # Check if we have cached results - parameters = self._get_default_parameters(strategy) - cached_result = self.cache_manager.get_backtest_result( - symbol, strategy, parameters, config.interval - ) - - if cached_result and not last_update: - self.logger.info("Using cached result for %s/%s", symbol, strategy) - return self._dict_to_result( - cached_result, symbol, strategy, parameters, config - ) - - # Get data and check if we need to update - data = self.data_manager.get_data( - symbol, - config.start_date, - config.end_date, - config.interval, - config.use_cache, - config.asset_type, - ) - - if data is None or data.empty: - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics={}, - error="No data available", - ) - - # Check if we have new data since last cached result - if cached_result and last_update: - last_data_point = pd.to_datetime( - cached_result.get("end_date", config.start_date), utc=True - ) - - # Ensure data index is in UTC for comparison - data_last_point = data.index[-1] - if data_last_point.tz is None: - data_last_point = data_last_point.tz_localize("UTC") - else: - data_last_point = data_last_point.tz_convert("UTC") - - if data_last_point <= last_data_point: - self.logger.info("No new data for %s/%s", symbol, strategy) - return self._dict_to_result( - cached_result, symbol, strategy, parameters, config - ) - - # Run backtest - return self.run_backtest(symbol, strategy, config) - - def _execute_backtest( - self, - symbol: str, - strategy: str, - data: pd.DataFrame, - parameters: dict[str, Any], - config: BacktestConfig, - ) -> BacktestResult: - """Execute the actual backtest logic.""" - try: - # Get strategy class - strategy_class = self._get_strategy_class(strategy) - if not strategy_class: - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics={}, - error=f"Strategy {strategy} not found", - ) - - # Initialize strategy - strategy_instance = strategy_class(**parameters) - - # Prepare data for backtesting library (requires uppercase OHLCV) - bt_data = self._prepare_data_for_backtesting_lib(data) - - if bt_data is None or bt_data.empty: - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics={}, - error="Data preparation failed", - ) - - # Create strategy adapter for backtesting library - StrategyAdapter = create_backtesting_strategy_adapter(strategy_instance) - - # Run backtest using the backtesting library - bt = Backtest( - bt_data, - StrategyAdapter, - cash=config.initial_capital, - commission=config.commission, - exclusive_orders=True, - ) - - # Execute backtest - bt_results = bt.run() - - # Convert backtesting library results to our format - result = self._convert_backtesting_results(bt_results, bt_data, config) - - # Extract metrics from backtesting library results - metrics = self._extract_metrics_from_bt_results(bt_results) - - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics=metrics, - equity_curve=( - result.get("equity_curve") if config.save_equity_curve else None - ), - trades=result.get("trades") if config.save_trades else None, - start_date=config.start_date, - end_date=config.end_date, - ) - - except Exception as e: - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics={}, - error=str(e), - ) - - def _execute_portfolio_backtest( - self, - data_dict: dict[str, pd.DataFrame], - strategy: str, - weights: dict[str, float], - config: BacktestConfig, - ) -> BacktestResult: - """Execute portfolio backtest.""" - try: - # Align all data to common date range - aligned_data = self._align_portfolio_data(data_dict) - - if aligned_data.empty: - return BacktestResult( - symbol="PORTFOLIO", - strategy=strategy, - parameters=weights, - config=config, - metrics={}, - error="No aligned data for portfolio", - ) - - # Calculate portfolio returns - portfolio_returns = self._calculate_portfolio_returns(aligned_data, weights) - - # Create portfolio equity curve - initial_capital = config.initial_capital - equity_curve = (1 + portfolio_returns).cumprod() * initial_capital - - # Calculate portfolio metrics - portfolio_data = { - "returns": portfolio_returns, - "equity_curve": equity_curve, - "weights": weights, - } - - metrics = self.result_analyzer.calculate_portfolio_metrics( - portfolio_data, initial_capital - ) - - return BacktestResult( - symbol="PORTFOLIO", - strategy=strategy, - parameters=weights, - config=config, - metrics=metrics, - equity_curve=( - equity_curve.to_frame("equity") - if config.save_equity_curve - else None - ), - ) - - except Exception as e: - return BacktestResult( - symbol="PORTFOLIO", - strategy=strategy, - parameters=weights, - config=config, - metrics={}, - error=str(e), - ) - - def _process_batch( - self, batch: list[tuple[str, str]], config: BacktestConfig - ) -> list[BacktestResult]: - """Process batch of symbol/strategy combinations. - - Uses a module-level worker to avoid pickling bound methods or objects that - are not serializable by multiprocessing. Each worker constructs its own - engine and runs the single backtest there. - """ - results: list[BacktestResult] = [] - - # Build serializable cfg_kwargs for workers (they will construct BacktestConfig) - for i in range( - 0, len(batch), max(1, len(batch)) - ): # keep batching but here we pass full batch to executor.map - # Prepare args for each (symbol, strategy) - worker_args = [] - for symbol, strategy in batch: - cfg_kwargs = { - "symbols": [symbol], - "strategies": [strategy], - "start_date": config.start_date, - "end_date": config.end_date, - "period": getattr(config, "period", None), - "initial_capital": getattr(config, "initial_capital", 10000), - "interval": getattr(config, "interval", "1d"), - "max_workers": getattr(config, "max_workers", None), - # propagate strategies_path from parent cfg (may be present on _TmpCfg) - "strategies_path": getattr(config, "strategies_path", None), - # include commonly expected config attributes so worker-side _TmpCfg has them - "use_cache": getattr(config, "use_cache", True), - "commission": getattr(config, "commission", 0.001), - "save_trades": getattr(config, "save_trades", False), - "save_equity_curve": getattr(config, "save_equity_curve", False), - # Additional worker-facing attributes to avoid attribute errors in fallback _TmpCfg - "override_old_trades": getattr(config, "override_old_trades", True), - "memory_limit_gb": getattr(config, "memory_limit_gb", 8.0), - "asset_type": getattr(config, "asset_type", None), - "futures_mode": getattr(config, "futures_mode", False), - "leverage": getattr(config, "leverage", 1.0), - } - worker_args.append((symbol, strategy, cfg_kwargs)) - - # Use ProcessPoolExecutor with module-level worker to avoid pickling issues - try: - with concurrent.futures.ProcessPoolExecutor( - max_workers=self.max_workers - ) as executor: - for worker_res in executor.map(_run_backtest_worker, worker_args): - # worker_res is a serializable dict - sym = worker_res.get("symbol") - strat = worker_res.get("strategy") - err = worker_res.get("error") - metrics = worker_res.get("metrics", {}) or {} - duration = worker_res.get("duration_seconds", None) - data_points = worker_res.get("data_points", None) - - if err: - self.logger.error( - "Batch backtest failed for %s/%s: %s", sym, strat, err - ) - self.stats["errors"] += 1 - results.append( - BacktestResult( - symbol=sym or "", - strategy=strat or "", - parameters={}, - config=config, - metrics={}, - error=err, - ) - ) - else: - # Construct a minimal BacktestResult for downstream processing - br = BacktestResult( - symbol=sym or "", - strategy=strat or "", - parameters={}, - config=config, - metrics=metrics, - trades=worker_res.get("trades_raw"), - start_date=getattr(config, "start_date", None), - end_date=getattr(config, "end_date", None), - duration_seconds=duration or 0, - data_points=int(data_points) - if data_points is not None - else 0, - error=None, - ) - # Attach raw backtest payloads if present so engine.run can persist them - try: - br.bt_results_raw = worker_res.get( - "bt_results_raw", None - ) - except Exception: - pass - # Reflect worker-level cache hits in parent engine stats - try: - if worker_res.get("cache_hit"): - self.stats["cache_hits"] += 1 - except Exception: - pass - results.append(br) - except Exception as e: - self.logger.error("Failed to execute worker batch: %s", e) - # Convert all batch items to error BacktestResult - for symbol, strategy in batch: - self.stats["errors"] += 1 - results.append( - BacktestResult( - symbol=symbol, - strategy=strategy, - parameters={}, - config=config, - metrics={}, - error=str(e), - ) - ) - - # we've processed the whole provided batch once; break - break - - return results - - def _run_single_backtest_task( - self, symbol: str, strategy: str, config: BacktestConfig - ) -> BacktestResult: - """Task function for multiprocessing.""" - # Create new instances for this process - data_manager = UnifiedDataManager() - cache_manager = UnifiedCacheManager() - - # Create temporary engine for this process - temp_engine = UnifiedBacktestEngine(data_manager, cache_manager, max_workers=1) - return temp_engine.run_backtest(symbol, strategy, config) - - def _prepare_data_with_indicators( - self, data: pd.DataFrame, strategy_instance - ) -> pd.DataFrame: - """Prepare data with technical indicators required by strategy.""" - prepared_data = data.copy() - - # Add basic indicators that most strategies need - prepared_data = self._add_basic_indicators(prepared_data) - - # Add strategy-specific indicators - if hasattr(strategy_instance, "add_indicators"): - prepared_data = strategy_instance.add_indicators(prepared_data) - - return prepared_data - - def _add_basic_indicators(self, data: pd.DataFrame) -> pd.DataFrame: - """Add basic technical indicators.""" - df = data.copy() - - # Simple moving averages - for period in [10, 20, 50]: - df[f"sma_{period}"] = df["close"].rolling(period).mean() - - # RSI - df["rsi_14"] = self._calculate_rsi(df["close"].values, 14) - - # MACD - macd_line, signal_line, histogram = self._calculate_macd(df["close"].values) - df["macd"] = macd_line - df["macd_signal"] = signal_line - df["macd_histogram"] = histogram - - # Bollinger Bands - sma_20 = df["close"].rolling(20).mean() - std_20 = df["close"].rolling(20).std() - df["bb_upper"] = sma_20 + (std_20 * 2) - df["bb_lower"] = sma_20 - (std_20 * 2) - df["bb_middle"] = sma_20 - - return df - - def _simulate_trading( - self, data: pd.DataFrame, strategy_instance, config: BacktestConfig - ) -> dict[str, Any]: - """Simulate trading based on strategy signals.""" - trades = [] - equity_curve = [] - - capital = config.initial_capital - position = 0 - position_size = 0 - - # Pre-generate all signals for the entire dataset - try: - strategy_data = self._transform_data_for_strategy(data) - all_signals = strategy_instance.generate_signals(strategy_data) - except Exception as e: - self.logger.debug( - "Strategy %s failed: %s", strategy_instance.__class__.__name__, e - ) - # If strategy fails, create zero signals - all_signals = pd.Series(0, index=data.index) - - for i, (timestamp, row) in enumerate(data.iterrows()): - # Get pre-generated signal for this timestamp - signal = all_signals.iloc[i] if i < len(all_signals) else 0 - - # Execute trades based on signal - if signal == 1 and position <= 0: # Buy signal - if position < 0: # Close short position - pnl = (position_size * row["close"] - position_size * position) * -1 - capital += pnl - trades.append( - { - "timestamp": timestamp, - "action": "cover", - "price": row["close"], - "size": abs(position_size), - "pnl": pnl, - } - ) - - # Open long position - use full capital minus commission for BuyAndHold - available_capital = capital / ( - 1 + config.commission - ) # Account for commission in calculation - position_size = available_capital / row["close"] - position = row["close"] - capital -= position_size * row["close"] + ( - position_size * row["close"] * config.commission - ) - - trades.append( - { - "timestamp": timestamp, - "action": "buy", - "price": row["close"], - "size": position_size, - "pnl": 0, - } - ) - - elif signal == -1 and position >= 0: # Sell signal - if position > 0: # Close long position - pnl = position_size * (row["close"] - position) - capital += pnl + (position_size * row["close"]) - trades.append( - { - "timestamp": timestamp, - "action": "sell", - "price": row["close"], - "size": position_size, - "pnl": pnl, - } - ) - position = 0 - position_size = 0 - - # Calculate current portfolio value - if position > 0: - portfolio_value = capital + (position_size * row["close"]) - elif position < 0: - portfolio_value = capital - (position_size * (row["close"] - position)) - else: - portfolio_value = capital - - equity_curve.append({"timestamp": timestamp, "equity": portfolio_value}) - - trades_df = pd.DataFrame(trades) if trades else pd.DataFrame() - - return { - "trades": trades_df, - "equity_curve": pd.DataFrame(equity_curve), - "final_capital": ( - equity_curve[-1]["equity"] if equity_curve else config.initial_capital - ), - } - - def _get_strategy_signal(self, strategy_instance, data: pd.DataFrame) -> int: - """Get trading signal from strategy.""" - if hasattr(strategy_instance, "generate_signals"): - try: - # Transform data to uppercase columns for strategy compatibility - strategy_data = self._transform_data_for_strategy(data) - # Use the correct method name (plural) - signals = strategy_instance.generate_signals(strategy_data) - if len(signals) > 0: - return signals.iloc[-1] # Return last signal - return 0 - except Exception as e: - # Log the actual error for debugging - self.logger.debug( - "Strategy %s failed: %s", strategy_instance.__class__.__name__, e - ) - # Strategy failed - return 0 (no signal) to generate zero metrics - return 0 - - # No generate_signals method - strategy is invalid, return 0 - return 0 - - def _transform_data_for_strategy(self, data: pd.DataFrame) -> pd.DataFrame: - """Transform data columns to uppercase format expected by external strategies.""" - if data is None or data.empty: - return data - - # Only select OHLCV columns that strategies expect - required_columns = ["open", "high", "low", "close", "volume"] - - # Check if all required columns exist - missing_columns = [col for col in required_columns if col not in data.columns] - if missing_columns: - raise ValueError(f"Missing required columns: {missing_columns}") - - # Select only OHLCV columns - df = data[required_columns].copy() - - # Transform lowercase columns to uppercase for strategy compatibility - column_mapping = { - "open": "Open", - "high": "High", - "low": "Low", - "close": "Close", - "volume": "Volume", - } - - # Rename columns - df = df.rename(columns=column_mapping) - - return df - - def _align_portfolio_data(self, data_dict: dict[str, pd.DataFrame]) -> pd.DataFrame: - """Align multiple asset data to common date range.""" - if not data_dict: - return pd.DataFrame() - - # Find common date range - all_dates = None - for symbol, data in data_dict.items(): - all_dates = ( - set(data.index) - if all_dates is None - else all_dates.intersection(set(data.index)) - ) - - if not all_dates: - return pd.DataFrame() - - # Create aligned dataframe - common_dates = sorted(list(all_dates)) - aligned_data = pd.DataFrame(index=common_dates) - - for symbol, data in data_dict.items(): - aligned_data[f"{symbol}_close"] = data.loc[common_dates, "close"] - - return aligned_data.dropna() - - def _calculate_portfolio_returns( - self, aligned_data: pd.DataFrame, weights: dict[str, float] - ) -> pd.Series: - """Calculate portfolio returns.""" - returns = pd.Series(index=aligned_data.index, dtype=float) - - for i in range(1, len(aligned_data)): - portfolio_return = 0 - for symbol, weight in weights.items(): - col_name = f"{symbol}_close" - if col_name in aligned_data.columns: - asset_return = ( - aligned_data[col_name].iloc[i] - / aligned_data[col_name].iloc[i - 1] - ) - 1 - portfolio_return += weight * asset_return - - returns.iloc[i] = portfolio_return - - return returns.fillna(0) - - @staticmethod - # @jit(nopython=True) # Removed for compatibility - def _calculate_rsi(prices: np.ndarray, period: int = 14) -> np.ndarray: - """Fast RSI calculation using Numba.""" - deltas = np.diff(prices) - gains = np.where(deltas > 0, deltas, 0) - losses = np.where(deltas < 0, -deltas, 0) - - avg_gains = np.full_like(prices, np.nan) - avg_losses = np.full_like(prices, np.nan) - rsi = np.full_like(prices, np.nan) - - if len(gains) >= period: - avg_gains[period] = np.mean(gains[:period]) - avg_losses[period] = np.mean(losses[:period]) - - for i in range(period + 1, len(prices)): - avg_gains[i] = (avg_gains[i - 1] * (period - 1) + gains[i - 1]) / period - avg_losses[i] = ( - avg_losses[i - 1] * (period - 1) + losses[i - 1] - ) / period - - if avg_losses[i] == 0: - rsi[i] = 100 - else: - rs = avg_gains[i] / avg_losses[i] - rsi[i] = 100 - (100 / (1 + rs)) - - return rsi - - @staticmethod - # @jit(nopython=True) # Removed for compatibility - def _calculate_macd( - prices: np.ndarray, fast: int = 12, slow: int = 26, signal: int = 9 - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Fast MACD calculation using Numba.""" - ema_fast = np.full_like(prices, np.nan) - ema_slow = np.full_like(prices, np.nan) - - # Calculate EMAs - alpha_fast = 2.0 / (fast + 1.0) - alpha_slow = 2.0 / (slow + 1.0) - - ema_fast[0] = prices[0] - ema_slow[0] = prices[0] - - for i in range(1, len(prices)): - ema_fast[i] = alpha_fast * prices[i] + (1 - alpha_fast) * ema_fast[i - 1] - ema_slow[i] = alpha_slow * prices[i] + (1 - alpha_slow) * ema_slow[i - 1] - - macd_line = ema_fast - ema_slow - - # Calculate signal line (EMA of MACD) - signal_line = np.full_like(prices, np.nan) - alpha_signal = 2.0 / (signal + 1.0) - - # Start signal line calculation after we have enough MACD data - signal_start = max(fast, slow) - if len(macd_line) > signal_start: - signal_line[signal_start] = macd_line[signal_start] - for i in range(signal_start + 1, len(prices)): - signal_line[i] = ( - alpha_signal * macd_line[i] - + (1 - alpha_signal) * signal_line[i - 1] - ) - - histogram = macd_line - signal_line - - return macd_line, signal_line, histogram - - def _calculate_batch_size(self, num_symbols: int, memory_limit_gb: float) -> int: - """Calculate optimal batch size based on memory constraints.""" - estimated_memory_per_symbol_mb = 50 - available_memory_mb = memory_limit_gb * 1024 * 0.8 - - max_batch_size = int(available_memory_mb / estimated_memory_per_symbol_mb) - return min(max_batch_size, num_symbols, 100) - - def _get_strategy_class(self, strategy_name: str) -> type | None: - """Get strategy class by name using StrategyFactory.""" - try: - from .strategy import StrategyFactory - - # Create an instance and get its class - strategy_instance = StrategyFactory.create_strategy(strategy_name, {}) - return strategy_instance.__class__ - except Exception as e: - self.logger.error("Failed to load strategy %s: %s", strategy_name, e) - return None - - def _get_default_parameters(self, strategy_name: str) -> dict[str, Any]: - """Get default parameters for a strategy.""" - default_params = { - "rsi": {"period": 14, "overbought": 70, "oversold": 30}, - "macd": {"fast": 12, "slow": 26, "signal": 9}, - "bollinger_bands": {"period": 20, "deviation": 2}, - "sma_crossover": {"fast_period": 10, "slow_period": 20}, - } - return default_params.get(strategy_name.lower(), {}) - - def _dict_to_result( - self, - cached_dict: dict, - symbol: str, - strategy: str, - parameters: dict, - config: BacktestConfig, - ) -> BacktestResult: - """Convert cached dictionary to BacktestResult object.""" - import pandas as pd - - # Handle trades data from cache - trades = cached_dict.get("trades") - if trades is not None and isinstance(trades, dict): - # Convert trades dict back to DataFrame - trades = pd.DataFrame(trades) - elif trades is not None and not isinstance(trades, pd.DataFrame): - trades = None - - # Handle equity_curve data from cache - equity_curve = cached_dict.get("equity_curve") - if equity_curve is not None and isinstance(equity_curve, dict): - # Convert equity_curve dict back to DataFrame - equity_curve = pd.DataFrame(equity_curve) - elif equity_curve is not None and not isinstance(equity_curve, pd.DataFrame): - equity_curve = None - - return BacktestResult( - symbol=symbol, - strategy=strategy, - parameters=parameters, - config=config, - metrics=cached_dict.get("metrics", {}), - trades=trades, - equity_curve=equity_curve, - start_date=cached_dict.get("start_date"), - end_date=cached_dict.get("end_date"), - duration_seconds=cached_dict.get("duration_seconds", 0), - data_points=cached_dict.get("data_points", 0), - error=cached_dict.get("error"), - ) - - def _log_stats(self): - """Log performance statistics.""" - self.logger.info("Batch backtest completed:") - self.logger.info(" Total backtests: %s", self.stats["backtests_run"]) - self.logger.info(" Cache hits: %s", self.stats["cache_hits"]) - self.logger.info(" Cache misses: %s", self.stats["cache_misses"]) - self.logger.info(" Errors: %s", self.stats["errors"]) - self.logger.info(" Total time: %.2fs", self.stats["total_time"]) - if self.stats["backtests_run"] > 0: - avg_time = self.stats["total_time"] / self.stats["backtests_run"] - self.logger.info(" Avg time per backtest: %.2fs", avg_time) - - def get_performance_stats(self) -> dict[str, Any]: - """Get engine performance statistics.""" - return self.stats.copy() - - def run(self, manifest: dict[str, Any], outdir: Path | str) -> dict[str, Any]: - """ - Manifest-driven executor. - - This method expands the provided manifest (as produced by the CLI) into - BacktestConfig objects and runs batch backtests for each requested - (interval x strategies x symbols) combination. Results are persisted - to the DB via src.database.unified_models (best-effort). - - Returns a summary dict with counts and plan_hash. - """ - import json as _json - from pathlib import Path as _Path - - outdir = _Path(outdir) - outdir.mkdir(parents=True, exist_ok=True) - - plan = manifest.get("plan", {}) - symbols = plan.get("symbols", []) or [] - strategies = plan.get("strategies", []) or [] - intervals = plan.get("intervals", []) or ["1d"] - start = plan.get("start") - end = plan.get("end") - period_mode = plan.get("period_mode", "max") - plan_hash = plan.get("plan_hash") - target_metric = plan.get("metric", DEFAULT_METRIC) - - # Resolve period_mode -> if 'max' leave start/end None so data manager uses full range - if period_mode == "max": - start_date = None - end_date = None - else: - start_date = start - end_date = end - - # Create run row in DB (best-effort) - run_obj = None - run_id = None - try: - from src.database import unified_models # type: ignore[import-not-found] - - try: - # Prefer robust ensure_run_for_manifest which will attempt fallback creation - if hasattr(unified_models, "ensure_run_for_manifest"): - run_obj = unified_models.ensure_run_for_manifest(manifest) - else: - run_obj = unified_models.create_run_from_manifest(manifest) - run_id = getattr(run_obj, "run_id", None) - except Exception: - run_obj = None - run_id = None - except Exception: - run_obj = None - run_id = None - - # Prepare a persistence_context passed to lower-level helpers - # If run_id couldn't be created/resolved, disable persistence to avoid null run_id inserts. - if run_id is None: - persistence_context = None - else: - persistence_context = { - "run_id": run_id, - "target_metric": target_metric, - "plan_hash": plan_hash, - } - - total_results = 0 - errors = 0 - persisted = 0 - results_summary = [] - - # For each interval, create a BacktestConfig and run batch backtests - for interval in intervals: - try: - # Respect export flags from manifest so workers capture trades/equity when requested. - exports = plan.get("exports", []) or [] - if isinstance(exports, str): - exports = [exports] - - # Capture trades by default when DB persistence is active so we can store - # detailed executions into unified_models (trades table and trades_raw). - # Still honor explicit exports flags when provided. - save_trades = ( - "all" in exports - or "trades" in exports - or "trade" in exports - or (persistence_context is not None) - ) - save_equity = ( - "all" in exports or "equity" in exports or "equity_curve" in exports - ) - - cfg_kwargs = { - "symbols": symbols, - "strategies": strategies, - "start_date": start_date, - "end_date": end_date, - "period": period_mode, - "initial_capital": plan.get("initial_capital", 10000), - "interval": interval, - "max_workers": plan.get("max_workers", None), - # propagate strategies_path from manifest so workers can initialize loaders - "strategies_path": plan.get("strategies_path"), - "save_trades": save_trades, - "save_equity_curve": save_equity, - } - # Build BacktestConfig - try: - cfg = BacktestConfig(**cfg_kwargs) - except Exception: - # Fallback: construct minimal config object-like dict - class _TmpCfg: - def __init__(self, **kw): - self.__dict__.update(kw) - - cfg = _TmpCfg(**cfg_kwargs) - - # Ensure fallback config has expected attributes with sensible defaults - # so later code can access them regardless of how cfg was constructed. - _defaults = { - "initial_capital": 10000, - "interval": getattr(cfg, "interval", "1d"), - "max_workers": getattr(cfg, "max_workers", None), - "use_cache": getattr(cfg, "use_cache", True), - "commission": getattr(cfg, "commission", 0.001), - "save_trades": getattr(cfg, "save_trades", False), - "save_equity_curve": getattr(cfg, "save_equity_curve", False), - "override_old_trades": getattr(cfg, "override_old_trades", True), - "memory_limit_gb": getattr(cfg, "memory_limit_gb", 8.0), - "asset_type": getattr(cfg, "asset_type", None), - "futures_mode": getattr(cfg, "futures_mode", False), - "leverage": getattr(cfg, "leverage", 1.0), - } - for _k, _v in _defaults.items(): - if not hasattr(cfg, _k): - try: - setattr(cfg, _k, _v) - except Exception: - # be defensive if cfg disallows setattr - pass - - # Run batch - batch_results = self.run_batch_backtests(cfg) - total_results += len(batch_results) - - # Persist individual results (best-effort) using direct_backtest helper - try: - import src.core.direct_backtest as direct_mod # type: ignore[import-not-found] - - # Only attempt persistence when we have a valid persistence_context (run_id resolved) - if persistence_context: - for r in batch_results: - # Map BacktestResult dataclass to expected dict for persistence - rd = { - "symbol": r.symbol, - "strategy": r.strategy, - "timeframe": getattr(r.config, "interval", interval), - "metrics": r.metrics or {}, - "trades": r.trades if hasattr(r, "trades") else None, - "bt_results": getattr(r, "bt_results_raw", None), - "start_date": getattr(r, "start_date", start_date), - "end_date": getattr(r, "end_date", end_date), - "error": getattr(r, "error", None), - } - - # Force a persistence stub if worker returned no metrics/trades/bt_results - # but did not set an explicit error. This ensures full lineage for the run. - if ( - not rd.get("metrics") - and not rd.get("trades") - and not rd.get("bt_results") - and not rd.get("error") - ): - rd["error"] = "no_result" - - try: - direct_mod._persist_result_to_db( - rd, persistence_context - ) - persisted += 1 - except Exception: - errors += 1 - else: - # Persistence disabled (no run_id); skip storing individual results - pass - except Exception: - # If persistence helper unavailable, skip persistence but continue - pass - - # Summarize top strategies for this interval - for r in batch_results[:5]: - results_summary.append( - { - "symbol": r.symbol, - "strategy": r.strategy, - "interval": getattr(r.config, "interval", interval), - "metric": (r.metrics or {}).get(target_metric), - "error": getattr(r, "error", None), - } - ) - - except Exception as e: - errors += 1 - logging.getLogger(__name__).exception( - "Failed running interval %s: %s", interval, e - ) - continue - - summary = { - "plan_hash": plan_hash, - "total_results": total_results, - "persisted": persisted, - "errors": errors, - "results_sample": results_summary, - } - - # Best-effort: finalize ranks/aggregates and upsert BestStrategy rows into unified_models - try: - if run_id is not None and target_metric: - try: - from src.database import ( - unified_models, # type: ignore[import-not-found] - ) - - sess = unified_models.Session() - try: - # Get distinct symbols for run - symbols = ( - sess.query(unified_models.BacktestResult.symbol) - .filter(unified_models.BacktestResult.run_id == run_id) - .distinct() - .all() - ) - symbols = [s[0] for s in symbols] - - def _is_higher_better(metric_name: str) -> bool: - mn = (metric_name or "").lower() - if "drawdown" in mn or "max_drawdown" in mn or "mdd" in mn: - return False - return True - - for symbol in symbols: - rows = ( - sess.query(unified_models.BacktestResult) - .filter( - unified_models.BacktestResult.run_id == run_id, - unified_models.BacktestResult.symbol == symbol, - ) - .all() - ) - - entries = [] - higher_better = _is_higher_better(target_metric) - for r in rows: - mval = None - try: - if r.metrics and isinstance(r.metrics, dict): - raw = r.metrics.get(target_metric) - mval = None if raw is None else float(raw) - except Exception as exc: - logging.getLogger(__name__).debug( - "Failed to parse metric %s: %s", - target_metric, - exc, - ) - # Treat None as worst - sort_key = ( - float("-inf") - if higher_better - else float("inf") - if mval is None - else mval - ) - if mval is None: - sort_key = ( - float("-inf") if higher_better else float("inf") - ) - entries.append((sort_key, mval is None, r)) - - # Sort and assign ranks - entries.sort(key=lambda x: x[0], reverse=higher_better) - for idx, (_sort_key, _is_null, row) in enumerate(entries): - try: - row.rank_in_symbol = idx + 1 - sess.add(row) - except Exception: - pass - - # Persist SymbolAggregate and BestStrategy for top entry - if entries: - best_row = entries[0][2] - topn = [] - for e in entries[:3]: - r = e[2] - topn.append( - { - "strategy": r.strategy, - "interval": r.interval, - "rank": r.rank_in_symbol, - "metric": None - if r.metrics is None - else r.metrics.get(target_metric), - } - ) - existing_agg = ( - sess.query(unified_models.SymbolAggregate) - .filter( - unified_models.SymbolAggregate.run_id == run_id, - unified_models.SymbolAggregate.symbol == symbol, - unified_models.SymbolAggregate.best_by - == target_metric, - ) - .one_or_none() - ) - summary_json = {"top": topn} - if existing_agg: - existing_agg.best_result = best_row.result_id - existing_agg.summary = summary_json - sess.add(existing_agg) - else: - agg = unified_models.SymbolAggregate( - run_id=run_id, - symbol=symbol, - best_by=target_metric, - best_result=best_row.result_id, - summary=summary_json, - ) - sess.add(agg) - - # Upsert BestStrategy - try: - bs_existing = ( - sess.query(unified_models.BestStrategy) - .filter( - unified_models.BestStrategy.symbol - == symbol, - unified_models.BestStrategy.timeframe - == best_row.interval, - ) - .one_or_none() - ) - - def _num(mdict, key): - try: - if mdict and isinstance(mdict, dict): - v = mdict.get(key) - return ( - float(v) if v is not None else None - ) - except Exception: - return None - return None - - sortino_val = _num( - best_row.metrics, "sortino_ratio" - ) or _num(best_row.metrics, "Sortino_Ratio") - calmar_val = _num( - best_row.metrics, "calmar_ratio" - ) or _num(best_row.metrics, "Calmar_Ratio") - sharpe_val = _num( - best_row.metrics, "sharpe_ratio" - ) or _num(best_row.metrics, "Sharpe_Ratio") - total_return_val = _num( - best_row.metrics, "total_return" - ) or _num(best_row.metrics, "Total_Return") - max_dd_val = _num( - best_row.metrics, "max_drawdown" - ) or _num(best_row.metrics, "Max_Drawdown") - - if bs_existing: - bs_existing.strategy = best_row.strategy - bs_existing.sortino_ratio = sortino_val - bs_existing.calmar_ratio = calmar_val - bs_existing.sharpe_ratio = sharpe_val - bs_existing.total_return = total_return_val - bs_existing.max_drawdown = max_dd_val - bs_existing.backtest_result_id = getattr( - best_row, "result_id", None - ) - bs_existing.updated_at = datetime.utcnow() - sess.add(bs_existing) - else: - bs = unified_models.BestStrategy( - symbol=symbol, - timeframe=best_row.interval, - strategy=best_row.strategy, - sortino_ratio=sortino_val, - calmar_ratio=calmar_val, - sharpe_ratio=sharpe_val, - total_return=total_return_val, - max_drawdown=max_dd_val, - backtest_result_id=getattr( - best_row, "result_id", None - ), - updated_at=datetime.utcnow(), - ) - sess.add(bs) - except Exception: - logging.getLogger(__name__).exception( - "Failed to upsert BestStrategy for %s", symbol - ) - - sess.commit() - finally: - try: - sess.close() - except Exception: - pass - except Exception: - logging.getLogger(__name__).exception( - "Failed to finalize BestStrategy for run %s", run_id - ) - except Exception: - # Non-fatal: continue even if finalization fails - pass - - # Write summary file - try: - summary_path = outdir / "engine_run_summary.json" - with summary_path.open("w", encoding="utf-8") as fh: - _json.dump(summary, fh, indent=2, sort_keys=True, ensure_ascii=False) - except Exception: - pass - - return summary - - def clear_cache(self, symbol: str | None = None, strategy: str | None = None): - """Clear cached results.""" - self.cache_manager.clear_cache(cache_type="backtest", symbol=symbol) - - def _prepare_data_for_backtesting_lib(self, data: pd.DataFrame) -> pd.DataFrame: - """Prepare data for the backtesting library (requires uppercase OHLCV columns).""" - try: - # Check if we have lowercase columns and convert them - if all( - col in data.columns - for col in ["open", "high", "low", "close", "volume"] - ): - bt_data = data.rename( - columns={ - "open": "Open", - "high": "High", - "low": "Low", - "close": "Close", - "volume": "Volume", - } - )[["Open", "High", "Low", "Close", "Volume"]].copy() - # Check if we already have uppercase columns - elif all( - col in data.columns - for col in ["Open", "High", "Low", "Close", "Volume"] - ): - bt_data = data[["Open", "High", "Low", "Close", "Volume"]].copy() - else: - self.logger.error("Missing required OHLCV columns in data") - return None - - # Ensure no NaN values - bt_data = bt_data.dropna() - - return bt_data - - except Exception as e: - self.logger.error("Error preparing data for backtesting library: %s", e) - return None - - def _extract_metrics_from_bt_results(self, bt_results) -> dict[str, Any]: - """Extract metrics from backtesting library results. - - This function is defensive: backtesting library results may contain pandas - Timestamps/Timedeltas or other non-scalar types. Coerce values to floats - where sensible and fall back to None/0 when conversion fails. - """ - import math - - try: - - def _as_float(v): - """Safely coerce a value to float or return None.""" - if v is None: - return None - # Already a float/int - if isinstance(v, (int, float)): - if isinstance(v, bool): - return float(v) - if math.isfinite(v): - return float(v) - return None - # Numpy numeric types - try: - import numpy as _np - - if isinstance(v, _np.generic): - return float(v.item()) - except Exception: - pass - # Pandas Timestamp/Timedelta -> convert to numeric where appropriate - try: - import pandas as _pd - - if isinstance(v, _pd.Timedelta): - # convert to total days as a numeric proxy (timedeltas appear for volatility sometimes) - try: - return float(v.total_seconds()) - except Exception: - return None - if isinstance(v, _pd.Timestamp): - # Timestamp is not numeric; return None - return None - except Exception: - pass - # Strings that may include percent signs or commas - if isinstance(v, str): - try: - s = v.strip().replace("%", "").replace(",", "") - return float(s) - except Exception: - return None - # Fallback: try numeric conversion - try: - return float(v) - except Exception: - return None - - def _get_first(keys, default=None): - for k in keys: - try: - if isinstance(bt_results, dict) and k in bt_results: - return bt_results.get(k) - except Exception: - pass - return default - - # Map keys with fallbacks - total_return = ( - _as_float( - _get_first(["Return [%]", "Total_Return", "total_return"], 0.0) - ) - or 0.0 - ) - sharpe = ( - _as_float( - _get_first(["Sharpe Ratio", "Sharpe_Ratio", "sharpe_ratio"], 0.0) - ) - or 0.0 - ) - sortino = ( - _as_float( - _get_first(["Sortino Ratio", "Sortino_Ratio", "sortino_ratio"], 0.0) - ) - or 0.0 - ) - calmar = ( - _as_float( - _get_first(["Calmar Ratio", "Calmar_Ratio", "calmar_ratio"], 0.0) - ) - or 0.0 - ) - max_dd = _as_float( - _get_first(["Max. Drawdown [%]", "Max_Drawdown", "max_drawdown"], 0.0) - ) - max_dd = 0.0 if max_dd is None else abs(max_dd) - volatility = ( - _as_float(_get_first(["Volatility [%]", "volatility"], 0.0)) or 0.0 - ) - num_trades = _get_first(["# Trades", "num_trades", "Trades"], 0) or 0 - try: - num_trades = int(num_trades) - except Exception: - num_trades = 0 - win_rate = _as_float(_get_first(["Win Rate [%]", "win_rate"], 0.0)) or 0.0 - profit_factor = ( - _as_float(_get_first(["Profit Factor", "profit_factor"], 1.0)) or 1.0 - ) - best_trade = ( - _as_float(_get_first(["Best Trade [%]", "best_trade"], 0.0)) or 0.0 - ) - worst_trade = ( - _as_float(_get_first(["Worst Trade [%]", "worst_trade"], 0.0)) or 0.0 - ) - avg_trade = ( - _as_float(_get_first(["Avg. Trade [%]", "avg_trade"], 0.0)) or 0.0 - ) - avg_trade_duration = ( - _as_float( - _get_first(["Avg. Trade Duration", "avg_trade_duration"], 0.0) - ) - or 0.0 - ) - start_value = _as_float(_get_first(["Start", "start_value"], 0.0)) or 0.0 - end_value = ( - _as_float( - _get_first(["End", "end_value", "Equity Final [$]"], start_value) - ) - or start_value - ) - buy_hold = ( - _as_float(_get_first(["Buy & Hold Return [%]", "buy_hold_return"], 0.0)) - or 0.0 - ) - exposure = ( - _as_float(_get_first(["Exposure Time [%]", "exposure_time"], 0.0)) - or 0.0 - ) - - metrics = { - "total_return": total_return, - "sharpe_ratio": sharpe, - "sortino_ratio": sortino, - "calmar_ratio": calmar, - "max_drawdown": max_dd, - "volatility": volatility, - "num_trades": num_trades, - "win_rate": win_rate, - "profit_factor": profit_factor, - "best_trade": best_trade, - "worst_trade": worst_trade, - "avg_trade": avg_trade, - "avg_trade_duration": avg_trade_duration, - "start_value": start_value, - "end_value": end_value, - "buy_hold_return": buy_hold, - "exposure_time": exposure, - } - - return metrics - except Exception as e: - self.logger.error( - "Error extracting metrics from backtesting results: %s", e - ) - return {} - - def _convert_backtesting_results( - self, bt_results, bt_data: pd.DataFrame, config: BacktestConfig - ) -> dict[str, Any]: - """Convert backtesting library results to our internal format.""" - try: - # Get trades from backtesting library - trades_df = None - equity_curve_df = None - - # Try to get trades if available - try: - if hasattr(bt_results, "_trades") and bt_results._trades is not None: - trades_df = bt_results._trades.copy() - - # Get equity curve from backtesting library - if ( - hasattr(bt_results, "_equity_curve") - and bt_results._equity_curve is not None - ): - equity_curve_df = bt_results._equity_curve.copy() - - except Exception as e: - self.logger.debug("Could not extract detailed trade data: %s", e) - - result = { - "trades": trades_df, - "equity_curve": equity_curve_df, - "final_value": float(bt_results.get("End", config.initial_capital)), - "total_trades": int(bt_results.get("# Trades", 0)), - } - - return result - - except Exception as e: - self.logger.error("Error converting backtesting results: %s", e) - return { - "trades": None, - "equity_curve": None, - "final_value": config.initial_capital, - "total_trades": 0, - } diff --git a/src/core/cache_manager.py b/src/core/cache_manager.py deleted file mode 100644 index 2d6a271..0000000 --- a/src/core/cache_manager.py +++ /dev/null @@ -1,833 +0,0 @@ -""" -Unified Cache Manager - Consolidates all caching functionality. -Supports data, backtest results, and optimization caching with intelligent management. -""" - -from __future__ import annotations - -import gzip -import hashlib -import json -import logging -import os -import pickle -import sqlite3 -import threading -from dataclasses import dataclass -from datetime import datetime, timedelta -from pathlib import Path -from typing import Any - -import pandas as pd - -# Optional Redis for recent overlay cache -try: - import redis as _redis # type: ignore[import-not-found] -except Exception: # pragma: no cover - optional - _redis = None - - -@dataclass -class CacheEntry: - """Cache entry metadata.""" - - key: str - cache_type: str # 'data', 'backtest', 'optimization' - symbol: str - created_at: datetime - last_accessed: datetime - expires_at: datetime | None - size_bytes: int - source: str | None = None - interval: str | None = None - data_type: str | None = None # 'spot', 'futures', etc. - parameters_hash: str | None = None - version: str = "1.0" - - -class UnifiedCacheManager: - """ - Unified cache manager that consolidates all caching functionality. - Handles data caching, backtest results, and optimization results. - """ - - def __init__(self, cache_dir: str = "cache", max_size_gb: float = 10.0): - self.cache_dir = Path(cache_dir) - self.max_size_bytes = int(max_size_gb * 1024**3) - self.lock = threading.RLock() - - # Create directory structure - self.data_dir = self.cache_dir / "data" - self.backtest_dir = self.cache_dir / "backtests" - self.optimization_dir = self.cache_dir / "optimizations" - self.metadata_db = self.cache_dir / "cache.db" - - for dir_path in [self.data_dir, self.backtest_dir, self.optimization_dir]: - dir_path.mkdir(parents=True, exist_ok=True) - - self._init_database() - self.logger = logging.getLogger(__name__) - - # Optional Redis client for recent overlay layer - self.redis_client = None - try: - use_redis = os.getenv("USE_REDIS_RECENT", "false").lower() == "true" - redis_url = os.getenv("REDIS_URL", "") - if use_redis and _redis is not None and redis_url: - self.redis_client = _redis.from_url(redis_url, decode_responses=False) - # ping to verify - try: - self.redis_client.ping() - self.logger.info("Redis recent overlay enabled (%s)", redis_url) - except Exception: - self.redis_client = None - except Exception: - self.redis_client = None - - def _init_database(self) -> None: - """Initialize SQLite database for metadata.""" - with sqlite3.connect(self.metadata_db) as conn: - conn.execute( - """ - CREATE TABLE IF NOT EXISTS cache_entries ( - key TEXT PRIMARY KEY, - cache_type TEXT NOT NULL, - symbol TEXT NOT NULL, - created_at TEXT NOT NULL, - last_accessed TEXT NOT NULL, - expires_at TEXT, - size_bytes INTEGER NOT NULL, - source TEXT, - interval TEXT, - data_type TEXT, - parameters_hash TEXT, - version TEXT DEFAULT '1.0', - file_path TEXT NOT NULL - ) - """ - ) - - # Create indexes for performance - indexes = [ - "CREATE INDEX IF NOT EXISTS idx_cache_type ON cache_entries (cache_type)", - "CREATE INDEX IF NOT EXISTS idx_symbol ON cache_entries (symbol)", - "CREATE INDEX IF NOT EXISTS idx_expires_at ON cache_entries (expires_at)", - "CREATE INDEX IF NOT EXISTS idx_last_accessed ON cache_entries (last_accessed)", - "CREATE INDEX IF NOT EXISTS idx_source ON cache_entries (source)", - "CREATE INDEX IF NOT EXISTS idx_data_type ON cache_entries (data_type)", - ] - - for index_sql in indexes: - conn.execute(index_sql) - - def cache_data( - self, - symbol: str, - data: pd.DataFrame, - interval: str = "1d", - source: str | None = None, - data_type: str | None = None, - ttl_hours: int = 48, - ) -> str: - """ - Cache market data. - - Args: - symbol: Symbol identifier - data: DataFrame with OHLCV data - interval: Data interval - source: Data source name - data_type: Data type ('spot', 'futures', etc.) - ttl_hours: Time to live in hours - - Returns: - Cache key - """ - with self.lock: - key = self._generate_key( - "data", - symbol=symbol, - interval=interval, - source=source, - data_type=data_type, - ) - - file_path = self._get_file_path("data", key) - compressed_data = self._compress_data(data) - - # Write compressed data - file_path.write_bytes(compressed_data) - - # Create cache entry - now = datetime.now() - entry = CacheEntry( - key=key, - cache_type="data", - symbol=symbol, - created_at=now, - last_accessed=now, - expires_at=now + timedelta(hours=ttl_hours), - size_bytes=len(compressed_data), - source=source, - interval=interval, - data_type=data_type, - ) - - self._save_entry(entry, file_path) - self._cleanup_if_needed() - - return key - - def get_data( - self, - symbol: str, - start_date: str | None = None, - end_date: str | None = None, - interval: str = "1d", - source: str | None = None, - data_type: str | None = None, - ) -> pd.DataFrame | None: - """ - Retrieve cached market data. - - Args: - symbol: Symbol identifier - start_date: Optional start date filter - end_date: Optional end date filter - interval: Data interval - source: Optional source filter - data_type: Optional data type filter - - Returns: - DataFrame or None if not found/expired - """ - with self.lock: - # Find matching cache entries - entries = self._find_entries( - "data", - symbol=symbol, - interval=interval, - source=source, - data_type=data_type, - ) - - if not entries: - return None - - # Get the most recent non-expired entry - valid_entries = [e for e in entries if not self._is_expired(e)] - if not valid_entries: - # Clean up expired entries - for entry in entries: - self._remove_entry(entry.key) - return None - - # Sort by creation date (most recent first) - valid_entries.sort(key=lambda x: x.created_at, reverse=True) - entry = valid_entries[0] - - # Load and decompress data - file_path = self._get_file_path("data", entry.key) - if not file_path.exists(): - self._remove_entry(entry.key) - return None - - try: - compressed_data = file_path.read_bytes() - data = self._decompress_data(compressed_data) - - # Update access time - self._update_access_time(entry.key) - - # Filter by date range if specified - if start_date or end_date: - if start_date: - start = pd.to_datetime(start_date, utc=True) - # If data index is timezone-aware, ensure comparison consistency - if hasattr(data.index, "tz") and data.index.tz is not None: - if start.tz is None: - start = start.tz_localize("UTC") - else: - # If data index is timezone-naive but start is aware, make start naive - if start.tz is not None: - start = start.tz_localize(None) - data = data[data.index >= start] - if end_date: - end = pd.to_datetime(end_date, utc=True) - # If data index is timezone-aware, ensure comparison consistency - if hasattr(data.index, "tz") and data.index.tz is not None: - if end.tz is None: - end = end.tz_localize("UTC") - else: - # If data index is timezone-naive but end is aware, make end naive - if end.tz is not None: - end = end.tz_localize(None) - data = data[data.index <= end] - - return data if not data.empty else None - - except Exception as e: - self.logger.warning("Failed to load cached data for %s: %s", symbol, e) - self._remove_entry(entry.key) - return None - - def cache_backtest_result( - self, - symbol: str, - strategy: str, - parameters: dict[str, Any], - result: dict[str, Any], - interval: str = "1d", - ttl_days: int = 30, - ) -> str: - """Cache backtest result.""" - with self.lock: - params_hash = self._hash_parameters(parameters) - key = self._generate_key( - "backtest", - symbol=symbol, - strategy=strategy, - parameters_hash=params_hash, - interval=interval, - ) - - file_path = self._get_file_path("backtest", key) - - # Add metadata to result - result_with_meta = { - "result": result, - "symbol": symbol, - "strategy": strategy, - "parameters": parameters, - "interval": interval, - "cached_at": datetime.now().isoformat(), - } - - compressed_data = self._compress_data(result_with_meta) - file_path.write_bytes(compressed_data) - - # Create cache entry - now = datetime.now() - entry = CacheEntry( - key=key, - cache_type="backtest", - symbol=symbol, - created_at=now, - last_accessed=now, - expires_at=now + timedelta(days=ttl_days), - size_bytes=len(compressed_data), - interval=interval, - parameters_hash=params_hash, - ) - - self._save_entry(entry, file_path) - self._cleanup_if_needed() - - return key - - def get_backtest_result( - self, - symbol: str, - strategy: str, - parameters: dict[str, Any], - interval: str = "1d", - ) -> dict[str, Any] | None: - """Retrieve cached backtest result.""" - with self.lock: - params_hash = self._hash_parameters(parameters) - entries = self._find_entries( - "backtest", - symbol=symbol, - parameters_hash=params_hash, - interval=interval, - ) - - if not entries: - return None - - # Get the most recent non-expired entry - valid_entries = [e for e in entries if not self._is_expired(e)] - if not valid_entries: - for entry in entries: - self._remove_entry(entry.key) - return None - - entry = valid_entries[0] - file_path = self._get_file_path("backtest", entry.key) - - if not file_path.exists(): - self._remove_entry(entry.key) - return None - - try: - compressed_data = file_path.read_bytes() - cached_data = self._decompress_data(compressed_data) - - self._update_access_time(entry.key) - result = cached_data.get("result") - return result if result is not None else {} - - except Exception as e: - self.logger.warning("Failed to load cached backtest: %s", e) - self._remove_entry(entry.key) - return None - - def cache_optimization_result( - self, - symbol: str, - strategy: str, - optimization_config: dict[str, Any], - result: dict[str, Any], - interval: str = "1d", - ttl_days: int = 60, - ) -> str: - """Cache optimization result.""" - with self.lock: - config_hash = self._hash_parameters(optimization_config) - key = self._generate_key( - "optimization", - symbol=symbol, - strategy=strategy, - parameters_hash=config_hash, - interval=interval, - ) - - file_path = self._get_file_path("optimization", key) - - result_with_meta = { - "result": result, - "symbol": symbol, - "strategy": strategy, - "optimization_config": optimization_config, - "interval": interval, - "cached_at": datetime.now().isoformat(), - } - - compressed_data = self._compress_data(result_with_meta) - file_path.write_bytes(compressed_data) - - # Create cache entry - now = datetime.now() - entry = CacheEntry( - key=key, - cache_type="optimization", - symbol=symbol, - created_at=now, - last_accessed=now, - expires_at=now + timedelta(days=ttl_days), - size_bytes=len(compressed_data), - interval=interval, - parameters_hash=config_hash, - ) - - self._save_entry(entry, file_path) - self._cleanup_if_needed() - - return key - - def get_optimization_result( - self, - symbol: str, - strategy: str, - optimization_config: dict[str, Any], - interval: str = "1d", - ) -> dict[str, Any] | None: - """Retrieve cached optimization result.""" - with self.lock: - config_hash = self._hash_parameters(optimization_config) - entries = self._find_entries( - "optimization", - symbol=symbol, - parameters_hash=config_hash, - interval=interval, - ) - - if not entries: - return None - - valid_entries = [e for e in entries if not self._is_expired(e)] - if not valid_entries: - for entry in entries: - self._remove_entry(entry.key) - return None - - entry = valid_entries[0] - file_path = self._get_file_path("optimization", entry.key) - - if not file_path.exists(): - self._remove_entry(entry.key) - return None - - try: - compressed_data = file_path.read_bytes() - cached_data = self._decompress_data(compressed_data) - - self._update_access_time(entry.key) - result = cached_data.get("result") - return result if result is not None else {} - - except Exception as e: - self.logger.warning("Failed to load cached optimization: %s", e) - self._remove_entry(entry.key) - return None - - def clear_cache( - self, - cache_type: str | None = None, - symbol: str | None = None, - source: str | None = None, - older_than_days: int | None = None, - ) -> None: - """Clear cache entries based on filters.""" - with self.lock: - conditions = [] - params = [] - - if cache_type: - conditions.append("cache_type = ?") - params.append(cache_type) - - if symbol: - conditions.append("symbol = ?") - params.append(symbol) - - if source: - conditions.append("source = ?") - params.append(source) - - if older_than_days: - cutoff = (datetime.now() - timedelta(days=older_than_days)).isoformat() - conditions.append("created_at < ?") - params.append(cutoff) - - where_clause = " AND ".join(conditions) if conditions else "1=1" - - with sqlite3.connect(self.metadata_db) as conn: - # Use parameterized query to prevent SQL injection - if conditions: - query = f"SELECT key, cache_type FROM cache_entries WHERE {where_clause}" # nosec B608 - else: - query = "SELECT key, cache_type FROM cache_entries" - cursor = conn.execute(query, params) - - entries_to_remove = cursor.fetchall() - - # Remove files - for key, ct in entries_to_remove: - file_path = self._get_file_path(ct, key) - if file_path.exists(): - file_path.unlink() - - # Remove metadata - if conditions: - delete_query = f"DELETE FROM cache_entries WHERE {where_clause}" # nosec B608 - else: - delete_query = "DELETE FROM cache_entries" - conn.execute(delete_query, params) - - self.logger.info("Cleared %s cache entries", len(entries_to_remove)) - - def get_cache_stats(self) -> dict[str, Any]: - """Get comprehensive cache statistics.""" - with sqlite3.connect(self.metadata_db) as conn: - # Overall stats - cursor = conn.execute( - """ - SELECT - cache_type, - COUNT(*) as count, - SUM(size_bytes) as total_size, - AVG(size_bytes) as avg_size, - MIN(created_at) as oldest, - MAX(created_at) as newest - FROM cache_entries - GROUP BY cache_type - """ - ) - - stats_by_type = {} - total_size = 0 - - for row in cursor: - cache_type, count, size_sum, avg_size, oldest, newest = row - size_sum = size_sum or 0 - total_size += size_sum - - stats_by_type[cache_type] = { - "count": count, - "total_size_bytes": size_sum, - "total_size_mb": size_sum / 1024**2, - "avg_size_bytes": avg_size or 0, - "oldest": oldest, - "newest": newest, - } - - # Source distribution for data cache - cursor = conn.execute( - """ - SELECT source, COUNT(*), SUM(size_bytes) - FROM cache_entries - WHERE cache_type = 'data' AND source IS NOT NULL - GROUP BY source - """ - ) - - source_stats = {} - for source, count, size_sum in cursor: - source_stats[source] = {"count": count, "size_bytes": size_sum or 0} - - return { - "total_size_bytes": total_size, - "total_size_mb": total_size / 1024**2, - "total_size_gb": total_size / 1024**3, - "max_size_gb": self.max_size_bytes / 1024**3, - "utilization_percent": (total_size / self.max_size_bytes) * 100, - "by_type": stats_by_type, - "by_source": source_stats, - } - - def _generate_key(self, cache_type: str, **kwargs: Any) -> str: - """Generate unique cache key.""" - key_parts = [cache_type] - for k, v in sorted(kwargs.items()): - if v is not None: - key_parts.append(f"{k}={v}") - - key_string = "|".join(key_parts) - return hashlib.sha256(key_string.encode()).hexdigest() - - def _get_file_path(self, cache_type: str, key: str) -> Path: - """Get file path for cache entry.""" - if cache_type == "data": - return self.data_dir / f"{key}.gz" - if cache_type == "backtest": - return self.backtest_dir / f"{key}.gz" - if cache_type == "optimization": - return self.optimization_dir / f"{key}.gz" - msg = f"Unknown cache type: {cache_type}" - raise ValueError(msg) - - def _compress_data(self, data: Any) -> bytes: - """Compress data using gzip.""" - serialized = pickle.dumps(data) - - return gzip.compress(serialized) - - def _decompress_data(self, compressed_data: bytes) -> Any: - """Decompress data.""" - decompressed = gzip.decompress(compressed_data) - # Note: pickle.loads() can be unsafe with untrusted data - # In production, consider using safer serialization formats - return pickle.loads(decompressed) # nosec B301 - - # -------- Optional Redis recent overlay helpers --------- - def _redis_recent_key(self, symbol: str, interval: str) -> str: - return f"data:recent:{symbol}:{interval}" - - def get_recent_overlay_from_redis( - self, symbol: str, interval: str - ) -> pd.DataFrame | None: - try: - if not self.redis_client: - return None - key = self._redis_recent_key(symbol, interval) - blob = self.redis_client.get(key) - if not blob: - return None - data = self._decompress_data(blob) - if isinstance(data, pd.DataFrame) and not data.empty: - return data - return None - except Exception: - return None - - def set_recent_overlay_to_redis( - self, symbol: str, interval: str, df: pd.DataFrame, ttl_hours: int = 24 - ) -> None: - try: - if not self.redis_client or df is None or df.empty: - return - key = self._redis_recent_key(symbol, interval) - blob = self._compress_data(df) - self.redis_client.setex(key, int(ttl_hours * 3600), blob) - except Exception: - return - - def _hash_parameters(self, parameters: dict[str, Any]) -> str: - """Generate hash for parameters.""" - params_str = json.dumps(parameters, sort_keys=True) - return hashlib.sha256(params_str.encode()).hexdigest()[:16] - - def _save_entry(self, entry: CacheEntry, file_path: Path) -> None: - """Save cache entry metadata.""" - with sqlite3.connect(self.metadata_db) as conn: - conn.execute( - """ - INSERT OR REPLACE INTO cache_entries - (key, cache_type, symbol, created_at, last_accessed, expires_at, - size_bytes, source, interval, data_type, parameters_hash, version, file_path) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - entry.key, - entry.cache_type, - entry.symbol, - entry.created_at.isoformat(), - entry.last_accessed.isoformat(), - entry.expires_at.isoformat() if entry.expires_at else None, - entry.size_bytes, - entry.source, - entry.interval, - entry.data_type, - entry.parameters_hash, - entry.version, - str(file_path), - ), - ) - - def _find_entries(self, cache_type: str, **filters: Any) -> list[CacheEntry]: - """Find cache entries matching filters.""" - conditions = ["cache_type = ?"] - params = [cache_type] - - for key, value in filters.items(): - if value is not None: - conditions.append(f"{key} = ?") - params.append(value) - - where_clause = " AND ".join(conditions) - - with sqlite3.connect(self.metadata_db) as conn: - # Use parameterized query to prevent SQL injection - query = f"SELECT * FROM cache_entries WHERE {where_clause}" # nosec B608 - cursor = conn.execute(query, params) - - entries = [] - for row in cursor: - entry = CacheEntry( - key=row[0], - cache_type=row[1], - symbol=row[2], - created_at=datetime.fromisoformat(row[3]).replace(tzinfo=None), - last_accessed=datetime.fromisoformat(row[4]).replace(tzinfo=None), - expires_at=datetime.fromisoformat(row[5]).replace(tzinfo=None) - if row[5] - else None, - size_bytes=row[6], - source=row[7], - interval=row[8], - data_type=row[9], - parameters_hash=row[10], - version=row[11], - ) - entries.append(entry) - - return entries - - def _is_expired(self, entry: CacheEntry) -> bool: - """Check if cache entry is expired.""" - if not entry.expires_at: - return False - - from datetime import timezone - - # Always use UTC for consistent comparison - now = datetime.now(timezone.utc).replace(tzinfo=None) - expires_at = entry.expires_at - - return now > expires_at - - def _update_access_time(self, key: str) -> None: - """Update last access time.""" - from datetime import timezone - - with sqlite3.connect(self.metadata_db) as conn: - conn.execute( - "UPDATE cache_entries SET last_accessed = ? WHERE key = ?", - (datetime.now(timezone.utc).replace(tzinfo=None).isoformat(), key), - ) - - def _remove_entry(self, key: str) -> None: - """Remove cache entry and its file.""" - with sqlite3.connect(self.metadata_db) as conn: - cursor = conn.execute( - "SELECT cache_type FROM cache_entries WHERE key = ?", (key,) - ) - row = cursor.fetchone() - - if row: - cache_type = row[0] - file_path = self._get_file_path(cache_type, key) - if file_path.exists(): - file_path.unlink() - - conn.execute("DELETE FROM cache_entries WHERE key = ?", (key,)) - - def _cleanup_if_needed(self) -> None: - """Clean up cache if size exceeds limit.""" - stats = self.get_cache_stats() - total_size = stats["total_size_bytes"] - - if total_size > self.max_size_bytes: - self.logger.info( - "Cache size (%.2f GB) exceeds limit, cleaning up...", - total_size / 1024**3, - ) - - # Remove expired entries first - self._cleanup_expired() - - # If still over limit, remove LRU entries - stats = self.get_cache_stats() - if stats["total_size_bytes"] > self.max_size_bytes: - self._cleanup_lru() - - def _cleanup_expired(self) -> None: - """Remove expired cache entries.""" - now = datetime.now().isoformat() - - with sqlite3.connect(self.metadata_db) as conn: - cursor = conn.execute( - "SELECT key, cache_type FROM cache_entries WHERE expires_at < ?", (now,) - ) - - expired_entries = cursor.fetchall() - - for key, cache_type in expired_entries: - file_path = self._get_file_path(cache_type, key) - if file_path.exists(): - file_path.unlink() - - conn.execute("DELETE FROM cache_entries WHERE expires_at < ?", (now,)) - - self.logger.info("Removed %s expired cache entries", len(expired_entries)) - - def _cleanup_lru(self) -> None: - """Remove least recently used entries.""" - target_size = int(self.max_size_bytes * 0.8) # Clean to 80% of limit - - with sqlite3.connect(self.metadata_db) as conn: - cursor = conn.execute( - """ - SELECT key, cache_type, size_bytes - FROM cache_entries - ORDER BY last_accessed ASC - """ - ) - - current_size = self.get_cache_stats()["total_size_bytes"] - removed_count = 0 - - for key, cache_type, size_bytes in cursor: - if current_size <= target_size: - break - - file_path = self._get_file_path(cache_type, key) - if file_path.exists(): - file_path.unlink() - - conn.execute("DELETE FROM cache_entries WHERE key = ?", (key,)) - current_size -= size_bytes - removed_count += 1 - - self.logger.info("Removed %s LRU cache entries", removed_count) diff --git a/src/core/collection_manager.py b/src/core/collection_manager.py deleted file mode 100644 index af8663c..0000000 --- a/src/core/collection_manager.py +++ /dev/null @@ -1,957 +0,0 @@ -""" -Portfolio Manager - Handles portfolio comparison and investment prioritization. -Provides comprehensive portfolio analysis and investment recommendations. -""" - -from __future__ import annotations - -import logging -import warnings -from dataclasses import asdict, dataclass -from datetime import datetime -from typing import Any - -import numpy as np - -from .backtest_engine import BacktestResult -from .result_analyzer import UnifiedResultAnalyzer - -warnings.filterwarnings("ignore") - - -@dataclass -class PortfolioSummary: - """Summary statistics for a portfolio.""" - - name: str - total_assets: int - total_strategies: int - best_performer: str - worst_performer: str - avg_return: float - avg_sharpe: float - max_drawdown: float - risk_score: float - return_score: float - overall_score: float - investment_priority: int - recommended_allocation: float - risk_category: str # 'Conservative', 'Moderate', 'Aggressive' - - -@dataclass -class InvestmentRecommendation: - """Investment recommendation for a portfolio.""" - - portfolio_name: str - priority_rank: int - recommended_allocation_pct: float - expected_annual_return: float - expected_volatility: float - max_drawdown_risk: float - confidence_score: float - risk_category: str - investment_rationale: str - key_strengths: list[str] - key_risks: list[str] - minimum_investment_period: str - - -class PortfolioManager: - """ - Portfolio Manager for comparing portfolios and providing investment prioritization. - Analyzes multiple portfolios and provides investment recommendations. - """ - - def __init__(self): - self.result_analyzer = UnifiedResultAnalyzer() - self.logger = logging.getLogger(__name__) - - # Risk scoring weights - self.risk_weights = { - "max_drawdown": 0.3, - "volatility": 0.25, - "var_95": 0.2, - "sharpe_ratio": 0.15, # Higher is better - "sortino_ratio": 0.1, # Higher is better - } - - # Return scoring weights - self.return_weights = { - "total_return": 0.4, - "annualized_return": 0.3, - "sharpe_ratio": 0.2, - "win_rate": 0.1, - } - - def analyze_portfolios( - self, portfolios: dict[str, list[BacktestResult]] - ) -> dict[str, Any]: - """ - Analyze multiple portfolios and generate comprehensive comparison. - - Args: - portfolios: Dictionary mapping portfolio names to lists of BacktestResults - - Returns: - Comprehensive portfolio analysis - """ - self.logger.info("Analyzing %s portfolios...", len(portfolios)) - - portfolio_summaries = {} - detailed_analysis = {} - - # Analyze each portfolio - for portfolio_name, results in portfolios.items(): - self.logger.info("Analyzing portfolio: %s", portfolio_name) - - # Calculate portfolio summary - summary = self._calculate_portfolio_summary(portfolio_name, results) - portfolio_summaries[portfolio_name] = summary - - # Calculate detailed metrics - detailed_metrics = self._calculate_detailed_metrics(results) - detailed_analysis[portfolio_name] = detailed_metrics - - # Rank portfolios and generate recommendations - ranked_portfolios = self._rank_portfolios(portfolio_summaries) - investment_recommendations = self._generate_investment_recommendations( - ranked_portfolios, detailed_analysis - ) - - # Generate overall analysis - return { - "analysis_date": datetime.now().isoformat(), - "portfolios_analyzed": len(portfolios), - "portfolio_summaries": { - name: asdict(summary) for name, summary in portfolio_summaries.items() - }, - "detailed_analysis": detailed_analysis, - "ranked_portfolios": ranked_portfolios, - "investment_recommendations": [ - asdict(rec) for rec in investment_recommendations - ], - "market_analysis": self._generate_market_analysis(portfolio_summaries), - "risk_analysis": self._generate_risk_analysis(portfolio_summaries), - "diversification_analysis": self._analyze_diversification_opportunities( - portfolios - ), - } - - def generate_investment_plan( - self, - total_capital: float, - portfolios: dict[str, list[BacktestResult]], - risk_tolerance: str = "moderate", - ) -> dict[str, Any]: - """ - Generate specific investment plan with capital allocation. - - Args: - total_capital: Total capital to allocate - portfolios: Portfolio analysis results - risk_tolerance: 'conservative', 'moderate', 'aggressive' - - Returns: - Detailed investment plan - """ - self.logger.info( - "Generating investment plan for $%.2f with %s risk tolerance", - total_capital, - risk_tolerance, - ) - - # Analyze portfolios - analysis = self.analyze_portfolios(portfolios) - recommendations = analysis["investment_recommendations"] - - # Filter recommendations based on risk tolerance - suitable_recommendations = self._filter_by_risk_tolerance( - recommendations, risk_tolerance - ) - - # Calculate allocations - allocations = self._calculate_capital_allocations( - suitable_recommendations, total_capital, risk_tolerance - ) - - # Generate implementation timeline - implementation_plan = self._generate_implementation_plan(allocations) - - # Risk management plan - risk_management = self._generate_risk_management_plan(allocations, analysis) - - return { - "plan_date": datetime.now().isoformat(), - "total_capital": total_capital, - "risk_tolerance": risk_tolerance, - "allocations": allocations, - "implementation_plan": implementation_plan, - "risk_management": risk_management, - "expected_portfolio_metrics": self._calculate_expected_portfolio_metrics( - allocations - ), - "monitoring_recommendations": self._generate_monitoring_recommendations(), - "rebalancing_strategy": self._generate_rebalancing_strategy(allocations), - } - - def _calculate_portfolio_summary( - self, name: str, results: list[BacktestResult] - ) -> PortfolioSummary: - """Calculate summary statistics for a portfolio.""" - if not results: - return PortfolioSummary( - name=name, - total_assets=0, - total_strategies=0, - best_performer="N/A", - worst_performer="N/A", - avg_return=0, - avg_sharpe=0, - max_drawdown=0, - risk_score=0, - return_score=0, - overall_score=0, - investment_priority=999, - recommended_allocation=0, - risk_category="Unknown", - ) - - # Filter successful results - successful_results = [r for r in results if not r.error and r.metrics] - - if not successful_results: - return PortfolioSummary( - name=name, - total_assets=len(results), - total_strategies=0, - best_performer="N/A", - worst_performer="N/A", - avg_return=0, - avg_sharpe=0, - max_drawdown=0, - risk_score=0, - return_score=0, - overall_score=0, - investment_priority=999, - recommended_allocation=0, - risk_category="High Risk", - ) - - # Extract metrics - returns = [r.metrics.get("total_return", 0) for r in successful_results] - sharpes = [r.metrics.get("sharpe_ratio", 0) for r in successful_results] - drawdowns = [r.metrics.get("max_drawdown", 0) for r in successful_results] - - # Find best and worst performers - best_idx = np.argmax(returns) - worst_idx = np.argmin(returns) - - best_performer = f"{successful_results[best_idx].symbol}/{successful_results[best_idx].strategy}" - worst_performer = f"{successful_results[worst_idx].symbol}/{successful_results[worst_idx].strategy}" - - # Calculate scores - risk_score = self._calculate_risk_score(successful_results) - return_score = self._calculate_return_score(successful_results) - overall_score = (return_score * 0.6) + ( - risk_score * 0.4 - ) # Weight returns higher - - # Determine risk category - risk_category = self._determine_risk_category( - risk_score, np.mean(drawdowns), np.std(returns) - ) - - return PortfolioSummary( - name=name, - total_assets=len(set(r.symbol for r in results)), - total_strategies=len(set(r.strategy for r in results)), - best_performer=best_performer, - worst_performer=worst_performer, - avg_return=np.mean(returns), - avg_sharpe=np.mean(sharpes), - max_drawdown=np.mean(drawdowns), - risk_score=risk_score, - return_score=return_score, - overall_score=overall_score, - investment_priority=0, # Will be set during ranking - recommended_allocation=0, # Will be calculated later - risk_category=risk_category, - ) - - def _calculate_detailed_metrics( - self, results: list[BacktestResult] - ) -> dict[str, Any]: - """Calculate detailed metrics for a portfolio.""" - successful_results = [r for r in results if not r.error and r.metrics] - - if not successful_results: - return {} - - # Aggregate all metrics - all_metrics = {} - metric_names = set() - for result in successful_results: - metric_names.update(result.metrics.keys()) - - for metric in metric_names: - values = [ - r.metrics.get(metric, 0) - for r in successful_results - if metric in r.metrics - ] - if values: - all_metrics[metric] = { - "mean": np.mean(values), - "std": np.std(values), - "min": np.min(values), - "max": np.max(values), - "median": np.median(values), - "count": len(values), - } - - # Strategy analysis - strategy_performance = {} - for strategy in set(r.strategy for r in successful_results): - strategy_results = [r for r in successful_results if r.strategy == strategy] - strategy_returns = [ - r.metrics.get("total_return", 0) for r in strategy_results - ] - - strategy_performance[strategy] = { - "count": len(strategy_results), - "avg_return": np.mean(strategy_returns), - "success_rate": len([r for r in strategy_returns if r > 0]) - / len(strategy_returns) - * 100, - "best_return": np.max(strategy_returns), - "worst_return": np.min(strategy_returns), - } - - # Asset analysis - asset_performance = {} - for symbol in set(r.symbol for r in successful_results): - symbol_results = [r for r in successful_results if r.symbol == symbol] - symbol_returns = [r.metrics.get("total_return", 0) for r in symbol_results] - - asset_performance[symbol] = { - "count": len(symbol_results), - "avg_return": np.mean(symbol_returns), - "consistency": ( - 1 - (np.std(symbol_returns) / np.mean(symbol_returns)) - if np.mean(symbol_returns) != 0 - else 0 - ), - "best_strategy": max( - symbol_results, key=lambda x: x.metrics.get("total_return", 0) - ).strategy, - } - - return { - "summary_metrics": all_metrics, - "strategy_performance": strategy_performance, - "asset_performance": asset_performance, - "total_combinations": len(results), - "successful_combinations": len(successful_results), - "success_rate": ( - len(successful_results) / len(results) * 100 if results else 0 - ), - } - - def _rank_portfolios( - self, summaries: dict[str, PortfolioSummary] - ) -> list[tuple[str, PortfolioSummary]]: - """Rank portfolios by overall score.""" - # Sort by overall score (descending) - ranked = sorted( - summaries.items(), key=lambda x: x[1].overall_score, reverse=True - ) - - # Update priority rankings - for i, (_name, summary) in enumerate(ranked): - summary.investment_priority = i + 1 - - return ranked - - def _generate_investment_recommendations( - self, - ranked_portfolios: list[tuple[str, PortfolioSummary]], - detailed_analysis: dict[str, Any], - ) -> list[InvestmentRecommendation]: - """Generate investment recommendations for each portfolio.""" - recommendations = [] - total_score = sum(summary.overall_score for _, summary in ranked_portfolios) - - for i, (name, summary) in enumerate(ranked_portfolios): - # Calculate recommended allocation based on score - if total_score > 0: - base_allocation = (summary.overall_score / total_score) * 100 - else: - base_allocation = 100 / len(ranked_portfolios) - - # Adjust allocation based on risk category - risk_adjustment = self._get_risk_adjustment(summary.risk_category) - recommended_allocation = min( - base_allocation * risk_adjustment, 40 - ) # Cap at 40% - - # Generate rationale and key points - rationale = self._generate_investment_rationale( - summary, detailed_analysis.get(name, {}) - ) - strengths = self._identify_key_strengths( - summary, detailed_analysis.get(name, {}) - ) - risks = self._identify_key_risks(summary, detailed_analysis.get(name, {})) - - # Calculate confidence score - confidence = self._calculate_confidence_score( - summary, detailed_analysis.get(name, {}) - ) - - recommendation = InvestmentRecommendation( - portfolio_name=name, - priority_rank=i + 1, - recommended_allocation_pct=recommended_allocation, - expected_annual_return=summary.avg_return, - expected_volatility=self._estimate_volatility( - detailed_analysis.get(name, {}) - ), - max_drawdown_risk=abs(summary.max_drawdown), - confidence_score=confidence, - risk_category=summary.risk_category, - investment_rationale=rationale, - key_strengths=strengths, - key_risks=risks, - minimum_investment_period=self._recommend_investment_period( - summary.risk_category - ), - ) - - recommendations.append(recommendation) - - return recommendations - - def _calculate_risk_score(self, results: list[BacktestResult]) -> float: - """Calculate risk score for portfolio (0-100, higher is better).""" - risk_metrics = [] - - for result in results: - metrics = result.metrics - - # Individual risk components (normalized to 0-100) - max_dd = abs(metrics.get("max_drawdown", 0)) - volatility = metrics.get("volatility", 0) - var_95 = abs(metrics.get("var_95", 0)) - sharpe = metrics.get("sharpe_ratio", 0) - sortino = metrics.get("sortino_ratio", 0) - - # Convert to scores (lower risk = higher score) - dd_score = max(0, 100 - max_dd * 2) # Max drawdown penalty - vol_score = max(0, 100 - volatility) # Volatility penalty - var_score = max(0, 100 - var_95 * 10) # VaR penalty - sharpe_score = min(100, sharpe * 20) # Sharpe bonus - sortino_score = min(100, sortino * 20) # Sortino bonus - - # Weighted combination - risk_score = ( - dd_score * self.risk_weights["max_drawdown"] - + vol_score * self.risk_weights["volatility"] - + var_score * self.risk_weights["var_95"] - + sharpe_score * self.risk_weights["sharpe_ratio"] - + sortino_score * self.risk_weights["sortino_ratio"] - ) - - risk_metrics.append(risk_score) - - return np.mean(risk_metrics) if risk_metrics else 0 - - def _calculate_return_score(self, results: list[BacktestResult]) -> float: - """Calculate return score for portfolio (0-100, higher is better).""" - return_metrics = [] - - for result in results: - metrics = result.metrics - - # Individual return components - total_return = metrics.get("total_return", 0) - annual_return = metrics.get("annualized_return", 0) - sharpe = metrics.get("sharpe_ratio", 0) - win_rate = metrics.get("win_rate", 0) - - # Convert to scores - total_score = min(100, max(0, total_return)) # Cap at 100% - annual_score = min(100, max(0, annual_return * 2)) # Scale annual return - sharpe_score = min(100, sharpe * 20) # Sharpe bonus - win_score = win_rate # Already in percentage - - # Weighted combination - return_score = ( - total_score * self.return_weights["total_return"] - + annual_score * self.return_weights["annualized_return"] - + sharpe_score * self.return_weights["sharpe_ratio"] - + win_score * self.return_weights["win_rate"] - ) - - return_metrics.append(return_score) - - return np.mean(return_metrics) if return_metrics else 0 - - def _determine_risk_category( - self, risk_score: float, avg_drawdown: float, return_volatility: float - ) -> str: - """Determine risk category based on metrics.""" - if risk_score >= 70 and abs(avg_drawdown) <= 10 and return_volatility <= 15: - return "Conservative" - if risk_score >= 50 and abs(avg_drawdown) <= 20 and return_volatility <= 25: - return "Moderate" - return "Aggressive" - - def _generate_market_analysis( - self, summaries: dict[str, PortfolioSummary] - ) -> dict[str, Any]: - """Generate overall market analysis.""" - if not summaries: - return {} - - all_returns = [s.avg_return for s in summaries.values()] - all_sharpes = [s.avg_sharpe for s in summaries.values()] - - return { - "market_sentiment": ( - "Bullish" - if np.mean(all_returns) > 5 - else "Bearish" - if np.mean(all_returns) < -2 - else "Neutral" - ), - "average_market_return": np.mean(all_returns), - "market_volatility": np.std(all_returns), - "risk_adjusted_performance": np.mean(all_sharpes), - "top_performing_category": max( - summaries.keys(), key=lambda k: summaries[k].avg_return - ), - "most_consistent_category": max( - summaries.keys(), key=lambda k: summaries[k].avg_sharpe - ), - "recommendations": self._generate_market_recommendations(summaries), - } - - def _generate_risk_analysis( - self, summaries: dict[str, PortfolioSummary] - ) -> dict[str, Any]: - """Generate risk analysis across portfolios.""" - risk_categories = {} - for summary in summaries.values(): - category = summary.risk_category - if category not in risk_categories: - risk_categories[category] = [] - risk_categories[category].append(summary) - - risk_analysis = {} - for category, portfolios in risk_categories.items(): - risk_analysis[category] = { - "count": len(portfolios), - "avg_return": np.mean([p.avg_return for p in portfolios]), - "avg_risk_score": np.mean([p.risk_score for p in portfolios]), - "recommended_allocation": self._get_category_allocation(category), - "portfolios": [p.name for p in portfolios], - } - - return { - "by_category": risk_analysis, - "overall_risk_level": self._assess_overall_risk_level(summaries), - "diversification_score": self._calculate_diversification_score(summaries), - "risk_recommendations": self._generate_risk_recommendations(risk_analysis), - } - - def _analyze_diversification_opportunities( - self, portfolios: dict[str, list[BacktestResult]] - ) -> dict[str, Any]: - """Analyze diversification opportunities across portfolios.""" - # Asset type analysis - all_symbols = set() - all_strategies = set() - portfolio_overlap = {} - - for name, results in portfolios.items(): - symbols = set(r.symbol for r in results) - strategies = set(r.strategy for r in results) - - all_symbols.update(symbols) - all_strategies.update(strategies) - - portfolio_overlap[name] = { - "symbols": symbols, - "strategies": strategies, - "asset_types": self._classify_asset_types(symbols), - } - - # Calculate overlaps - overlap_analysis = {} - portfolio_names = list(portfolio_overlap.keys()) - - for i, name1 in enumerate(portfolio_names): - for name2 in portfolio_names[i + 1 :]: - symbols1 = portfolio_overlap[name1]["symbols"] - symbols2 = portfolio_overlap[name2]["symbols"] - - overlap = len(symbols1.intersection(symbols2)) - total_unique = len(symbols1.union(symbols2)) - - overlap_analysis[f"{name1}_vs_{name2}"] = { - "symbol_overlap": overlap, - "total_symbols": total_unique, - "overlap_percentage": ( - (overlap / total_unique * 100) if total_unique > 0 else 0 - ), - } - - return { - "total_unique_symbols": len(all_symbols), - "total_unique_strategies": len(all_strategies), - "portfolio_overlaps": overlap_analysis, - "diversification_opportunities": self._identify_diversification_gaps( - portfolio_overlap - ), - "recommended_portfolio_mix": self._recommend_portfolio_mix( - portfolio_overlap - ), - } - - def _filter_by_risk_tolerance( - self, recommendations: list[dict], risk_tolerance: str - ) -> list[dict]: - """Filter recommendations based on risk tolerance.""" - risk_mapping = { - "conservative": ["Conservative"], - "moderate": ["Conservative", "Moderate"], - "aggressive": ["Conservative", "Moderate", "Aggressive"], - } - - allowed_categories = risk_mapping.get( - risk_tolerance, ["Conservative", "Moderate"] - ) - - return [ - rec for rec in recommendations if rec["risk_category"] in allowed_categories - ] - - def _calculate_capital_allocations( - self, recommendations: list[dict], total_capital: float, risk_tolerance: str - ) -> list[dict]: - """Calculate specific capital allocations.""" - if not recommendations: - return [] - - # Adjust allocations based on risk tolerance - risk_multipliers = { - "conservative": {"Conservative": 1.5, "Moderate": 0.5, "Aggressive": 0.1}, - "moderate": {"Conservative": 1.0, "Moderate": 1.2, "Aggressive": 0.8}, - "aggressive": {"Conservative": 0.7, "Moderate": 1.0, "Aggressive": 1.3}, - } - - multipliers = risk_multipliers.get(risk_tolerance, risk_multipliers["moderate"]) - - # Apply multipliers - adjusted_allocations = [] - for rec in recommendations: - adjusted_pct = rec["recommended_allocation_pct"] * multipliers.get( - rec["risk_category"], 1.0 - ) - adjusted_allocations.append(adjusted_pct) - - # Normalize to 100% - total_adjusted = sum(adjusted_allocations) - if total_adjusted > 0: - normalized_allocations = [ - pct / total_adjusted * 100 for pct in adjusted_allocations - ] - else: - normalized_allocations = [100 / len(recommendations)] * len(recommendations) - - # Calculate dollar amounts - allocations = [] - for i, rec in enumerate(recommendations): - allocation_pct = normalized_allocations[i] - allocation_amount = total_capital * (allocation_pct / 100) - - allocations.append( - { - "portfolio_name": rec["portfolio_name"], - "allocation_percentage": allocation_pct, - "allocation_amount": allocation_amount, - "priority_rank": rec["priority_rank"], - "risk_category": rec["risk_category"], - "expected_return": rec["expected_annual_return"], - } - ) - - return allocations - - def _generate_implementation_plan(self, allocations: list[dict]) -> dict[str, Any]: - """Generate implementation timeline.""" - # Sort by priority - sorted_allocations = sorted(allocations, key=lambda x: x["priority_rank"]) - - implementation_phases = [] - cumulative_allocation = 0 - - for i, allocation in enumerate(sorted_allocations): - phase_start = i * 2 # 2 weeks between phases - phase_end = phase_start + 1 - - cumulative_allocation += allocation["allocation_percentage"] - - implementation_phases.append( - { - "phase": i + 1, - "week_start": phase_start, - "week_end": phase_end, - "portfolio": allocation["portfolio_name"], - "amount": allocation["allocation_amount"], - "percentage": allocation["allocation_percentage"], - "cumulative_percentage": cumulative_allocation, - "priority": allocation["priority_rank"], - } - ) - - return { - "total_phases": len(implementation_phases), - "estimated_duration_weeks": len(implementation_phases) * 2, - "phases": implementation_phases, - "risk_management_notes": [ - "Start with highest-ranked portfolios", - "Monitor performance after each phase", - "Adjust subsequent allocations based on early results", - "Maintain 5-10% cash reserve for opportunities", - ], - } - - def _generate_risk_management_plan( - self, allocations: list[dict], analysis: dict[str, Any] - ) -> dict[str, Any]: - """Generate risk management plan.""" - sum(a["allocation_amount"] for a in allocations) - - # Calculate portfolio risk metrics - sum( - a["expected_return"] * a["allocation_percentage"] / 100 for a in allocations - ) - - return { - "portfolio_limits": { - "max_single_portfolio_pct": 40, - "max_aggressive_allocation_pct": 30, - "min_conservative_allocation_pct": 20, - }, - "stop_loss_rules": { - "individual_portfolio_stop_loss": -15, # % - "total_portfolio_stop_loss": -10, # % - "review_trigger": -5, # % - }, - "rebalancing_triggers": { - "time_based": "Quarterly", - "drift_threshold": 5, # % deviation from target - "performance_threshold": 10, # % underperformance - }, - "monitoring_schedule": { - "daily": ["Market conditions", "Major news events"], - "weekly": ["Portfolio performance", "Risk metrics"], - "monthly": ["Full portfolio review", "Rebalancing assessment"], - "quarterly": ["Strategy review", "Allocation adjustments"], - }, - "risk_metrics_targets": { - "max_portfolio_volatility": 20, - "target_sharpe_ratio": 1.0, - "max_correlation_single_asset": 0.3, - }, - } - - def _calculate_expected_portfolio_metrics( - self, allocations: list[dict] - ) -> dict[str, float]: - """Calculate expected metrics for the combined portfolio.""" - if not allocations: - return {} - - # Weighted calculations - weights = [a["allocation_percentage"] / 100 for a in allocations] - returns = [a["expected_return"] for a in allocations] - - expected_return = sum(w * r for w, r in zip(weights, returns)) - - # Simplified risk calculation (would need correlation matrix for full calculation) - portfolio_volatility = np.sqrt( - sum(w**2 * (r * 0.5) ** 2 for w, r in zip(weights, returns)) - ) - - return { - "expected_annual_return": expected_return, - "expected_volatility": portfolio_volatility, - "expected_sharpe_ratio": ( - expected_return / portfolio_volatility - if portfolio_volatility > 0 - else 0 - ), - "diversification_benefit": len(allocations) / 10, # Simplified - "risk_score": sum(w * (100 - abs(r)) for w, r in zip(weights, returns)), - } - - # Helper methods for various calculations... - def _get_risk_adjustment(self, risk_category: str) -> float: - """Get risk adjustment multiplier.""" - return {"Conservative": 1.2, "Moderate": 1.0, "Aggressive": 0.8}.get( - risk_category, 1.0 - ) - - def _estimate_volatility(self, detailed_analysis: dict) -> float: - """Estimate portfolio volatility.""" - if not detailed_analysis or "summary_metrics" not in detailed_analysis: - return 20.0 # Default estimate - - volatility_data = detailed_analysis["summary_metrics"].get("volatility", {}) - return volatility_data.get("mean", 20.0) - - def _generate_investment_rationale( - self, summary: PortfolioSummary, detailed_analysis: dict - ) -> str: - """Generate investment rationale.""" - if summary.overall_score >= 70: - return ( - f"Strong performer with {summary.avg_return}% average return and " - f"{summary.risk_category.lower()} risk profile." - ) - if summary.overall_score >= 50: - return "Solid performer with balanced risk-return profile suitable for diversified portfolios." - return "Higher risk option that may be suitable for aggressive investors seeking potential upside." - - def _identify_key_strengths( - self, summary: PortfolioSummary, detailed_analysis: dict - ) -> list[str]: - """Identify key strengths.""" - strengths = [] - - if summary.avg_return > 10: - strengths.append(f"High average return of {summary.avg_return}%") - if summary.avg_sharpe > 1: - strengths.append( - f"Strong risk-adjusted returns (Sharpe: {summary.avg_sharpe})" - ) - if abs(summary.max_drawdown) < 10: - strengths.append("Low drawdown risk") - if summary.total_assets > 10: - strengths.append("Well-diversified across multiple assets") - - return strengths[:3] # Limit to top 3 - - def _identify_key_risks( - self, summary: PortfolioSummary, detailed_analysis: dict - ) -> list[str]: - """Identify key risks.""" - risks = [] - - if abs(summary.max_drawdown) > 20: - risks.append(f"High drawdown risk ({abs(summary.max_drawdown):.1f}%)") - if summary.avg_sharpe < 0.5: - risks.append("Poor risk-adjusted returns") - if summary.total_assets < 5: - risks.append("Limited diversification") - if summary.risk_category == "Aggressive": - risks.append("High volatility and risk") - - return risks[:3] # Limit to top 3 - - def _calculate_confidence_score( - self, summary: PortfolioSummary, detailed_analysis: dict - ) -> float: - """Calculate confidence score.""" - base_score = summary.overall_score - - # Adjust based on data quality - if detailed_analysis.get("success_rate", 0) > 80: - base_score *= 1.1 - elif detailed_analysis.get("success_rate", 0) < 50: - base_score *= 0.9 - - # Adjust based on consistency - if summary.total_assets > 10 and summary.total_strategies > 3: - base_score *= 1.05 - - return min(100, base_score) - - def _recommend_investment_period(self, risk_category: str) -> str: - """Recommend minimum investment period.""" - return { - "Conservative": "6-12 months", - "Moderate": "12-24 months", - "Aggressive": "24+ months", - }.get(risk_category, "12-24 months") - - def _generate_monitoring_recommendations(self) -> list[str]: - """Generate monitoring recommendations.""" - return [ - "Review portfolio performance weekly", - "Monitor individual strategy performance monthly", - "Assess correlation changes quarterly", - "Rebalance when allocation drifts >5% from targets", - "Consider strategy replacement if underperforming for 6+ months", - ] - - def _generate_rebalancing_strategy(self, allocations: list[dict]) -> dict[str, Any]: - """Generate rebalancing strategy.""" - return { - "frequency": "Quarterly", - "drift_threshold": 5, # % - "method": "Threshold-based with time override", - "rules": [ - "Rebalance if any allocation drifts >5% from target", - "Mandatory rebalancing every 6 months regardless of drift", - "Emergency rebalancing if portfolio loses >10%", - "Consider tax implications before rebalancing", - ], - } - - # Additional helper methods would be implemented here... - def _generate_market_recommendations(self, summaries: dict) -> list[str]: - return ["Monitor market conditions", "Consider defensive strategies if needed"] - - def _get_category_allocation(self, category: str) -> float: - return {"Conservative": 40, "Moderate": 35, "Aggressive": 25}.get(category, 30) - - def _assess_overall_risk_level(self, summaries: dict) -> str: - avg_risk = np.mean([s.risk_score for s in summaries.values()]) - return "Low" if avg_risk > 70 else "Medium" if avg_risk > 50 else "High" - - def _calculate_diversification_score(self, summaries: dict) -> float: - total_assets = sum(s.total_assets for s in summaries.values()) - return min(100, total_assets * 2) # Simplified calculation - - def _generate_risk_recommendations(self, risk_analysis: dict) -> list[str]: - return [ - "Maintain diversification", - "Monitor correlation changes", - "Review risk limits regularly", - ] - - def _classify_asset_types(self, symbols: set) -> dict[str, int]: - crypto_count = len( - [ - s - for s in symbols - if any(c in s.upper() for c in ["BTC", "ETH", "USD", "USDT"]) - ] - ) - forex_count = len([s for s in symbols if s.endswith("=X")]) - stock_count = len(symbols) - crypto_count - forex_count - - return {"stocks": stock_count, "crypto": crypto_count, "forex": forex_count} - - def _identify_diversification_gaps(self, portfolio_overlap: dict) -> list[str]: - return [ - "Consider adding international exposure", - "Evaluate sector concentration", - ] - - def _recommend_portfolio_mix(self, portfolio_overlap: dict) -> dict[str, float]: - return {"Primary": 60, "Secondary": 25, "Satellite": 15} diff --git a/src/core/data_manager.py b/src/core/data_manager.py deleted file mode 100644 index 88c184d..0000000 --- a/src/core/data_manager.py +++ /dev/null @@ -1,1643 +0,0 @@ -""" -Unified Data Manager - Consolidates all data fetching and management functionality. -Supports multiple data sources including Bybit for crypto futures. -""" - -from __future__ import annotations - -import logging -import os -import time -import warnings -from abc import ABC, abstractmethod -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List, Optional - -import pandas as pd -import requests -from requests.adapters import HTTPAdapter -from urllib3.util.retry import Retry - -from .cache_manager import UnifiedCacheManager - -warnings.filterwarnings("ignore") - - -@dataclass -class DataSourceConfig: - """Configuration for data sources.""" - - name: str - priority: int - rate_limit: float - max_retries: int - timeout: float - supports_batch: bool = False - supports_futures: bool = False - asset_types: List[str] | None = None - max_symbols_per_request: int = 1 - - -class DataSource(ABC): - """Abstract base class for all data sources.""" - - def __init__(self, config: DataSourceConfig) -> None: - self.config = config - self.last_request_time = 0 - self.session = self._create_session() - self.logger = logging.getLogger(f"{__name__}.{config.name}") - - def transform_symbol(self, symbol: str, asset_type: str | None = None) -> str: - """Transform symbol to fit this data source's format.""" - return symbol # Default: no transformation - - def _create_session(self) -> requests.Session: - """Create HTTP session with retry strategy.""" - session = requests.Session() - retry_strategy = Retry( - total=self.config.max_retries, - backoff_factor=1, - status_forcelist=[429, 500, 502, 503, 504], - ) - adapter = HTTPAdapter(max_retries=retry_strategy) - session.mount("http://", adapter) - session.mount("https://", adapter) - return session - - def _rate_limit(self) -> None: - """Apply rate limiting.""" - elapsed = time.time() - self.last_request_time - if elapsed < self.config.rate_limit: - time.sleep(self.config.rate_limit - elapsed) - self.last_request_time = int(time.time()) - - @abstractmethod - def fetch_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Optional[pd.DataFrame]: - """Fetch data for a single symbol.""" - - @abstractmethod - def fetch_batch_data( - self, - symbols: List[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Dict[str, pd.DataFrame]: - """Fetch data for multiple symbols.""" - - @abstractmethod - def get_available_symbols(self, asset_type: str | None = None) -> List[str]: - """Get available symbols for this source.""" - - def standardize_data(self, df: pd.DataFrame) -> pd.DataFrame: - """Standardize data format across all sources.""" - if df.empty: - return df - - df = df.copy() - - # Standardize column names - column_mapping = { - "Open": "open", - "open": "open", - "High": "high", - "high": "high", - "Low": "low", - "low": "low", - "Close": "close", - "close": "close", - "Adj Close": "adj_close", - "adj_close": "adj_close", - "Volume": "volume", - "volume": "volume", - } - - df.columns = [column_mapping.get(col, col.lower()) for col in df.columns] - - # Ensure required columns exist - required_cols = ["open", "high", "low", "close"] - missing_cols = [col for col in required_cols if col not in df.columns] - if missing_cols: - msg = f"Missing required columns: {missing_cols}" - raise ValueError(msg) - - # Convert to numeric - numeric_cols = ["open", "high", "low", "close", "volume"] - for col in numeric_cols: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce") - - # Ensure datetime index - if not isinstance(df.index, pd.DatetimeIndex): - df.index = pd.to_datetime(df.index) - - # Sort by date - df = df.sort_index() - - # Remove invalid data - df = df.dropna(subset=["close"]) - df = df[ - (df["high"] >= df["low"]) - & (df["high"] >= df["open"]) - & (df["high"] >= df["close"]) - & (df["low"] <= df["open"]) - & (df["low"] <= df["close"]) - ] - - return df - - -class YahooFinanceSource(DataSource): - """Yahoo Finance data source - primary for stocks, forex, commodities.""" - - def __init__(self) -> None: - config = DataSourceConfig( - name="yahoo_finance", - priority=1, - rate_limit=1.5, - max_retries=3, - timeout=30, - supports_batch=True, - supports_futures=True, - asset_types=["stocks", "forex", "commodities", "indices", "crypto"], - max_symbols_per_request=100, - ) - super().__init__(config) - - def transform_symbol(self, symbol: str, asset_type: str | None = None) -> str: - """Transform symbol for Yahoo Finance format.""" - # Yahoo Finance forex format - if asset_type == "forex" or "=" in symbol: - return symbol # Already in correct format (EURUSD=X) - - # Handle forex pairs without =X - forex_pairs = [ - "EURUSD", - "GBPUSD", - "USDJPY", - "USDCHF", - "AUDUSD", - "USDCAD", - "NZDUSD", - "EURJPY", - "GBPJPY", - "EURGBP", - "AUDJPY", - "EURAUD", - "EURCHF", - "AUDNZD", - "GBPAUD", - "GBPCAD", - ] - if symbol in forex_pairs: - return f"{symbol}=X" - - # Crypto format - Yahoo uses dash format and typically USD quote - if asset_type == "crypto" or any( - crypto in symbol.upper() for crypto in ["BTC", "ETH", "ADA", "SOL"] - ): - up = symbol.upper() - if "USDT" in up and "-" not in up: - # Map USDT quote to Yahoo's USD convention, e.g., IMXUSDT -> IMX-USD - return up.replace("USDT", "-USD") - if "USD" in up and "-" not in up: - return up.replace("USD", "-USD") - - return symbol - - def fetch_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Optional[pd.DataFrame]: - """Fetch data from Yahoo Finance. - - Supports a 'period' kwarg (e.g. 'max', '1y') which will be preferred over - start/end if provided. This mirrors yfinance.Ticker.history semantics. - """ - import yfinance as yf - - self._rate_limit() - - # Transform symbol to Yahoo Finance format - asset_type = kwargs.get("asset_type") - transformed_symbol = self.transform_symbol(symbol, asset_type) - - # Allow callers to pass 'period' to request the provider's period-based download - period = kwargs.get("period") or kwargs.get("period_mode") or None - - try: - ticker = yf.Ticker(transformed_symbol) - if period: - # Use period-based download (yfinance handles interval constraints) - data = ticker.history(period=period, interval=interval) - else: - data = ticker.history(start=start_date, end=end_date, interval=interval) - - if data is None or data.empty: - return None - - return self.standardize_data(data) - - except Exception as e: - self.logger.warning("Yahoo Finance fetch failed for %s: %s", symbol, e) - return None - - def fetch_batch_data( - self, - symbols: List[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Dict[str, pd.DataFrame]: - """Fetch batch data from Yahoo Finance. - - If a 'period' kwarg is provided it will be used instead of start/end - (matches yfinance.download semantics). - """ - import yfinance as yf - - self._rate_limit() - - period = kwargs.get("period") or kwargs.get("period_mode") or None - - try: - if period: - data = yf.download( - symbols, - period=period, - interval=interval, - group_by="ticker", - progress=False, - ) - else: - data = yf.download( - symbols, - start=start_date, - end=end_date, - interval=interval, - group_by="ticker", - progress=False, - ) - - result = {} - if len(symbols) == 1: - symbol = symbols[0] - if not getattr(data, "empty", False): - result[symbol] = self.standardize_data(data) - else: - # yfinance.download returns a DataFrame with a top-level column for each ticker - for symbol in symbols: - try: - if symbol in data.columns.levels[0]: - symbol_data = data[symbol] - if not getattr(symbol_data, "empty", False): - result[symbol] = self.standardize_data(symbol_data) - except Exception as exc: - # some downloads return a flat DataFrame for single-column cases; ignore failures per-symbol - self.logger.debug( - "Batch fetch postprocess failed for %s: %s", symbol, exc - ) - continue - - return result - - except Exception as e: - self.logger.warning("Yahoo Finance batch fetch failed: %s", e) - return {} - - def get_available_symbols(self, asset_type: str | None = None) -> List[str]: - """Get available symbols - not implemented for Yahoo Finance source.""" - # Yahoo Finance doesn't provide a direct API for symbol listing - # Would need external data or hardcoded list - self.logger.warning( - "get_available_symbols not implemented for Yahoo Finance source" - ) - return [] - - -class BybitSource(DataSource): - """Bybit data source - primary for crypto futures trading.""" - - def __init__( - self, - api_key: Optional[str] = None, - api_secret: Optional[str] = None, - testnet: bool = False, - ) -> None: - config = DataSourceConfig( - name="bybit", - priority=1, # Primary for crypto - rate_limit=0.1, # 10 requests per second - max_retries=3, - timeout=30, - supports_batch=True, - supports_futures=True, - asset_types=["crypto", "crypto_futures"], - max_symbols_per_request=50, - ) - super().__init__(config) - - self.api_key = api_key - self.api_secret = api_secret - self.testnet = testnet - - # Bybit endpoints - if testnet: - self.base_url = "https://api-testnet.bybit.com" - else: - self.base_url = "https://api.bybit.com" - - def fetch_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - category: str = "linear", - **kwargs, - ) -> Optional[pd.DataFrame]: - """ - Fetch data from Bybit. - - Args: - symbol: Trading symbol (e.g., 'BTCUSDT') - start_date: Start date - end_date: End date - interval: Kline interval ('1', '3', '5', '15', '30', '60', '120', '240', - '360', '720', 'D', 'W', 'M') - category: Product category ('spot', 'linear', 'inverse', 'option') - """ - self._rate_limit() - - try: - # Convert interval to Bybit format - bybit_interval = self._convert_interval(interval) - if not bybit_interval: - self.logger.error("Unsupported interval: %s", interval) - return None - - # Convert dates to timestamps (robust to strings and tokens) - start_dt = pd.to_datetime(start_date, errors="coerce") - end_dt = pd.to_datetime(end_date, errors="coerce") - if pd.isna(end_dt): - end_dt = pd.Timestamp.utcnow() - if pd.isna(start_dt): - # Default window based on interval - try: - if interval in {"1m", "3m", "5m", "15m", "30m"}: - start_dt = end_dt - pd.Timedelta(days=7) - elif interval in {"1h", "2h", "4h", "6h", "12h"}: - start_dt = end_dt - pd.Timedelta(days=90) - else: - start_dt = end_dt - pd.Timedelta(days=365) - except Exception: - start_dt = end_dt - pd.Timedelta(days=90) - - start_ts = int(start_dt.timestamp() * 1000) - end_ts = int(end_dt.timestamp() * 1000) - - # Fetch kline data - url = f"{self.base_url}/v5/market/kline" - params = { - "category": category, - "symbol": symbol, - "interval": bybit_interval, - "start": start_ts, - "end": end_ts, - "limit": 1000, - } - - all_data = [] - current_end = end_ts - - # Fetch data in chunks (Bybit returns max 1000 records per request) - while current_end > start_ts: - params["end"] = current_end - - response = self.session.get( - url, params=params, timeout=self.config.timeout - ) - response.raise_for_status() - - data = response.json() - - if data.get("retCode") != 0: - self.logger.error("Bybit API error: %s", data.get("retMsg")) - break - - klines = data.get("result", {}).get("list", []) - if not klines: - break - - all_data.extend(klines) - - # Update end timestamp for next iteration - current_end = int(klines[-1][0]) - 1 - - # Rate limit between requests - time.sleep(self.config.rate_limit) - - if not all_data: - return None - - # Convert to DataFrame - df = pd.DataFrame( - all_data, - columns=[ - "timestamp", - "open", - "high", - "low", - "close", - "volume", - "turnover", - ], - ) - - # Convert timestamp to datetime - df["timestamp"] = pd.to_datetime(df["timestamp"].astype(int), unit="ms") - df.set_index("timestamp", inplace=True) - df = df.sort_index() - - # Convert to numeric - numeric_cols = ["open", "high", "low", "close", "volume", "turnover"] - for col in numeric_cols: - df[col] = pd.to_numeric(df[col], errors="coerce") - - return self.standardize_data(df) - - except Exception as e: - self.logger.warning("Bybit fetch failed for %s: %s", symbol, e) - return None - - def fetch_batch_data( - self, - symbols: List[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Dict[str, pd.DataFrame]: - """Fetch batch data from Bybit (sequential due to rate limits).""" - result = {} - - for symbol in symbols: - data = self.fetch_data(symbol, start_date, end_date, interval, **kwargs) - if data is not None: - result[symbol] = data - - return result - - def get_available_symbols(self, asset_type: str = "linear") -> List[str]: - """Get available trading symbols from Bybit.""" - try: - url = f"{self.base_url}/v5/market/instruments-info" - params = {"category": asset_type} - - response = self.session.get(url, params=params, timeout=self.config.timeout) - response.raise_for_status() - - data = response.json() - - if data.get("retCode") != 0: - self.logger.error("Bybit API error: %s", data.get("retMsg")) - return [] - - instruments = data.get("result", {}).get("list", []) - symbols = [ - inst.get("symbol") - for inst in instruments - if inst.get("status") == "Trading" - ] - - return symbols - - except Exception as e: - self.logger.error("Failed to fetch Bybit symbols: %s", e) - return [] - - def get_futures_symbols(self) -> List[str]: - """Get crypto futures symbols.""" - return self.get_available_symbols("linear") - - def get_spot_symbols(self) -> List[str]: - """Get crypto spot symbols.""" - return self.get_available_symbols("spot") - - def _convert_interval(self, interval: str) -> Optional[str]: - """Convert standard interval to Bybit format.""" - mapping = { - "1m": "1", - "3m": "3", - "5m": "5", - "15m": "15", - "30m": "30", - "1h": "60", - "2h": "120", - "4h": "240", - "6h": "360", - "12h": "720", - "1d": "D", - "1w": "W", - "1M": "M", - } - return mapping.get(interval) - - -class AlphaVantageSource(DataSource): - """Alpha Vantage source for additional stock data.""" - - def __init__(self, api_key: str) -> None: - config = DataSourceConfig( - name="alpha_vantage", - priority=3, - rate_limit=12, # 5 requests per minute - max_retries=3, - timeout=30, - supports_batch=False, - asset_types=["stocks", "forex", "commodities"], - max_symbols_per_request=1, - ) - super().__init__(config) - self.api_key = api_key - self.base_url = "https://www.alphavantage.co/query" - - def fetch_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Optional[pd.DataFrame]: - """Fetch data from Alpha Vantage.""" - self._rate_limit() - - try: - function = self._get_function(interval) - params = { - "function": function, - "symbol": symbol, - "apikey": self.api_key, - "outputsize": "full", - "datatype": "json", - } - - if interval not in ["1d", "1w", "1M"]: - params["interval"] = self._convert_interval(interval) - - response = self.session.get( - self.base_url, params=params, timeout=self.config.timeout - ) - data = response.json() - - # Find time series data - time_series_key = None - for key in data.keys(): - if "Time Series" in key: - time_series_key = key - break - - if not time_series_key: - return None - - # Convert to DataFrame - df = pd.DataFrame.from_dict(data[time_series_key], orient="index") - df.index = pd.to_datetime(df.index) - df = df.sort_index() - - # Standardize column names - df.columns = [ - col.split(". ")[-1].lower().replace(" ", "_") for col in df.columns - ] - - # Filter by date range using UTC timezone - start = pd.to_datetime(start_date, utc=True) - end = pd.to_datetime(end_date, utc=True) - - # Convert data index to UTC for consistent comparison - if df.index.tz is None: - df.index = df.index.tz_localize("UTC") - else: - df.index = df.index.tz_convert("UTC") - - df = df[(df.index >= start) & (df.index <= end)] - - return self.standardize_data(df) if not df.empty else None - - except Exception as e: - self.logger.warning("Alpha Vantage fetch failed for %s: %s", symbol, e) - return None - - def fetch_batch_data( - self, - symbols: List[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs, - ) -> Dict[str, pd.DataFrame]: - """Sequential fetch for Alpha Vantage.""" - result = {} - for symbol in symbols: - data = self.fetch_data(symbol, start_date, end_date, interval, **kwargs) - if data is not None: - result[symbol] = data - return result - - def get_available_symbols(self, asset_type: str | None = None) -> List[str]: - """Get available symbols - not implemented for Alpha Vantage source.""" - # Alpha Vantage doesn't provide a direct API for symbol listing - # Would require subscription to premium endpoints or external data - self.logger.warning( - "get_available_symbols not implemented for Alpha Vantage source" - ) - return [] - - def _get_function(self, interval: str) -> str: - """Get Alpha Vantage function name.""" - if interval in ["1m", "5m", "15m", "30m", "60m"]: - return "TIME_SERIES_INTRADAY" - if interval == "1d": - return "TIME_SERIES_DAILY_ADJUSTED" - if interval == "1w": - return "TIME_SERIES_WEEKLY_ADJUSTED" - if interval == "1M": - return "TIME_SERIES_MONTHLY_ADJUSTED" - return "TIME_SERIES_DAILY_ADJUSTED" - - def _convert_interval(self, interval: str) -> str: - """Convert to Alpha Vantage format.""" - mapping = { - "1m": "1min", - "5m": "5min", - "15m": "15min", - "30m": "30min", - "1h": "60min", - } - return mapping.get(interval, "1min") - - -class UnifiedDataManager: - """ - Unified data manager that consolidates all data fetching functionality. - Automatically routes requests to appropriate data sources based on asset type. - """ - - def __init__(self, cache_manager: UnifiedCacheManager | None = None) -> None: - self.cache_manager = cache_manager or UnifiedCacheManager() - self.sources: dict[str, DataSource] = {} - self.logger = logging.getLogger(__name__) - - # Initialize default sources - self._initialize_sources() - - def _initialize_sources(self) -> None: - """Initialize available data sources.""" - import os - - # Yahoo Finance (always available - fallback) - self.add_source(YahooFinanceSource()) - - # Enhanced Alpha Vantage (good for stocks/forex/crypto) - av_key = os.getenv("ALPHA_VANTAGE_API_KEY") - if av_key: - try: - self.add_source(EnhancedAlphaVantageSource()) - except Exception as e: - self.logger.warning("Could not add Enhanced Alpha Vantage: %s", e) - # Fallback to existing implementation - try: - self.add_source(AlphaVantageSource(av_key)) - except: - pass - - # Twelve Data (excellent coverage) - twelve_key = os.getenv("TWELVE_DATA_API_KEY") - if twelve_key: - try: - self.add_source(TwelveDataSource()) - except Exception as e: - self.logger.warning("Could not add Twelve Data: %s", e) - - # Bybit for crypto futures (specialized) - bybit_key = os.getenv("BYBIT_API_KEY") - bybit_secret = os.getenv("BYBIT_API_SECRET") - testnet = os.getenv("BYBIT_TESTNET", "false").lower() == "true" - - self.add_source(BybitSource(bybit_key, bybit_secret, testnet)) - - def add_source(self, source: DataSource): - """Add a data source.""" - self.sources[source.config.name] = source - self.logger.debug("Added data source: %s", source.config.name) - - def get_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - use_cache: bool = True, - asset_type: str | None = None, - **kwargs, - ) -> Optional[pd.DataFrame]: - """ - Get data for a symbol with intelligent source routing. - - Args: - symbol: Symbol to fetch - start_date: Start date (YYYY-MM-DD) - end_date: End date (YYYY-MM-DD) - interval: Data interval - use_cache: Whether to use cached data - asset_type: Asset type hint ('crypto', 'stocks', 'forex', etc.) - **kwargs: Additional parameters for specific sources - """ - # If a native provider period was requested (e.g., period='max'), skip cache reads to ensure - # we fetch the full available history from the source. We'll still write-through to cache below. - period_requested = kwargs.get("period") or kwargs.get("period_mode") - - # Check cache first (only when no explicit provider period was requested) - if use_cache and not period_requested: - # Legacy fast-path: return any single cached hit immediately (maintains test expectations) - legacy_cached = self.cache_manager.get_data( - symbol, start_date, end_date, interval - ) - if legacy_cached is not None: - self.logger.debug("Cache hit (legacy) for %s", symbol) - return legacy_cached - - # Split cache: attempt to merge a full snapshot with a recent overlay - # Try Redis overlay first if available - full_df = self.cache_manager.get_data( - symbol, start_date, end_date, interval, data_type="full" - ) - recent_df = None - try: - recent_df = self.cache_manager.get_recent_overlay_from_redis( - symbol, interval - ) - except Exception: - recent_df = None - if recent_df is None: - recent_df = self.cache_manager.get_data( - symbol, start_date, end_date, interval, data_type="recent" - ) - merged = None - if ( - full_df is not None - and not full_df.empty - and recent_df is not None - and not recent_df.empty - ): - try: - merged = ( - pd.concat([full_df, recent_df]) - .sort_index() - .loc[lambda df: ~df.index.duplicated(keep="last")] - ) - except Exception: - merged = full_df - elif full_df is not None and not full_df.empty: - merged = full_df - elif recent_df is not None and not recent_df.empty: - merged = recent_df - - if merged is not None and not merged.empty: - # If requested range extends beyond merged coverage, auto-extend by fetching missing windows - try: - req_start = pd.to_datetime(start_date) - req_end = pd.to_datetime(end_date) - c_start = merged.index[0] - c_end = merged.index[-1] - need_before = req_start < c_start - need_after = req_end > c_end - except Exception: - need_before = need_after = False - - if need_before: - try: - df_b = self.get_data( - symbol, - start_date, - c_start.strftime("%Y-%m-%d"), - interval, - use_cache=False, - asset_type=asset_type, - period_mode=period_requested, - ) - if df_b is not None and not df_b.empty: - merged = ( - pd.concat([df_b, merged]) - .sort_index() - .loc[lambda df: ~df.index.duplicated(keep="last")] - ) - except Exception: - pass - if need_after: - try: - df_a = self.get_data( - symbol, - c_end.strftime("%Y-%m-%d"), - end_date, - interval, - use_cache=False, - asset_type=asset_type, - period_mode=period_requested, - ) - if df_a is not None and not df_a.empty: - merged = ( - pd.concat([merged, df_a]) - .sort_index() - .loc[lambda df: ~df.index.duplicated(keep="last")] - ) - except Exception: - pass - - return merged - - # Determine asset type if not provided - if not asset_type: - asset_type = self._detect_asset_type(symbol) - - # Get appropriate sources for asset type - suitable_sources = self._get_sources_for_asset_type(asset_type) - - # Try each source in priority order - for source in suitable_sources: - try: - # Pass asset_type to enable symbol transformation - kwargs["asset_type"] = asset_type - data = source.fetch_data( - symbol, start_date, end_date, interval, **kwargs - ) - if data is not None and not data.empty: - # Always write-through to cache on a fresh fetch. - # Use split-caching: store 'full' when provider period requested, else 'recent'. - cache_kind = "full" if period_requested else "recent" - ttl_hours = 24 if cache_kind == "recent" else 24 * 30 - try: - self.cache_manager.cache_data( - symbol, - data, - interval, - source.config.name, - data_type=cache_kind, - ttl_hours=ttl_hours, - ) - except Exception as e: - self.logger.warning( - "Failed to cache data for %s from %s: %s", - symbol, - source.config.name, - e, - ) - - self.logger.info( - "Successfully fetched %s from %s", symbol, source.config.name - ) - # Freshness check for daily bars - if interval == "1d": - try: - last_bar = data.index[-1].date() - from pandas.tseries.offsets import BDay - - expected = ( - pd.Timestamp(datetime.utcnow().date()) - BDay(1) - ).date() - if last_bar < expected: - self.logger.warning( - "Data for %s seems stale: last=%s expected>=%s", - symbol, - last_bar, - expected, - ) - except Exception: - pass - return data - - except Exception as e: - self.logger.warning( - "Source %s failed for %s: %s", source.config.name, symbol, e - ) - continue - - self.logger.error("All sources failed for %s", symbol) - return None - - def get_batch_data( - self, - symbols: List[str], - start_date: str, - end_date: str, - interval: str = "1d", - use_cache: bool = True, - asset_type: str | None = None, - **kwargs, - ) -> Dict[str, pd.DataFrame]: - """Get data for multiple symbols with intelligent batching and cache-first behavior.""" - result: Dict[str, pd.DataFrame] = {} - - # Group symbols by asset type for optimal source selection - symbol_groups = self._group_symbols_by_type(symbols, asset_type) - - for group_type, group_symbols in symbol_groups.items(): - sources = self._get_sources_for_asset_type(group_type) - - # If caching enabled, try to satisfy from cache first to avoid external requests - missing_symbols: List[str] = [] - if use_cache: - for symbol in list(group_symbols): - try: - full_df = self.cache_manager.get_data( - symbol, start_date, end_date, interval, data_type="full" - ) - recent_df = self.cache_manager.get_data( - symbol, start_date, end_date, interval, data_type="recent" - ) - merged = None - if ( - full_df is not None - and not full_df.empty - and recent_df is not None - and not recent_df.empty - ): - merged = ( - pd.concat([full_df, recent_df]) - .sort_index() - .loc[lambda df: ~df.index.duplicated(keep="last")] - ) - elif full_df is not None and not full_df.empty: - merged = full_df - elif recent_df is not None and not recent_df.empty: - merged = recent_df - - if merged is not None and not merged.empty: - result[symbol] = merged - # Track that we used cache for this symbol - continue - missing_symbols.append(symbol) - except Exception as e: - self.logger.warning("Cache lookup failed for %s: %s", symbol, e) - missing_symbols.append(symbol) - else: - missing_symbols = list(group_symbols) - - # Try batch-capable sources for missing symbols - for source in sources: - if not missing_symbols: - break - - if source.config.supports_batch and len(missing_symbols) > 1: - try: - batch_data = source.fetch_batch_data( - missing_symbols, start_date, end_date, interval, **kwargs - ) - - # Add fetched data to result and update cache - fetched_symbols = [] - for symbol, data in batch_data.items(): - if data is not None and not data.empty: - result[symbol] = data - fetched_symbols.append(symbol) - if use_cache: - try: - self.cache_manager.cache_data( - symbol, data, interval, source.config.name - ) - except Exception as e: - self.logger.warning( - "Failed to cache data for %s from %s: %s", - symbol, - source.config.name, - e, - ) - - # Remove fetched symbols from missing list - if fetched_symbols: - missing_symbols = [ - s for s in missing_symbols if s not in fetched_symbols - ] - - except Exception as e: - self.logger.warning( - "Batch fetch failed from %s: %s", source.config.name, e - ) - - # Fall back to individual requests for any remaining missing symbols - for symbol in missing_symbols: - try: - individual_data = self.get_data( - symbol, - start_date, - end_date, - interval, - use_cache, - group_type, - **kwargs, - ) - if individual_data is not None: - result[symbol] = individual_data - except Exception as e: - self.logger.warning("Individual fetch failed for %s: %s", symbol, e) - - return result - - def get_crypto_futures_data( - self, - symbol: str, - start_date: str, - end_date: str, - interval: str = "1d", - use_cache: bool = True, - ) -> Optional[pd.DataFrame]: - """Get crypto futures data specifically from Bybit.""" - bybit_source = self.sources.get("bybit") - if not bybit_source: - self.logger.error("Bybit source not available for futures data") - return None - - # Check cache first - if use_cache: - cached_data = self.cache_manager.get_data( - symbol, start_date, end_date, interval, "futures" - ) - if cached_data is not None: - return cached_data - - try: - data = bybit_source.fetch_data( - symbol, start_date, end_date, interval, category="linear" - ) - - if data is not None and use_cache: - self.cache_manager.cache_data( - symbol, data, interval, "bybit", data_type="futures" - ) - - return data - - except Exception as e: - self.logger.error("Failed to fetch futures data for %s: %s", symbol, e) - return None - - def _detect_asset_type(self, symbol: str) -> str: - """Detect asset type from symbol.""" - symbol_upper = symbol.upper() - - # Crypto patterns - if any( - pattern in symbol_upper for pattern in ["USDT", "BTC", "ETH", "BNB", "ADA"] - ) or (symbol_upper.endswith("USD") and len(symbol_upper) > 6): - return "crypto" - if "-USD" in symbol_upper: - return "crypto" - - # Forex patterns - if symbol_upper.endswith("=X") or len(symbol_upper) == 6: - return "forex" - - # Futures patterns - if symbol_upper.endswith("=F"): - return "commodities" - - # Default to stocks - return "stocks" - - # Global override for source ordering per asset type (process-wide) - _global_source_order_overrides: dict[str, list[str]] = {} - - @classmethod - def set_source_order_override( - cls, asset_type: str, ordered_sources: list[str] - ) -> None: - cls._global_source_order_overrides[asset_type] = list(ordered_sources) - - def _get_sources_for_asset_type(self, asset_type: str) -> List[DataSource]: - """Get appropriate sources for asset type, sorted by priority or override.""" - suitable_sources = [] - - for source in self.sources.values(): - if not source.config.asset_types or asset_type in source.config.asset_types: - suitable_sources.append(source) - - # Optional filtering for crypto: allow disabling Yahoo/AlphaVantage via env, - # and prefer Bybit/Twelve when available to reduce noisy fallbacks. - if asset_type == "crypto": - import os as _os - - disable_yahoo = _os.getenv("DISABLE_YAHOO_CRYPTO", "false").lower() in { - "1", - "true", - "yes", - } - disable_av = _os.getenv("DISABLE_AV_CRYPTO", "false").lower() in { - "1", - "true", - "yes", - } - names = {s.config.name for s in suitable_sources} - has_primary = any(n in names for n in {"bybit", "twelve_data"}) - if disable_yahoo or has_primary: - suitable_sources = [ - s for s in suitable_sources if s.config.name != "yahoo_finance" - ] - if disable_av or has_primary: - suitable_sources = [ - s for s in suitable_sources if s.config.name != "alpha_vantage" - ] - - override = self._global_source_order_overrides.get(asset_type) - if override: - order_idx = {name: i for i, name in enumerate(override)} - suitable_sources.sort(key=lambda x: order_idx.get(x.config.name, 10_000)) - else: - if asset_type == "crypto": - suitable_sources.sort( - key=lambda x: (0 if x.config.name == "bybit" else x.config.priority) - ) - else: - suitable_sources.sort(key=lambda x: x.config.priority) - - return suitable_sources - - def probe_and_set_order( - self, - asset_type: str, - symbols: list[str], - interval: str = "1d", - sample_size: int = 5, - ) -> list[str]: - """Probe sources for coverage and set a global ordering by longest history. - - Skips cache and uses provider period='max'. Returns ordered source names. - """ - sym_sample = symbols[: max(1, min(sample_size, len(symbols)))] - candidates = [s for s in self._get_sources_for_asset_type(asset_type)] - scores: list[tuple[str, int, pd.Timestamp | None]] = [] - - for src in candidates: - total_rows = 0 - earliest: pd.Timestamp | None = None - for s in sym_sample: - try: - df = src.fetch_data( - s, - start_date="1900-01-01", - end_date=datetime.utcnow().date().isoformat(), - interval=interval, - asset_type=asset_type, - period="max", - period_mode="max", - ) - if df is not None and not df.empty: - total_rows += len(df) - f = df.index[0] - earliest = f if earliest is None or f < earliest else earliest - except Exception as exc: - self.logger.debug( - "Probe error for %s via %s: %s", s, src.config.name, exc - ) - continue - scores.append((src.config.name, total_rows, earliest)) - - def _key(t: tuple[str, int, pd.Timestamp | None]): - name, rows, first = t - first_val = first.value if hasattr(first, "value") else 2**63 - 1 - return (-rows, first_val) - - ordered = [name for name, *_ in sorted(scores, key=_key)] - if ordered: - self.set_source_order_override(asset_type, ordered) - return ordered - - def _group_symbols_by_type( - self, symbols: List[str], default_type: Optional[str] = None - ) -> Dict[str, List[str]]: - """Group symbols by detected asset type.""" - groups: Dict[str, List[str]] = {} - - for symbol in symbols: - asset_type = default_type or self._detect_asset_type(symbol) - if asset_type not in groups: - groups[asset_type] = [] - groups[asset_type].append(symbol) - - return groups - - def get_available_crypto_futures(self) -> List[str]: - """Get available crypto futures symbols.""" - bybit_source = self.sources.get("bybit") - if bybit_source: - return bybit_source.get_futures_symbols() - return [] - - def get_source_status(self) -> Dict[str, Dict[str, Any]]: - """Get status of all data sources.""" - status = {} - for name, source in self.sources.items(): - status[name] = { - "priority": source.config.priority, - "rate_limit": source.config.rate_limit, - "supports_batch": source.config.supports_batch, - "supports_futures": source.config.supports_futures, - "asset_types": source.config.asset_types, - "max_symbols_per_request": source.config.max_symbols_per_request, - } - return status - - -# Additional Data Sources - - -class EnhancedAlphaVantageSource(DataSource): - """Enhanced Alpha Vantage data source - excellent for stocks, forex, crypto.""" - - def __init__(self) -> None: - config = DataSourceConfig( - name="alpha_vantage_enhanced", - priority=2, - rate_limit=5.0, # 5 calls per minute for free tier - max_retries=3, - timeout=30.0, - supports_batch=False, - asset_types=["stock", "forex", "crypto", "commodity"], - ) - super().__init__(config) - self.api_key = os.getenv("ALPHA_VANTAGE_API_KEY", "demo") - self.base_url = "https://www.alphavantage.co/query" - - def transform_symbol(self, symbol: str, asset_type: str | None = None) -> str: - """Transform symbol for Alpha Vantage format.""" - # Alpha Vantage forex format (no =X suffix) - if "=X" in symbol: - return symbol.replace("=X", "") - - # Alpha Vantage crypto format (no dash) - if "-USD" in symbol: - return symbol.replace("-USD", "USD") - - return symbol - - def fetch_data( - self, - symbol: str, - start_date: datetime, - end_date: datetime, - interval: str = "1d", - **kwargs, - ) -> Optional[pd.DataFrame]: - """Fetch data from Alpha Vantage.""" - try: - self._rate_limit() - - # Transform symbol to Alpha Vantage format - asset_type = kwargs.get("asset_type") - transformed_symbol = self.transform_symbol(symbol, asset_type) - - # Map intervals - av_interval = self._map_interval(interval) - function = self._get_function(transformed_symbol, interval) - - params = { - "function": function, - "symbol": transformed_symbol, - "apikey": self.api_key, - "outputsize": "full", - "datatype": "json", - } - - if interval in ["1min", "5min", "15min", "30min", "60min"]: - params["interval"] = av_interval - - response = self.session.get( - self.base_url, params=params, timeout=self.config.timeout - ) - response.raise_for_status() - - data = response.json() - - # Check for API errors - if "Error Message" in data: - self.logger.error("Alpha Vantage error: %s", data["Error Message"]) - return None - - if "Note" in data: - self.logger.warning("Alpha Vantage rate limit: %s", data["Note"]) - return None - - # Parse data - time_series_key = self._get_time_series_key(data) - if not time_series_key: - return None - - df = self._parse_time_series(data[time_series_key]) - if df is not None: - df = self._filter_date_range(df, start_date, end_date) - - return df - - except Exception as e: - self.logger.error("Error fetching %s from Alpha Vantage: %s", symbol, e) - return None - - def _map_interval(self, interval: str) -> str: - """Map internal intervals to Alpha Vantage intervals.""" - mapping = { - "1min": "1min", - "5min": "5min", - "15min": "15min", - "30min": "30min", - "1h": "60min", - "1d": "daily", - } - return mapping.get(interval, "daily") - - def _get_function(self, symbol: str, interval: str) -> str: - """Get appropriate Alpha Vantage function.""" - if "/" in symbol: # Forex - if interval == "1d": - return "FX_DAILY" - return "FX_INTRADAY" - if any(crypto in symbol.upper() for crypto in ["BTC", "ETH", "LTC", "XRP"]): - if interval == "1d": - return "DIGITAL_CURRENCY_DAILY" - return "CRYPTO_INTRADAY" - # Stocks - if interval == "1d": - return "TIME_SERIES_DAILY" - return "TIME_SERIES_INTRADAY" - - def _get_time_series_key(self, data: dict) -> Optional[str]: - """Find the time series key in the response.""" - for key in data: - if "Time Series" in key: - return key - return None - - def _parse_time_series(self, time_series: dict) -> Optional[pd.DataFrame]: - """Parse time series data into DataFrame.""" - try: - df = pd.DataFrame.from_dict(time_series, orient="index") - df.index = pd.to_datetime(df.index) - df = df.sort_index() - - # Standardize column names - column_mapping = {} - for col in df.columns: - if "open" in col.lower(): - column_mapping[col] = "Open" - elif "high" in col.lower(): - column_mapping[col] = "High" - elif "low" in col.lower(): - column_mapping[col] = "Low" - elif "close" in col.lower(): - column_mapping[col] = "Close" - elif "volume" in col.lower(): - column_mapping[col] = "Volume" - - df = df.rename(columns=column_mapping) - - # Convert to numeric - for col in ["Open", "High", "Low", "Close", "Volume"]: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce") - - return df - - except Exception as e: - self.logger.error("Error parsing Alpha Vantage data: %s", e) - return None - - def fetch_batch_data( - self, - symbols: list[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs: Any, - ) -> dict[str, pd.DataFrame]: - """Fetch data for multiple symbols.""" - result = {} - for symbol in symbols: - data = self.fetch_data(symbol, start_date, end_date, interval, **kwargs) - if data is not None: - result[symbol] = data - return result - - def get_available_symbols(self, asset_type: str | None = None) -> list[str]: - """Get available symbols for this source.""" - # Alpha Vantage doesn't provide a comprehensive symbol list - # Return common symbols based on asset type - if asset_type == "stock": - return ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META"] - if asset_type == "forex": - return ["EUR/USD", "GBP/USD", "USD/JPY", "USD/CHF"] - if asset_type == "crypto": - return ["BTC/USD", "ETH/USD", "LTC/USD", "XRP/USD"] - return [] - - -class TwelveDataSource(DataSource): - """Twelve Data source - excellent coverage for stocks, forex, crypto, indices.""" - - def __init__(self) -> None: - config = DataSourceConfig( - name="twelve_data", - priority=2, - rate_limit=1.0, # 8 requests per minute for free tier - max_retries=3, - timeout=30.0, - supports_batch=True, - max_symbols_per_request=8, - asset_types=["stock", "forex", "crypto", "index", "etf"], - ) - super().__init__(config) - self.api_key = os.getenv("TWELVE_DATA_API_KEY", "demo") - self.base_url = "https://api.twelvedata.com" - - def transform_symbol(self, symbol: str, asset_type: str | None = None) -> str: - """Transform symbol for Twelve Data format.""" - # Twelve Data forex format (use slash format) - if "=X" in symbol: - base_symbol = symbol.replace("=X", "") - if len(base_symbol) == 6: # EURUSD -> EUR/USD - return f"{base_symbol[:3]}/{base_symbol[3:]}" - - # Twelve Data crypto format (no dash) - up = symbol.upper() - if "-USD" in up: - return up.replace("-USD", "USD") - if "-USDT" in up: - up = up.replace("-USDT", "/USDT") - # fallthrough to exchange append below - # return here was removed to allow exchange tagging - # IMXUSDT -> IMX/USDT - if up.endswith("USDT") and "/" not in up and "-" not in up: - up = f"{up[:-4]}/USDT" - - # Optional exchange routing for crypto, e.g., IMX/USDT:BINANCE - try: - import os as _os - - exch = _os.getenv("TWELVE_DATA_CRYPTO_EXCHANGE", "").strip() - if exch and ":" not in up and (asset_type == "crypto" or "/" in up): - up = f"{up}:{exch.upper()}" - except Exception: - pass - - return up - - def fetch_data( - self, - symbol: str, - start_date: datetime, - end_date: datetime, - interval: str = "1d", - **kwargs, - ) -> Optional[pd.DataFrame]: - """Fetch data from Twelve Data.""" - try: - self._rate_limit() - - # Transform symbol to Twelve Data format - asset_type = kwargs.get("asset_type") - transformed_symbol = self.transform_symbol(symbol, asset_type) - - # Coerce dates (supports strings or datetime-like) - start_dt = pd.to_datetime(start_date, errors="coerce") - end_dt = pd.to_datetime(end_date, errors="coerce") - if pd.isna(end_dt): - end_dt = pd.Timestamp.utcnow() - if pd.isna(start_dt): - # default to one year window for daily; smaller for intraday - if interval in {"1m", "5m", "15m", "30m", "1h", "4h"}: - start_dt = end_dt - pd.Timedelta(days=30) - else: - start_dt = end_dt - pd.Timedelta(days=365) - - params = { - "symbol": transformed_symbol, - "interval": self._map_interval(interval), - "start_date": start_dt.strftime("%Y-%m-%d"), - "end_date": end_dt.strftime("%Y-%m-%d"), - "apikey": self.api_key, - "format": "JSON", - "outputsize": 5000, - } - - url = f"{self.base_url}/time_series" - response = self.session.get(url, params=params, timeout=self.config.timeout) - response.raise_for_status() - - data = response.json() - - if "code" in data and data["code"] != 200: - self.logger.error( - "Twelve Data error: %s", data.get("message", "Unknown error") - ) - return None - - if "values" not in data: - self.logger.warning("No data returned for %s", symbol) - return None - - return self._parse_twelve_data(data["values"]) - - except Exception as e: - self.logger.error("Error fetching %s from Twelve Data: %s", symbol, e) - return None - - def _map_interval(self, interval: str) -> str: - """Map internal intervals to Twelve Data intervals.""" - mapping = { - "1min": "1min", - "5min": "5min", - "15min": "15min", - "30min": "30min", - "1h": "1h", - "4h": "4h", - "1d": "1day", - "1wk": "1week", - } - return mapping.get(interval, "1day") - - def _parse_twelve_data(self, values: list) -> Optional[pd.DataFrame]: - """Parse Twelve Data values into DataFrame.""" - try: - df = pd.DataFrame(values) - df["datetime"] = pd.to_datetime(df["datetime"]) - df = df.set_index("datetime") - - # Convert to numeric and rename columns - for col in ["open", "high", "low", "close", "volume"]: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce") - - df = df.rename( - columns={ - "open": "Open", - "high": "High", - "low": "Low", - "close": "Close", - "volume": "Volume", - } - ) - - # Select standard columns - columns = ["Open", "High", "Low", "Close"] - if "Volume" in df.columns: - columns.append("Volume") - - df = df[columns] - return df.sort_index() - - except Exception as e: - self.logger.error("Error parsing Twelve Data: %s", e) - return None - - def fetch_batch_data( - self, - symbols: list[str], - start_date: str, - end_date: str, - interval: str = "1d", - **kwargs: Any, - ) -> dict[str, pd.DataFrame]: - """Fetch data for multiple symbols.""" - result = {} - for symbol in symbols: - data = self.fetch_data(symbol, start_date, end_date, interval, **kwargs) - if data is not None: - result[symbol] = data - return result - - def get_available_symbols(self, asset_type: str | None = None) -> list[str]: - """Get available symbols for this source.""" - # Twelve Data doesn't provide a comprehensive symbol list in free tier - # Return common symbols based on asset type - if asset_type == "stock": - return ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META", "NVDA", "NFLX"] - if asset_type == "forex": - return ["EUR/USD", "GBP/USD", "USD/JPY", "USD/CHF", "AUD/USD", "USD/CAD"] - if asset_type == "crypto": - return ["BTC/USD", "ETH/USD", "LTC/USD", "XRP/USD", "ADA/USD", "DOT/USD"] - return [] - - -# (end of module) diff --git a/src/core/direct_backtest.py b/src/core/direct_backtest.py deleted file mode 100644 index 02d69e8..0000000 --- a/src/core/direct_backtest.py +++ /dev/null @@ -1,880 +0,0 @@ -""" -Direct Backtesting Library Integration -Direct backtesting using the backtesting library. - -This file was extended to support optional persistence of backtest results into the -project database via the lightweight unified_models helper (src.database.unified_models). -Pass an optional persistence_context (dict) to run_direct_backtest / run_strategy_comparison -to enable DB writes. Persistence is best-effort: guarded imports and safe upsert logic. -""" - -from __future__ import annotations - -import logging -from typing import Any, Dict, List, Optional - -import pandas as pd -from backtesting import Backtest - -from .backtest_engine import create_backtesting_strategy_adapter -from .data_manager import UnifiedDataManager -from .strategy import StrategyFactory - - -# Local utilities used when persisting -def _persist_result_to_db( - result: Dict[str, Any], persistence_context: Dict[str, Any] -) -> None: - """ - Persist a single backtest result into the DB using src.database.unified_models. - This function is best-effort and will not raise on failures (only logs). - persistence_context must include at least: - - run_id (str) - - target_metric (str) optional - - session_factory or rely on unified_models.Session - Added debug logging to help diagnose missing metrics persistence. - """ - logger = logging.getLogger(__name__) - logger.debug( - "Called _persist_result_to_db for symbol=%s strategy=%s", - result.get("symbol"), - result.get("strategy"), - ) - logger.debug( - "Persistence context keys: %s", - list((persistence_context or {}).keys()) - if persistence_context is not None - else None, - ) - - try: - from src.database import unified_models # type: ignore[import-not-found] - from src.utils.trades_parser import ( - parse_trades_from_string, # type: ignore[import-not-found] - ) - except Exception: - logger.debug( - "Persistence not available (unified_models or trades parser missing)" - ) - return - - try: - sess = unified_models.Session() - except Exception as e: - logger.exception("Failed to create unified_models.Session(): %s", e) - return - try: - run_id = persistence_context.get("run_id") - # If run_id is missing or falsy, avoid attempting DB writes which will violate NOT NULL constraints. - if not run_id: - logging.getLogger(__name__).debug( - "Persistence context provided but run_id is missing; skipping DB persistence for %s", - result.get("symbol"), - ) - return - - # Check for existing BacktestResult (idempotency) - existing = ( - sess.query(unified_models.BacktestResult) - .filter( - unified_models.BacktestResult.run_id == run_id, - unified_models.BacktestResult.symbol == result.get("symbol"), - unified_models.BacktestResult.strategy == result.get("strategy"), - unified_models.BacktestResult.interval == result.get("timeframe"), - ) - .one_or_none() - ) - - # Prepare payload - metrics = result.get("metrics") or {} - # Try to convert native backtesting stats to a plain dict unconditionally - raw_stats = result.get("bt_results") - engine_ctx = None - try: - if raw_stats is not None: - engine_ctx = ( - raw_stats if isinstance(raw_stats, dict) else dict(raw_stats) - ) - except Exception: - engine_ctx = None - - # If metrics were not provided, derive a few canonical ones from engine_ctx - # so downstream ranking (target_metric) has values to work with. - if not metrics and engine_ctx and isinstance(engine_ctx, dict): - try: - - def _as_float(v): - try: - return float(v) - except Exception: - return None - - # Backtesting.py common keys - sortino = engine_ctx.get("Sortino Ratio") - calmar = engine_ctx.get("Calmar Ratio") - sharpe = engine_ctx.get("Sharpe Ratio") - total_ret = engine_ctx.get("Return [%]") - max_dd = engine_ctx.get("Max. Drawdown [%]") or engine_ctx.get( - "Max Drawdown [%]" - ) - num_trades = engine_ctx.get("# Trades") - - derived = {} - if sortino is not None: - derived["sortino_ratio"] = _as_float(sortino) - derived["Sortino_Ratio"] = derived["sortino_ratio"] - if calmar is not None: - derived["calmar_ratio"] = _as_float(calmar) - derived["Calmar_Ratio"] = derived["calmar_ratio"] - if sharpe is not None: - derived["sharpe_ratio"] = _as_float(sharpe) - derived["Sharpe_Ratio"] = derived["sharpe_ratio"] - if total_ret is not None: - derived["total_return"] = _as_float(total_ret) - derived["Total_Return"] = derived["total_return"] - if max_dd is not None: - derived["max_drawdown"] = _as_float(max_dd) - derived["Max_Drawdown"] = derived["max_drawdown"] - if num_trades is not None: - # leave as float to keep consistent handling downstream - derived["num_trades"] = _as_float(num_trades) - - metrics = derived - except Exception: - # Best-effort only; leave metrics as-is if derivation fails - pass - - # Sanitize JSON-like payloads: replace NaN/Inf with None and convert numpy/pandas objects. - def _sanitize_jsonable(obj): - try: - import math - except Exception: - math = None - try: - import pandas as _pd # type: ignore[import-not-found] - except Exception: - _pd = None - try: - import numpy as _np # type: ignore[import-not-found] - except Exception: - _np = None - - # Pandas DataFrame/Series first: convert then recurse to sanitize nested values - try: - if _pd is not None and isinstance(obj, _pd.DataFrame): - recs = obj.to_dict(orient="records") - return _sanitize_jsonable(recs) - if _pd is not None and isinstance(obj, _pd.Series): - return _sanitize_jsonable(obj.to_dict()) - except Exception: - pass - - # Primitive safe types - if obj is None: - return None - if isinstance(obj, (str, bool, int)): - return obj - # Floats: guard against NaN / Inf which are invalid in JSONB - if isinstance(obj, float): - try: - if math is not None and (math.isnan(obj) or math.isinf(obj)): - return None - except Exception: - return None - return obj - # Numpy scalars - try: - if _np is not None and isinstance(obj, _np.generic): - return _sanitize_jsonable(obj.item()) - except Exception: - pass - # Dicts and lists: recurse - if isinstance(obj, dict): - out = {} - for k, v in obj.items(): - try: - out[k] = _sanitize_jsonable(v) - except Exception: - out[k] = None - return out - if isinstance(obj, (list, tuple)): - return [_sanitize_jsonable(v) for v in obj] - # Fallback: try to coerce to string safely - try: - return str(obj) - except Exception: - return None - - # Apply sanitization before persisting into JSONB columns - try: - metrics = _sanitize_jsonable(metrics) - except Exception: - metrics = {} - try: - engine_ctx = _sanitize_jsonable(engine_ctx) - except Exception: - engine_ctx = None - - trades_raw = None - trades_obj = result.get("trades") - if trades_obj is not None: - try: - if isinstance(trades_obj, pd.DataFrame): - trades_raw = trades_obj.to_csv(index=False) - else: - # If it's a list/dict or other, try json - import json as _json # local import - - trades_raw = _json.dumps(trades_obj) - except Exception: - trades_raw = str(trades_obj) - - # Attach equity curve into engine_ctx for reporting if available - try: - eq = result.get("equity_curve") - if eq is not None: - if engine_ctx is None: - engine_ctx = {} - engine_ctx["_equity_curve"] = _sanitize_jsonable(eq) - # Re-sanitize engine_ctx to ensure no NaN/Inf slipped in - engine_ctx = _sanitize_jsonable(engine_ctx) - except Exception: - pass - - start_at = None - end_at = None - # Try to infer start/end from engine context or trades/data if present - if "start_date" in result and "end_date" in result: - try: - import dateutil.parser as _parser # type: ignore[import-not-found] - - start_at = _parser.parse(result["start_date"]) - end_at = _parser.parse(result["end_date"]) - except Exception: - start_at = None - end_at = None - - if existing: - # Update existing row (idempotent upsert behavior) - existing.metrics = metrics - existing.engine_ctx = engine_ctx - existing.trades_raw = trades_raw - existing.error = result.get("error") - if start_at is not None: - existing.start_at_utc = start_at - if end_at is not None: - existing.end_at_utc = end_at - sess.add(existing) - sess.flush() - result_id = existing.result_id - else: - br = unified_models.BacktestResult( - run_id=run_id, - symbol=result.get("symbol"), - strategy=result.get("strategy"), - interval=result.get("timeframe"), - start_at_utc=start_at, - end_at_utc=end_at, - rank_in_symbol=None, - metrics=metrics, - engine_ctx=engine_ctx, - trades_raw=trades_raw, - error=result.get("error"), - ) - sess.add(br) - sess.flush() - result_id = br.result_id - - # Persist trades normalized rows if possible - if trades_raw: - try: - # Ensure new optional columns exist (best-effort, safe if already present) - try: - unified_models.create_tables() - except Exception: - pass - parsed_trades = parse_trades_from_string(trades_raw) - # Cleanup existing trades for this result (to keep idempotent) - sess.query(unified_models.Trade).filter( - unified_models.Trade.result_id == result_id - ).delete() - for t in parsed_trades: - # Try to parse entry/exit timestamps if available - def _parse_dt(val): - try: - if val is None: - return None - import dateutil.parser as _parser # type: ignore[import-not-found] - - return _parser.parse(str(val)) - except Exception: - return None - - tr = unified_models.Trade( - result_id=result_id, - trade_index=int(t.get("trade_index", 0)), - entry_time=_parse_dt( - t.get("entry_time") - or t.get("EntryTime") - or t.get("entry time") - ), - exit_time=_parse_dt( - t.get("exit_time") - or t.get("ExitTime") - or t.get("exit time") - ), - size=str(t.get("size")) if t.get("size") is not None else None, - entry_bar=int(t.get("entry_bar")) - if t.get("entry_bar") is not None - else None, - exit_bar=int(t.get("exit_bar")) - if t.get("exit_bar") is not None - else None, - entry_price=str(t.get("entry_price")) - if t.get("entry_price") is not None - else None, - exit_price=str(t.get("exit_price")) - if t.get("exit_price") is not None - else None, - pnl=str(t.get("pnl")) if t.get("pnl") is not None else None, - duration=str(t.get("duration")) - if t.get("duration") is not None - else None, - tag=str(t.get("tag")) if t.get("tag") is not None else None, - entry_signals=str(t.get("entry_signals")) - if t.get("entry_signals") is not None - else None, - exit_signals=str(t.get("exit_signals")) - if t.get("exit_signals") is not None - else None, - ) - sess.add(tr) - sess.flush() - except Exception: - logging.getLogger(__name__).exception( - "Failed to persist trades for result %s", result.get("symbol") - ) - - sess.commit() - except Exception: - sess.rollback() - logging.getLogger(__name__).exception( - "Failed to persist backtest result for %s", result.get("symbol") - ) - finally: - sess.close() - - -def finalize_persistence_for_run(run_id: str, target_metric: Optional[str]) -> None: - """ - Finalize DB persistence for a run: compute per-symbol ranks by target metric, - upsert SymbolAggregate summaries and canonical BestStrategy rows. - - This is a best-effort helper and will log/continue on failures. - """ - if not run_id or not target_metric: - logging.getLogger(__name__).debug( - "finalize_persistence_for_run skipped (missing run_id or target_metric)" - ) - return - - def _is_higher_better(metric_name: str) -> bool: - mn = (metric_name or "").lower() - if "drawdown" in mn or "max_drawdown" in mn or "mdd" in mn: - return False - return True - - sess = None - try: - from src.database import unified_models # type: ignore[import-not-found] - - sess = unified_models.Session() - - # Get distinct symbols for run - symbols = ( - sess.query(unified_models.BacktestResult.symbol) - .filter(unified_models.BacktestResult.run_id == run_id) - .distinct() - .all() - ) - symbols = [s[0] for s in symbols] - - for symbol in symbols: - rows = ( - sess.query(unified_models.BacktestResult) - .filter( - unified_models.BacktestResult.run_id == run_id, - unified_models.BacktestResult.symbol == symbol, - ) - .all() - ) - - entries = [] - higher_better = _is_higher_better(target_metric) - for r in rows: - mval = None - try: - if r.metrics and isinstance(r.metrics, dict): - raw = r.metrics.get(target_metric) - mval = None if raw is None else float(raw) - except Exception as exc: - logging.getLogger(__name__).debug( - "Failed to parse metric %s: %s", target_metric, exc - ) - sort_key = ( - (float("-inf") if higher_better else float("inf")) - if mval is None - else mval - ) - entries.append((sort_key, mval is None, r)) - - entries.sort(key=lambda x: x[0], reverse=higher_better) - - for idx, (_sk, _is_null, row) in enumerate(entries): - row.rank_in_symbol = idx + 1 - sess.add(row) - - if entries: - best_row = entries[0][2] - topn = [] - for e in entries[:3]: - r = e[2] - topn.append( - { - "strategy": r.strategy, - "interval": r.interval, - "rank": r.rank_in_symbol, - "metric": None - if r.metrics is None - else r.metrics.get(target_metric), - } - ) - # Upsert SymbolAggregate - existing_agg = ( - sess.query(unified_models.SymbolAggregate) - .filter( - unified_models.SymbolAggregate.run_id == run_id, - unified_models.SymbolAggregate.symbol == symbol, - unified_models.SymbolAggregate.best_by == target_metric, - ) - .one_or_none() - ) - summary = {"top": topn} - if existing_agg: - existing_agg.best_result = best_row.result_id - existing_agg.summary = summary - sess.add(existing_agg) - else: - agg = unified_models.SymbolAggregate( - run_id=run_id, - symbol=symbol, - best_by=target_metric, - best_result=best_row.result_id, - summary=summary, - ) - sess.add(agg) - - # Upsert BestStrategy - try: - bs_existing = ( - sess.query(unified_models.BestStrategy) - .filter( - unified_models.BestStrategy.symbol == symbol, - unified_models.BestStrategy.timeframe == best_row.interval, - ) - .one_or_none() - ) - - def _num(mdict, key): - try: - if mdict and isinstance(mdict, dict): - v = mdict.get(key) - return float(v) if v is not None else None - except Exception: - return None - return None - - sortino_val = _num(best_row.metrics, "sortino_ratio") or _num( - best_row.metrics, "Sortino_Ratio" - ) - calmar_val = _num(best_row.metrics, "calmar_ratio") or _num( - best_row.metrics, "Calmar_Ratio" - ) - sharpe_val = _num(best_row.metrics, "sharpe_ratio") or _num( - best_row.metrics, "Sharpe_Ratio" - ) - total_return_val = _num(best_row.metrics, "total_return") or _num( - best_row.metrics, "Total_Return" - ) - max_dd_val = _num(best_row.metrics, "max_drawdown") or _num( - best_row.metrics, "Max_Drawdown" - ) - - from datetime import datetime as _dt - - if bs_existing: - bs_existing.strategy = best_row.strategy - bs_existing.sortino_ratio = sortino_val - bs_existing.calmar_ratio = calmar_val - bs_existing.sharpe_ratio = sharpe_val - bs_existing.total_return = total_return_val - bs_existing.max_drawdown = max_dd_val - bs_existing.backtest_result_id = getattr( - best_row, "result_id", None - ) - bs_existing.updated_at = _dt.utcnow() - sess.add(bs_existing) - else: - bs = unified_models.BestStrategy( - symbol=symbol, - timeframe=best_row.interval, - strategy=best_row.strategy, - sortino_ratio=sortino_val, - calmar_ratio=calmar_val, - sharpe_ratio=sharpe_val, - total_return=total_return_val, - max_drawdown=max_dd_val, - backtest_result_id=getattr(best_row, "result_id", None), - updated_at=_dt.utcnow(), - ) - sess.add(bs) - except Exception: - logging.getLogger(__name__).exception( - "Failed to upsert BestStrategy for %s", symbol - ) - - sess.commit() - except Exception: - try: - if sess: - sess.rollback() - except Exception: - pass - logging.getLogger(__name__).exception( - "Failed to finalize ranks/aggregates for run %s", run_id - ) - finally: - try: - if sess: - sess.close() - except Exception: - pass - - -def run_direct_backtest( - symbol: str, - strategy_name: str, - start_date: str, - end_date: str, - timeframe: str = "1d", - initial_capital: float = 10000.0, - commission: float = 0.001, - period: Optional[str] = None, - use_cache: bool = True, - persistence_context: Optional[Dict[str, Any]] = None, -) -> Dict[str, Any]: - """ - Run backtest using backtesting library directly. - Returns ground truth results without wrapper complexity. - - If persistence_context is provided (dict), the function will attempt to persist - the result into the DB via src.database.unified_models. - """ - logger = logging.getLogger(__name__) - - try: - # Get data - data_manager = UnifiedDataManager() - # If 'period' is provided, data sources like Yahoo will prefer it over start/end. - data = data_manager.get_data( - symbol, - start_date, - end_date, - timeframe, - use_cache=use_cache, - period=period, - period_mode=period, - ) - - if data is None or data.empty: - res = { - "symbol": symbol, - "strategy": strategy_name, - "timeframe": timeframe, - "error": "No data available", - "metrics": {}, - "trades": None, - "backtest_object": None, - } - # Attempt to persist even no-data case - if persistence_context: - _persist_result_to_db(res, persistence_context) - return res - - # Prepare data for backtesting library - bt_data = data.rename( - columns={ - "open": "Open", - "high": "High", - "low": "Low", - "close": "Close", - "volume": "Volume", - } - )[["Open", "High", "Low", "Close", "Volume"]] - - # Convert to UTC then remove timezone for backtesting library compatibility - if bt_data.index.tz is None: - bt_data.index = bt_data.index.tz_localize("UTC") - else: - bt_data.index = bt_data.index.tz_convert("UTC") - bt_data.index = bt_data.index.tz_localize(None) - - # Create strategy - strategy = StrategyFactory.create_strategy(strategy_name) - StrategyClass = create_backtesting_strategy_adapter(strategy) - - # Run backtest with backtesting library - bt = Backtest( - bt_data, - StrategyClass, - cash=initial_capital, - commission=commission, - finalize_trades=True, # Ensure all trades are captured - ) - - # Run and keep native stats object from backtesting library - result = bt.run() - - # Extract trades if available - trades = None - if hasattr(result, "_trades") and not result._trades.empty: - trades = result._trades.copy() - - # Extract equity curve if available - equity_curve = None - try: - if hasattr(result, "_equity_curve") and result._equity_curve is not None: - equity_curve = result._equity_curve.copy() - except Exception: - equity_curve = None - - ret = { - "symbol": symbol, - "strategy": strategy_name, - "timeframe": timeframe, - "error": None, - # Do not extract custom metrics; return native stats instead - "metrics": None, - "trades": trades, - "equity_curve": equity_curve, - "backtest_object": bt, # Include for plotting - "bt_results": result, # Native stats/series from backtesting library - "start_date": start_date, - "end_date": end_date, - } - - # Persist if requested - if persistence_context: - try: - _persist_result_to_db(ret, persistence_context) - except Exception: - logger.exception( - "Failed to persist result for %s/%s", symbol, strategy_name - ) - - return ret - - except Exception as e: - logger.error("Direct backtest failed for %s/%s: %s", symbol, strategy_name, e) - res = { - "symbol": symbol, - "strategy": strategy_name, - "timeframe": timeframe, - "error": str(e), - "metrics": {}, - "trades": None, - "backtest_object": None, - } - if persistence_context: - _persist_result_to_db(res, persistence_context) - return res - - -def run_strategy_comparison( - symbol: str, - strategies: List[str], - start_date: str, - end_date: str, - timeframe: str = "1d", - initial_capital: float = 10000.0, - persistence_context: Optional[Dict[str, Any]] = None, -) -> Dict[str, Any]: - """ - Compare multiple strategies for a symbol using backtesting library. - Returns complete analysis with rankings and plot data. - - If persistence_context is provided, each individual strategy result will be persisted. - """ - logger = logging.getLogger(__name__) - logger.info( - "Running strategy comparison for %s: %d strategies", symbol, len(strategies) - ) - - results = [] - best_result = None - best_sortino = -999.0 - - for strategy_name in strategies: - result = run_direct_backtest( - symbol, - strategy_name, - start_date, - end_date, - timeframe, - initial_capital, - persistence_context=persistence_context, - ) - - results.append(result) - - # Track best strategy by Sortino: prefer native bt_results, fallback to metrics['sortino_ratio'] - if not result["error"]: - try: - native = result.get("bt_results") or {} - sortino = native.get("Sortino Ratio", None) - if sortino is None: - # Fallback to normalized metrics key when native field absent - sortino = (result.get("metrics") or {}).get("sortino_ratio") - sortino_val = float("nan") if sortino is None else float(sortino) - except Exception as exc: - logging.getLogger(__name__).debug("Failed to parse Sortino: %s", exc) - sortino_val = float("nan") - - # Treat NaN as very poor - if sortino_val == sortino_val and sortino_val > best_sortino: - best_sortino = sortino_val - best_result = result - - # Sort by native Sortino Ratio - def _sort_key(res: Dict[str, Any]) -> float: - try: - native = res.get("bt_results") or {} - v = native.get("Sortino Ratio", None) - if v is None: - v = (res.get("metrics") or {}).get("sortino_ratio") - val = float(v) if v is not None else float("nan") - # push NaN to the end by returning -inf when NaN - return val if val == val else float("-inf") - except Exception: - return float("-inf") - - results.sort(key=_sort_key, reverse=True) - - # Add rankings - for i, result in enumerate(results): - result["rank"] = i + 1 - - out = { - "symbol": symbol, - "timeframe": timeframe, - "results": results, - "best_strategy": best_result, - "total_strategies": len(strategies), - "successful_strategies": len( - [ - r - for r in results - if not r["error"] - and (lambda _n: (float(_n) if _n is not None else 0.0) > 0.0)( - (r.get("bt_results") or {}).get("# Trades", None) - ) - ] - ), - "date_range": f"{start_date} to {end_date}", - } - - # If persistence context contains a run_id and target_metric, finalize ranking/aggregates - try: - run_id = persistence_context.get("run_id") if persistence_context else None - target_metric = ( - persistence_context.get("target_metric") if persistence_context else None - ) - finalize_persistence_for_run(run_id, target_metric) - except Exception: - logging.getLogger(__name__).debug( - "No persistence_context provided or failed to finalize ranks/aggregates" - ) - - # Safety net: directly upsert BestStrategy from in-memory best_result when possible. - # This covers environments where DB state wasn't fully populated yet by finalize. - try: - if persistence_context and best_result and best_result.get("strategy"): - from src.database import unified_models # type: ignore[import-not-found] - - sess = unified_models.Session() - try: - bs_existing = ( - sess.query(unified_models.BestStrategy) - .filter( - unified_models.BestStrategy.symbol == symbol, - unified_models.BestStrategy.timeframe == timeframe, - ) - .one_or_none() - ) - - m = best_result.get("metrics") or {} - - def _num(d, k): - try: - if d and isinstance(d, dict): - v = d.get(k) - return float(v) if v is not None else None - except Exception: - return None - return None - - sortino_val = _num(m, "sortino_ratio") or _num(m, "Sortino_Ratio") - calmar_val = _num(m, "calmar_ratio") or _num(m, "Calmar_Ratio") - sharpe_val = _num(m, "sharpe_ratio") or _num(m, "Sharpe_Ratio") - total_return_val = _num(m, "total_return") or _num(m, "Total_Return") - max_dd_val = _num(m, "max_drawdown") or _num(m, "Max_Drawdown") - - from datetime import datetime as _dt - - if bs_existing: - bs_existing.strategy = best_result.get("strategy") - bs_existing.sortino_ratio = sortino_val - bs_existing.calmar_ratio = calmar_val - bs_existing.sharpe_ratio = sharpe_val - bs_existing.total_return = total_return_val - bs_existing.max_drawdown = max_dd_val - bs_existing.updated_at = _dt.utcnow() - sess.add(bs_existing) - else: - bs = unified_models.BestStrategy( - symbol=symbol, - timeframe=timeframe, - strategy=best_result.get("strategy"), - sortino_ratio=sortino_val, - calmar_ratio=calmar_val, - sharpe_ratio=sharpe_val, - total_return=total_return_val, - max_drawdown=max_dd_val, - updated_at=_dt.utcnow(), - ) - sess.add(bs) - sess.commit() - except Exception: - try: - sess.rollback() - except Exception: - pass - finally: - try: - sess.close() - except Exception: - pass - except Exception: - logging.getLogger(__name__).debug("BestStrategy safety upsert skipped") - - return out diff --git a/src/core/external_strategy_loader.py b/src/core/external_strategy_loader.py deleted file mode 100644 index c26147b..0000000 --- a/src/core/external_strategy_loader.py +++ /dev/null @@ -1,335 +0,0 @@ -""" -External Strategy Loader - -Loads and manages external trading strategies from separate repositories. -Provides unified interface for strategy testing and execution. -""" - -from __future__ import annotations - -import importlib.util -import logging -import sys -from pathlib import Path -from typing import Any - -logger = logging.getLogger(__name__) - - -class ExternalStrategyLoader: - """ - Loads and manages external trading strategies - - Discovers strategy modules from external repositories and provides - a unified interface for the quant-system to use them. - """ - - def __init__(self, strategies_path: str | None = None): - """ - Initialize External Strategy Loader - - Args: - strategies_path: Path to external strategies directory - (defaults to ../quant-strategies relative to project root) - """ - if strategies_path is None: - # Default to external_strategies directory (mounted in Docker) - project_root = Path(__file__).parent.parent.parent - default_strategies_path = project_root / "external_strategies" - self.strategies_path = default_strategies_path - else: - self.strategies_path = Path(strategies_path) - self.loaded_strategies: dict[str, type] = {} - self._discover_strategies() - - def _discover_strategies(self) -> None: - """Discover available strategy modules. - - Prefer importing strategies from the 'algorithms/python' subdirectory of the - provided strategies_path. If that subdirectory is missing but the provided - strategies_path itself contains standalone Python strategy files (common when - mounting ./quant-strategies/algorithms/python directly to the container root), - fall back to loading .py files from the root of strategies_path. - This keeps imports safe while allowing flexible mount layouts. - """ - try: - alg_py = Path(self.strategies_path) / "algorithms" / "python" - search_dir = None - - # Primary: explicit algorithms/python directory - if alg_py.exists() and alg_py.is_dir(): - search_dir = alg_py - else: - # Fallback: if the strategies_path itself directly contains .py files, - # use that directory (handles mounts like ./quant-strategies/algorithms/python:/app/external_strategies) - sp = Path(self.strategies_path) - if sp.exists() and any(sp.glob("*.py")): - search_dir = sp - - if search_dir is None: - logger.warning( - "algorithms/python directory not found under strategies_path: %s", - alg_py, - ) - return - - for strategy_file in search_dir.glob("*.py"): - if strategy_file.name.startswith("_"): - continue - self._load_strategy_file(strategy_file) - - except Exception as e: - logger.error("Error discovering strategies in algorithms/python: %s", e) - - def _load_strategy_file(self, strategy_file: Path) -> None: - """ - Load a single strategy from a Python file - - Args: - strategy_file: Path to strategy Python file - """ - try: - # Load the strategy module - strategy_name = strategy_file.stem - spec = importlib.util.spec_from_file_location( - f"external_strategy_{strategy_name}", strategy_file - ) - if spec is None or spec.loader is None: - logger.error("Could not load spec for %s", strategy_name) - return - - module = importlib.util.module_from_spec(spec) - sys.modules[f"external_strategy_{strategy_name}"] = module - spec.loader.exec_module(module) - - # Look for strategy class in the module - strategy_class = None - for attr_name in dir(module): - attr = getattr(module, attr_name) - if ( - isinstance(attr, type) - and attr_name.lower().endswith("strategy") - and attr_name != "BaseStrategy" - ): - strategy_class = attr - break - - if strategy_class: - self.loaded_strategies[strategy_name] = strategy_class - logger.info("Loaded external strategy: %s", strategy_name) - else: - logger.warning("No strategy class found in %s", strategy_file.name) - - except Exception as e: - logger.error("Failed to load strategy %s: %s", strategy_file.name, e) - - def _load_strategy_dir(self, strategy_dir: Path) -> None: - """ - Load a single strategy from directory - - Args: - strategy_dir: Path to strategy directory - """ - try: - # Look for quant_system adapter - adapter_path = strategy_dir / "adapters" / "quant_system.py" - if not adapter_path.exists(): - logger.warning( - "No quant_system adapter found for %s", strategy_dir.name - ) - return - - # Load the adapter module - spec = importlib.util.spec_from_file_location( - f"{strategy_dir.name}_adapter", adapter_path - ) - if spec is None or spec.loader is None: - logger.error("Could not load spec for %s", strategy_dir.name) - return - - module = importlib.util.module_from_spec(spec) - sys.modules[f"{strategy_dir.name}_adapter"] = module - spec.loader.exec_module(module) - - # Find the adapter class (should end with 'Adapter') - adapter_class = None - for attr_name in dir(module): - attr = getattr(module, attr_name) - if ( - isinstance(attr, type) - and attr_name.endswith("Adapter") - and attr_name != "Adapter" - ): - adapter_class = attr - break - - if adapter_class is None: - logger.error("No adapter class found in %s", strategy_dir.name) - return - - # Store the strategy - strategy_name = strategy_dir.name.replace("-", "_") - self.loaded_strategies[strategy_name] = adapter_class - logger.info("Loaded strategy: %s", strategy_name) - - except Exception as e: - logger.error("Failed to load strategy %s: %s", strategy_dir.name, e) - - def get_strategy(self, strategy_name: str, **kwargs: Any) -> Any: - """ - Get a strategy instance by name - - Args: - strategy_name: Name of the strategy - **kwargs: Parameters for strategy initialization - - Returns: - Strategy adapter instance - - Raises: - ValueError: If strategy not found - """ - if strategy_name not in self.loaded_strategies: - available = list(self.loaded_strategies.keys()) - msg = f"Strategy '{strategy_name}' not found. Available: {available}" - raise ValueError(msg) - - strategy_class = self.loaded_strategies[strategy_name] - return strategy_class(**kwargs) - - def list_strategies(self) -> list[str]: - """Get list of available strategy names""" - return list(self.loaded_strategies.keys()) - - def list_strategy_candidates(self) -> list[str]: - """ - Non-import-based discovery: list candidate strategy names (file stems and dirs) - without attempting to import them. This is safe in minimal environments and - useful for CLI discovery (--strategies=all) when imports would fail due to - missing optional dependencies. - """ - candidates: set[str] = set() - try: - if not self.strategies_path or not Path(self.strategies_path).exists(): - return [] - sp = Path(self.strategies_path) - # Python files in root - for f in sp.glob("*.py"): - if not f.name.startswith("_") and f.name != "README.py": - candidates.add(f.stem) - # Files under algorithms/python - alg_py = sp / "algorithms" / "python" - if alg_py.exists(): - for f in alg_py.glob("*.py"): - if not f.name.startswith("_"): - candidates.add(f.stem) - # Files under algorithms/original (some are .py) - alg_orig = sp / "algorithms" / "original" - if alg_orig.exists(): - for f in alg_orig.glob("*.py"): - if not f.name.startswith("_"): - candidates.add(f.stem) - # Directory-based strategies - for d in sp.iterdir(): - if d.is_dir() and not d.name.startswith("."): - candidates.add(d.name.replace("-", "_")) - except Exception: - # Best-effort: return whatever we have collected so far - pass - return sorted(candidates) - - def get_strategy_info(self, strategy_name: str) -> dict[str, Any]: - """ - Get information about a strategy - - Args: - strategy_name: Name of the strategy - - Returns: - Dictionary with strategy information - """ - if strategy_name not in self.loaded_strategies: - msg = f"Strategy '{strategy_name}' not found" - raise ValueError(msg) - - # Create a temporary instance to get info - strategy = self.get_strategy(strategy_name) - if hasattr(strategy, "get_strategy_info"): - strategy_info = strategy.get_strategy_info() - return strategy_info if strategy_info is not None else {} - return { - "name": strategy_name, - "type": "External", - "parameters": getattr(strategy, "parameters", {}), - "description": f"External strategy: {strategy_name}", - } - - def validate_strategy_data(self, strategy_name: str, data: Any) -> bool: - """ - Validate data for a specific strategy - - Args: - strategy_name: Name of the strategy - data: Data to validate - - Returns: - True if data is valid, False otherwise - """ - strategy = self.get_strategy(strategy_name) - if hasattr(strategy, "validate_data"): - result = strategy.validate_data(data) - return result if isinstance(result, bool) else True - return True - - -# Global strategy loader instance -_strategy_loader: ExternalStrategyLoader | None = None - - -def get_strategy_loader(strategies_path: str | None = None) -> ExternalStrategyLoader: - """ - Get global strategy loader instance - - Args: - strategies_path: Path to strategies directory (only used on first call) - - Returns: - ExternalStrategyLoader instance - - Behavior: - - If strategies_path is provided, use it. - - Otherwise prefer the project 'external_strategies' directory (for Docker mounts). - - If that doesn't exist, fall back to the bundled 'quant-strategies' directory. - - If neither exists, initialize loader with None (loader will simply have no strategies). - """ - global _strategy_loader - if _strategy_loader is None: - resolved = strategies_path - if resolved is None: - # Resolve sensible defaults relative to project root - project_root = Path(__file__).parent.parent.parent - external_dir = project_root / "external_strategies" - quant_dir = project_root / "quant-strategies" - if external_dir.exists(): - resolved = str(external_dir) - elif quant_dir.exists(): - resolved = str(quant_dir) - else: - resolved = None - _strategy_loader = ExternalStrategyLoader(resolved) - return _strategy_loader - - -def load_external_strategy(strategy_name: str, **kwargs: Any) -> Any: - """ - Convenience function to load an external strategy - - Args: - strategy_name: Name of the strategy - **kwargs: Strategy parameters - - Returns: - Strategy adapter instance - """ - loader = get_strategy_loader() - return loader.get_strategy(strategy_name, **kwargs) diff --git a/src/core/result_analyzer.py b/src/core/result_analyzer.py deleted file mode 100644 index a15703e..0000000 --- a/src/core/result_analyzer.py +++ /dev/null @@ -1,578 +0,0 @@ -""" -Unified Result Analyzer - Consolidates all result analysis functionality. -Calculates comprehensive metrics for backtests, portfolios, and optimizations. -""" - -from __future__ import annotations - -import logging -import warnings -from typing import Any - -import numpy as np -import pandas as pd -from scipy import stats - -warnings.filterwarnings("ignore") - - -class UnifiedResultAnalyzer: - """ - Unified result analyzer that consolidates all result analysis functionality. - Provides comprehensive metrics calculation for different types of results. - """ - - def __init__(self): - self.logger = logging.getLogger(__name__) - - def calculate_metrics( - self, backtest_result: dict[str, Any], initial_capital: float - ) -> dict[str, float]: - """ - Calculate comprehensive metrics for a single backtest result. - - Args: - backtest_result: Backtest result dictionary with equity_curve and trades - initial_capital: Initial capital amount - - Returns: - Dictionary of calculated metrics - """ - try: - equity_curve = backtest_result.get("equity_curve") - trades = backtest_result.get("trades") - final_capital = backtest_result.get("final_capital", initial_capital) - - if equity_curve is None or equity_curve.empty: - return self._get_zero_metrics() - - # Convert equity curve to pandas Series if needed - equity_values = ( - equity_curve["equity"] - if isinstance(equity_curve, pd.DataFrame) - else equity_curve - ) - - # Calculate returns - returns = equity_values.pct_change().dropna() - - # Basic metrics - metrics = { - "total_return": ((final_capital - initial_capital) / initial_capital) - * 100, - "annualized_return": self._calculate_annualized_return( - equity_values, initial_capital - ), - "volatility": self._calculate_volatility(returns), - "sharpe_ratio": self._calculate_sharpe_ratio(returns), - "sortino_ratio": self._calculate_sortino_ratio(returns), - "calmar_ratio": self._calculate_calmar_ratio( - equity_values, initial_capital - ), - "max_drawdown": self._calculate_max_drawdown(equity_values), - "max_drawdown_duration": self._calculate_max_drawdown_duration( - equity_values - ), - "var_95": self._calculate_var(returns, 0.05), - "cvar_95": self._calculate_cvar(returns, 0.05), - "skewness": self._calculate_skewness(returns), - "kurtosis": self._calculate_kurtosis(returns), - "win_rate": 0, - "profit_factor": 0, - "avg_win": 0, - "avg_loss": 0, - "largest_win": 0, - "largest_loss": 0, - "num_trades": 0, - "avg_trade_duration": 0, - "expectancy": 0, - } - - # Trade-specific metrics - if trades is not None and not trades.empty: - trade_metrics = self._calculate_trade_metrics(trades) - metrics.update(trade_metrics) - - # Risk metrics - risk_metrics = self._calculate_risk_metrics(returns, equity_values) - metrics.update(risk_metrics) - - return metrics - - except Exception as e: - self.logger.error("Error calculating metrics: %s", e) - return self._get_zero_metrics() - - def calculate_portfolio_metrics( - self, portfolio_data: dict[str, Any], initial_capital: float - ) -> dict[str, float]: - """ - Calculate metrics for portfolio backtests. - - Args: - portfolio_data: Portfolio data with returns, equity_curve, weights - initial_capital: Initial capital amount - - Returns: - Dictionary of portfolio metrics - """ - try: - returns = portfolio_data.get("returns") - equity_curve = portfolio_data.get("equity_curve") - weights = portfolio_data.get("weights", {}) - - if returns is None or equity_curve is None: - return self._get_zero_metrics() - - # Basic portfolio metrics - return { - "total_return": ( - (equity_curve.iloc[-1] - initial_capital) / initial_capital - ) - * 100, - "annualized_return": self._calculate_annualized_return( - equity_curve, initial_capital - ), - "volatility": self._calculate_volatility(returns), - "sharpe_ratio": self._calculate_sharpe_ratio(returns), - "sortino_ratio": self._calculate_sortino_ratio(returns), - "max_drawdown": self._calculate_max_drawdown(equity_curve), - "var_95": self._calculate_var(returns, 0.05), - "cvar_95": self._calculate_cvar(returns, 0.05), - "num_assets": len(weights), - "effective_assets": self._calculate_effective_number_assets(weights), - "concentration_ratio": max(weights.values()) if weights else 0, - "diversification_ratio": self._calculate_diversification_ratio(weights), - } - - except Exception as e: - self.logger.error("Error calculating portfolio metrics: %s", e) - return self._get_zero_metrics() - - def calculate_optimization_metrics( - self, optimization_results: dict[str, Any] - ) -> dict[str, float]: - """ - Calculate metrics for optimization results. - - Args: - optimization_results: Optimization results data - - Returns: - Dictionary of optimization metrics - """ - try: - history = optimization_results.get("optimization_history", []) - final_population = optimization_results.get("final_population", []) - - if not history: - return {} - - # Extract scores from history - scores = [entry.get("score", 0) for entry in history if "score" in entry] - best_scores = [ - entry.get("best_score", 0) for entry in history if "best_score" in entry - ] - - return { - "convergence_speed": self._calculate_convergence_speed(best_scores), - "final_diversity": self._calculate_population_diversity( - final_population - ), - "improvement_rate": self._calculate_improvement_rate(best_scores), - "stability_ratio": self._calculate_stability_ratio(best_scores), - "exploration_ratio": self._calculate_exploration_ratio(scores), - "total_evaluations": len(scores), - "successful_evaluations": len([s for s in scores if s > 0]), - "best_score": max(scores) if scores else 0, - "avg_score": np.mean(scores) if scores else 0, - "score_std": np.std(scores) if scores else 0, - } - - except Exception as e: - self.logger.error("Error calculating optimization metrics: %s", e) - return {} - - def compare_results(self, results: list[dict[str, Any]]) -> dict[str, Any]: - """ - Compare multiple backtest results. - - Args: - results: List of backtest result dictionaries - - Returns: - Comparison analysis - """ - if not results: - return {} - - try: - # Extract metrics from all results - all_metrics = [] - for result in results: - if result.get("metrics"): - all_metrics.append(result["metrics"]) - - if not all_metrics: - return {} - - # Calculate statistics across results - metric_names = set() - for metrics in all_metrics: - metric_names.update(metrics.keys()) - - comparison = {} - for metric in metric_names: - values = [m.get(metric, 0) for m in all_metrics if metric in m] - if values: - comparison[f"{metric}_mean"] = np.mean(values) - comparison[f"{metric}_std"] = np.std(values) - comparison[f"{metric}_min"] = np.min(values) - comparison[f"{metric}_max"] = np.max(values) - comparison[f"{metric}_median"] = np.median(values) - - # Ranking analysis - if "total_return" in metric_names: - returns = [m.get("total_return", 0) for m in all_metrics] - comparison["best_performer_idx"] = np.argmax(returns) - comparison["worst_performer_idx"] = np.argmin(returns) - - return comparison - - except Exception as e: - self.logger.error("Error comparing results: %s", e) - return {} - - def _calculate_annualized_return( - self, equity_curve: pd.Series, initial_capital: float - ) -> float: - """Calculate annualized return.""" - if len(equity_curve) < 2: - return 0 - - total_days = (equity_curve.index[-1] - equity_curve.index[0]).days - if total_days <= 0: - return 0 - - total_return = (equity_curve.iloc[-1] - initial_capital) / initial_capital - years = total_days / 365.25 - - if years <= 0: - return 0 - - return ((1 + total_return) ** (1 / years) - 1) * 100 - - def _calculate_volatility(self, returns: pd.Series) -> float: - """Calculate annualized volatility.""" - if len(returns) < 2: - return 0 - - return returns.std() * np.sqrt(252) * 100 # Assuming daily returns - - def _calculate_sharpe_ratio( - self, returns: pd.Series, risk_free_rate: float = 0.02 - ) -> float: - """Calculate Sharpe ratio.""" - if len(returns) < 2 or returns.std() == 0: - return 0 - - excess_returns = returns - (risk_free_rate / 252) # Daily risk-free rate - return (excess_returns.mean() / returns.std()) * np.sqrt(252) - - def _calculate_sortino_ratio( - self, returns: pd.Series, risk_free_rate: float = 0.02 - ) -> float: - """Calculate Sortino ratio.""" - if len(returns) < 2: - return 0 - - excess_returns = returns - (risk_free_rate / 252) - downside_returns = returns[returns < 0] - - if len(downside_returns) == 0 or downside_returns.std() == 0: - return 0 - - return (excess_returns.mean() / downside_returns.std()) * np.sqrt(252) - - def _calculate_calmar_ratio( - self, equity_curve: pd.Series, initial_capital: float - ) -> float: - """Calculate Calmar ratio.""" - annualized_return = self._calculate_annualized_return( - equity_curve, initial_capital - ) - max_drawdown = abs(self._calculate_max_drawdown(equity_curve)) - - if max_drawdown == 0: - return 0 - - return annualized_return / max_drawdown - - def _calculate_max_drawdown(self, equity_curve: pd.Series) -> float: - """Calculate maximum drawdown percentage.""" - if len(equity_curve) < 2: - return 0 - - peak = equity_curve.expanding().max() - drawdown = (equity_curve - peak) / peak - return drawdown.min() * 100 - - def _calculate_max_drawdown_duration(self, equity_curve: pd.Series) -> int: - """Calculate maximum drawdown duration in days.""" - if len(equity_curve) < 2: - return 0 - - peak = equity_curve.expanding().max() - drawdown = equity_curve < peak - - # Find consecutive drawdown periods - drawdown_periods = [] - current_period = 0 - - for is_drawdown in drawdown: - if is_drawdown: - current_period += 1 - else: - if current_period > 0: - drawdown_periods.append(current_period) - current_period = 0 - - if current_period > 0: - drawdown_periods.append(current_period) - - return max(drawdown_periods) if drawdown_periods else 0 - - def _calculate_var(self, returns: pd.Series, confidence: float) -> float: - """Calculate Value at Risk.""" - if len(returns) < 2: - return 0 - - return np.percentile(returns, confidence * 100) * 100 - - def _calculate_cvar(self, returns: pd.Series, confidence: float) -> float: - """Calculate Conditional Value at Risk (Expected Shortfall).""" - if len(returns) < 2: - return 0 - - var = np.percentile(returns, confidence * 100) - cvar = returns[returns <= var].mean() - return cvar * 100 - - def _calculate_skewness(self, returns: pd.Series) -> float: - """Calculate skewness of returns.""" - if len(returns) < 3: - return 0 - - return stats.skew(returns) - - def _calculate_kurtosis(self, returns: pd.Series) -> float: - """Calculate excess kurtosis of returns.""" - if len(returns) < 4: - return 0 - - return stats.kurtosis(returns) - - def _calculate_trade_metrics(self, trades: pd.DataFrame) -> dict[str, float]: - """Calculate trade-specific metrics.""" - if trades.empty: - return { - "win_rate": 0, - "profit_factor": 0, - "avg_win": 0, - "avg_loss": 0, - "largest_win": 0, - "largest_loss": 0, - "num_trades": 0, - "avg_trade_duration": 0, - "expectancy": 0, - } - - # Filter trades with PnL information - trades_with_pnl = ( - trades[trades["pnl"] != 0] if "pnl" in trades.columns else pd.DataFrame() - ) - - if trades_with_pnl.empty: - return { - "win_rate": 0, - "profit_factor": 0, - "avg_win": 0, - "avg_loss": 0, - "largest_win": 0, - "largest_loss": 0, - "num_trades": len(trades), - "avg_trade_duration": 0, - "expectancy": 0, - } - - pnl_values = trades_with_pnl["pnl"] - winning_trades = pnl_values[pnl_values > 0] - losing_trades = pnl_values[pnl_values < 0] - - num_winning = len(winning_trades) - len(losing_trades) - total_trades = len(pnl_values) - - win_rate = (num_winning / total_trades * 100) if total_trades > 0 else 0 - - gross_profit = winning_trades.sum() if not winning_trades.empty else 0 - gross_loss = abs(losing_trades.sum()) if not losing_trades.empty else 0 - profit_factor = (gross_profit / gross_loss) if gross_loss > 0 else 0 - - avg_win = winning_trades.mean() if not winning_trades.empty else 0 - avg_loss = losing_trades.mean() if not losing_trades.empty else 0 - - largest_win = winning_trades.max() if not winning_trades.empty else 0 - largest_loss = losing_trades.min() if not losing_trades.empty else 0 - - expectancy = pnl_values.mean() if not pnl_values.empty else 0 - - return { - "win_rate": win_rate, - "profit_factor": profit_factor, - "avg_win": avg_win, - "avg_loss": avg_loss, - "largest_win": largest_win, - "largest_loss": largest_loss, - "num_trades": total_trades, - "expectancy": expectancy, - } - - def _calculate_risk_metrics( - self, returns: pd.Series, equity_curve: pd.Series - ) -> dict[str, float]: - """Calculate additional risk metrics.""" - if len(returns) < 2: - return {} - - # Beta calculation (simplified, using market proxy) - # For now, return 1.0 as placeholder - beta = 1.0 - - # Tracking error (simplified) - tracking_error = returns.std() * np.sqrt(252) * 100 - - # Information ratio (simplified) - information_ratio = ( - returns.mean() / returns.std() * np.sqrt(252) if returns.std() > 0 else 0 - ) - - return { - "beta": beta, - "tracking_error": tracking_error, - "information_ratio": information_ratio, - } - - def _calculate_effective_number_assets(self, weights: dict[str, float]) -> float: - """Calculate effective number of assets (Herfindahl index).""" - if not weights: - return 0 - - weight_values = list(weights.values()) - sum_squared_weights = sum(w**2 for w in weight_values) - return 1 / sum_squared_weights if sum_squared_weights > 0 else 0 - - def _calculate_diversification_ratio(self, weights: dict[str, float]) -> float: - """Calculate diversification ratio.""" - if not weights: - return 0 - - # Simplified calculation - would need correlation matrix for full calculation - num_assets = len(weights) - equal_weight = 1.0 / num_assets - - # Calculate deviation from equal weighting - weight_values = list(weights.values()) - return 1 - sum(abs(w - equal_weight) for w in weight_values) / 2 - - def _calculate_convergence_speed(self, best_scores: list[float]) -> float: - """Calculate how quickly optimization converged.""" - if len(best_scores) < 2: - return 0 - - # Find the generation where 95% of final improvement was achieved - final_score = best_scores[-1] - initial_score = best_scores[0] - target_improvement = (final_score - initial_score) * 0.95 - - for i, score in enumerate(best_scores): - if score - initial_score >= target_improvement: - return i / len(best_scores) - - return 1.0 - - def _calculate_population_diversity(self, population: list[dict]) -> float: - """Calculate diversity in final population.""" - if len(population) < 2: - return 0 - - # Calculate variance in scores as proxy for diversity - scores = [p.get("score", 0) for p in population if "score" in p] - if not scores: - return 0 - - return np.std(scores) / np.mean(scores) if np.mean(scores) > 0 else 0 - - def _calculate_improvement_rate(self, best_scores: list[float]) -> float: - """Calculate rate of improvement over optimization.""" - if len(best_scores) < 2: - return 0 - - improvements = [ - best_scores[i] - best_scores[i - 1] for i in range(1, len(best_scores)) - ] - positive_improvements = [imp for imp in improvements if imp > 0] - - return len(positive_improvements) / len(improvements) if improvements else 0 - - def _calculate_stability_ratio(self, best_scores: list[float]) -> float: - """Calculate stability of optimization (low variance in later generations).""" - if len(best_scores) < 10: - return 0 - - # Compare variance in first half vs second half - mid_point = len(best_scores) // 2 - first_half_var = np.var(best_scores[:mid_point]) - second_half_var = np.var(best_scores[mid_point:]) - - if first_half_var == 0: - return 1.0 if second_half_var == 0 else 0.0 - - return 1 - (second_half_var / first_half_var) - - def _calculate_exploration_ratio(self, all_scores: list[float]) -> float: - """Calculate how well the optimization explored the search space.""" - if len(all_scores) < 2: - return 0 - - # Calculate ratio of unique scores to total evaluations - unique_scores = len(set(all_scores)) - total_scores = len(all_scores) - - return unique_scores / total_scores - - def _get_zero_metrics(self) -> dict[str, float]: - """Return dictionary of zero metrics for failed calculations.""" - return { - "total_return": 0, - "annualized_return": 0, - "volatility": 0, - "sharpe_ratio": 0, - "sortino_ratio": 0, - "calmar_ratio": 0, - "max_drawdown": 0, - "max_drawdown_duration": 0, - "var_95": 0, - "cvar_95": 0, - "skewness": 0, - "kurtosis": 0, - "win_rate": 0, - "profit_factor": 0, - "avg_win": 0, - "avg_loss": 0, - "largest_win": 0, - "largest_loss": 0, - "num_trades": 0, - "avg_trade_duration": 0, - "expectancy": 0, - } diff --git a/src/core/strategy.py b/src/core/strategy.py deleted file mode 100644 index e4810dd..0000000 --- a/src/core/strategy.py +++ /dev/null @@ -1,205 +0,0 @@ -""" -Trading Strategy Framework - -Provides base classes and utilities for implementing trading strategies. -Supports both built-in and external strategies. -""" - -from __future__ import annotations - -import logging -from abc import ABC, abstractmethod -from typing import Any - -import pandas as pd - -from .external_strategy_loader import get_strategy_loader - -logger = logging.getLogger(__name__) - - -class BaseStrategy(ABC): - """ - Abstract base class for trading strategies - - All strategies should inherit from this class and implement - the required methods. - """ - - def __init__(self, name: str): - """ - Initialize base strategy - - Args: - name: Strategy name - """ - self.name = name - self.parameters: dict[str, Any] = {} - - @abstractmethod - def generate_signals(self, data: pd.DataFrame) -> pd.Series: - """ - Generate trading signals - - Args: - data: DataFrame with OHLCV data - - Returns: - Series of signals: 1 (buy), -1 (sell), 0 (hold) - """ - - def get_strategy_info(self) -> dict[str, Any]: - """Get strategy information""" - return { - "name": self.name, - "type": "Base", - "parameters": self.parameters, - "description": f"Trading strategy: {self.name}", - } - - def validate_data(self, data: pd.DataFrame) -> bool: - """ - Validate input data - - Args: - data: DataFrame with OHLCV data - - Returns: - True if data is valid, False otherwise - """ - required_columns = ["Open", "High", "Low", "Close", "Volume"] - return all(col in data.columns for col in required_columns) - - -class BuyAndHoldStrategy(BaseStrategy): - """ - Simple Buy and Hold Strategy - - Generates a buy signal at the start and holds the position. - """ - - def __init__(self) -> None: - super().__init__("Buy and Hold") - self.parameters = {} - - def generate_signals(self, data: pd.DataFrame) -> pd.Series: - """Generate buy and hold signals""" - signals = [0] * len(data) - if len(signals) > 0: - signals[0] = 1 # Buy at the start - return pd.Series(signals, index=data.index) - - -class StrategyFactory: - """ - Factory class for creating strategy instances - - Supports both built-in and external strategies. - """ - - # Built-in strategies - BUILTIN_STRATEGIES = {"BuyAndHold": BuyAndHoldStrategy} - - @classmethod - def create_strategy( - cls, strategy_name: str, parameters: dict[str, Any] | None = None - ) -> Any: - """ - Create a strategy instance - - Args: - strategy_name: Name of the strategy - parameters: Strategy parameters - - Returns: - Strategy instance - - Raises: - ValueError: If strategy not found - """ - if parameters is None: - parameters = {} - - # Check built-in strategies first - if strategy_name in cls.BUILTIN_STRATEGIES: - strategy_class = cls.BUILTIN_STRATEGIES[strategy_name] - return strategy_class(**parameters) - - # Try external strategies - try: - loader = get_strategy_loader() - return loader.get_strategy(strategy_name, **parameters) - except ValueError: - pass - - # Strategy not found - available_builtin = list(cls.BUILTIN_STRATEGIES.keys()) - available_external = get_strategy_loader().list_strategies() - available_all = available_builtin + available_external - - msg = f"Strategy '{strategy_name}' not found. Available strategies: {available_all}" - raise ValueError(msg) - - @classmethod - def list_strategies(cls) -> dict[str, list[str]]: - """ - List all available strategies - - Returns: - Dictionary with 'builtin' and 'external' strategy lists - """ - builtin = list(cls.BUILTIN_STRATEGIES.keys()) - external = get_strategy_loader().list_strategies() - - return {"builtin": builtin, "external": external, "all": builtin + external} - - @classmethod - def get_strategy_info(cls, strategy_name: str) -> dict[str, Any]: - """ - Get information about a strategy - - Args: - strategy_name: Name of the strategy - - Returns: - Dictionary with strategy information - """ - # Check built-in strategies - if strategy_name in cls.BUILTIN_STRATEGIES: - strategy = cls.create_strategy(strategy_name) - strategy_info = strategy.get_strategy_info() - return strategy_info if strategy_info is not None else {} - - # Check external strategies - try: - loader = get_strategy_loader() - return loader.get_strategy_info(strategy_name) - except ValueError: - msg = f"Strategy '{strategy_name}' not found" - raise ValueError(msg) - - -def create_strategy( - strategy_name: str, parameters: dict[str, Any] | None = None -) -> Any: - """ - Convenience function to create a strategy - - Args: - strategy_name: Name of the strategy - parameters: Strategy parameters - - Returns: - Strategy instance - """ - return StrategyFactory.create_strategy(strategy_name, parameters) - - -def list_available_strategies() -> dict[str, list[str]]: - """ - Convenience function to list available strategies - - Returns: - Dictionary with strategy lists - """ - return StrategyFactory.list_strategies() diff --git a/src/data/__init__.py b/src/data/__init__.py new file mode 100644 index 0000000..a9a2c5b --- /dev/null +++ b/src/data/__init__.py @@ -0,0 +1 @@ +__all__ = [] diff --git a/src/data/alpaca_source.py b/src/data/alpaca_source.py new file mode 100644 index 0000000..c246fd5 --- /dev/null +++ b/src/data/alpaca_source.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import os +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + + +class AlpacaSource(DataSource): + """Template for Alpaca Market Data API. + + Configure env vars ALPACA_API_KEY_ID and ALPACA_API_SECRET_KEY. + Implement fetch logic for bars endpoint as needed. + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("ALPACA_API_KEY_ID") + self.api_secret = os.environ.get("ALPACA_API_SECRET_KEY") + if not (self.api_key and self.api_secret): + raise OSError("ALPACA_API_KEY_ID and ALPACA_API_SECRET_KEY env vars are required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=0.25) + + def _map_tf(self, tf: str) -> str: + tf = tf.lower() + if tf.endswith("m"): + return f"{int(tf[:-1])}Min" + if tf.endswith("h"): + return f"{int(tf[:-1])}Hour" + if tf.endswith("d"): + return "1Day" + raise ValueError(f"Unsupported timeframe for Alpaca: {tf}") + + def _is_crypto(self, sym: str) -> bool: + s = sym.upper() + return "/" in s or s.endswith("USD") or s.endswith("USDT") + + def _map_crypto_symbol(self, sym: str) -> str: + s = sym.upper().replace("USDT", "USD") + if "/" not in s: + # e.g., BTCUSD -> BTC/USD + if s.endswith("USD"): + return f"{s[:-3]}/USD" + return s + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("alpaca", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (alpaca) with only_cached=True") + + headers = { + "APCA-API-KEY-ID": self.api_key, + "APCA-API-SECRET-KEY": self.api_secret, + } + rows = [] + session = create_retry_session() + page_token = None + + sym_fetch = map_symbol("alpaca", symbol) + if self._is_crypto(sym_fetch): + # Crypto markets + mapped = self._map_crypto_symbol(sym_fetch) + url = "https://data.alpaca.markets/v1beta3/crypto/us/bars" + params = { + "symbols": mapped, + "timeframe": self._map_tf(tf), + "limit": 10000, + "start": "2015-01-01T00:00:00Z", + } + while True: + self._limiter.acquire() + p = params.copy() + if page_token: + p["page_token"] = page_token + resp = session.get(url, params=p, headers=headers, timeout=30) + resp.raise_for_status() + data = resp.json() + bars = (data.get("bars") or {}).get(mapped, []) + for b in bars: + ts = pd.to_datetime(b["t"], utc=True) + rows.append( + [ + ts, + float(b.get("o", 0.0)), + float(b.get("h", 0.0)), + float(b.get("l", 0.0)), + float(b.get("c", 0.0)), + float(b.get("v", 0.0)), + ] + ) + page_token = data.get("next_page_token") + if not page_token: + break + else: + # Stocks/ETFs + url = "https://data.alpaca.markets/v2/stocks/bars" + params = { + "symbols": sym_fetch, + "timeframe": self._map_tf(tf), + "limit": 10000, + "adjustment": "raw", + "feed": "sip", + "start": "1990-01-01T00:00:00Z", + } + while True: + self._limiter.acquire() + p = params.copy() + if page_token: + p["page_token"] = page_token + resp = session.get(url, params=p, headers=headers, timeout=30) + resp.raise_for_status() + data = resp.json() + bars = (data.get("bars") or {}).get(sym_fetch, []) + for b in bars: + ts = pd.to_datetime(b["t"], utc=True) + rows.append( + [ + ts, + float(b.get("o", 0.0)), + float(b.get("h", 0.0)), + float(b.get("l", 0.0)), + float(b.get("c", 0.0)), + float(b.get("v", 0.0)), + ] + ) + page_token = data.get("next_page_token") + if not page_token: + break + + if not rows: + raise RuntimeError(f"No data from Alpaca for {symbol} {tf}") + + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("alpaca", symbol, tf, df) + return df diff --git a/src/data/alphavantage_source.py b/src/data/alphavantage_source.py new file mode 100644 index 0000000..d69623d --- /dev/null +++ b/src/data/alphavantage_source.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import os +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter + + +class AlphaVantageSource(DataSource): + """Alpha Vantage (daily focus, no resampling). + + Env: ALPHAVANTAGE_API_KEY + Supports: 1d daily for FX and US equities/ETFs. Intraday not recommended due to limits. + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("ALPHAVANTAGE_API_KEY") + if not self.api_key: + raise OSError("ALPHAVANTAGE_API_KEY env var is required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=12.0) # respect 5 req/min free tier + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + if tf != "1d": + raise ValueError("AlphaVantageSource supports only 1d without resampling") + cached = self.cache.load("alphavantage", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (alphavantage) with only_cached=True") + + s = symbol.replace("/", "").upper() + session = create_retry_session() + self._limiter.acquire() + + # FX or Equity detection + if len(s) == 6 and s.isalpha(): + # FX_DAILY + from_sym, to_sym = s[:3], s[3:] + url = "https://www.alphavantage.co/query" + params = { + "function": "FX_DAILY", + "from_symbol": from_sym, + "to_symbol": to_sym, + "apikey": self.api_key, + "outputsize": "full", + "datatype": "json", + } + resp = session.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() or {} + series = data.get("Time Series FX (Daily)", {}) + rows = [] + for k in sorted(series.keys()): + r = series[k] + ts = pd.to_datetime(k, utc=True) + rows.append( + [ + ts, + float(r.get("1. open", 0.0)), + float(r.get("2. high", 0.0)), + float(r.get("3. low", 0.0)), + float(r.get("4. close", 0.0)), + 0.0, + ] + ) + else: + # TIME_SERIES_DAILY_ADJUSTED for equities/ETFs + url = "https://www.alphavantage.co/query" + params = { + "function": "TIME_SERIES_DAILY_ADJUSTED", + "symbol": symbol.upper(), + "outputsize": "full", + "apikey": self.api_key, + "datatype": "json", + } + resp = session.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() or {} + series = data.get("Time Series (Daily)", {}) + rows = [] + for k in sorted(series.keys()): + r = series[k] + ts = pd.to_datetime(k, utc=True) + rows.append( + [ + ts, + float(r.get("1. open", 0.0)), + float(r.get("2. high", 0.0)), + float(r.get("3. low", 0.0)), + float(r.get("4. close", 0.0)), + float(r.get("6. volume", 0.0)), + ] + ) + + if not rows: + raise RuntimeError(f"No data from AlphaVantage for {symbol} {tf}") + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("alphavantage", symbol, tf, df) + return df diff --git a/src/data/base.py b/src/data/base.py new file mode 100644 index 0000000..e889c50 --- /dev/null +++ b/src/data/base.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from pathlib import Path + +import pandas as pd + + +class DataSource(ABC): + def __init__(self, cache_dir: Path): + self.cache_dir = Path(cache_dir) + self.cache_dir.mkdir(parents=True, exist_ok=True) + + @abstractmethod + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + """Return OHLCV DataFrame indexed by UTC datetime, columns: [Open, High, Low, Close, Volume]. + + If `only_cached` is True, must return cached data or raise an error if missing. + """ diff --git a/src/data/cache.py b/src/data/cache.py new file mode 100644 index 0000000..7357ff6 --- /dev/null +++ b/src/data/cache.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + + +class ParquetCache: + def __init__(self, root: Path): + self.root = Path(root) + self.root.mkdir(parents=True, exist_ok=True) + + def _path(self, source: str, symbol: str, timeframe: str) -> Path: + sym = symbol.replace("/", "-") + return self.root / source / f"{sym}_{timeframe}.parquet" + + def load(self, source: str, symbol: str, timeframe: str) -> pd.DataFrame | None: + p = self._path(source, symbol, timeframe) + if not p.exists(): + return None + try: + df = pd.read_parquet(p) + return df + except Exception: + return None + + def save(self, source: str, symbol: str, timeframe: str, df: pd.DataFrame) -> None: + p = self._path(source, symbol, timeframe) + p.parent.mkdir(parents=True, exist_ok=True) + df.to_parquet(p, compression="zstd") diff --git a/src/data/ccxt_source.py b/src/data/ccxt_source.py new file mode 100644 index 0000000..087a2ef --- /dev/null +++ b/src/data/ccxt_source.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import time +from pathlib import Path + +import ccxt +import pandas as pd + +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + +CCXT_TF_MAP: dict[str, str] = { + "1m": "1m", + "3m": "3m", + "5m": "5m", + "15m": "15m", + "30m": "30m", + "1h": "1h", + "2h": "2h", + "4h": "4h", + "6h": "6h", + "12h": "12h", + "1d": "1d", + "1w": "1w", + "1M": "1M", +} + + +class CCXTSource(DataSource): + def __init__(self, exchange: str, cache_dir: Path): + super().__init__(cache_dir) + self.exchange_name = exchange + self.exchange = getattr(ccxt, exchange)({"enableRateLimit": True}) + self.cache = ParquetCache(cache_dir) + # Extra inter-call limiter (in addition to ccxt's internal rate limit) + self._limiter = RateLimiter(min_interval=self.exchange.rateLimit / 1000.0) + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe + cached = self.cache.load(self.exchange_name, symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError( + f"Cache miss for {symbol} {tf} ({self.exchange_name}) with only_cached=True" + ) + + if tf not in CCXT_TF_MAP: + raise ValueError(f"Unsupported timeframe for ccxt: {tf}") + + # Map common variants to CCXT's expected format (e.g., BTCUSDT -> BTC/USDT) + sym_fetch = map_symbol(self.exchange_name, symbol) + + ohlcv = [] + limit = 1000 + since = None + backoff = 1.0 + max_backoff = 60.0 + while True: + self._limiter.acquire() + try: + batch = self.exchange.fetch_ohlcv(sym_fetch, timeframe=tf, since=since, limit=limit) + except Exception as e: + # Handle ccxt-specific throttle/availability errors with backoff + import ccxt + + if isinstance( + e, + ccxt.RateLimitExceeded + | ccxt.DDoSProtection + | ccxt.ExchangeNotAvailable + | ccxt.NetworkError, + ): + time.sleep(backoff) + backoff = min(max_backoff, backoff * 2) + continue + raise + backoff = 1.0 + if not batch: + break + ohlcv.extend(batch) + if len(batch) < limit: + break + since = batch[-1][0] + 1 + # extra safety sleep to respect exchange rate limits + time.sleep(self.exchange.rateLimit / 1000.0) + + if not ohlcv: + raise RuntimeError(f"No data for {symbol} {tf} on {self.exchange_name}") + + df = pd.DataFrame(ohlcv, columns=["Date", "Open", "High", "Low", "Close", "Volume"]) # type: ignore + df["Date"] = pd.to_datetime(df["Date"], unit="ms") + df = df.set_index("Date").sort_index() + + self.cache.save(self.exchange_name, symbol, tf, df) + return df diff --git a/src/data/finnhub_source.py b/src/data/finnhub_source.py new file mode 100644 index 0000000..323cfb0 --- /dev/null +++ b/src/data/finnhub_source.py @@ -0,0 +1,118 @@ +from __future__ import annotations + +import os +from datetime import datetime, timedelta, timezone +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + + +class FinnhubSource(DataSource): + """Finnhub.io data source (focused on FX intraday). + + Env: + - FINNHUB_API_KEY + + Timeframes supported (no resampling): 1m, 5m, 15m, 30m, 1h, 4h, 1d + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("FINNHUB_API_KEY") + if not self.api_key: + raise OSError("FINNHUB_API_KEY env var is required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=0.25) + + def _map_tf(self, tf: str) -> str: + tf = tf.lower() + if tf in ("1m", "5m", "15m", "30m"): + return tf[:-1] # 1,5,15,30 + if tf == "1h": + return "60" + if tf == "4h": + return "240" + if tf == "1d": + return "D" + raise ValueError(f"Unsupported timeframe for Finnhub: {tf}") + + def _is_fx(self, symbol: str) -> bool: + s = symbol.replace("/", "").upper() + return len(s) == 6 and s.isalpha() + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("finnhub", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (finnhub) with only_cached=True") + + mapped = map_symbol("finnhub", symbol) + res = self._map_tf(tf) + + # Finnhub candles require a from/to Unix time range + end = datetime.now(timezone.utc) + # Pull a generous lookback window without pagination; adjust by tf + lookback_days = 365 * 5 if tf == "1d" else 365 + start = end - timedelta(days=lookback_days) + + session = create_retry_session() + self._limiter.acquire() + + # FX route + if self._is_fx(symbol): + url = "https://finnhub.io/api/v1/forex/candle" + params = { + "symbol": mapped, # e.g., OANDA:EUR_USD + "resolution": res, + "from": int(start.timestamp()), + "to": int(end.timestamp()), + "token": self.api_key, + } + else: + # Equities/ETFs (if used): /stock/candle + url = "https://finnhub.io/api/v1/stock/candle" + params = { + "symbol": mapped, + "resolution": res, + "from": int(start.timestamp()), + "to": int(end.timestamp()), + "token": self.api_key, + } + + resp = session.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() + if not data or data.get("s") != "ok": + raise RuntimeError(f"No data from Finnhub for {symbol} {tf}") + # Finnhub returns arrays: t, o, h, low, c, v + rows = [] + for t, o, h, low, c, v in zip( + data.get("t", []), + data.get("o", []), + data.get("h", []), + data.get("l", []), + data.get("c", []), + data.get("v", []), + strict=False, + ): + ts = pd.to_datetime(t, unit="s", utc=True) + rows.append([ts, float(o), float(h), float(low), float(c), float(v)]) + + if not rows: + raise RuntimeError(f"No data from Finnhub for {symbol} {tf}") + + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("finnhub", symbol, tf, df) + return df diff --git a/src/data/polygon_source.py b/src/data/polygon_source.py new file mode 100644 index 0000000..32d93f3 --- /dev/null +++ b/src/data/polygon_source.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import os +from datetime import datetime, timedelta, timezone +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + + +class PolygonSource(DataSource): + """Template for Polygon.io data source. + + Configure env var POLYGON_API_KEY. + Implement fetch logic for desired endpoint(s), e.g., aggregates. + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("POLYGON_API_KEY") + if not self.api_key: + raise OSError("POLYGON_API_KEY env var is required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=0.25) + + def _map_tf(self, tf: str) -> tuple[int, str]: + tf = tf.lower() + if tf.endswith("m"): + return int(tf[:-1]), "minute" + if tf.endswith("h"): + return int(tf[:-1]), "hour" + if tf.endswith("d"): + return int(tf[:-1]), "day" + raise ValueError(f"Unsupported timeframe for Polygon: {tf}") + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("polygon", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (polygon) with only_cached=True") + + mult, span = self._map_tf(tf) + # Fetch in yearly chunks to respect response size limits + start = datetime(1990, 1, 1, tzinfo=timezone.utc) + end = datetime.now(timezone.utc) + + rows = [] + session = create_retry_session() + sym_fetch = map_symbol("polygon", symbol) + while start < end: + self._limiter.acquire() + chunk_end = min(start + timedelta(days=365 * 2), end) + url = ( + f"https://api.polygon.io/v2/aggs/ticker/{sym_fetch}/range/{mult}/{span}/" + f"{start.date()}" # from + f"/{chunk_end.date()}" # to + ) + params = { + "adjusted": "true", + "sort": "asc", + "limit": 50000, + "apiKey": self.api_key, + } + resp = session.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() + results = data.get("results", []) or [] + for r in results: + ts = pd.to_datetime(r["t"], unit="ms", utc=True) + rows.append( + [ + ts, + float(r.get("o", 0.0)), + float(r.get("h", 0.0)), + float(r.get("l", 0.0)), + float(r.get("c", 0.0)), + float(r.get("v", 0.0)), + ] + ) + start = chunk_end + timedelta(days=1) + + if not rows: + raise RuntimeError(f"No data from Polygon for {symbol} {tf}") + + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("polygon", symbol, tf, df) + return df diff --git a/src/data/ratelimiter.py b/src/data/ratelimiter.py new file mode 100644 index 0000000..e96767e --- /dev/null +++ b/src/data/ratelimiter.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +import time +from threading import Lock + + +class RateLimiter: + """Simple time-based rate limiter to space out API calls. + + Ensures at least `min_interval` seconds between successive `acquire()` calls. + """ + + def __init__(self, min_interval: float = 1.0): + self.min_interval = float(min_interval) + self._last = 0.0 + self._lock = Lock() + + def acquire(self): + with self._lock: + now = time.time() + delta = now - self._last + if delta < self.min_interval: + time.sleep(self.min_interval - delta) + self._last = time.time() diff --git a/src/data/symbol_mapper.py b/src/data/symbol_mapper.py new file mode 100644 index 0000000..4d6c052 --- /dev/null +++ b/src/data/symbol_mapper.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +import re + + +def _map_yfinance(symbol: str) -> str: + s = symbol.strip() + # Normalize common punctuation to Yahoo style first + class_share_map = { + "BRK.B": "BRK-B", + "BRK.A": "BRK-A", + "BF.B": "BF-B", + "BF.A": "BF-A", + "HEI.A": "HEI-A", + "HEI.B": "HEI-B", + "LEN.B": "LEN-B", + } + if s.upper() in class_share_map: + return class_share_map[s.upper()] + # Indices common aliases -> Yahoo caret codes + index_map = { + "SPX": "^GSPC", + "SP500": "^GSPC", + "GSPC": "^GSPC", + "NDX": "^NDX", + "DJI": "^DJI", + "RUT": "^RUT", + "VIX": "^VIX", + # International common names + "DAX": "^GDAXI", + "CAC": "^FCHI", + "FTSE": "^FTSE", + "NIKKEI": "^N225", + "N225": "^N225", + "HSI": "^HSI", + "EUROSTOXX50": "^STOXX50E", + "SX5E": "^STOXX50E", + } + if s.upper() in index_map: + return index_map[s.upper()] + + # Forex: canonical EURUSD or EUR/USD -> EURUSD=X on Yahoo + if re.fullmatch(r"[A-Z]{6}", s): + return f"{s}=X" + if re.fullmatch(r"[A-Z]{3}/[A-Z]{3}", s): + base, quote = s.split("/") + return f"{base}{quote}=X" + + # Futures: if user passed root like GC, CL, SI, NG, ZC, ZS, ZW map to =F + futures_roots = { + # Metals + "GC", + "SI", + "HG", + "PL", + "PA", + # Energy + "CL", + "NG", + "HO", + "RB", + "BZ", + # Grains/softs/livestock + "ZC", + "ZS", + "ZM", + "ZL", + "ZW", + "ZO", + "KC", + "SB", + "CC", + "CT", + "OJ", + "LE", + "HE", + "GF", + # Rates + "ZB", + "ZN", + "ZF", + "ZT", + } + if s.upper() in futures_roots: + return f"{s}=F" + + # Crypto: BTCUSD / BTC/USDT / BTCUSDT -> BTC-USD on Yahoo + up = s.upper() + if re.fullmatch(r"[A-Z]{2,6}USD(T)?", up): + base = up[:-4] if up.endswith("USDT") else up[:-3] + return f"{base}-USD" + if "/" in up and (up.endswith("/USD") or up.endswith("/USDT")): + base = up.split("/")[0] + return f"{base}-USD" + + return s + + +def _strip_yahoo_decoration(symbol: str) -> str: + s = symbol.strip() + # Remove Yahoo-specific adornments for providers that don't use them + if s.startswith("^"): + s = s[1:] + s = re.sub(r"(=F|=X)$", "", s) + # Convert share class hyphen form back to dot (e.g., BRK-B -> BRK.B) + if re.fullmatch(r"[A-Z]{1,5}-[A-Z]", s): + s = s.replace("-", ".") + return s + + +def map_symbol(provider: str, symbol: str) -> str: + p = provider.lower() + if p == "yfinance": + return _map_yfinance(symbol) + if p == "finnhub": + s = symbol.strip().upper() + # FX majors: EURUSD -> OANDA:EUR_USD; EUR/USD -> OANDA:EUR_USD + if re.fullmatch(r"[A-Z]{6}", s): + return f"OANDA:{s[:3]}_{s[3:]}" + if re.fullmatch(r"[A-Z]{3}/[A-Z]{3}", s): + base, quote = s.split("/") + return f"OANDA:{base}_{quote}" + return s + if p == "twelvedata": + s = symbol.strip().upper() + # FX: EURUSD -> EUR/USD + if re.fullmatch(r"[A-Z]{6}", s): + return f"{s[:3]}/{s[3:]}" + return s + if p == "alphavantage": + # AV handled in-source; keep symbol as-is + return symbol.strip() + # polygon/tiingo/alpaca typically don't accept Yahoo adornments + if p in {"polygon", "tiingo", "alpaca"}: + return _strip_yahoo_decoration(symbol) + return symbol diff --git a/src/data/tiingo_source.py b/src/data/tiingo_source.py new file mode 100644 index 0000000..b0d314c --- /dev/null +++ b/src/data/tiingo_source.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import os +from datetime import datetime, timedelta, timezone +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + + +class TiingoSource(DataSource): + """Template for Tiingo data source. + + Configure env var TIINGO_API_KEY. + Implement fetch logic for IEX/Tiingo endpoints as needed. + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("TIINGO_API_KEY") + if not self.api_key: + raise OSError("TIINGO_API_KEY env var is required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=0.25) + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("tiingo", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (tiingo) with only_cached=True") + + session = create_retry_session() + headers = {"Content-Type": "application/json"} + params_base = {"token": self.api_key} + + rows = [] + sym_fetch = map_symbol("tiingo", symbol) + + if tf.endswith("d"): + # Daily: /tiingo/daily/{ticker}/prices + url = f"https://api.tiingo.com/tiingo/daily/{sym_fetch}/prices" + params = params_base | {"startDate": "1990-01-01"} + self._limiter.acquire() + resp = session.get(url, params=params, headers=headers, timeout=30) + resp.raise_for_status() + data = resp.json() or [] + for r in data: + ts = pd.to_datetime(r["date"], utc=True) + rows.append( + [ + ts, + float(r.get("open", 0.0)), + float(r.get("high", 0.0)), + float(r.get("low", 0.0)), + float(r.get("close", 0.0)), + float(r.get("volume", 0.0)), + ] + ) + else: + # Intraday via IEX endpoint: /iex/{ticker}/prices with resampleFreq + def map_tf(tf: str) -> str: + if tf.endswith("m"): + return f"{int(tf[:-1])}min" + if tf.endswith("h"): + return f"{int(tf[:-1]) * 60}min" + raise ValueError(f"Unsupported intraday timeframe for Tiingo IEX: {tf}") + + resample = map_tf(tf) + start = datetime(2010, 1, 1, tzinfo=timezone.utc) + end = datetime.now(timezone.utc) + while start < end: + chunk_end = min(start + timedelta(days=30), end) + url = f"https://api.tiingo.com/iex/{sym_fetch}/prices" + params = params_base | { + "startDate": start.date().isoformat(), + "endDate": chunk_end.date().isoformat(), + "resampleFreq": resample, + } + self._limiter.acquire() + resp = session.get(url, params=params, headers=headers, timeout=30) + resp.raise_for_status() + data = resp.json() or [] + for r in data: + ts = pd.to_datetime(r["date"], utc=True) + rows.append( + [ + ts, + float(r.get("open", 0.0)), + float(r.get("high", 0.0)), + float(r.get("low", 0.0)), + float(r.get("close", 0.0)), + float(r.get("volume", 0.0)), + ] + ) + start = chunk_end + timedelta(days=1) + + if not rows: + raise RuntimeError(f"No data from Tiingo for {symbol} {tf}") + + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("tiingo", symbol, tf, df) + return df diff --git a/src/data/twelvedata_source.py b/src/data/twelvedata_source.py new file mode 100644 index 0000000..b8e2df0 --- /dev/null +++ b/src/data/twelvedata_source.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import os +from datetime import datetime, timedelta, timezone +from pathlib import Path + +import pandas as pd + +from ..utils.http import create_retry_session +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + + +class TwelveDataSource(DataSource): + """TwelveData source (focused on FX intraday). + + Env: TWELVEDATA_API_KEY + Timeframes supported: 1m,5m,15m,30m,1h,2h,4h,1d (subject to plan limits) + """ + + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.api_key = os.environ.get("TWELVEDATA_API_KEY") + if not self.api_key: + raise OSError("TWELVEDATA_API_KEY env var is required") + self.cache = ParquetCache(cache_dir) + self._limiter = RateLimiter(min_interval=0.25) + + def _map_tf(self, tf: str) -> str: + tf = tf.lower() + m = { + "1m": "1min", + "5m": "5min", + "15m": "15min", + "30m": "30min", + "1h": "1h", + "2h": "2h", + "4h": "4h", + "1d": "1day", + } + if tf in m: + return m[tf] + raise ValueError(f"Unsupported timeframe for TwelveData: {tf}") + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("twelvedata", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (twelvedata) with only_cached=True") + + interval = self._map_tf(tf) + sym_fetch = map_symbol("twelvedata", symbol) # e.g., EURUSD -> EUR/USD + # TwelveData time_series returns JSON with 'values' and 'datetime' fields + # We'll fetch a broad range using 'start_date'; TD also supports 'outputsize' + start = (datetime.now(timezone.utc) - timedelta(days=365)).date().isoformat() + params = { + "symbol": sym_fetch, + "interval": interval, + "start_date": start, + "apikey": self.api_key, + "format": "JSON", + "order": "ASC", + "dp": 8, + } + + session = create_retry_session() + self._limiter.acquire() + url = "https://api.twelvedata.com/time_series" + resp = session.get(url, params=params, timeout=30) + resp.raise_for_status() + data = resp.json() or {} + values = data.get("values") or [] + rows = [] + for r in values: + ts = pd.to_datetime(r.get("datetime"), utc=True) + if not isinstance(ts, pd.Timestamp): + continue + rows.append( + [ + ts, + float(r.get("open", 0.0)), + float(r.get("high", 0.0)), + float(r.get("low", 0.0)), + float(r.get("close", 0.0)), + float(r.get("volume", 0.0)), + ] + ) + if not rows: + raise RuntimeError(f"No data from TwelveData for {symbol} {tf}") + df = pd.DataFrame( + rows, columns=["Date", "Open", "High", "Low", "Close", "Volume"] + ).set_index("Date") + df.index = df.index.tz_convert(None) + df = df.sort_index() + self.cache.save("twelvedata", symbol, tf, df) + return df diff --git a/src/data/yfinance_source.py b/src/data/yfinance_source.py new file mode 100644 index 0000000..9c7d543 --- /dev/null +++ b/src/data/yfinance_source.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import time +from pathlib import Path + +import pandas as pd +import yfinance as yf + +from .base import DataSource +from .cache import ParquetCache +from .ratelimiter import RateLimiter +from .symbol_mapper import map_symbol + +YFINANCE_TF_MAP: dict[str, str] = { + "1m": "1m", + "2m": "2m", + "5m": "5m", + "15m": "15m", + "30m": "30m", + "60m": "60m", + "90m": "90m", + "1h": "60m", + "1d": "1d", + "1w": "1wk", + "1wk": "1wk", + "1mo": "1mo", +} + + +class YFinanceSource(DataSource): + def __init__(self, cache_dir: Path): + super().__init__(cache_dir) + self.cache = ParquetCache(cache_dir) + # Space requests to avoid Yahoo rate limits + self._limiter = RateLimiter(min_interval=1.0) + # Optional yfinance-cache integration + self._yfc = None + try: + from yfinance_cache import YFCache # type: ignore + + # Use a dedicated cache dir inside data cache + self._yfc = YFCache(cache_dir=str(Path(cache_dir) / "yfinance-http")) + except Exception: + self._yfc = None + + def fetch(self, symbol: str, timeframe: str, only_cached: bool = False) -> pd.DataFrame: + tf = timeframe.lower() + cached = self.cache.load("yfinance", symbol, tf) + if cached is not None and len(cached) > 0: + return cached + if only_cached: + raise RuntimeError(f"Cache miss for {symbol} {tf} (yfinance) with only_cached=True") + + if tf not in YFINANCE_TF_MAP: + raise ValueError(f"Unsupported timeframe for yfinance: {tf}") + + interval = YFINANCE_TF_MAP[tf] + self._limiter.acquire() + sym_fetch = map_symbol("yfinance", symbol) + + # Retry/backoff wrapper for yfinance with multiple fallbacks + def try_download() -> pd.DataFrame: + # 1) yfinance-cache (if available) + if self._yfc is not None: + try: + ticker = self._yfc.ticker.Ticker(sym_fetch) # type: ignore[attr-defined] + df_yfc = ticker.history(period="max", interval=interval, auto_adjust=False) + if df_yfc is not None and not df_yfc.empty: + return df_yfc + except Exception: + pass + # 2) yf.download + try: + df_dl = yf.download( + sym_fetch, period="max", interval=interval, auto_adjust=False, progress=False + ) + if df_dl is not None and not df_dl.empty: + return df_dl + except Exception: + pass + # 3) Ticker().history direct (often more robust for futures like ZW=F) + t = yf.Ticker(sym_fetch) + return t.history(period="max", interval=interval, auto_adjust=False) + + backoff = 1.0 + max_backoff = 30.0 + for _ in range(5): + try: + df = try_download() + break + except Exception: + time.sleep(backoff) + backoff = min(max_backoff, backoff * 2) + else: + # last attempt + df = try_download() + if df is None or df.empty: + raise RuntimeError(f"No data for {symbol} {tf}") + df = df.rename(columns={c: c.split()[0] for c in df.columns}) + df.index = df.index.tz_localize(None) + + self.cache.save("yfinance", symbol, tf, df) + return df diff --git a/src/database/__init__.py b/src/database/__init__.py deleted file mode 100644 index 2afabb5..0000000 --- a/src/database/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Database modules for data storage and retrieval.""" - -from __future__ import annotations - -from .db_connection import get_db_session - -__all__ = ["get_db_session"] diff --git a/src/database/db_connection.py b/src/database/db_connection.py deleted file mode 100644 index 1010cf9..0000000 --- a/src/database/db_connection.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Database connection management for PostgreSQL.""" - -from __future__ import annotations - -import logging -import os -from contextlib import asynccontextmanager -from typing import AsyncGenerator, Optional - -from sqlalchemy import Engine, create_engine -from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine -from sqlalchemy.orm import Session, sessionmaker -from sqlalchemy.pool import QueuePool - -logger = logging.getLogger(__name__) - - -class DatabaseManager: - """Manages database connections and sessions.""" - - def __init__(self): - self._sync_engine: Optional[Engine] = None - self._async_engine: Optional[AsyncEngine] = None - self._session_factory: Optional[sessionmaker] = None - self._async_session_factory: Optional[sessionmaker] = None - - def _get_database_url(self, async_mode: bool = False) -> str: - """Get database URL from environment variables.""" - database_url = os.getenv( - "DATABASE_URL", - "postgresql://quantuser:quantpass@localhost:5432/quant_system", - ) - - if async_mode and database_url.startswith("postgresql://"): - # Convert to async URL - database_url = database_url.replace( - "postgresql://", "postgresql+asyncpg://", 1 - ) - elif not async_mode and database_url.startswith("postgresql+asyncpg://"): - # Convert to sync URL - database_url = database_url.replace( - "postgresql+asyncpg://", "postgresql://", 1 - ) - - return database_url - - @property - def sync_engine(self) -> Engine: - """Get synchronous database engine.""" - if self._sync_engine is None: - database_url = self._get_database_url(async_mode=False) - self._sync_engine = create_engine( - database_url, - poolclass=QueuePool, - pool_size=10, - max_overflow=20, - pool_pre_ping=True, - echo=os.getenv("LOG_LEVEL") == "DEBUG", - ) - logger.info("✅ Synchronous database engine created") - return self._sync_engine - - @property - def async_engine(self) -> AsyncEngine: - """Get asynchronous database engine.""" - if self._async_engine is None: - database_url = self._get_database_url(async_mode=True) - self._async_engine = create_async_engine( - database_url, - pool_size=10, - max_overflow=20, - pool_pre_ping=True, - echo=os.getenv("LOG_LEVEL") == "DEBUG", - ) - logger.info("✅ Asynchronous database engine created") - return self._async_engine - - def get_sync_session(self) -> Session: - """Create a new synchronous database session.""" - if self._session_factory is None: - self._session_factory = sessionmaker( - bind=self.sync_engine, autocommit=False, autoflush=False - ) - return self._session_factory() - - def get_async_session_factory(self) -> sessionmaker: - """Get async session factory.""" - if self._async_session_factory is None: - self._async_session_factory = sessionmaker( - bind=self.async_engine, - class_=AsyncSession, - autocommit=False, - autoflush=False, - ) - return self._async_session_factory - - @asynccontextmanager - async def get_async_session(self) -> AsyncGenerator[AsyncSession, None]: - """Get async database session with automatic cleanup.""" - session_factory = self.get_async_session_factory() - async with session_factory() as session: - try: - yield session - await session.commit() - except Exception: - await session.rollback() - raise - finally: - await session.close() - - async def close(self): - """Close all database connections.""" - if self._async_engine: - await self._async_engine.dispose() - logger.info("✅ Async database engine closed") - - if self._sync_engine: - self._sync_engine.dispose() - logger.info("✅ Sync database engine closed") - - -# Global database manager instance -db_manager = DatabaseManager() - - -# Convenience functions -def get_db_session() -> Session: - """Get a synchronous database session.""" - return db_manager.get_sync_session() - - -async def get_async_db_session() -> AsyncGenerator[AsyncSession, None]: - """Get an asynchronous database session.""" - async with db_manager.get_async_session() as session: - yield session - - -def get_sync_engine() -> Engine: - """Get the synchronous database engine.""" - return db_manager.sync_engine - - -def get_async_engine() -> AsyncEngine: - """Get the asynchronous database engine.""" - return db_manager.async_engine diff --git a/src/database/models.py b/src/database/models.py deleted file mode 100644 index ab733bb..0000000 --- a/src/database/models.py +++ /dev/null @@ -1,337 +0,0 @@ -"""Database models for the Quant Trading System.""" - -from __future__ import annotations - -import uuid -from decimal import Decimal -from typing import List, Optional - -from sqlalchemy import ( - ARRAY, - BigInteger, - Column, - Date, - DateTime, - ForeignKey, - Index, - Integer, - Numeric, - String, - Text, - UniqueConstraint, -) -from sqlalchemy.dialects.postgresql import JSONB, UUID -from sqlalchemy.ext.declarative import declarative_base -from sqlalchemy.orm import relationship -from sqlalchemy.sql import func - -Base = declarative_base() - - -class PriceHistory(Base): - """Market price data storage.""" - - __tablename__ = "price_history" - __table_args__ = ( - UniqueConstraint( - "symbol", - "timestamp", - "data_source", - name="uq_price_symbol_timestamp_source", - ), - Index("idx_price_symbol_timestamp", "symbol", "timestamp"), - Index("idx_price_data_source", "data_source"), - {"schema": "market_data"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - symbol = Column(String(20), nullable=False) - timestamp = Column(DateTime(timezone=True), nullable=False) - open = Column(Numeric(20, 8), nullable=False) - high = Column(Numeric(20, 8), nullable=False) - low = Column(Numeric(20, 8), nullable=False) - close = Column(Numeric(20, 8), nullable=False) - volume = Column(BigInteger) - data_source = Column(String(50), nullable=False) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - - def __repr__(self) -> str: - return f"" - - -class BacktestResult(Base): - """Backtest results and performance metrics.""" - - __tablename__ = "results" - __table_args__ = ( - Index("idx_backtest_created_at", "created_at"), - Index("idx_backtest_strategy", "strategy"), - Index("idx_backtest_sortino_ratio", "sortino_ratio"), - Index("idx_backtest_total_return", "total_return"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - name = Column(String(255), nullable=False) - symbols = Column(ARRAY(Text), nullable=False) - strategy = Column(String(100), nullable=False) - timeframe = Column(String(10), nullable=False) - start_date = Column(Date, nullable=False) - end_date = Column(Date, nullable=False) - initial_capital = Column(Numeric(20, 2), nullable=False) - final_value = Column(Numeric(20, 2), nullable=False) - total_return = Column(Numeric(10, 4), nullable=False) - - # Primary metrics (Sortino-focused approach) - sortino_ratio = Column(Numeric(10, 4)) # Primary metric - calmar_ratio = Column(Numeric(10, 4)) # Secondary metric - sharpe_ratio = Column(Numeric(10, 4)) # Tertiary metric - profit_factor = Column(Numeric(10, 4)) # Supplementary metric - - # Risk metrics - max_drawdown = Column(Numeric(10, 4)) - volatility = Column(Numeric(10, 4)) - downside_deviation = Column(Numeric(10, 4)) - - # Performance metrics - win_rate = Column(Numeric(10, 4)) - average_win = Column(Numeric(10, 4)) - average_loss = Column(Numeric(10, 4)) - trades_count = Column(Integer) - - # Configuration and metadata - parameters = Column(JSONB) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - - def __repr__(self) -> str: - return f"" - - @property - def primary_metric(self) -> Optional[Decimal]: - """Get the primary performance metric (Sortino ratio).""" - return self.sortino_ratio - - @property - def metric_hierarchy(self) -> dict: - """Get all metrics in order of importance.""" - return { - "primary": self.sortino_ratio, - "secondary": self.calmar_ratio, - "tertiary": self.sharpe_ratio, - "supplementary": self.profit_factor, - } - - -class PortfolioConfiguration(Base): - """Portfolio configuration and settings.""" - - __tablename__ = "configurations" - __table_args__ = ( - Index("idx_portfolio_name", "name"), - Index("idx_portfolio_created_at", "created_at"), - Index("idx_portfolio_optimization_metric", "optimization_metric"), - {"schema": "portfolios"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - name = Column(String(255), nullable=False, unique=True) - symbols = Column(ARRAY(Text), nullable=False) - weights = Column(ARRAY(Numeric)) - initial_capital = Column(Numeric(20, 2), nullable=False) - commission = Column(Numeric(8, 6), default=0.001) - slippage = Column(Numeric(8, 6), default=0.002) - - # Performance optimization settings (Sortino-first approach) - optimization_metric = Column(String(50), default="sortino_ratio") - secondary_metrics = Column( - ARRAY(Text), default=["calmar_ratio", "sharpe_ratio", "profit_factor"] - ) - - # Configuration details - config = Column(JSONB) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - updated_at = Column( - DateTime(timezone=True), server_default=func.now(), onupdate=func.now() - ) - - # Relationships - backtest_results = relationship( - "BacktestResult", back_populates="portfolio", lazy="dynamic" - ) - - def __repr__(self) -> str: - return f"" - - @property - def is_sortino_optimized(self) -> bool: - """Check if portfolio uses Sortino ratio as primary metric.""" - return self.optimization_metric == "sortino_ratio" - - @property - def metric_priority(self) -> List[str]: - """Get metric priority list with primary metric first.""" - metrics = [self.optimization_metric] - if self.secondary_metrics: - metrics.extend( - [m for m in self.secondary_metrics if m != self.optimization_metric] - ) - return metrics - - -class BestStrategy(Base): - """Best performing strategy for each symbol/timeframe combination.""" - - __tablename__ = "best_strategies" - __table_args__ = ( - UniqueConstraint("symbol", "timeframe", name="uq_best_symbol_timeframe"), - Index("idx_best_symbol_timeframe", "symbol", "timeframe"), - Index("idx_best_sortino", "sortino_ratio"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - symbol = Column(String(20), nullable=False) - timeframe = Column(String(10), nullable=False) - strategy = Column(String(100), nullable=False) - - # Performance metrics - sortino_ratio = Column(Numeric(10, 4)) - calmar_ratio = Column(Numeric(10, 4)) - sharpe_ratio = Column(Numeric(10, 4)) - total_return = Column(Numeric(10, 4)) - max_drawdown = Column(Numeric(10, 4)) - - # Additional data - backtest_result_id = Column(UUID(as_uuid=True)) - updated_at = Column( - DateTime(timezone=True), server_default=func.now(), onupdate=func.now() - ) - - -class Trade(Base): - """Individual trades from backtesting.""" - - __tablename__ = "trades" - __table_args__ = ( - Index("idx_trade_symbol_strategy", "symbol", "strategy"), - Index("idx_trade_datetime", "trade_datetime"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - symbol = Column(String(20), nullable=False) - strategy = Column(String(100), nullable=False) - timeframe = Column(String(10), nullable=False) - - # Trade details - trade_datetime = Column(DateTime(timezone=True), nullable=False) - side = Column(String(10), nullable=False) # 'Buy' or 'Sell' - size = Column(Numeric(15, 6), nullable=False) - price = Column(Numeric(15, 6), nullable=False) - - # Trade metadata - equity_before = Column(Numeric(15, 2)) - equity_after = Column(Numeric(15, 2)) - backtest_result_id = Column(UUID(as_uuid=True)) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - - -# Add relationship to BacktestResult -BacktestResult.portfolio_id = Column( - UUID(as_uuid=True), ForeignKey("portfolios.configurations.id") -) -BacktestResult.portfolio = relationship( - "PortfolioConfiguration", back_populates="backtest_results" -) - - -class OptimizationResult(Base): - """Strategy optimization results.""" - - __tablename__ = "optimization_results" - __table_args__ = ( - Index("idx_optimization_created_at", "created_at"), - Index("idx_optimization_portfolio_id", "portfolio_id"), - Index("idx_optimization_best_sortino", "best_sortino_ratio"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - portfolio_id = Column( - UUID(as_uuid=True), ForeignKey("portfolios.configurations.id"), nullable=False - ) - strategy = Column(String(100), nullable=False) - optimization_metric = Column(String(50), default="sortino_ratio") - - # Best results found - best_sortino_ratio = Column(Numeric(10, 4)) - best_calmar_ratio = Column(Numeric(10, 4)) - best_sharpe_ratio = Column(Numeric(10, 4)) - best_profit_factor = Column(Numeric(10, 4)) - best_parameters = Column(JSONB) - - # Optimization metadata - iterations = Column(Integer) - optimization_time = Column(Numeric(10, 2)) # seconds - parameter_ranges = Column(JSONB) - results_summary = Column(JSONB) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - - # Relationships - portfolio = relationship("PortfolioConfiguration") - - def __repr__(self) -> str: - return f"" - - -class AIRecommendation(Base): - """AI-generated investment recommendations.""" - - __tablename__ = "ai_recommendations" - __table_args__ = ( - Index("idx_ai_created_at", "created_at"), - Index("idx_ai_risk_tolerance", "risk_tolerance"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - portfolio_name = Column(String(255)) - quarter = Column(String(10)) - year = Column(Integer) - risk_tolerance = Column(String(50)) - total_score = Column(Numeric(10, 4)) - confidence = Column(Numeric(5, 4)) - diversification_score = Column(Numeric(5, 4)) - total_assets = Column(Integer) - expected_return = Column(Numeric(10, 4)) - portfolio_risk = Column(Numeric(10, 4)) - overall_reasoning = Column(Text) - warnings = Column(ARRAY(String)) - correlation_analysis = Column(JSONB) - llm_model = Column(String(100)) - created_at = Column(DateTime(timezone=True), server_default=func.now()) - - -class AssetRecommendation(Base): - """Individual asset recommendations from AI analysis.""" - - __tablename__ = "asset_recommendations" - __table_args__ = ( - Index("idx_asset_rec_symbol", "symbol"), - Index("idx_asset_rec_confidence", "confidence_score"), - {"schema": "backtests"}, - ) - - id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - ai_recommendation_id = Column( - UUID(as_uuid=True), - ForeignKey("backtests.ai_recommendations.id"), - nullable=False, - ) - symbol = Column(String(20), nullable=False) - strategy = Column(String(100), nullable=False) - timeframe = Column(String(10), nullable=False) - recommendation_type = Column(String(10), nullable=False) # BUY, SELL, HOLD - confidence_score = Column(Numeric(5, 4), nullable=False) - reasoning = Column(Text) - created_at = Column(DateTime(timezone=True), server_default=func.now()) diff --git a/src/database/send_data.py b/src/database/send_data.py deleted file mode 100644 index 4b239cb..0000000 --- a/src/database/send_data.py +++ /dev/null @@ -1,67 +0,0 @@ -from __future__ import annotations - -import json -import logging -from typing import Any - -import requests -from sqlalchemy.orm import Session - -from src.database.db_connection import get_db_session - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class DataSender: - """Handles sending data to various endpoints such as APIs, databases, and messaging systems.""" - - @staticmethod - def send_to_api( - endpoint: str, data: dict[str, Any], headers: dict[str, str] | None = None - ) -> dict: - """Sends data to an external API endpoint.""" - headers = headers or {"Content-Type": "application/json"} - try: - response = requests.post(endpoint, json=data, headers=headers, timeout=10) - response.raise_for_status() - logger.info("✅ Successfully sent data to %s", endpoint) - return response.json() - except requests.exceptions.RequestException as e: - logger.error("❌ API request failed: %s", e) - return {"status": "error", "message": str(e)} - - @staticmethod - def save_to_database( - data: dict[str, Any], table_model, session: Session | None = None - ): - """Saves data to a database table using SQLAlchemy.""" - if session is None: - session = get_db_session() - - try: - record = table_model(**data) - session.add(record) - session.commit() - logger.info("✅ Data successfully saved to %s", table_model.__tablename__) - return {"status": "success", "message": "Data saved successfully"} - except Exception as e: - session.rollback() - logger.error("❌ Database save failed: %s", e) - return {"status": "error", "message": str(e)} - finally: - session.close() - - @staticmethod - def send_to_messaging_queue(queue_name: str, data: dict[str, Any]): - """Sends data to a messaging queue (RabbitMQ, Kafka, etc.).""" - try: - # Hypothetical message queue connection - from src.messaging.queue_service import QueueService # Hypothetical module - - QueueService.publish(queue_name, json.dumps(data)) - logger.info("✅ Data successfully sent to queue: %s", queue_name) - return {"status": "success", "message": f"Data sent to queue {queue_name}"} - except Exception as e: - logger.error("❌ Messaging queue send failed: %s", e) - return {"status": "error", "message": str(e)} diff --git a/src/database/unified_models.py b/src/database/unified_models.py deleted file mode 100644 index b0f72be..0000000 --- a/src/database/unified_models.py +++ /dev/null @@ -1,480 +0,0 @@ -""" -Lightweight SQLAlchemy models and helpers for the Unified CLI run lineage and results. - -This module provides: -- Declarative models for runs, backtest_results, trades, symbol_aggregates, run_artifacts. -- Helper functions: create_tables, create_run_from_manifest, find_run_by_plan_hash. - -It is intentionally defensive: tries to reuse src.database.db_connection.get_engine() if available, -falls back to a sqlite file-based engine when not. Designed for best-effort use by the CLI. -""" - -from __future__ import annotations - -import os -import uuid -from datetime import datetime -from typing import Any, Dict, Optional - -from sqlalchemy import ( - Column, - DateTime, - ForeignKey, - Integer, - String, - Text, - UniqueConstraint, - create_engine, -) -from sqlalchemy.dialects.postgresql import ( - JSONB as PG_JSONB, # type: ignore[import-not-found] -) -from sqlalchemy.exc import IntegrityError -from sqlalchemy.orm import declarative_base, relationship, scoped_session, sessionmaker - -# Prefer JSONB for Postgres, fallback to generic JSON -try: - from sqlalchemy import JSON as SQLJSON # type: ignore[import-not-found] -except Exception: - SQLJSON = Text - -Base = declarative_base() - - -# Engine/session helpers -def _get_engine(): - # Try to reuse project's db_connection engine helpers if present - # Prefer sync engine so this module stays simple. - # Test/CI override: force lightweight SQLite to avoid external DB dependency - try: - force_sqlite = False - # Common signals for test/CI environments available at import time - if os.environ.get("UNIFIED_MODELS_SQLITE", "").lower() in { - "1", - "true", - "yes", - } or os.environ.get("CI", "").lower() in {"1", "true", "yes"}: - force_sqlite = True - elif os.environ.get("PYTEST_CURRENT_TEST"): - # Usually set by pytest while collecting/running tests - force_sqlite = True - elif os.environ.get("TESTING", "").lower() in {"1", "true", "yes"}: - force_sqlite = True - - if force_sqlite: - database_url = f"sqlite:///{os.path.abspath('quant_unified_test.db')}" - return create_engine(database_url, echo=False, future=True) - except Exception: - pass - try: - from src.database.db_connection import ( - get_sync_engine, # type: ignore[import-not-found] - ) - - eng = get_sync_engine() - if eng is not None: - return eng - except Exception: - pass - try: - # As a secondary option, try the DatabaseManager property if exported - from src.database.db_connection import ( - db_manager, # type: ignore[import-not-found] - ) - - eng = getattr(db_manager, "sync_engine", None) - if eng is not None: - return eng - except Exception: - pass - - # Fallback: use DATABASE_URL env var or sqlite file - database_url = ( - os.environ.get("DATABASE_URL") - or f"sqlite:///{os.path.abspath('quant_unified.db')}" - ) - eng = create_engine(database_url, echo=False, future=True) - return eng - - -ENGINE = _get_engine() -Session = scoped_session( - sessionmaker(bind=ENGINE, autoflush=False, future=True, expire_on_commit=False) -) - - -# Helper to pick JSON type depending on DB -def JSON_TYPE(): - url = str(ENGINE.url).lower() if ENGINE and ENGINE.url else "" - if "postgres" in url or "psql" in url: - return PG_JSONB - return SQLJSON - - -class Run(Base): - __tablename__ = "runs" - # Note: default schema left to DB config; migrations can add schema `quant` if desired. - run_id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4())) - started_at_utc = Column( - DateTime(timezone=True), nullable=False, default=datetime.utcnow - ) - finished_at_utc = Column(DateTime(timezone=True), nullable=True) - actor = Column(String(128), nullable=False) - action = Column(String(64), nullable=False) - collection_ref = Column(Text, nullable=False) - strategies_mode = Column(String(256), nullable=False) - intervals_mode = Column(String(256), nullable=False) - target_metric = Column(String(64), nullable=False) - period_mode = Column(String(64), nullable=False) - args_json = Column(JSON_TYPE(), nullable=False) - git_sha_app = Column(String(64), nullable=True) - git_sha_strat = Column(String(64), nullable=True) - data_source = Column(String(128), nullable=True) - plan_hash = Column(String(128), nullable=False, unique=True, index=True) - status = Column(String(32), nullable=False, default="running") - error_summary = Column(Text, nullable=True) - - -class BacktestResult(Base): - __tablename__ = "backtest_results" - result_id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4())) - run_id = Column( - String(36), - ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - symbol = Column(String(64), nullable=False, index=True) - strategy = Column(String(256), nullable=False, index=True) - interval = Column(String(32), nullable=False, index=True) - start_at_utc = Column(DateTime(timezone=True), nullable=True) - end_at_utc = Column(DateTime(timezone=True), nullable=True) - rank_in_symbol = Column(Integer, nullable=True) - metrics = Column(JSON_TYPE(), nullable=False) - engine_ctx = Column(JSON_TYPE(), nullable=True) - trades_raw = Column(Text, nullable=True) - error = Column(Text, nullable=True) - - __table_args__ = ( - UniqueConstraint( - "run_id", - "symbol", - "strategy", - "interval", - name="uq_run_symbol_strategy_interval", - ), - ) - - run = relationship("Run", backref="results") - - -class Trade(Base): - __tablename__ = "trades" - trade_id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4())) - result_id = Column( - String(36), - ForeignKey("backtest_results.result_id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - trade_index = Column(Integer, nullable=False) - # Optional timestamps for entry/exit (UTC) - entry_time = Column(DateTime(timezone=True), nullable=True) - exit_time = Column(DateTime(timezone=True), nullable=True) - size = Column(String(64), nullable=True) - entry_bar = Column(Integer, nullable=True) - exit_bar = Column(Integer, nullable=True) - entry_price = Column(String(64), nullable=True) - exit_price = Column(String(64), nullable=True) - pnl = Column(String(64), nullable=True) - duration = Column(String(64), nullable=True) - tag = Column(String(128), nullable=True) - entry_signals = Column(Text, nullable=True) - exit_signals = Column(Text, nullable=True) - - __table_args__ = ( - UniqueConstraint("result_id", "trade_index", name="uq_result_trade_index"), - ) - - -class SymbolAggregate(Base): - __tablename__ = "symbol_aggregates" - id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4())) - run_id = Column( - String(36), - ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - symbol = Column(String(64), nullable=False) - best_by = Column(String(64), nullable=False) - best_result = Column( - String(36), - ForeignKey("backtest_results.result_id", ondelete="CASCADE"), - nullable=False, - ) - summary = Column(JSON_TYPE(), nullable=False) - - __table_args__ = ( - UniqueConstraint("run_id", "symbol", "best_by", name="uq_run_symbol_bestby"), - ) - - -class RunArtifact(Base): - __tablename__ = "run_artifacts" - artifact_id = Column( - String(36), primary_key=True, default=lambda: str(uuid.uuid4()) - ) - run_id = Column( - String(36), - ForeignKey("runs.run_id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - artifact_type = Column(String(64), nullable=False) - path_or_uri = Column(Text, nullable=False) - meta = Column(JSON_TYPE(), nullable=True) - - -class BestStrategy(Base): - """Best performing strategy for each symbol/timeframe combination (lightweight).""" - - __tablename__ = "best_strategies" - __table_args__ = ( - UniqueConstraint("symbol", "timeframe", name="uq_best_symbol_timeframe"), - ) - - id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4())) - symbol = Column(String(64), nullable=False, index=True) - timeframe = Column(String(32), nullable=False, index=True) - strategy = Column(String(256), nullable=False) - - # Performance metrics - sortino_ratio = Column( - SQLJSON().type if False else SQLJSON, nullable=True - ) # keep flexible; actual usage stores numbers - calmar_ratio = Column(SQLJSON().type if False else SQLJSON, nullable=True) - sharpe_ratio = Column(SQLJSON().type if False else SQLJSON, nullable=True) - total_return = Column(SQLJSON().type if False else SQLJSON, nullable=True) - max_drawdown = Column(SQLJSON().type if False else SQLJSON, nullable=True) - - backtest_result_id = Column(String(36), nullable=True) - updated_at = Column(DateTime(timezone=True), nullable=True) - - -def create_tables(): - Base.metadata.create_all(ENGINE) - # Best-effort migration: ensure new optional columns exist - try: - _ensure_trade_time_columns() - except Exception: - pass - - -def drop_tables(): - """Drop all tables for a full reset (dangerous).""" - try: - Base.metadata.drop_all(ENGINE) - except Exception: - # best-effort; caller can recreate afterwards - pass - - -def _ensure_trade_time_columns() -> None: - """Add entry_time and exit_time columns to trades if missing (best-effort). - - Uses SQLAlchemy Inspector to detect existing columns. Adds TIMESTAMPTZ for Postgres - and TEXT for SQLite (stored as ISO strings). - """ - try: - from sqlalchemy import inspect, text - - insp = inspect(ENGINE) - cols = {c.get("name") for c in insp.get_columns("trades")} - to_add = [] - if "entry_time" not in cols: - to_add.append("entry_time") - if "exit_time" not in cols: - to_add.append("exit_time") - if not to_add: - return - url = str(ENGINE.url).lower() if ENGINE and ENGINE.url else "" - with ENGINE.begin() as conn: - for col in to_add: - if "postgres" in url or "psql" in url: - conn.execute( - text( - f"ALTER TABLE trades ADD COLUMN IF NOT EXISTS {col} TIMESTAMPTZ NULL" - ) - ) - else: - # SQLite and others: check again to avoid errors, then add as TEXT - if col not in {c.get("name") for c in insp.get_columns("trades")}: - conn.execute(text(f"ALTER TABLE trades ADD COLUMN {col} TEXT")) - except Exception: - # Silent; optional migration - pass - - -# Convenience helpers used by CLI -def create_run_from_manifest(manifest: Dict[str, Any]) -> Optional[Run]: - """ - Insert a Run row from manifest dict. If a run with same plan_hash exists, return it. - """ - sess = Session() - plan_hash = manifest.get("plan", {}).get("plan_hash") - if not plan_hash: - raise ValueError("Manifest missing plan.plan_hash") - try: - existing = sess.query(Run).filter(Run.plan_hash == plan_hash).one_or_none() - if existing: - return existing - # Defensive truncation to avoid DB column length violations (e.g., long strategy lists) - try: - strategies_raw = manifest["plan"].get("strategies", []) - if isinstance(strategies_raw, (list, tuple)): - strategies_mode_raw = ",".join([str(s) for s in strategies_raw]) - else: - strategies_mode_raw = str(strategies_raw) - except Exception: - strategies_mode_raw = "" - - if len(strategies_mode_raw) > 256: - strategies_mode = strategies_mode_raw[:252] + "..." - else: - strategies_mode = strategies_mode_raw - - try: - intervals_raw = manifest["plan"].get("intervals", []) - if isinstance(intervals_raw, (list, tuple)): - intervals_mode_raw = ",".join([str(i) for i in intervals_raw]) - else: - intervals_mode_raw = str(intervals_raw) - except Exception: - intervals_mode_raw = "" - - if len(intervals_mode_raw) > 256: - intervals_mode = intervals_mode_raw[:252] + "..." - else: - intervals_mode = intervals_mode_raw - - run = Run( - actor=manifest["plan"].get("actor", "cli"), - action=manifest["plan"].get("action", "backtest"), - collection_ref=manifest["plan"].get("collection", ""), - strategies_mode=strategies_mode, - intervals_mode=intervals_mode, - target_metric=manifest["plan"].get("metric", ""), - period_mode=manifest["plan"].get("period_mode", ""), - args_json=manifest["plan"], - git_sha_app=manifest["plan"].get("git_sha_app"), - git_sha_strat=manifest["plan"].get("git_sha_strat"), - plan_hash=plan_hash, - status="running", - ) - sess.add(run) - sess.commit() - return run - except IntegrityError: - sess.rollback() - return sess.query(Run).filter(Run.plan_hash == plan_hash).one_or_none() - finally: - sess.close() - - -def ensure_run_for_manifest(manifest: Dict[str, Any]) -> Optional[Run]: - """ - Ensure a Run exists for the given manifest. - Tries create_run_from_manifest first. If that fails, attempts a manual upsert. - Returns a Run instance or None on failure. - """ - plan_hash = manifest.get("plan", {}).get("plan_hash") - if not plan_hash: - return None - - # First try the existing helper which handles most common cases - try: - run = create_run_from_manifest(manifest) - if run: - return run - except Exception: - # fall through to manual attempt - pass - - sess = Session() - try: - # Try to find existing run by plan_hash - existing = sess.query(Run).filter(Run.plan_hash == plan_hash).one_or_none() - if existing: - return existing - - # Build a minimal Run object from manifest safely - plan = manifest.get("plan", {}) or {} - # Defensive truncation for safety when constructing Run from manifest 'plan' - try: - strategies_raw = plan.get("strategies", []) - if isinstance(strategies_raw, (list, tuple)): - strategies_mode_raw = ",".join([str(s) for s in strategies_raw]) - else: - strategies_mode_raw = str(strategies_raw) - except Exception: - strategies_mode_raw = "" - - if len(strategies_mode_raw) > 256: - strategies_mode = strategies_mode_raw[:252] + "..." - else: - strategies_mode = strategies_mode_raw - - try: - intervals_raw = plan.get("intervals", []) - if isinstance(intervals_raw, (list, tuple)): - intervals_mode_raw = ",".join([str(i) for i in intervals_raw]) - else: - intervals_mode_raw = str(intervals_raw) - except Exception: - intervals_mode_raw = "" - - if len(intervals_mode_raw) > 256: - intervals_mode = intervals_mode_raw[:252] + "..." - else: - intervals_mode = intervals_mode_raw - - run = Run( - actor=plan.get("actor", "cli"), - action=plan.get("action", "backtest"), - collection_ref=plan.get("collection", ""), - strategies_mode=strategies_mode, - intervals_mode=intervals_mode, - target_metric=plan.get("metric", ""), - period_mode=plan.get("period_mode", ""), - args_json=plan, - plan_hash=plan_hash, - status="running", - ) - sess.add(run) - sess.commit() - return run - except IntegrityError: - # If another process inserted concurrently, return that row - try: - sess.rollback() - return sess.query(Run).filter(Run.plan_hash == plan_hash).one_or_none() - except Exception: - sess.rollback() - return None - except Exception: - try: - sess.rollback() - except Exception: - pass - return None - finally: - sess.close() - - -def find_run_by_plan_hash(plan_hash: str) -> Optional[Run]: - sess = Session() - try: - return sess.query(Run).filter(Run.plan_hash == plan_hash).one_or_none() - finally: - sess.close() diff --git a/src/main.py b/src/main.py new file mode 100644 index 0000000..eddcdff --- /dev/null +++ b/src/main.py @@ -0,0 +1,237 @@ +from __future__ import annotations + +import logging +import os +from datetime import datetime +from pathlib import Path + +import typer + +from .backtest.runner import BacktestRunner +from .config import load_config +from .reporting.all_csv_export import AllCSVExporter +from .reporting.csv_export import CSVExporter +from .reporting.health import HealthReporter +from .reporting.html import HTMLReporter +from .reporting.markdown import MarkdownReporter +from .reporting.tradingview import TradingViewExporter + +app = typer.Typer(add_completion=False, no_args_is_help=True) + + +@app.command() +def run( + config: str = typer.Option("config/example.yaml", help="Path to YAML config"), + output_dir: str | None = typer.Option( + None, help="Reports output dir (default: reports/)" + ), + strategies_path: str | None = typer.Option( + None, help="Path to external strategies repo (overrides env STRATEGIES_PATH)" + ), + only_cached: bool = typer.Option(False, help="Use only cached Parquet data; do not fetch"), + top_n: int = typer.Option(3, help="Top-N per symbol for CSV/HTML reports"), + inline_css: bool = typer.Option(False, help="Inline minimal CSS for offline HTML report"), +): + # Load .env if present + try: + from dotenv import load_dotenv + + load_dotenv() + except Exception: + pass + # Basic logging + logging.basicConfig(level=os.environ.get("LOG_LEVEL", "INFO")) + + # Cache HTTP where possible to reduce provider calls + try: + import requests_cache + + requests_cache.install_cache("http_cache", expire_after=43200) # 12 hours + except Exception: + pass + + cfg = load_config(config) + env_cache = os.environ.get("DATA_CACHE_DIR") + if env_cache: + cfg.cache_dir = env_cache + + ts = datetime.utcnow().strftime("%Y%m%d-%H%M%S") + base_out = Path(output_dir) if output_dir else Path("reports") / ts + base_out.mkdir(parents=True, exist_ok=True) + + strategies_root = ( + Path(strategies_path) + if strategies_path + else Path(os.environ.get("STRATEGIES_PATH", "/ext/strategies")) + ) + + start_ts = datetime.utcnow() + run_id = os.environ.get("RUN_ID", ts) + runner = BacktestRunner(cfg, strategies_root=strategies_root, run_id=run_id) + if not getattr(runner, "external_index", {}): + typer.secho( + ( + f"No strategies discovered under {strategies_root}.\n" + "- Ensure STRATEGIES_PATH points to the container path (e.g., /ext/strategies).\n" + "- Or pass --strategies-path /ext/strategies.\n" + "- Verify your strategy classes subclass BaseStrategy and import without errors.\n" + ), + err=True, + fg=typer.colors.RED, + ) + raise typer.Exit(code=1) + results = runner.run_all(only_cached=only_cached) + if not results: + tips = [ + "No backtest results produced.", + f"- Strategies discovered: {len(getattr(runner, 'external_index', {}))}", + f"- Collections: {len(cfg.collections)}, Timeframes: {len(cfg.timeframes)}", + "Possible causes:", + " • Using --only-cached but no Parquet data is cached (warm the cache or disable).", + " • Unsupported timeframe for the selected data source (adjust config).", + " • Strategy generated invalid/no signals for all parameter sets (check logic/params).", + " • Data providers returned no data (rate limits/network/API keys).", + ] + typer.secho("\n".join(tips), err=True, fg=typer.colors.RED) + raise typer.Exit(code=2) + + end_ts = datetime.utcnow() + + # Exports + CSVExporter(base_out).export(results) + # Consolidated CSVs from results cache + try: + AllCSVExporter(base_out, runner.results_cache, run_id, top_n=top_n).export(results) + except Exception: + pass + MarkdownReporter(base_out).export(results) + TradingViewExporter(base_out).export(results) + # HTML report with Tailwind (dark mode) + try: + HTMLReporter( + base_out, runner.results_cache, run_id, top_n=top_n, inline_css=inline_css + ).export(results) + except Exception: + pass + + # Emit run summary JSON + try: + import json + + summary = { + "started_at": start_ts.isoformat() + "Z", + "finished_at": end_ts.isoformat() + "Z", + "duration_sec": (end_ts - start_ts).total_seconds(), + "metric": cfg.metric, + "results_count": len(results), + "metrics": getattr(runner, "metrics", {}), + "failures_count": len(getattr(runner, "failures", [])), + "failures": getattr(runner, "failures", []), + } + (base_out / "summary.json").write_text(json.dumps(summary, indent=2)) + except Exception: + pass + + # Emit Prometheus-style metrics + try: + m = [] + duration = (end_ts - start_ts).total_seconds() + rm = getattr(runner, "metrics", {}) + + def line(k, v): + m.append(f"quant_{k} {v}") + + line("run_duration_seconds", duration) + line("results_count", len(results)) + for k in ( + "result_cache_hits", + "result_cache_misses", + "param_evals", + "symbols_tested", + "strategies_count", + ): + if k in rm: + line(k, rm[k]) + (base_out / "metrics.prom").write_text("\n".join(m) + "\n") + except Exception: + pass + + typer.echo(f"Done. Reports in: {base_out}") + + # Health report + try: + HealthReporter(base_out).export(getattr(runner, "failures", [])) + except Exception: + pass + + +@app.command() +def list_strategies( + strategies_path: str | None = typer.Option( + None, help="Path to external strategies repo (overrides env STRATEGIES_PATH)" + ), +): + from .strategies.registry import discover_external_strategies + + strategies_root = ( + Path(strategies_path) + if strategies_path + else Path(os.environ.get("STRATEGIES_PATH", "/ext/strategies")) + ) + index = discover_external_strategies(strategies_root) + if not index: + typer.echo(f"No strategies found under {strategies_root}") + raise typer.Exit(code=1) + typer.echo(f"Found {len(index)} strategies:") + for name in sorted(index.keys()): + typer.echo(f"- {name}") + + +@app.command() +def discover_symbols( + exchange: str = typer.Option("binance", help="CCXT exchange id (e.g., binance, bybit)"), + quote: str = typer.Option("USDT", help="Quote currency filter (e.g., USDT, USD)"), + top_n: int = typer.Option(50, help="Top N symbols by 24h volume"), + min_volume: float = typer.Option(0.0, help="Minimum 24h volume to include"), + name: str = typer.Option("crypto_discovered", help="Collection name to embed in YAML"), + output: str | None = typer.Option(None, help="Path to write YAML (default: print to stdout)"), +): + import yaml + + from .utils.symbols import DiscoverOptions, discover_ccxt_symbols + + opts = DiscoverOptions(exchange=exchange, quote=quote, top_n=top_n, min_volume=min_volume) + pairs = discover_ccxt_symbols(opts) + symbols = [s for s, _ in pairs] + cfg = { + "metric": "sortino", + "engine": "vectorbt", + "asset_workers": 4, + "param_workers": 2, + "max_fetch_concurrency": 2, + "cache_dir": ".cache/data", + "collections": [ + { + "name": name, + "source": exchange, + "exchange": exchange, + "quote": quote, + "fees": 0.0006, + "slippage": 0.0005, + "symbols": symbols, + } + ], + "timeframes": ["1d", "4h", "1h"], + "strategies": [], + } + text = yaml.safe_dump(cfg, sort_keys=False) + if output: + Path(output).parent.mkdir(parents=True, exist_ok=True) + Path(output).write_text(text) + typer.echo(f"Wrote: {output}") + else: + typer.echo(text) + + +if __name__ == "__main__": + app() diff --git a/src/reporting/__init__.py b/src/reporting/__init__.py index df20666..a9a2c5b 100644 --- a/src/reporting/__init__.py +++ b/src/reporting/__init__.py @@ -1,9 +1 @@ -"""Reporting module for portfolio analysis and visualization.""" - -from __future__ import annotations - -# The DetailedPortfolioReporter implementation lives in collection_report.py. -# Expose it at package level for callers that import src.reporting.DetailedPortfolioReporter -from .collection_report import DetailedPortfolioReporter - -__all__ = ["DetailedPortfolioReporter"] +__all__ = [] diff --git a/src/reporting/ai_report_generator.py b/src/reporting/ai_report_generator.py deleted file mode 100644 index 2c23a71..0000000 --- a/src/reporting/ai_report_generator.py +++ /dev/null @@ -1,157 +0,0 @@ -"""AI Report Generator for Investment Recommendations.""" - -from __future__ import annotations - -from pathlib import Path - -from src.ai.models import PortfolioRecommendation - - -class AIReportGenerator: - """Generates HTML reports for AI investment recommendations.""" - - def __init__(self): - # Base dir is unified with other exports - self.base_dir = Path("exports/ai_reco") - self.base_dir.mkdir(parents=True, exist_ok=True) - - def generate_html_report( - self, - recommendation: PortfolioRecommendation, - portfolio_name: str, - year: str, - quarter: str, - interval: str, - ) -> str: - """Generate HTML report under exports/ai_reco/// with unified name.""" - - quarterly_dir = self.base_dir / str(year) / str(quarter) - quarterly_dir.mkdir(parents=True, exist_ok=True) - - sanitized = ( - portfolio_name.replace(" ", "_").replace("/", "_").strip("_") - or "All_Collections" - ) - safe_interval = (interval or "multi").replace("/", "-") - filename = f"{sanitized}_Collection_{year}_{quarter}_{safe_interval}.html" - output_path = quarterly_dir / filename - - html_content = self._create_html_content(recommendation, portfolio_name) - - output_path.write_text(html_content, encoding="utf-8") - return str(output_path) - - def _create_html_content( - self, recommendation: PortfolioRecommendation, portfolio_name: str - ) -> str: - """Create HTML content for AI recommendations.""" - - asset_rows = "" - for asset in recommendation.asset_recommendations: - badge_class = ( - "bg-emerald-500/10 text-emerald-300" - if asset.recommendation_type == "BUY" - else "bg-amber-500/10 text-amber-300" - if asset.recommendation_type == "HOLD" - else "bg-rose-500/10 text-rose-300" - ) - - asset_rows += f""" - - {asset.symbol} - {asset.strategy} - {asset.timeframe} - {asset.allocation_percentage:.1f}% - {asset.risk_level} - {asset.confidence_score:.3f} - {asset.sortino_ratio:.3f} - {asset.sharpe_ratio:.3f} - {asset.total_return:.2f}% - {asset.risk_per_trade:.1f}% - {asset.position_size:.1f}% - {asset.stop_loss:.0f} - {asset.take_profit:.0f} - {asset.recommendation_type} - """ - - html_template = f""" - - - - - AI Investment Recommendations: {portfolio_name} - - - - - -
-
-

AI Investment Recommendations

-

Portfolio: {portfolio_name} • Risk Profile: {recommendation.risk_profile.title()}

-
- Download CSV -
-
- -
-

Summary

-
-
-
Total Assets
-
{recommendation.total_assets}
-
-
-
Expected Return
-
{recommendation.expected_return:.2f}%
-
-
-
Confidence
-
{recommendation.confidence_score:.3f}
-
-
-

{recommendation.reasoning}

-
- -
-

Asset Recommendations

-
- - - - - - - - - - - - - - - - - - - - - {asset_rows} - -
SymbolStrategyTimeframeAllocationRisk LevelConfidenceSortinoSharpeReturnRisk/TradePos. SizeSLTPAction
-
-
-
- -""" - - # Set CSV link dynamically (same directory, same base name with .csv) - try: - # Simple replacement: add a small inline script to patch href at runtime - html_template += "\n\n" - except Exception: - pass - - return html_template diff --git a/src/reporting/all_csv_export.py b/src/reporting/all_csv_export.py new file mode 100644 index 0000000..632008b --- /dev/null +++ b/src/reporting/all_csv_export.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Any + +from ..backtest.results_cache import ResultsCache +from ..backtest.runner import BestResult + + +class AllCSVExporter: + def __init__(self, out_dir: Path, results_cache: ResultsCache, run_id: str, top_n: int = 3): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + self.cache = results_cache + self.run_id = run_id + self.top_n = top_n + + def export(self, best_results: list[BestResult]): + all_rows = self.cache.list_by_run(self.run_id) + # All results CSV + path_all = self.out_dir / "all_results.csv" + with open(path_all, "w", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "collection", + "symbol", + "timeframe", + "strategy", + "metric", + "metric_value", + "params", + "sharpe", + "sortino", + "profit", + "trades", + "max_drawdown", + ] + ) + for r in all_rows: + stats = r.get("stats", {}) + w.writerow( + [ + r["collection"], + r["symbol"], + r["timeframe"], + r["strategy"], + r["metric"], + f"{r['metric_value']:.6f}", + r["params"], + f"{stats.get('sharpe', float('nan')):.6f}", + f"{stats.get('sortino', float('nan')):.6f}", + f"{stats.get('profit', float('nan')):.6f}", + stats.get("trades", 0), + f"{stats.get('max_drawdown', float('nan')):.6f}", + ] + ) + + # Top-N per (collection, symbol) + path_topn = self.out_dir / f"top{self.top_n}.csv" + # group + grouped: dict[tuple, list[dict[str, Any]]] = {} + for r in all_rows: + key = (r["collection"], r["symbol"]) + grouped.setdefault(key, []).append(r) + with open(path_topn, "w", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "collection", + "symbol", + "timeframe", + "strategy", + "metric", + "metric_value", + "params", + "sharpe", + "sortino", + "profit", + "trades", + "max_drawdown", + ] + ) + for _key, rows in grouped.items(): + rows = sorted(rows, key=lambda x: x["metric_value"], reverse=True)[: self.top_n] + for r in rows: + stats = r.get("stats", {}) + w.writerow( + [ + r["collection"], + r["symbol"], + r["timeframe"], + r["strategy"], + r["metric"], + f"{r['metric_value']:.6f}", + r["params"], + f"{stats.get('sharpe', float('nan')):.6f}", + f"{stats.get('sortino', float('nan')):.6f}", + f"{stats.get('profit', float('nan')):.6f}", + stats.get("trades", 0), + f"{stats.get('max_drawdown', float('nan')):.6f}", + ] + ) diff --git a/src/reporting/collection_report.py b/src/reporting/collection_report.py deleted file mode 100644 index 10e95ca..0000000 --- a/src/reporting/collection_report.py +++ /dev/null @@ -1,1141 +0,0 @@ -"""Clean Portfolio Report Generator (DB-sourced, Tailwind-ready) - -This reporter reads only from the database (unified_models lightweight schema) to -render a per-asset HTML report. It prefers detailed stats saved in -unified_models.BacktestResult.engine_ctx and overlays values from -unified_models.BacktestResult.metrics when needed. No JSON files are used. - -Styling: Uses Tailwind. In production, set TAILWIND_CSS_HREF to a built CSS -file (e.g., /assets/tailwind.min.css). If unset and no local CSS is found under -exports/reports/assets/tailwind.min.css, falls back to the CDN for dev only. -""" - -from __future__ import annotations - -import json -from pathlib import Path -from typing import Any, Dict - -from src.reporting.report_organizer import ReportOrganizer - - -class DetailedPortfolioReporter: - """Generates detailed visual reports using only DB data (unified_models).""" - - def __init__(self): - self.report_organizer = ReportOrganizer() - - def generate_comprehensive_report( - self, - portfolio_config: Dict[str, Any], - start_date: str, - end_date: str, - strategies: list[str], - timeframes: list[str] | None = None, - filename_interval: str | None = None, - ) -> str: - if timeframes is None: - timeframes = ["1d"] - - symbols = portfolio_config.get("symbols") or [] - assets_data: Dict[str, Dict[str, Any]] = {} - for symbol in symbols: - assets_data[symbol] = self._get_asset_data( - symbol, preferred_timeframes=timeframes or ["1d"] - ) - - html = self._create_html_report( - portfolio_config, - assets_data, - start_date, - end_date, - strategies=strategies, - timeframes=timeframes, - ) - # Choose interval token for filename: - # - If explicit filename_interval is provided, use it (e.g., "multi" for --interval all) - # - Else prefer '1d' if included, otherwise first of timeframes - interval = "1d" - try: - if filename_interval: - interval = filename_interval - elif timeframes: - interval = "1d" if "1d" in timeframes else timeframes[0] - except Exception: - interval = "1d" - return self._save_report( - html, portfolio_config.get("name") or "portfolio", interval - ) - - def _get_asset_data( - self, symbol: str, preferred_timeframes: list[str] | None = None - ) -> Dict[str, Any]: - try: - from src.database import unified_models as um - except Exception: - um = None - # Primary DB models (fallback for metrics when unified tables are empty) - try: - from src.database import models as dbm - from src.database.db_connection import ( - get_db_session as get_primary_session, # type: ignore[import-not-found] - ) - except Exception: - dbm = None - get_primary_session = None # type: ignore[assignment] - - sess = um.Session() if um else None - try: - # Prefer best strategy for requested timeframes (e.g., ['1d']) - u_bs = None - if um and sess: - try: - q = sess.query(um.BestStrategy).filter( - um.BestStrategy.symbol == symbol - ) - if preferred_timeframes: - q_pref = ( - q.filter( - um.BestStrategy.timeframe.in_(preferred_timeframes) - ) - .order_by(um.BestStrategy.updated_at.desc()) - .limit(1) - ) - u_bs = q_pref.one_or_none() - # Fallback to any timeframe if none found for preference - if not u_bs: - u_bs = ( - q.order_by(um.BestStrategy.updated_at.desc()) - .limit(1) - .one_or_none() - ) - except Exception: - u_bs = None - - # Secondary fallback to primary models BestStrategy (backtests schema) - b_bs = None - if not u_bs and dbm is not None and get_primary_session is not None: - try: - s2 = get_primary_session() - except Exception: - s2 = None - if s2 is not None: - try: - q2 = s2.query(dbm.BestStrategy).filter( - dbm.BestStrategy.symbol == symbol - ) - if preferred_timeframes: - q2 = q2.filter( - dbm.BestStrategy.timeframe.in_(preferred_timeframes) - ) - b_bs = ( - q2.order_by(dbm.BestStrategy.updated_at.desc()) - .limit(1) - .one_or_none() - ) - except Exception: - b_bs = None - finally: - try: - s2.close() - except Exception: - pass - if not u_bs and not b_bs: - return { - "best_strategy": "N/A", - "best_timeframe": "1d", - "data": {"overview": self._empty_overview(), "orders": []}, - } - - timeframe = getattr(u_bs, "timeframe", None) or getattr( - b_bs, "timeframe", "1d" - ) - overview = self._empty_overview() - - def _f(v): - try: - return float(v) if v is not None else 0.0 - except Exception: - return 0.0 - - # Pull from unified BestStrategy or fallback BestStrategy - src_bs = u_bs if u_bs is not None else b_bs - overview["PSR"] = _f(getattr(src_bs, "sortino_ratio", 0)) - overview["sharpe_ratio"] = _f(getattr(src_bs, "sharpe_ratio", 0)) - overview["net_profit"] = _f(getattr(src_bs, "total_return", 0)) - overview["max_drawdown"] = abs(_f(getattr(src_bs, "max_drawdown", 0))) - # optional calmar - try: - overview["calmar_ratio"] = _f(getattr(u_bs, "calmar_ratio", 0)) - except Exception: - pass - - stats_full: Dict[str, Any] = {} - period_start_str: str | None = None - period_end_str: str | None = None - - # Find corresponding BacktestResult for richer stats - br = None - try: - if getattr(u_bs, "backtest_result_id", None): - br = ( - sess.query(um.BacktestResult) - .filter(um.BacktestResult.result_id == u_bs.backtest_result_id) - .one_or_none() - ) - # If BestStrategy doesn't carry result_id, align by the declared best strategy - if not br: - br = ( - sess.query(um.BacktestResult) - .filter(um.BacktestResult.symbol == symbol) - .filter(um.BacktestResult.interval == timeframe) - .filter( - um.BacktestResult.strategy == getattr(u_bs, "strategy", "") - ) - .order_by(um.BacktestResult.end_at_utc.desc().nullslast()) - .first() - ) - # Last fallback: latest any strategy (kept for resilience) - if not br: - br = ( - sess.query(um.BacktestResult) - .filter(um.BacktestResult.symbol == symbol) - .filter(um.BacktestResult.interval == timeframe) - .order_by(um.BacktestResult.end_at_utc.desc().nullslast()) - .first() - ) - except Exception: - br = None - - # Prefer engine_ctx for canonical backtesting library stats - if br and isinstance(br.engine_ctx, dict): - stats_full.update(br.engine_ctx) - # Try to derive period from engine context when DB timestamps are missing - try: - if not period_start_str and isinstance( - stats_full.get("Start"), str - ): - period_start_str = stats_full.get("Start")[:10] - if not period_end_str and isinstance(stats_full.get("End"), str): - period_end_str = stats_full.get("End")[:10] - except Exception: - pass - - # Overlay metrics if engine_ctx lacks fields - if br and isinstance(br.metrics, dict): - m = br.metrics or {} - stats_full.setdefault( - "Sortino Ratio", m.get("sortino_ratio") or m.get("Sortino_Ratio") - ) - stats_full.setdefault( - "Sharpe Ratio", m.get("sharpe_ratio") or m.get("Sharpe_Ratio") - ) - stats_full.setdefault( - "Return [%]", m.get("total_return") or m.get("Total_Return") - ) - stats_full.setdefault( - "Max. Drawdown [%]", m.get("max_drawdown") or m.get("Max_Drawdown") - ) - stats_full.setdefault( - "Win Rate [%]", m.get("win_rate") or m.get("Win_Rate") - ) - - # If unified BacktestResult missing, try fallback primary results to populate overview keys - if not br and b_bs is not None: - try: - overview["PSR"] = ( - _f(getattr(b_bs, "sortino_ratio", 0)) or overview["PSR"] - ) - overview["sharpe_ratio"] = ( - _f(getattr(b_bs, "sharpe_ratio", 0)) or overview["sharpe_ratio"] - ) - overview["net_profit"] = ( - _f(getattr(b_bs, "total_return", 0)) or overview["net_profit"] - ) - md = _f(getattr(b_bs, "max_drawdown", 0)) - if md: - overview["max_drawdown"] = abs(md) - except Exception: - pass - - # Capture period from DB result for display and derived annualized stats - try: - if br: - sd = getattr(br, "start_at_utc", None) - ed = getattr(br, "end_at_utc", None) - if sd and not period_start_str: - try: - period_start_str = sd.date().isoformat() - except Exception: - pass - if ed and not period_end_str: - try: - period_end_str = ed.date().isoformat() - except Exception: - pass - except Exception: - pass - - # Compute Return (Ann.) [%] if possible - try: - if ( - br - and ("Return (Ann.) [%]" not in stats_full) - and stats_full.get("Return [%]") is not None - ): - sd = getattr(br, "start_at_utc", None) - ed = getattr(br, "end_at_utc", None) - if sd and ed: - days = max((ed - sd).days, 1) - total = 1.0 + float(stats_full["Return [%]"]) / 100.0 - ann = (total ** (365.0 / float(days))) - 1.0 - stats_full["Return (Ann.) [%]"] = ann * 100.0 - except Exception: - pass - - # Compute Equity Final if missing from initial_capital - try: - if ( - br - and ("Equity Final [$]" not in stats_full) - and stats_full.get("Return [%]") is not None - ): - init_cap = None - if getattr(br, "run_id", None): - run = ( - sess.query(um.Run) - .filter(um.Run.run_id == br.run_id) - .one_or_none() - ) - if run and isinstance(run.args_json, dict): - init_cap = run.args_json.get("initial_capital") - if init_cap is None: - init_cap = 10000.0 - stats_full["Equity Final [$]"] = float(init_cap) * ( - 1.0 + float(stats_full["Return [%]"]) / 100.0 - ) - except Exception: - pass - - # Push enriched values into overview tiles - def _pull(name_engine: str, key_overview: str): - try: - v = stats_full.get(name_engine) - if v is None: - return - overview[key_overview] = float(v) - except Exception: - pass - - _pull("Sortino Ratio", "PSR") - _pull("Sharpe Ratio", "sharpe_ratio") - _pull("Return [%]", "net_profit") - try: - v = stats_full.get("Max. Drawdown [%]") - if v is not None: - overview["max_drawdown"] = abs(float(v)) - except Exception: - pass - - # Ensure the summary metrics table has sensible defaults even if engine_ctx is missing - # Populate from BestStrategy/overview when BacktestResult engine_ctx is unavailable. - try: - if stats_full is None: - stats_full = {} - # Backfill core fields if absent - if stats_full.get("Sortino Ratio") is None: - stats_full["Sortino Ratio"] = overview.get("PSR") - if stats_full.get("Sharpe Ratio") is None: - stats_full["Sharpe Ratio"] = overview.get("sharpe_ratio") - if stats_full.get("Return [%]") is None: - stats_full["Return [%]"] = overview.get("net_profit") - if stats_full.get("Max. Drawdown [%]") is None: - md = overview.get("max_drawdown") - if md is not None: - # Backtesting.py reports DD as negative percent; keep sign convention for the table - stats_full["Max. Drawdown [%]"] = -abs(float(md)) - except Exception: - pass - - # Trades: prefer normalized Trade table, else parse trades_raw - trades: list[dict] = [] - try: - if br and getattr(br, "result_id", None): - rows = ( - sess.query(um.Trade) - .filter(um.Trade.result_id == br.result_id) - .order_by(um.Trade.trade_index.asc()) - .all() - ) - for t in rows: - trades.append( - { - "idx": getattr(t, "trade_index", None), - "entry_time": getattr(t, "entry_time", None), - "exit_time": getattr(t, "exit_time", None), - "entry_bar": getattr(t, "entry_bar", None), - "exit_bar": getattr(t, "exit_bar", None), - "entry_price": getattr(t, "entry_price", None), - "exit_price": getattr(t, "exit_price", None), - "size": getattr(t, "size", None), - "pnl": getattr(t, "pnl", None), - "duration": getattr(t, "duration", None), - "tag": getattr(t, "tag", None), - } - ) - elif br and getattr(br, "trades_raw", None): - try: - raw = json.loads(br.trades_raw) - if isinstance(raw, list): - for i, tr in enumerate(raw): - if not isinstance(tr, dict): - continue - trades.append( - { - "idx": tr.get("index") or i, - "entry_time": tr.get("EntryTime") - or tr.get("entry_time"), - "exit_time": tr.get("ExitTime") - or tr.get("exit_time"), - "entry_bar": tr.get("entry_bar") - or tr.get("EntryBar") - or tr.get("entry"), - "exit_bar": tr.get("exit_bar") - or tr.get("ExitBar") - or tr.get("exit"), - "entry_price": tr.get("entry_price"), - "exit_price": tr.get("exit_price"), - "size": tr.get("size"), - "pnl": tr.get("pnl"), - "duration": tr.get("duration"), - "tag": tr.get("tag"), - } - ) - except Exception: - pass - except Exception: - trades = [] - - # As a last resort, attempt to derive total_orders from primary DB Trade rows if unified has none - if not trades and dbm is not None and get_primary_session is not None: - try: - s3 = get_primary_session() - except Exception: - s3 = None - if s3 is not None: - try: - cnt = ( - s3.query(dbm.Trade) - .filter(dbm.Trade.symbol == symbol) - .count() - ) - if cnt and cnt > 0: - overview["total_orders"] = int(cnt) - except Exception: - pass - finally: - try: - s3.close() - except Exception: - pass - - # Set total_orders from persisted trades; do not compute trades locally - try: - overview["total_orders"] = len(trades) - except Exception: - overview["total_orders"] = 0 - - return { - "best_strategy": ( - getattr(u_bs, "strategy", None) - or getattr(b_bs, "strategy", "") - or "N/A" - ), - "best_timeframe": timeframe, - "stats_full": stats_full, - "data": {"overview": overview, "orders": trades}, - "period_start": period_start_str, - "period_end": period_end_str, - } - finally: - if sess is not None: - try: - sess.close() - except Exception: - pass - - def _empty_overview(self) -> Dict[str, Any]: - return { - "PSR": 0.0, - "sharpe_ratio": 0.0, - "total_orders": 0, - "net_profit": 0.0, - "max_drawdown": 0.0, - "calmar_ratio": 0.0, - } - - def _create_html_report( - self, - portfolio_config: dict, - assets_data: dict, - start_date: str, - end_date: str, - strategies: list[str] | None = None, - timeframes: list[str] | None = None, - ) -> str: - # Tailwind include: prefer local stylesheet or env var; fallback to CDN in dev - try: - import os - - tw_href = os.environ.get("TAILWIND_CSS_HREF", "").strip() - # If env var points to a local path that doesn't exist, ignore it to allow CDN fallback - if tw_href and not tw_href.startswith(("http://", "https://")): - try: - if not Path(tw_href).exists(): - tw_href = "" - except Exception: - tw_href = "" - if not tw_href: - cand = Path("exports/reports/assets/tailwind.min.css") - if cand.exists(): - tw_href = str(cand) - tailwind_tag = ( - f'' - if tw_href - else '' - ) - except Exception: - tailwind_tag = '' - # Plotly include (for inline equity charts) - plotly_tag = '' - - # Top overview (computed from assets_data) - total_assets = len(assets_data) - avg_sortino = 0.0 - winners = 0 - traders = 0 - vals = [] - for data in assets_data.values(): - ov = (data.get("data") or {}).get("overview") or {} - try: - vals.append(float(ov.get("PSR", 0) or 0)) - except Exception: - pass - try: - if float(ov.get("net_profit", 0) or 0) > 0: - winners += 1 - except Exception: - pass - try: - if int(ov.get("total_orders", 0) or 0) > 0: - traders += 1 - except Exception: - pass - if vals: - avg_sortino = sum(vals) / len(vals) - - # Backtest settings card (strategies, intervals, period) - strat_list = ", ".join(strategies or []) - tf_list = ", ".join(timeframes or []) - # Prefer derived period from assets_data (global earliest start, latest end) - try: - derived_starts = [] - derived_ends = [] - for v in assets_data.values(): - ps = v.get("period_start") - pe = v.get("period_end") - if isinstance(ps, str) and len(ps) >= 10: - derived_starts.append(ps[:10]) - if isinstance(pe, str) and len(pe) >= 10: - derived_ends.append(pe[:10]) - derived_start = min(derived_starts) if derived_starts else None - derived_end = max(derived_ends) if derived_ends else None - except Exception: - derived_start = None - derived_end = None - - period_str = ( - f"{derived_start} → {derived_end}" - if (derived_start and derived_end) - else (f"{start_date} → {end_date}" if (start_date and end_date) else "max") - ) - settings_card = f""" -
- Backtest Settings -
-
-
Intervals
{tf_list or "-"}
-
Strategies
{strat_list or "-"}
-
Period
{period_str}
-
-
-
- """ - - top_overview = f""" -
-
-
Assets
-
{total_assets}
-
-
-
Avg Sortino
-
{avg_sortino:.3f}
-
-
-
Positive Returns
-
{winners}
-
-
-
With Trades
-
{traders}
-
-
- """ - - # Sidebar TOC (TailAdmin-style): sticky on large screens, compact chips on mobile - toc_items = [ - f'
  • {sym}
    • ' - for sym in assets_data.keys() - ] - sidebar_html = ( - '" - ) - # Mobile chips - chips_html = ( - '
    ' - '
    ' - + "".join( - [ - f'{sym}' - for sym in assets_data.keys() - ] - ) - + "
    " - "
    " - ) - - # Asset sections - asset_sections = [] - for symbol, data in assets_data.items(): - overview = (data.get("data") or {}).get("overview") or {} - stats = data.get("stats_full") or {} - - def fmt(v: Any, prec=2, pct=False, money=False) -> str: - try: - if v is None: - return "-" - f = float(v) - if money: - return f"${f:,.{prec}f}" - if pct: - return f"{f:.{prec}f}%" - return f"{f:.{prec}f}" - except Exception: - return str(v) if v is not None else "-" - - metrics_row = f""" -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Equity FinalCommissionsReturnBuy & Hold ReturnSortinoSharpeReturn (Ann.)Max DDWin Rate
    {fmt(stats.get("Equity Final [$]"), 2, money=True)}{fmt(stats.get("Commissions [$]"), 2, money=True)}{fmt(stats.get("Return [%]"), 2, pct=True)}{fmt(stats.get("Buy & Hold Return [%]"), 2, pct=True)}{fmt(stats.get("Sortino Ratio"), 3)}{fmt(stats.get("Sharpe Ratio"), 3)}{fmt(stats.get("Return (Ann.) [%]"), 2, pct=True)}{fmt(stats.get("Max. Drawdown [%]"), 2, pct=True)}{fmt(stats.get("Win Rate [%]"), 2, pct=True)}
    -
    - """ - - # Build simple sparkline from any available equity series - equity_series = [] - try: - for k in ( - "equity_curve", - "equity", - "equity_values", - "Equity Curve", - "equity_series", - ): - v = stats.get(k) - if isinstance(v, (list, tuple)) and len(v) >= 2: - equity_series = [float(x) for x in v if x is not None] - break - # Backtesting.py direct stats often store '_equity_curve' as list of dicts - if not equity_series: - v2 = stats.get("_equity_curve") - if isinstance(v2, list) and len(v2) >= 2: - try: - pts = [] - for row in v2: - if isinstance(row, dict) and "Equity" in row: - pts.append(float(row.get("Equity"))) - if len(pts) >= 2: - equity_series = pts - except Exception: - pass - if not equity_series and isinstance(stats.get("series"), list): - s0 = stats["series"][0] - if isinstance(s0, dict) and isinstance(s0.get("y"), list): - equity_series = [float(x) for x in s0["y"] if x is not None] - except Exception: - equity_series = [] - - def _spark(points: list[float], width=600, height=80) -> str: - try: - if not points or len(points) < 2: - return "" - mn = min(points) - mx = max(points) - rng = (mx - mn) or 1.0 - step = width / (len(points) - 1) - cmds = [] - for i, v in enumerate(points): - x = i * step - y = height - ((float(v) - mn) / rng) * height - cmds.append(("M" if i == 0 else "L") + f" {x:.2f} {y:.2f}") - d = " ".join(cmds) - return ( - f'' - f'' - "" - ) - except Exception: - return "" - - spark = _spark(equity_series) - # Fallback: embed plot HTML if provided by engine_ctx - plot_embed = None - try: - for k in ("plot_html", "plot_div", "plot", "chart_html"): - v = stats.get(k) - if isinstance(v, str) and (" str: - try: - import re as _re - - return _re.sub(r"[^A-Za-z0-9_\-]", "_", s) - except Exception: - return s - - def _plotly_equity( - sym: str, eq: list[float], stats_obj: dict, orders: list[dict] - ) -> str: - try: - import json as _json - - if not eq or len(eq) < 2: - return "" - x = list(range(len(eq))) - dd = [] - try: - if isinstance(stats_obj.get("_equity_curve"), list): - vals = [] - has_dd = False - for r in stats_obj["_equity_curve"]: - if ( - isinstance(r, dict) - and r.get("DrawdownPct") is not None - ): - has_dd = True - vals.append(float(r.get("DrawdownPct"))) - if has_dd and len(vals) == len(eq): - dd = vals - except Exception: - dd = [] - div_id = f"plot_{_safe_id(sym)}" - data = [ - { - "x": x, - "y": eq, - "type": "scatter", - "mode": "lines", - "name": "Equity", - "line": {"color": "#22d3ee"}, - } - ] - layout = { - "margin": {"l": 30, "r": 10, "t": 10, "b": 30}, - "paper_bgcolor": "rgba(0,0,0,0)", - "plot_bgcolor": "rgba(0,0,0,0)", - "xaxis": {"showgrid": False, "zeroline": False}, - "yaxis": {"showgrid": False, "zeroline": False}, - "showlegend": True, - } - if dd: - data.append( - { - "x": x, - "y": dd, - "type": "scatter", - "mode": "lines", - "name": "Drawdown [%]", - "line": {"color": "#f43f5e"}, - "yaxis": "y2", - } - ) - layout["yaxis2"] = {"overlaying": "y", "side": "right"} - - # Buy & Hold overlay if metric present - try: - bnh = stats_obj.get("Buy & Hold Return [%]") - if bnh is not None and len(eq) >= 2: - eq0 = float(eq[0]) - eq_bnh_end = eq0 * (1.0 + float(bnh) / 100.0) - # Linear interpolation for lack of series - y_bnh = [ - eq0 + (eq_bnh_end - eq0) * (i / (len(x) - 1)) - for i in range(len(x)) - ] - data.append( - { - "x": x, - "y": y_bnh, - "type": "scatter", - "mode": "lines", - "name": "Buy & Hold", - "line": {"color": "#a3e635", "dash": "dash"}, - } - ) - except Exception: - pass - - # Entry/Exit markers from orders using entry_bar/exit_bar indices - try: - entries_x = [] - entries_y = [] - exits_x = [] - exits_y = [] - for od in orders or []: - eb = od.get("entry_bar") - xb = od.get("exit_bar") - if isinstance(eb, (int, float)) and 0 <= int(eb) < len(eq): - idx = int(eb) - entries_x.append(x[idx]) - entries_y.append(eq[idx]) - if isinstance(xb, (int, float)) and 0 <= int(xb) < len(eq): - idx = int(xb) - exits_x.append(x[idx]) - exits_y.append(eq[idx]) - if entries_x: - data.append( - { - "x": entries_x, - "y": entries_y, - "type": "scatter", - "mode": "markers", - "name": "Entry", - "marker": { - "color": "#22c55e", - "size": 6, - "symbol": "triangle-up", - }, - } - ) - if exits_x: - data.append( - { - "x": exits_x, - "y": exits_y, - "type": "scatter", - "mode": "markers", - "name": "Exit", - "marker": { - "color": "#ef4444", - "size": 6, - "symbol": "triangle-down", - }, - } - ) - except Exception: - pass - payload = _json.dumps( - { - "data": data, - "layout": layout, - "config": {"displayModeBar": False, "responsive": True}, - } - ) - return ( - f'
    ' - f"" - ) - except Exception: - return "" - - plotly_plot = _plotly_equity( - symbol, - equity_series, - stats, - (data.get("data") or {}).get("orders") or [], - ) - placeholder_plot = plot_embed or plotly_plot or spark - if not placeholder_plot: - placeholder_plot = '
    Plotting disabled in this environment.
    ' - plot_section = f""" -

    Equity Curve

    -
    {placeholder_plot}
    - """ - - # Trades table if any - trades = (data.get("data") or {}).get("orders") or [] - trades_html = "" - if isinstance(trades, list) and trades: - trade_rows = [] - for tr in trades[:200]: - - def _fmt_dt(v): - try: - import datetime as _dt - - if v is None: - return "" - if isinstance(v, str): - return v - if isinstance(v, (_dt.datetime, _dt.date)): - return v.isoformat() - except Exception: - return str(v) if v is not None else "" - return str(v) - - trade_rows.append( - f"" - f'{tr.get("idx", "")}' - f'{_fmt_dt(tr.get("entry_time"))}' - f'{_fmt_dt(tr.get("exit_time"))}' - f'{tr.get("size", "")}' - f'{tr.get("entry_price", "")}' - f'{tr.get("exit_price", "")}' - f'{tr.get("pnl", "")}' - f'{tr.get("duration", "")}' - f'{tr.get("tag", "")}' - f"" - ) - trades_html = f""" -

    Trades

    -
    - - - - - - - - - - - - - - - {"".join(trade_rows)} -
    #Entry TimeExit TimeSizeEntryExitPnLDurationTag
    -
    - """ - - asset_sections.append( - f""" -
    -
    -

    {symbol}

    -
    - Best: {(data.get("best_strategy") or "N/A")} - ⏰ {data.get("best_timeframe", "1d")} -
    -
    -

    Summary Metrics

    - {metrics_row} -
    -
    -
    Sortino
    -
    {overview.get("PSR", 0):.3f}
    -
    -
    -
    Sharpe
    -
    {overview.get("sharpe_ratio", 0):.3f}
    -
    -
    -
    Orders
    -
    {int(overview.get("total_orders", 0) or 0)}
    -
    -
    -
    Net Profit
    -
    0 else "text-rose-400")}\">{overview.get("net_profit", 0):.2f}%
    -
    -
    -
    Max Drawdown
    -
    -{overview.get("max_drawdown", 0):.2f}%
    -
    -
    -
    Calmar
    -
    {overview.get("calmar_ratio", 0):.3f}
    -
    -
    - {plot_section} - {trades_html} -
    - """ - ) - - # Use double braces for literal braces in .format() - # Footer: educational disclaimer + project link - footer_html = ( - '
    ' - '
    ' - "This report is for educational purposes only and does not constitute financial advice. " - 'Project: quant-system.' - "
    " - "
    " - ) - html_template = """ - - - - Collection Analysis: {{portfolio_name}} - {tailwind_tag} - {plotly_tag} - - - -
    -
    -

    {{portfolio_name}}

    -

    Real Backtesting Data • {{start_date}} → {{end_date}}

    -
    -
    - {sidebar_html} -
    - {settings_card} - {top_overview} - {chips_html} - {{asset_sections}} -
    -
    -
    - {footer_html} - - -""" - - # Brace-safe rendering: protect placeholders, escape all braces, then restore placeholders - tokens = { - "[[PORTFOLIO_NAME]]": "{portfolio_name}", - "[[START_DATE]]": "{start_date}", - "[[END_DATE]]": "{end_date}", - "[[ASSET_SECTIONS]]": "{asset_sections}", - "[[TAILWIND_TAG]]": "{tailwind_tag}", - "[[PLOTLY_TAG]]": "{plotly_tag}", - "[[SIDEBAR_HTML]]": "{sidebar_html}", - "[[TOP_OVERVIEW]]": "{top_overview}", - "[[CHIPS_HTML]]": "{chips_html}", - "[[SETTINGS_CARD]]": "{settings_card}", - "[[FOOTER_HTML]]": "{footer_html}", - } - # Mark placeholders - html_template_marked = ( - html_template.replace("{{portfolio_name}}", "[[PORTFOLIO_NAME]]") - .replace("{{start_date}}", "[[START_DATE]]") - .replace("{{end_date}}", "[[END_DATE]]") - .replace("{{asset_sections}}", "[[ASSET_SECTIONS]]") - .replace("{tailwind_tag}", "[[TAILWIND_TAG]]") - .replace("{plotly_tag}", "[[PLOTLY_TAG]]") - .replace("{sidebar_html}", "[[SIDEBAR_HTML]]") - .replace("{top_overview}", "[[TOP_OVERVIEW]]") - .replace("{chips_html}", "[[CHIPS_HTML]]") - .replace("{settings_card}", "[[SETTINGS_CARD]]") - .replace("{footer_html}", "[[FOOTER_HTML]]") - ) - # Escape all remaining braces so they render literally - html_template_escaped = html_template_marked.replace("{", "{{").replace( - "}", "}}" - ) - # Restore placeholders - for t, ph in tokens.items(): - html_template_escaped = html_template_escaped.replace(t, ph) - - # Choose header dates: prefer derived period - header_start = derived_start or start_date - header_end = derived_end or end_date - - return html_template_escaped.format( - portfolio_name=portfolio_config.get("name") or "Portfolio", - start_date=header_start, - end_date=header_end, - asset_sections="\n".join(asset_sections), - tailwind_tag=tailwind_tag, - plotly_tag=plotly_tag, - sidebar_html=sidebar_html, - top_overview=top_overview, - chips_html=chips_html, - settings_card=settings_card, - footer_html=footer_html, - ) - - def _save_report( - self, html_content: str, portfolio_name: str, interval: str - ) -> str: - # Save via organizer using unified naming (exports/reports//Q/_Collection__Q_.html) - tmp = Path("temp_report.html") - tmp.write_text(html_content, encoding="utf-8") - try: - return str( - self.report_organizer.organize_report( - str(tmp), portfolio_name, None, interval=interval - ) - ) - finally: - if tmp.exists(): - tmp.unlink() diff --git a/src/reporting/csv_export.py b/src/reporting/csv_export.py new file mode 100644 index 0000000..8b6300f --- /dev/null +++ b/src/reporting/csv_export.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +import csv +from pathlib import Path + +from ..backtest.runner import BestResult + + +class CSVExporter: + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + + def export(self, results: list[BestResult]): + path = self.out_dir / "summary.csv" + with open(path, "w", newline="") as f: + w = csv.writer(f) + w.writerow( + [ + "collection", + "symbol", + "timeframe", + "strategy", + "metric", + "metric_value", + "params", + "sharpe", + "sortino", + "profit", + "trades", + "max_drawdown", + ] + ) + for r in results: + w.writerow( + [ + r.collection, + r.symbol, + r.timeframe, + r.strategy, + r.metric_name, + f"{r.metric_value:.6f}", + r.params, + f"{r.stats.get('sharpe', float('nan')):.6f}", + f"{r.stats.get('sortino', float('nan')):.6f}", + f"{r.stats.get('profit', float('nan')):.6f}", + r.stats.get("trades", 0), + f"{r.stats.get('max_drawdown', float('nan')):.6f}", + ] + ) diff --git a/src/reporting/health.py b/src/reporting/health.py new file mode 100644 index 0000000..f52a27b --- /dev/null +++ b/src/reporting/health.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + + +class HealthReporter: + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + + def export(self, failures: list[dict[str, Any]]): + if not failures: + # Still emit a tiny note + (self.out_dir / "health.md").write_text("# Data Health\n\nNo data fetch failures.\n") + return + + lines: list[str] = [] + lines.append("# Data Health") + lines.append("") + lines.append(f"Failures: {len(failures)}") + lines.append("") + lines.append("| Collection | Symbol | Timeframe | Source | Error |") + lines.append("|---|---|---|---|---|") + for f in failures: + lines.append( + f"| {f.get('collection', '')} | {f.get('symbol', '')} | {f.get('timeframe', '')} | {f.get('source', '')} | {str(f.get('error', '')).replace('|', ' ').strip()} |" + ) + (self.out_dir / "health.md").write_text("\n".join(lines) + "\n") diff --git a/src/reporting/html.py b/src/reporting/html.py new file mode 100644 index 0000000..4cb5474 --- /dev/null +++ b/src/reporting/html.py @@ -0,0 +1,182 @@ +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from ..backtest.results_cache import ResultsCache + +if TYPE_CHECKING: + pass + + +class HTMLReporter: + def __init__( + self, + out_dir: Path, + results_cache: ResultsCache, + run_id: str, + top_n: int = 3, + inline_css: bool = False, + ): + self.out_dir = Path(out_dir) + self.cache = results_cache + self.run_id = run_id + self.top_n = top_n + self.inline_css = inline_css + + def export(self, best: list[object]): + # Load all rows for top-N sections from results cache + all_rows = self.cache.list_by_run(self.run_id) + # Fallback: if the current run reused cached results and didn't write + # rows with this run_id, synthesize rows from the provided BestResult list. + if not all_rows and best: + try: + from ..backtest.runner import BestResult as _BR # type: ignore + + tmp: list[dict[str, Any]] = [] + for b in best: + if isinstance(b, _BR): + tmp.append( + { + "collection": b.collection, + "symbol": b.symbol, + "timeframe": b.timeframe, + "strategy": b.strategy, + "params": b.params, + "metric": b.metric_name, + "metric_value": float(b.metric_value), + "stats": b.stats if isinstance(b.stats, dict) else {}, + } + ) + all_rows = tmp + except Exception: + pass + grouped: dict[tuple, list[dict[str, Any]]] = {} + for r in all_rows: + key = (r["collection"], r["symbol"]) + grouped.setdefault(key, []).append(r) + + def card_for_row(row: dict[str, Any]) -> str: + stats = row.get("stats", {}) or {} + return f""" +
    +
    +

    {row.get("collection", "")} / {row.get("symbol", "")}

    + {row.get("timeframe", "")} +
    +
    +
    Strategy: {row.get("strategy", "")}
    +
    Metric: {row.get("metric", "")} = {float(row.get("metric_value", float("nan"))):.6f}
    +
    Params: {row.get("params", {})}
    +
    +
    +
    Sharpe: {float(stats.get("sharpe", float("nan"))):.4f}
    +
    Sortino: {float(stats.get("sortino", float("nan"))):.4f}
    +
    Profit: {float(stats.get("profit", float("nan"))):.4f}
    +
    Trades: {int(stats.get("trades", 0))}
    +
    Max DD: {float(stats.get("max_drawdown", float("nan"))):.4f}
    +
    +
    + """ + + def table_for_topn(rows: list[dict[str, Any]]) -> str: + rows = sorted(rows, key=lambda x: x["metric_value"], reverse=True)[: self.top_n] + body = "\n".join( + f"{r['timeframe']}{r['strategy']}{r['metric']}{r['metric_value']:.6f}" + f"{r['params']}" + f"{r['stats'].get('sharpe', float('nan')):.4f}" + f"{r['stats'].get('sortino', float('nan')):.4f}" + f"{r['stats'].get('profit', float('nan')):.4f}" + f"{r['stats'].get('trades', 0)}" + f"{r['stats'].get('max_drawdown', float('nan')):.4f}" + for r in rows + ) + return f""" +
    + + + + + + + + + + + + + + + + {body} +
    TimeframeStrategyMetricValueParamsSharpeSortinoProfitTradesMax DD
    +
    + """ + + # Build a card per (collection, symbol) showing the true best across + # all timeframes/strategies by metric_value + cards = [] + for key in sorted(grouped.keys()): + rows = grouped[key] + if not rows: + continue + top = max(rows, key=lambda x: x["metric_value"]) # best overall + cards.append( + "
    " + card_for_row(top) + table_for_topn(rows) + "
    " + ) + + html = f""" + + + + + + Backtest Report + + + + + +
    +
    +

    Backtest Report

    + +
    +
    + {"".join(cards)} +
    +
    + + + """ + + if self.inline_css: + css_inline = ( + ":root{--bg:#020617;--panel:#0f172a;--text:#e2e8f0;--muted:#94a3b8} " + "body{background:var(--bg);color:var(--text);} " + ".container{max-width:72rem;margin:0 auto;padding:1.5rem} " + ".btn{padding:.25rem .75rem;border-radius:.375rem;background:#1e293b;color:#e2e8f0} " + ".grid{display:grid;gap:1rem} " + ".card{padding:1rem;border-radius:.75rem;background:var(--panel);box-shadow:0 1px 2px rgba(0,0,0,.3)} " + ".badge{font-size:.75rem;padding:.1rem .5rem;border-radius:.25rem;background:#1e293b} " + "table{width:100%;font-size:.875rem} thead{background:#334155;color:#cbd5e1} " + "th,td{padding:.5rem .75rem;text-align:left} code{font-family:ui-monospace,Menlo,Monaco,Consolas,monospace}" + ) + html = html.replace( + '', + f"", + ) + html = html.replace("tailwind.config = { darkMode: 'class' };", "") + html = html.replace("max-w-6xl mx-auto p-6", "container") + html = html.replace( + "px-3 py-1 rounded bg-slate-800 text-slate-200 hover:bg-slate-700", "btn" + ) + html = html.replace("p-4 rounded-lg bg-slate-800 text-slate-100 shadow", "card") + html = html.replace("text-xs px-2 py-1 rounded bg-slate-700", "badge") + + path = self.out_dir / "report.html" + path.write_text(html) diff --git a/src/reporting/markdown.py b/src/reporting/markdown.py new file mode 100644 index 0000000..6daee1f --- /dev/null +++ b/src/reporting/markdown.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from pathlib import Path + +from ..backtest.runner import BestResult + + +class MarkdownReporter: + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + + def export(self, results: list[BestResult]): + # Group by collection -> symbol + by_key = {} + for r in results: + by_key.setdefault((r.collection, r.symbol), []).append(r) + + lines: list[str] = [] + lines.append("# Backtest Report\n") + + for (collection, symbol), rows in sorted(by_key.items()): + lines.append(f"## {collection} / {symbol}\n") + # Sort rows by metric_value desc + rows = sorted(rows, key=lambda x: x.metric_value, reverse=True) + best = rows[0] + lines.append("- Best Combination:") + lines.append(f" - Timeframe: {best.timeframe}") + lines.append(f" - Strategy: {best.strategy}") + lines.append(f" - Metric: {best.metric_name} = {best.metric_value:.6f}") + lines.append(f" - Params: {best.params}") + lines.append("- Key Metrics:") + lines.append(f" - Sharpe: {best.stats.get('sharpe', float('nan')):.6f}") + lines.append(f" - Sortino: {best.stats.get('sortino', float('nan')):.6f}") + lines.append(f" - Profit: {best.stats.get('profit', float('nan')):.6f}") + lines.append(f" - Trades: {best.stats.get('trades', 0)}") + lines.append(f" - Max Drawdown: {best.stats.get('max_drawdown', float('nan')):.6f}") + lines.append("") + + path = self.out_dir / "report.md" + path.write_text("\n".join(lines)) diff --git a/src/reporting/report_organizer.py b/src/reporting/report_organizer.py deleted file mode 100644 index 5c21e4a..0000000 --- a/src/reporting/report_organizer.py +++ /dev/null @@ -1,230 +0,0 @@ -"""Report organizer utility for quarterly report management.""" - -from __future__ import annotations - -import shutil -from datetime import datetime, timezone -from pathlib import Path - - -class ReportOrganizer: - """Organizes reports by quarter and year, ensuring single report per portfolio per quarter.""" - - def __init__(self, base_reports_dir: str = "exports/reports"): - self.base_reports_dir = Path(base_reports_dir) - self.base_reports_dir.mkdir(exist_ok=True) - - def get_quarter_from_date(self, date: datetime) -> tuple[int, int]: - """Get year and quarter from date.""" - quarter = (date.month - 1) // 3 + 1 - return date.year, quarter - - def get_quarterly_dir(self, year: int, quarter: int) -> Path: - """Get the quarterly directory path.""" - return self.base_reports_dir / f"{year}" / f"Q{quarter}" - - def get_portfolio_name_from_filename(self, filename: str) -> str | None: - """Extract portfolio name from report filename.""" - if filename.startswith("portfolio_report_"): - # Format: portfolio_report_{portfolio_name}_{timestamp}.html - parts = filename.replace("portfolio_report_", "").split("_") - if len(parts) >= 2: - # Take all parts except the last one (timestamp) - return "_".join(parts[:-1]) - return None - - def organize_report( - self, - report_path: str, - portfolio_name: str, - report_date: datetime | None = None, - interval: str | None = None, - ) -> Path: - """ - Organize a report into quarterly structure. - - Args: - report_path: Path to the report file - portfolio_name: Name of the portfolio - report_date: Date of the report (defaults to current date) - - Returns: - Path to the organized report - """ - if report_date is None: - report_date = datetime.now(timezone.utc) - - year, quarter = self.get_quarter_from_date(report_date) - quarterly_dir = self.get_quarterly_dir(year, quarter) - quarterly_dir.mkdir(parents=True, exist_ok=True) - - # Clean portfolio name for filename - clean_portfolio_name = portfolio_name.replace(" ", "_").replace("/", "_") - interval_part = (interval or "multi").replace("/", "-") - - # Unified filename format: _Collection___.html - new_filename = ( - f"{clean_portfolio_name}_Collection_{year}_Q{quarter}_{interval_part}.html" - ) - target_path = quarterly_dir / new_filename - - # Check if report already exists for this portfolio/quarter - if target_path.exists(): - print(f"Overriding existing report: {target_path}") - target_path.unlink() # Remove existing report - - # Copy/move the report - source_path = Path(report_path) - if source_path.exists(): - shutil.copy2(source_path, target_path) - print(f"Report organized: {target_path}") - - # Also handle compressed version if it exists - compressed_source = source_path.with_suffix(".html.gz") - if compressed_source.exists(): - compressed_target = target_path.with_suffix(".html.gz") - shutil.copy2(compressed_source, compressed_target) - - return target_path - - def organize_existing_reports(self) -> None: - """Organize all existing reports in reports_output.""" - print("Organizing existing reports...") - - # Find all portfolio reports - for report_file in self.base_reports_dir.glob("portfolio_report_*.html"): - portfolio_name = self.get_portfolio_name_from_filename(report_file.name) - - if portfolio_name: - # Try to extract date from filename timestamp - try: - filename_parts = report_file.stem.split("_") - timestamp_part = filename_parts[-1] # Last part should be timestamp - - # Parse timestamp (format: YYYYMMDD_HHMMSS) - if len(timestamp_part) >= 8: - date_part = timestamp_part[:8] # YYYYMMDD - report_date = datetime.strptime(date_part, "%Y%m%d").replace( - tzinfo=timezone.utc - ) - else: - report_date = datetime.now(timezone.utc) - - except (ValueError, IndexError): - # If parsing fails, use current date - report_date = datetime.now(timezone.utc) - - # Organize the report (no interval info, use 'multi') - self.organize_report( - str(report_file), portfolio_name, report_date, interval="multi" - ) - - # Remove original file after organizing - report_file.unlink() - - # Also remove compressed version if exists - compressed_file = report_file.with_suffix(".html.gz") - if compressed_file.exists(): - compressed_file.unlink() - - def get_latest_report(self, portfolio_name: str) -> Path | None: - """Get the latest report for a portfolio.""" - clean_portfolio_name = portfolio_name.replace(" ", "_").replace("/", "_") - - latest_report = None - latest_date = None - - # Search through all quarterly directories - for year_dir in self.base_reports_dir.glob("????"): - if year_dir.is_dir(): - for quarter_dir in year_dir.glob("Q?"): - if quarter_dir.is_dir(): - # Prefer unified naming with interval; fallback to legacy - report_path = ( - quarter_dir - / f"{clean_portfolio_name}_Collection_{year_dir.name}_{quarter_dir.name}_1d.html" - ) - if not report_path.exists(): - # Try any interval by globbing - candidates = list( - quarter_dir.glob( - f"{clean_portfolio_name}_Collection_{year_dir.name}_{quarter_dir.name}_*.html" - ) - ) - if candidates: - report_path = candidates[0] - else: - # Legacy fallback - report_path = ( - quarter_dir - / f"{clean_portfolio_name}_Q{quarter_dir.name[1]}_{year_dir.name}.html" - ) - if report_path.exists(): - year = int(year_dir.name) - quarter = int(quarter_dir.name[1]) - date = datetime( - year, (quarter - 1) * 3 + 1, 1, tzinfo=timezone.utc - ) - - if latest_date is None or date > latest_date: - latest_date = date - latest_report = report_path - - return latest_report - - def list_quarterly_reports( - self, year: int | None = None - ) -> dict[str, dict[str, list]]: - """List all quarterly reports, optionally filtered by year.""" - reports: dict[str, dict[str, list]] = {} - - year_pattern = str(year) if year else "????" - - for year_dir in self.base_reports_dir.glob(year_pattern): - if year_dir.is_dir(): - year_str = year_dir.name - reports[year_str] = {} - - for quarter_dir in year_dir.glob("Q?"): - if quarter_dir.is_dir(): - quarter_str = quarter_dir.name - reports[year_str][quarter_str] = [] - - for report_file in quarter_dir.glob("*.html"): - reports[year_str][quarter_str].append(report_file.name) - - return reports - - def cleanup_old_reports(self, keep_quarters: int = 8) -> None: - """Clean up old reports, keeping only the last N quarters.""" - current_date = datetime.now(timezone.utc) - current_year, current_quarter = self.get_quarter_from_date(current_date) - - # Calculate cutoff date - cutoff_quarters = [] - year, quarter = current_year, current_quarter - - for _ in range(keep_quarters): - cutoff_quarters.append((year, quarter)) - quarter -= 1 - if quarter < 1: - quarter = 4 - year -= 1 - - # Remove directories older than cutoff - for year_dir in self.base_reports_dir.glob("????"): - if year_dir.is_dir(): - year_int = int(year_dir.name) - - for quarter_dir in year_dir.glob("Q?"): - if quarter_dir.is_dir(): - quarter_int = int(quarter_dir.name[1]) - - if (year_int, quarter_int) not in cutoff_quarters: - print(f"Removing old reports: {quarter_dir}") - shutil.rmtree(quarter_dir) - - # Remove empty year directories - if not list(year_dir.glob("Q?")): - print(f"Removing empty year directory: {year_dir}") - year_dir.rmdir() diff --git a/src/reporting/tailwind.input.css b/src/reporting/tailwind.input.css deleted file mode 100644 index adc9d8b..0000000 --- a/src/reporting/tailwind.input.css +++ /dev/null @@ -1,8 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; - -/* Optional: custom utility tweaks for cards/tables */ -.card { @apply rounded-xl border border-white/10 bg-white/5 backdrop-blur; } -.card-header { @apply px-5 py-4; } -.card-body { @apply p-5; } diff --git a/src/reporting/tradingview.py b/src/reporting/tradingview.py new file mode 100644 index 0000000..c8bd653 --- /dev/null +++ b/src/reporting/tradingview.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +from pathlib import Path + +from ..backtest.runner import BestResult + + +class TradingViewExporter: + def __init__(self, out_dir: Path): + self.out_dir = Path(out_dir) + self.out_dir.mkdir(parents=True, exist_ok=True) + + def export(self, results: list[BestResult]): + # Keep only the best strategy per (collection, symbol, timeframe) + best_per_key: dict[tuple[str, str, str], BestResult] = {} + for r in results: + key = (r.collection, r.symbol, r.timeframe) + prev = best_per_key.get(key) + if prev is None or float(r.metric_value) > float(prev.metric_value): + best_per_key[key] = r + + lines: list[str] = [] + lines.append("# TradingView Export\n") + lines.append("Copy/paste alert messages for the best strategy per symbol/timeframe.\n") + + # Sort for stable output + for key in sorted(best_per_key.keys()): + r = best_per_key[key] + sharpe = r.stats.get("sharpe") if isinstance(r.stats, dict) else None + sortino = r.stats.get("sortino") if isinstance(r.stats, dict) else None + calmar = r.stats.get("calmar") if isinstance(r.stats, dict) else None + + def fmt(x): + try: + return f"{float(x):.3f}" + except Exception: + return "N/A" + + msg = ( + f"🚨 QUANT SIGNAL: {r.symbol} 📊\n" + f"Strategy: {r.strategy}\n" + f"Timeframe: {r.timeframe}\n" + f"📈 Sharpe: {fmt(sharpe)}\n" + f"📊 Sortino: {fmt(sortino)}\n" + f"⚖️ Calmar: {fmt(calmar)}\n\n" + f"Price: {{close}}\n" + f"Time: {{timenow}}\n" + f"Action: {{strategy.order.action}}\n" + f"Qty: {{strategy.order.contracts}}\n\n" + f"#QuantTrading #{r.symbol} #{r.strategy}" + ) + lines.append(msg) + lines.append("") + + path = self.out_dir / "tradingview.md" + path.write_text("\n".join(lines)) diff --git a/src/strategies/__init__.py b/src/strategies/__init__.py new file mode 100644 index 0000000..a9a2c5b --- /dev/null +++ b/src/strategies/__init__.py @@ -0,0 +1 @@ +__all__ = [] diff --git a/src/strategies/adapters/__init__.py b/src/strategies/adapters/__init__.py new file mode 100644 index 0000000..a9a2c5b --- /dev/null +++ b/src/strategies/adapters/__init__.py @@ -0,0 +1 @@ +__all__ = [] diff --git a/src/strategies/adapters/ctor_signals_adapter.py b/src/strategies/adapters/ctor_signals_adapter.py new file mode 100644 index 0000000..dd17d52 --- /dev/null +++ b/src/strategies/adapters/ctor_signals_adapter.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import importlib + +import pandas as pd + +from ..base import BaseStrategy + + +class CtorSignalsAdapter(BaseStrategy): + """Adapter for external classes that: + + - accept parameters via __init__(**parameters) + - expose generate_signals(self, data: pd.DataFrame) -> pd.Series with values {1,-1,0} + + The adapter converts the 1/-1/0 signal series into entry/exit boolean series + compatible with the backtesting engine. + + Config example: + + strategies: + - name: bitcoin_strategy + module: src.strategies.adapters.ctor_signals_adapter + class: CtorSignalsAdapter + params: + external_module: my_pkg.bitcoin_strategy + external_class: BitcoinStrategy + grid: + lookback_period: [20, 50] + sma_period: [20, 50] + """ + + name = "external_ctor_signals" + + def param_grid(self) -> dict: + # Grid is supplied via config under params.grid and handled by the runner + return {} + + def generate_signals(self, df: pd.DataFrame, params: dict) -> tuple[pd.Series, pd.Series]: + mod_name = params.get("external_module") + cls_name = params.get("external_class") + if not mod_name or not cls_name: + raise ValueError("external_module and external_class are required in params") + + mod = importlib.import_module(mod_name) + Cls = getattr(mod, cls_name) + + # Remove adapter-specific keys and pass the rest to constructor + ctor_kwargs = { + k: v + for k, v in params.items() + if k not in {"external_module", "external_class", "grid"} + } + obj = Cls(**ctor_kwargs) + + # External generate_signals returns a Series in {1, 0, -1} + sig = obj.generate_signals(df) + if not isinstance(sig, pd.Series): + raise TypeError("External strategy must return a pandas Series of signals") + entries = (sig == 1).fillna(False) + exits = (sig == -1).fillna(False) + return entries, exits diff --git a/src/strategies/adapters/generic_adapter.py b/src/strategies/adapters/generic_adapter.py new file mode 100644 index 0000000..58a0218 --- /dev/null +++ b/src/strategies/adapters/generic_adapter.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +import importlib + +import pandas as pd + +from ..base import BaseStrategy + + +class GenericExternalAdapter(BaseStrategy): + """Adapter to wrap an external strategy without modifying its code. + + Usage in config (example): + + strategies: + - name: ext_rsi + module: src.strategies.adapters.generic_adapter + class: GenericExternalAdapter + params: + external_module: my_repo.rsi.module + external_class: RSIStrategy # or use external_function: rsi_generate + grid: + rsi_window: [14, 21] + rsi_buy: [30] + rsi_sell: [70] + + The runner will pass a merged params dict to generate_signals that includes both + static adapter params (external_module/class/function) and the current grid values. + """ + + name = "external_generic" + + def param_grid(self) -> dict: + # Grid is supplied via config under params.grid and handled by the runner + return {} + + def generate_signals(self, df: pd.DataFrame, params: dict) -> tuple[pd.Series, pd.Series]: + mod_name = params.get("external_module") + cls_name = params.get("external_class") + fn_name = params.get("external_function") + if not mod_name: + raise ValueError("external_module is required in params for GenericExternalAdapter") + + mod = importlib.import_module(mod_name) + + if cls_name: + Cls = getattr(mod, cls_name) + obj = Cls() + if hasattr(obj, "generate_signals"): + return obj.generate_signals(df, params) + # Callable class instance + return obj(df, params) + if fn_name: + func = getattr(mod, fn_name) + return func(df, params) + + raise ValueError("Provide either external_class or external_function in params") diff --git a/src/strategies/base.py b/src/strategies/base.py new file mode 100644 index 0000000..1dde495 --- /dev/null +++ b/src/strategies/base.py @@ -0,0 +1,21 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +import pandas as pd + + +class BaseStrategy(ABC): + name: str + + @abstractmethod + def param_grid(self) -> dict[str, list]: + """Return the parameter grid to search. Keys map to lists of candidate values.""" + + @abstractmethod + def generate_signals(self, df: pd.DataFrame, params: dict) -> tuple[pd.Series, pd.Series]: + """Return entries and exits boolean Series aligned to df index.""" + + def to_tradingview_pine(self, params: dict) -> str | None: + """Optional: return Pine v5 code snippet implementing the strategy with given params.""" + return None diff --git a/src/strategies/registry.py b/src/strategies/registry.py new file mode 100644 index 0000000..31b3eb4 --- /dev/null +++ b/src/strategies/registry.py @@ -0,0 +1,96 @@ +from __future__ import annotations + +import importlib +import importlib.util +import inspect +from pathlib import Path + +from ..config import StrategyConfig +from .base import BaseStrategy + + +def discover_external_strategies(strategies_root: Path) -> dict[str, type[BaseStrategy]]: + """Discover all BaseStrategy subclasses under the given path. + + Tries two approaches per .py file: + 1) Regular import assuming package structure (root added to sys.path) + 2) Fallback: load module from file via importlib.util.spec_from_file_location + """ + import sys + + if str(strategies_root) not in sys.path: + sys.path.insert(0, str(strategies_root)) + + found: dict[str, type[BaseStrategy]] = {} + + for py in strategies_root.rglob("*.py"): + if py.name.startswith("_"): + continue + rel = py.relative_to(strategies_root) + mod_name = ".".join(rel.with_suffix("").parts) + mod = None + # Try package import + try: + mod = importlib.import_module(mod_name) + except Exception: + # Fallback: load directly from file path + try: + spec = importlib.util.spec_from_file_location(mod_name, str(py)) + if spec and spec.loader: # type: ignore[attr-defined] + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) # type: ignore[attr-defined] + except Exception: + mod = None + if mod is None: + continue + # Native BaseStrategy subclasses + for _, obj in inspect.getmembers(mod, inspect.isclass): + if issubclass(obj, BaseStrategy) and obj is not BaseStrategy: + name = getattr(obj, "name", obj.__name__) + found[name] = obj + + # Auto-adapt external classes that define generate_signals(self, data) + for _, ext_cls in inspect.getmembers(mod, inspect.isclass): + if ext_cls is BaseStrategy or issubclass(ext_cls, BaseStrategy): + continue + if hasattr(ext_cls, "generate_signals") and callable(ext_cls.generate_signals): + ext_mod_name = mod.__name__ + ext_cls_name = ext_cls.__name__ + + def _make_adapter(module_name: str, class_name: str) -> type[BaseStrategy]: + from .adapters.ctor_signals_adapter import CtorSignalsAdapter + + class _AutoAdapter(BaseStrategy): # type: ignore[misc] + name = class_name + + def param_grid(self) -> dict: + return {} + + def generate_signals(self, df, params): + merged = { + **params, + "external_module": module_name, + "external_class": class_name, + } + return CtorSignalsAdapter().generate_signals(df, merged) + + _AutoAdapter.__name__ = f"AutoAdapter_{class_name}" + return _AutoAdapter + + adapter_cls = _make_adapter(ext_mod_name, ext_cls_name) + found[ext_cls_name] = adapter_cls + return found + + +def load_strategy( + cfg: StrategyConfig, strategies_root: Path, external_index: dict[str, type[BaseStrategy]] +): + """Load a strategy class either from module or from external discovery.""" + if cfg.module and cfg.cls: + mod = importlib.import_module(cfg.module) + cls = getattr(mod, cfg.cls) + return cls + # fallback by name from external index + if cfg.name in external_index: + return external_index[cfg.name] + raise ImportError(f"Strategy not found: {cfg}") diff --git a/src/utils/__init__.py b/src/utils/__init__.py deleted file mode 100644 index 761e423..0000000 --- a/src/utils/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Utility modules for the quant system.""" - -from __future__ import annotations - -__all__ = [] diff --git a/src/utils/config_manager.py b/src/utils/config_manager.py deleted file mode 100644 index 6c17ba7..0000000 --- a/src/utils/config_manager.py +++ /dev/null @@ -1,216 +0,0 @@ -"""Configuration management module for the quant system.""" - -from __future__ import annotations - -import contextlib -import json -import os -from pathlib import Path -from typing import Any - -import yaml - - -class ConfigManager: - """Manages configuration settings for the application. - - Supports loading from JSON or YAML files and environment variables. - """ - - def __init__(self, config_path: str | None = None): - """ - Initialize the config manager. - - Args: - config_path: Optional path to a configuration file - """ - self.config: dict[str, Any] = {} - - # Load default configuration - self._load_defaults() - - # Load from file if provided - if config_path: - self.load_config_file(config_path) - - # Override with environment variables - self._load_from_env() - - def _load_defaults(self) -> None: - """Load default configuration values.""" - self.config = { - "data": { - "default_interval": "1d", - "cache_dir": Path.home() / ".quant-py" / "cache", - }, - "backtest": { - "default_commission": 0.001, # 0.1% commission - "initial_capital": 10000, - }, - "logging": { - "level": "INFO", - "log_file": Path.home() / ".quant-py" / "logs" / "quant-py.log", - "debug_backtest": False, # Add debug flag for backtest operations - }, - } - - # Create necessary directories - Path(self.config["data"]["cache_dir"]).mkdir(parents=True, exist_ok=True) - Path(self.config["logging"]["log_file"]).parent.mkdir( - parents=True, exist_ok=True - ) - - def load_config_file(self, config_path: str) -> None: - """ - Load configuration from a file. - - Args: - config_path: Path to the configuration file (JSON or YAML) - """ - if not Path(config_path).exists(): - msg = f"Configuration file not found: {config_path}" - raise FileNotFoundError(msg) - - # Determine file type and load accordingly - if config_path.endswith(".json"): - with Path(config_path).open() as f: - file_config = json.load(f) - elif config_path.endswith((".yaml", ".yml")): - with Path(config_path).open() as f: - file_config = yaml.safe_load(f) - else: - msg = "Unsupported configuration file format. Use .json, .yaml, or .yml" - raise ValueError(msg) - - # Update configuration - self._update_nested_dict(self.config, file_config) - - def _load_from_env(self) -> None: - """ - Load configuration from environment variables. - - Environment variables should be in the format: - QUANTPY_SECTION_KEY=value - """ - prefix = "QUANTPY_" - - for env_var, value in os.environ.items(): - if env_var.startswith(prefix): - # Remove prefix and split into parts - parts = env_var[len(prefix) :].lower().split("_") - - if len(parts) >= 2: - section = parts[0] - key = "_".join(parts[1:]) - - # Create section if it doesn't exist - if section not in self.config: - self.config[section] = {} - - # Convert value to appropriate type if possible - parsed_value: str | bool | int | float = value - if value.lower() in ("true", "yes", "1"): - parsed_value = True - elif value.lower() in ("false", "no", "0"): - parsed_value = False - elif value.isdigit(): - parsed_value = int(value) - else: - with contextlib.suppress(ValueError): - parsed_value = float(value) - - self.config[section][key] = parsed_value - - def _update_nested_dict(self, d: dict[str, Any], u: dict[str, Any]) -> None: - """ - Update a nested dictionary with values from another dictionary. - - Args: - d: Target dictionary to update - u: Source dictionary with new values - """ - for k, v in u.items(): - if isinstance(v, dict) and k in d and isinstance(d[k], dict): - self._update_nested_dict(d[k], v) - else: - d[k] = v - - def get(self, path: str, default: Any = None) -> Any: - """ - Get a configuration value using dot notation for nested dictionaries. - - Args: - path: Path to the configuration value using dot notation (e.g., 'data.cache_dir') - default: Default value to return if the path doesn't exist - - Returns: - The configuration value or the default - """ - keys = path.split(".") - result = self.config - - for key in keys: - if not isinstance(result, dict) or key not in result: - return default - result = result[key] - - return result - - def set(self, path: str, value: Any) -> None: - """ - Set a configuration value using dot notation for nested dictionaries. - - Args: - path: Path to the configuration value using dot notation (e.g., 'data.cache_dir') - value: Value to set - """ - keys = path.split(".") - current = self.config - - # Navigate to the parent of the target key, creating nested dicts as needed - for key in keys[:-1]: - if key not in current or not isinstance(current[key], dict): - current[key] = {} - current = current[key] - - # Set the final value - current[keys[-1]] = value - - def save_to_file(self, file_path: str) -> None: - """ - Save the current configuration to a file. - - Args: - file_path: Path to save the configuration to - """ - # Create a serializable copy of the config - serializable_config = self._make_serializable(self.config) - - # Determine file type based on extension - if file_path.endswith(".json"): - with Path(file_path).open("w") as f: - json.dump(serializable_config, f, indent=4) - elif file_path.endswith((".yaml", ".yml")): - with Path(file_path).open("w") as f: - yaml.dump(serializable_config, f, default_flow_style=False) - else: - msg = "Unsupported file format. Use .json, .yaml, or .yml" - raise ValueError(msg) - - def _make_serializable(self, obj: Any) -> Any: - """ - Convert objects to serializable format. - - Args: - obj: Object to make serializable - - Returns: - Serializable version of the object - """ - if isinstance(obj, Path): - return str(obj) - if isinstance(obj, dict): - return {key: self._make_serializable(value) for key, value in obj.items()} - if isinstance(obj, list): - return [self._make_serializable(item) for item in obj] - return obj diff --git a/src/utils/csv_exporter.py b/src/utils/csv_exporter.py deleted file mode 100644 index ac577b3..0000000 --- a/src/utils/csv_exporter.py +++ /dev/null @@ -1,874 +0,0 @@ -""" -Raw Data CSV Export Utility - -Exports portfolio performance data with best strategies and timeframes to CSV format. -Based on the features.md specification and crypto_best_strategies.csv format. -""" - -from __future__ import annotations - -import logging -import re -from pathlib import Path -from typing import Any - -import pandas as pd -from bs4 import BeautifulSoup - -from src.database.db_connection import get_db_session -from src.database.models import BestStrategy - - -class RawDataCSVExporter: - """ - Export raw portfolio data with best strategies and performance metrics to CSV. - - Features: - - CSV export with symbol, best strategy, best timeframe, and performance metrics - - Bulk export for all assets from quarterly reports - - Customizable column selection (Sharpe, Sortino, profit, drawdown) - - Integration with existing quarterly report structure - """ - - def __init__(self, output_dir: str = "exports/csv"): - # Default output directory aligned with repo: exports/csv - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - self.reports_dir = Path("exports/reports") - self.logger = logging.getLogger(__name__) - - def export_from_database_primary( - self, - quarter: str, - year: str, - output_filename: str | None = None, - export_format: str = "full", - portfolio_name: str = "all", - portfolio_path: str | None = None, - interval: str | None = None, - ) -> list[str]: - """ - Export data directly from database - primary data source for CSV exports. - Filters by specific collection symbols if portfolio is provided. - - Args: - quarter: Quarter (Q1, Q2, Q3, Q4) - year: Year (YYYY) - output_filename: Custom filename, auto-generated if None - export_format: 'full', 'best-strategies', or 'quarterly' - portfolio_name: Portfolio collection name for filtering - portfolio_path: Path to portfolio config file for symbol filtering - - Returns: - List of paths to exported CSV files - """ - output_files = [] - - try: - db_session = get_db_session() - - # Load portfolio symbols for filtering if portfolio path provided - portfolio_symbols = None - if portfolio_path: - try: - import json - from pathlib import Path - - with Path(portfolio_path).open() as f: - portfolio_config = json.load(f) - # Get the first (and usually only) portfolio config - portfolio_key = list(portfolio_config.keys())[0] - portfolio_symbols = portfolio_config[portfolio_key].get( - "symbols", [] - ) - portfolio_name = portfolio_key # Use actual collection name - self.logger.info( - "Filtering by %s symbols from %s collection", - len(portfolio_symbols), - portfolio_name, - ) - except Exception as e: - self.logger.warning("Could not load portfolio config: %s", e) - - # Query best strategies from database with optional symbol filtering. - # Primary canonical table is backtests.best_strategies (models.BestStrategy). - # If that is empty (e.g., legacy or different persistence layer), fall back - # to the lightweight unified_models BestStrategy table (unified_models.BestStrategy). - query = db_session.query(BestStrategy) - - if portfolio_symbols: - query = query.filter(BestStrategy.symbol.in_(portfolio_symbols)) - # Filter by timeframe/interval if provided - if "interval" in locals() and interval: - try: - query = query.filter(BestStrategy.timeframe == interval) - except Exception: - pass - - best_strategies = query.all() - - # Fallback to unified_models if no rows found in canonical backtests schema - if not best_strategies: - try: - from src.database import unified_models - - sess2 = unified_models.Session() - try: - uq = sess2.query(unified_models.BestStrategy) - if portfolio_symbols: - uq = uq.filter( - unified_models.BestStrategy.symbol.in_( - portfolio_symbols - ) - ) - if "interval" in locals() and interval: - try: - uq = uq.filter( - unified_models.BestStrategy.timeframe == interval - ) - except Exception: - pass - unified_rows = uq.all() - if unified_rows: - # Map unified_models rows into a structure compatible with the rest of this function. - # unified_models.BestStrategy has attributes with same names used below (symbol, timeframe, strategy, sortino_ratio, calmar_ratio, sharpe_ratio, total_return, max_drawdown, updated_at) - best_strategies = unified_rows - self.logger.info( - "Fell back to unified_models BestStrategy table (%d rows)", - len(best_strategies), - ) - finally: - sess2.close() - except Exception: - # If fallback fails, continue with empty list to trigger no-data path below - pass - - if not best_strategies: - self.logger.warning( - "No strategies found in database for specified filters" - ) - return output_files - - self.logger.info( - "Found %s strategies in database for %s collection", - len(best_strategies), - portfolio_name, - ) - - # Convert to DataFrame - data = [] - for strategy in best_strategies: - data.append( - { - "Symbol": strategy.symbol, - "Strategy": strategy.strategy, - "Timeframe": strategy.timeframe, - "Sortino_Ratio": strategy.sortino_ratio or 0.0, - "Sharpe_Ratio": strategy.sharpe_ratio or 0.0, - "Calmar_Ratio": strategy.calmar_ratio or 0.0, - "Total_Return": strategy.total_return or 0.0, - "Max_Drawdown": strategy.max_drawdown or 0.0, - "Updated_At": strategy.updated_at.strftime("%Y-%m-%d %H:%M:%S") - if strategy.updated_at - else "", - "Quarter": quarter, - "Year": year, - } - ) - - df = pd.DataFrame(data) - - # Create output directory following standard naming convention - csv_output_dir = self.output_dir / year / quarter - csv_output_dir.mkdir(parents=True, exist_ok=True) - - # Generate filename following naming convention - # Prefer human-readable collection name from config when available - display_name = portfolio_name or "All_Collections" - if portfolio_path: - try: - import json - - with Path(portfolio_path).open() as f: - portfolio_config = json.load(f) - portfolio_key = list(portfolio_config.keys())[0] - display_name = ( - portfolio_config[portfolio_key].get("name") or display_name - ) - except Exception: - pass - - # Sanitize and build unified base filename: _Collection___ - sanitized = re.sub(r"\W+", "_", str(display_name)).strip("_") - safe_interval = (interval or "multi").replace("/", "-") - if output_filename: - base_filename = output_filename.replace(".csv", "") - else: - base_filename = ( - f"{sanitized}_Collection_{year}_{quarter}_{safe_interval}" - ) - - if export_format == "best-strategies": - filename = f"{base_filename}.csv" - # Keep only one row per symbol with highest Sortino ratio - df = ( - df.sort_values("Sortino_Ratio", ascending=False) - .groupby("Symbol") - .first() - .reset_index() - ) - # Select and rename columns for best strategies format - df = df[ - ["Symbol", "Strategy", "Timeframe", "Sortino_Ratio", "Total_Return"] - ].rename( - columns={ - "Symbol": "Asset", - "Strategy": "Best_Strategy", - "Timeframe": "Best_Timeframe", - "Sortino_Ratio": "Sortino_Ratio", - "Total_Return": "Total_Return_Pct", - } - ) - elif export_format == "quarterly": - filename = f"{base_filename}.csv" - # Create summary statistics - summary_data = [] - for symbol in df["Symbol"].unique(): - symbol_data = df[df["Symbol"] == symbol] - best_strategy = symbol_data.loc[ - symbol_data["Sortino_Ratio"].idxmax() - ] - summary_data.append( - { - "Asset": symbol, - "Best_Strategy": best_strategy["Strategy"], - "Best_Timeframe": best_strategy["Timeframe"], - "Best_Sortino": best_strategy["Sortino_Ratio"], - "Strategies_Tested": len(symbol_data), - "Avg_Return": symbol_data["Total_Return"].mean(), - "Best_Return": symbol_data["Total_Return"].max(), - } - ) - df = pd.DataFrame(summary_data) - else: # full - filename = f"{base_filename}.csv" - # Keep all data with proper column names - df = df.rename( - columns={ - "Symbol": "Asset", - "Strategy": "Strategy_Name", - "Timeframe": "Time_Resolution", - } - ) - - output_file = csv_output_dir / filename - - # Export to CSV - df.to_csv(output_file, index=False) - output_files.append(str(output_file)) - - self.logger.info("Exported %s records to %s", len(df), output_file) - - return output_files - - except Exception as e: - # Attempt unified_models fallback even if primary DB session creation failed early - try: - from src.database import unified_models - - # Load portfolio symbols for filtering if portfolio_path is provided - portfolio_symbols = None - if "portfolio_path" in locals() and portfolio_path: - try: - import json - - with Path(portfolio_path).open() as f: - portfolio_config = json.load(f) - portfolio_key = list(portfolio_config.keys())[0] - portfolio_symbols = portfolio_config[portfolio_key].get( - "symbols", [] - ) - portfolio_name = portfolio_key - except Exception: - pass - - sess2 = unified_models.Session() - try: - uq = sess2.query(unified_models.BestStrategy) - if portfolio_symbols: - uq = uq.filter( - unified_models.BestStrategy.symbol.in_(portfolio_symbols) - ) - if interval: - try: - uq = uq.filter( - unified_models.BestStrategy.timeframe == interval - ) - except Exception: - pass - best_strategies = uq.all() - finally: - try: - sess2.close() - except Exception: - pass - - if not best_strategies: - self.logger.error( - "Failed to export from database and unified_models had no rows: %s", - e, - ) - return output_files - - # Build DataFrame from unified_models rows (same as above) - data = [] - for strategy in best_strategies: - data.append( - { - "Symbol": strategy.symbol, - "Strategy": strategy.strategy, - "Timeframe": strategy.timeframe, - "Sortino_Ratio": strategy.sortino_ratio or 0.0, - "Sharpe_Ratio": strategy.sharpe_ratio or 0.0, - "Calmar_Ratio": strategy.calmar_ratio or 0.0, - "Total_Return": strategy.total_return or 0.0, - "Max_Drawdown": strategy.max_drawdown or 0.0, - "Updated_At": strategy.updated_at.strftime( - "%Y-%m-%d %H:%M:%S" - ) - if strategy.updated_at - else "", - "Quarter": quarter, - "Year": year, - } - ) - - df = pd.DataFrame(data) - csv_output_dir = self.output_dir / year / quarter - csv_output_dir.mkdir(parents=True, exist_ok=True) - - display_name = portfolio_name or "All_Collections" - if "portfolio_path" in locals() and portfolio_path: - try: - import json - - with Path(portfolio_path).open() as f: - portfolio_config = json.load(f) - portfolio_key = list(portfolio_config.keys())[0] - display_name = ( - portfolio_config[portfolio_key].get("name") - or display_name - ) - except Exception: - pass - - sanitized = re.sub(r"\W+", "_", str(display_name)).strip("_") - safe_interval = (interval or "multi").replace("/", "-") - base_filename = ( - f"{sanitized}_Collection_{year}_{quarter}_{safe_interval}" - ) - - if export_format == "best-strategies": - filename = f"{base_filename}.csv" - df = ( - df.sort_values("Sortino_Ratio", ascending=False) - .groupby("Symbol") - .first() - .reset_index() - ) - df = df[ - [ - "Symbol", - "Strategy", - "Timeframe", - "Sortino_Ratio", - "Total_Return", - ] - ].rename( - columns={ - "Symbol": "Asset", - "Strategy": "Best_Strategy", - "Timeframe": "Best_Timeframe", - "Sortino_Ratio": "Sortino_Ratio", - "Total_Return": "Total_Return_Pct", - } - ) - elif export_format == "quarterly": - filename = f"{base_filename}.csv" - summary_data = [] - for symbol in df["Symbol"].unique(): - symbol_data = df[df["Symbol"] == symbol] - best_strategy = symbol_data.loc[ - symbol_data["Sortino_Ratio"].idxmax() - ] - summary_data.append( - { - "Asset": symbol, - "Best_Strategy": best_strategy["Strategy"], - "Best_Timeframe": best_strategy["Timeframe"], - "Best_Sortino": best_strategy["Sortino_Ratio"], - "Strategies_Tested": len(symbol_data), - "Avg_Return": symbol_data["Total_Return"].mean(), - "Best_Return": symbol_data["Total_Return"].max(), - } - ) - df = pd.DataFrame(summary_data) - else: - filename = f"{base_filename}.csv" - df = df.rename( - columns={ - "Symbol": "Asset", - "Strategy": "Strategy_Name", - "Timeframe": "Time_Resolution", - } - ) - - output_file = csv_output_dir / filename - df.to_csv(output_file, index=False) - output_files.append(str(output_file)) - self.logger.info( - "Exported %s records to %s (unified_models)", len(df), output_file - ) - return output_files - except Exception as e2: - self.logger.error( - "Failed CSV export fallback: %s (original: %s)", e2, e - ) - return output_files - finally: - if "db_session" in locals(): - db_session.close() - - def export_from_quarterly_reports( - self, - quarter: str, - year: str, - output_filename: str | None = None, - export_format: str = "full", - collection_name: str | None = None, - interval: str | None = None, - ) -> list[str]: - """ - Extract data from existing quarterly reports and export to CSV. - Creates separate CSV files for each HTML report (e.g., Crypto_Portfolio_Q3_2025.csv). - - Args: - quarter: Quarter (Q1, Q2, Q3, Q4) - year: Year (YYYY) - output_filename: Custom filename, auto-generated if None (used for single file export) - export_format: 'full' or 'best-strategies' - - Returns: - List of paths to exported CSV files - """ - # Check if quarterly reports exist - quarterly_reports_dir = self.reports_dir / year / quarter - if not quarterly_reports_dir.exists(): - self.logger.warning("No quarterly reports found for %s %s", quarter, year) - return [] - - # Find HTML report files - html_files = list(quarterly_reports_dir.glob("*.html")) - if not html_files: - self.logger.warning("No HTML reports found in %s", quarterly_reports_dir) - return [] - - self.logger.info( - "Found %d HTML reports for %s %s", len(html_files), quarter, year - ) - - # Create quarterly directory structure under exports/csv - quarterly_dir = self.output_dir / year / quarter - quarterly_dir.mkdir(parents=True, exist_ok=True) - - exported_files = [] - - # Process each HTML report separately - for html_file in html_files: - # Extract data from this specific report - extracted_data = self._extract_data_from_html_report(html_file) - - if not extracted_data: - self.logger.warning("No data extracted from %s", html_file.name) - continue - - # Convert to DataFrame - df = pd.DataFrame(extracted_data) - - # Build unified filename - name_for_file = collection_name or html_file.stem - sanitized = re.sub(r"\W+", "_", str(name_for_file)).strip("_") - safe_interval = (interval or "multi").replace("/", "-") - csv_filename = ( - output_filename - if output_filename and len(html_files) == 1 - else f"{sanitized}_Collection_{year}_{quarter}_{safe_interval}.csv" - ) - - # Process based on format - if export_format == "best-strategies": - # Group by symbol and keep best performing strategy - if "Symbol" in df.columns and "Sortino_Ratio" in df.columns: - df = ( - df.sort_values("Sortino_Ratio", ascending=False) - .groupby("Symbol") - .first() - .reset_index() - ) - df = df[["Symbol", "Strategy", "Timeframe"]].rename( - columns={ - "Symbol": "Asset", - "Strategy": "Best Strategy", - "Timeframe": "Resolution", - } - ) - - # Add quarterly metadata - df["Quarter"] = quarter - df["Year"] = year - df["Export_Date"] = pd.Timestamp.now().strftime("%Y-%m-%d") - - # Sort by performance - if "Sortino_Ratio" in df.columns: - df = df.sort_values("Sortino_Ratio", ascending=False) - elif "Total_Return_Pct" in df.columns: - df = df.sort_values("Total_Return_Pct", ascending=False) - - # Export to quarterly directory - output_path = quarterly_dir / csv_filename - df.to_csv(output_path, index=False) - - exported_files.append(str(output_path)) - - self.logger.info( - "Exported %s data from %s to %s (%d rows)", - export_format, - html_file.name, - output_path, - len(df), - ) - - self.logger.info( - "Exported %d CSV files from quarterly reports for %s %s", - len(exported_files), - quarter, - year, - ) - - return exported_files - - def get_available_columns(self) -> list[str]: - """Get list of all available columns for export.""" - return [ - "Symbol", - "Strategy", - "Timeframe", - "Total_Return_Pct", - "Sortino_Ratio", - "Sharpe_Ratio", - "Calmar_Ratio", - "Max_Drawdown_Pct", - "Win_Rate_Pct", - "Profit_Factor", - "Number_of_Trades", - "Volatility_Pct", - "Downside_Deviation", - "Average_Win", - "Average_Loss", - "Longest_Win_Streak", - "Longest_Loss_Streak", - "Data_Points", - "Backtest_Duration_Seconds", - ] - - def _extract_data_from_html_report(self, html_file: Path) -> list[dict[str, Any]]: - """Extract performance data from HTML report files.""" - try: - with html_file.open("r", encoding="utf-8") as f: - soup = BeautifulSoup(f.read(), "html.parser") - - extracted_data = [] - - # Look for tables with performance data - tables = soup.find_all("table") - - for table in tables: - # Check if this is a performance metrics table - headers = table.find("tr") - if not headers: - continue - - header_cells = [ - th.get_text(strip=True) for th in headers.find_all(["th", "td"]) - ] - - # Look for tables that contain symbol/strategy information - if any( - keyword in " ".join(header_cells).lower() - for keyword in ["symbol", "strategy", "asset", "sortino", "sharpe"] - ): - rows = table.find_all("tr")[1:] # Skip header row - - for row in rows: - cells = [ - td.get_text(strip=True) for td in row.find_all(["td", "th"]) - ] - if len(cells) < 2: - continue - - # Try to extract data based on common patterns - row_data = self._parse_table_row(header_cells, cells) - if row_data: - extracted_data.append(row_data) - - # Also look for metric cards or divs with performance data - metric_cards = soup.find_all( - "div", class_=re.compile(r".*metric.*|.*card.*|.*performance.*", re.I) - ) - for card in metric_cards: - card_data = self._parse_metric_card(card) - if card_data: - extracted_data.append(card_data) - - # Tailwind report structure fallback: section[id^='asset-'] with h2 and spans - try: - sections = soup.select("section[id^='asset-']") - for sec in sections: - h2 = sec.find("h2") - symbol = h2.get_text(strip=True) if h2 else None - best_strategy = None - timeframe = None - for sp in sec.find_all("span"): - txt = sp.get_text(strip=True) - if txt.startswith("Best:") and best_strategy is None: - best_strategy = txt.replace("Best:", "").strip() - if "⏰" in txt and timeframe is None: - timeframe = txt.replace("⏰", "").strip() - if symbol and best_strategy: - extracted_data.append( - { - "Symbol": symbol, - "Strategy": best_strategy, - "Timeframe": timeframe or "1d", - } - ) - except Exception: - pass - - self.logger.info( - "Extracted %d data points from %s", len(extracted_data), html_file.name - ) - return extracted_data - - except Exception as e: - self.logger.error("Failed to parse HTML file %s: %s", html_file, e) - return [] - - def _parse_table_row( - self, headers: list[str], cells: list[str] - ) -> dict[str, Any] | None: - """Parse a table row and extract relevant metrics.""" - if len(headers) != len(cells): - return None - - row_data = {} - - # Map common header patterns to our standard columns - header_mapping = { - "symbol": "Symbol", - "asset": "Symbol", - "strategy": "Strategy", - "timeframe": "Timeframe", - "resolution": "Timeframe", - "total_return": "Total_Return_Pct", - "return": "Total_Return_Pct", - "sortino": "Sortino_Ratio", - "sharpe": "Sharpe_Ratio", - "calmar": "Calmar_Ratio", - "drawdown": "Max_Drawdown_Pct", - "win_rate": "Win_Rate_Pct", - "profit_factor": "Profit_Factor", - "trades": "Number_of_Trades", - "volatility": "Volatility_Pct", - } - - for i, header in enumerate(headers): - if i >= len(cells): - break - - header_lower = ( - header.lower() - .replace(" ", "_") - .replace("%", "") - .replace("(", "") - .replace(")", "") - ) - - # Find matching column name - mapped_column = None - for pattern, column in header_mapping.items(): - if pattern in header_lower: - mapped_column = column - break - - if mapped_column and cells[i]: - try: - # Try to convert numeric values - if mapped_column in [ - "Total_Return_Pct", - "Sortino_Ratio", - "Sharpe_Ratio", - "Calmar_Ratio", - "Max_Drawdown_Pct", - "Win_Rate_Pct", - "Profit_Factor", - "Volatility_Pct", - ]: - # Remove % signs and other formatting - clean_value = re.sub(r"[%$,\s]", "", cells[i]) - if clean_value and clean_value != "-": - row_data[mapped_column] = float(clean_value) - elif mapped_column == "Number_of_Trades": - clean_value = re.sub(r"[,\s]", "", cells[i]) - if clean_value and clean_value.isdigit(): - row_data[mapped_column] = int(clean_value) - else: - row_data[mapped_column] = cells[i] - except (ValueError, TypeError): - row_data[mapped_column] = cells[i] - - # Only return if we have at least symbol or strategy - if "Symbol" in row_data or "Strategy" in row_data: - # Set defaults - if "Timeframe" not in row_data: - row_data["Timeframe"] = "1d" - return row_data - - return None - - def _parse_metric_card(self, card) -> dict[str, Any] | None: - """Parse metric cards for performance data.""" - # This would need to be customized based on the actual HTML structure - # of the reports generated by the system - text = card.get_text(strip=True) - - # Look for patterns like "BTCUSDT: 45.2%" or "Strategy: BuyAndHold" - symbol_match = re.search(r"([A-Z0-9]+USDT?):?\s*([-+]?\d+\.?\d*%?)", text) - strategy_match = re.search(r"Strategy:?\s*([A-Za-z\s]+)", text) - - if symbol_match or strategy_match: - card_data = {} - if symbol_match: - card_data["Symbol"] = symbol_match.group(1) - if len(symbol_match.groups()) > 1: - try: - value = float(symbol_match.group(2).replace("%", "")) - card_data["Total_Return_Pct"] = value - except ValueError: - pass - - if strategy_match: - card_data["Strategy"] = strategy_match.group(1).strip() - - card_data["Timeframe"] = "1d" # Default - return card_data - - return None - - def _export_from_database( - self, - quarter: str, - year: str, - export_format: str = "full", - interval: str | None = None, - ) -> list[str]: - """ - Export data directly from database when HTML reports have no data. - - Args: - quarter: Quarter (Q1, Q2, Q3, Q4) - year: Year (YYYY) - export_format: Export format ('full', 'best-strategies', 'quarterly') - - Returns: - List of generated CSV file paths - """ - output_files = [] - - try: - db_session = get_db_session() - - # Query all best strategies from database - q = db_session.query(BestStrategy) - if "interval" in locals() and interval: - try: - q = q.filter(BestStrategy.timeframe == interval) - except Exception: - pass - best_strategies = q.all() - - if not best_strategies: - self.logger.warning("No strategies found in database") - return output_files - - self.logger.info("Found %s strategies in database", len(best_strategies)) - - # Convert to DataFrame - data = [] - for strategy in best_strategies: - data.append( - { - "Symbol": strategy.symbol, - "Strategy": strategy.strategy, - "Timeframe": strategy.timeframe, - "Sortino_Ratio": strategy.sortino_ratio, - "Sharpe_Ratio": strategy.sharpe_ratio, - "Calmar_Ratio": strategy.calmar_ratio, - "Total_Return": strategy.total_return, - "Max_Drawdown": strategy.max_drawdown, - "Updated_At": strategy.updated_at.strftime("%Y-%m-%d %H:%M:%S") - if strategy.updated_at - else "", - } - ) - - df = pd.DataFrame(data) - - # Create output directory - csv_output_dir = self.output_dir / year / quarter - csv_output_dir.mkdir(parents=True, exist_ok=True) - - # Generate filename (fallback method) using unified convention - safe_interval = (interval or "multi").replace("/", "-") - filename = ( - f"All_Collections_Collection_{year}_{quarter}_{safe_interval}.csv" - ) - if export_format == "best-strategies": - # Keep only one row per symbol with highest Sortino ratio - df = ( - df.sort_values("Sortino_Ratio", ascending=False) - .groupby("Symbol") - .first() - .reset_index() - ) - elif export_format == "quarterly": - pass - else: # full - pass - - output_file = csv_output_dir / filename - - # Export to CSV - df.to_csv(output_file, index=False) - output_files.append(str(output_file)) - - self.logger.info("Exported %s records to %s", len(df), output_file) - - return output_files - - except Exception as e: - self.logger.error("Failed to export from database: %s", e) - return output_files - finally: - if "db_session" in locals(): - db_session.close() diff --git a/src/utils/http.py b/src/utils/http.py new file mode 100644 index 0000000..78664c0 --- /dev/null +++ b/src/utils/http.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import requests +from requests.adapters import HTTPAdapter +from urllib3.util.retry import Retry + + +def create_retry_session( + total: int = 5, + backoff_factor: float = 0.5, + status_forcelist: tuple = (429, 500, 502, 503, 504), + allowed_methods: frozenset | None = None, +) -> requests.Session: + if allowed_methods is None: + allowed_methods = frozenset(["HEAD", "GET", "OPTIONS"]) # idempotent + retry = Retry( + total=total, + read=total, + connect=total, + backoff_factor=backoff_factor, + status_forcelist=status_forcelist, + allowed_methods=allowed_methods, + raise_on_status=False, + respect_retry_after_header=True, + ) + adapter = HTTPAdapter(max_retries=retry) + session = requests.Session() + session.mount("http://", adapter) + session.mount("https://", adapter) + return session diff --git a/src/utils/logger.py b/src/utils/logger.py deleted file mode 100644 index fdc72b3..0000000 --- a/src/utils/logger.py +++ /dev/null @@ -1,207 +0,0 @@ -"""Centralized logging utility for the quant system.""" - -from __future__ import annotations - -import logging -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, ClassVar - -from src.utils.config_manager import ConfigManager - - -class Logger: - """Centralized logging utility for the quant system. - - Provides consistent logging across all modules and CLI commands. - """ - - # Log levels mapping - LOG_LEVELS: ClassVar[dict[str, int]] = { - "DEBUG": logging.DEBUG, - "INFO": logging.INFO, - "WARNING": logging.WARNING, - "ERROR": logging.ERROR, - "CRITICAL": logging.CRITICAL, - } - - # Initialize class variables - _initialized = False - _config = None - _cli_log_file = None - _orig_stdout = None - _orig_stderr = None - - @classmethod - def initialize(cls, config: ConfigManager | None = None) -> None: - """Initialize the logging system with configuration.""" - if cls._initialized: - return - - # Load config if not provided - if config is None: - cls._config = ConfigManager() - else: - cls._config = config - - # Create log directory if it doesn't exist - log_dir = Path(cls._config.config["logging"]["log_file"]).parent - log_dir.mkdir(parents=True, exist_ok=True) - - # Create CLI logs directory - cli_log_dir = log_dir / "cli" - cli_log_dir.mkdir(parents=True, exist_ok=True) - - # Configure root logger - root_logger = logging.getLogger() - root_logger.setLevel( - cls.LOG_LEVELS.get( - cls._config.config["logging"]["level"].upper(), logging.INFO - ) - ) - - # Remove existing handlers to avoid duplicates - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) - - # Add file handler for the main log file - file_handler = logging.FileHandler(cls._config.config["logging"]["log_file"]) - formatter = logging.Formatter( - "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - ) - file_handler.setFormatter(formatter) - root_logger.addHandler(file_handler) - - # Add console handler - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setFormatter(formatter) - root_logger.addHandler(console_handler) - - cls._initialized = True - - @classmethod - def get_logger(cls, name: str) -> logging.Logger: - """Get a logger for a specific module.""" - if not cls._initialized: - cls.initialize() - return logging.getLogger(name) - - @classmethod - def setup_cli_logging(cls, command_name: str) -> str: - """Set up logging for CLI commands.""" - if not cls._initialized: - cls.initialize() - - # Create logs directory if it doesn't exist - Path("logs").mkdir(exist_ok=True) - - # Generate log filename with timestamp - timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") - log_file = f"logs/{command_name}_{timestamp}.log" - - # Create file handler with UTF-8 encoding - file_handler = logging.FileHandler(log_file, encoding="utf-8") - file_handler.setLevel(logging.DEBUG) - - # Create formatter - formatter = logging.Formatter( - "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - ) - file_handler.setFormatter(formatter) - - # Add handler to root logger - logging.getLogger().addHandler(file_handler) - - cls._cli_log_file = log_file - - return cls._cli_log_file - - @classmethod - def capture_stdout(cls) -> None: - """Capture stdout and stderr to the logger. - - Call this after setup_cli_logging for full CLI output capture. - """ - if not cls._initialized or not cls._cli_log_file: - return - - # Create stdout/stderr redirectors - class LoggerWriter: - def __init__(self, logger: Any, level: Any) -> None: - self.logger = logger - self.level = level - self.buffer: str = "" - - def write(self, message: str) -> None: - if message and not message.isspace(): - try: - self.buffer += message - if "\n" in self.buffer: - self.flush() - except UnicodeEncodeError: - # Handle Unicode encoding errors by replacing problematic characters - safe_message = message.encode("ascii", "replace").decode( - "ascii" - ) - self.buffer += safe_message - if "\n" in self.buffer: - self.flush() - - def flush(self) -> None: - if self.buffer: - try: - for line in self.buffer.rstrip().splitlines(): - self.level(line) - except UnicodeEncodeError: - # Handle Unicode encoding errors by replacing problematic characters - safe_buffer = self.buffer.encode("ascii", "replace").decode( - "ascii" - ) - for line in safe_buffer.rstrip().splitlines(): - self.level(line) - self.buffer = "" - - logger = logging.getLogger() - - # Save original stdout/stderr - cls._orig_stdout = sys.stdout - cls._orig_stderr = sys.stderr - - # Redirect stdout and stderr - sys.stdout = LoggerWriter(logger, logger.info) - sys.stderr = LoggerWriter(logger, logger.error) - - @classmethod - def restore_stdout(cls) -> None: - """Restore original stdout and stderr.""" - if hasattr(cls, "_orig_stdout") and hasattr(cls, "_orig_stderr"): - sys.stdout = cls._orig_stdout - sys.stderr = cls._orig_stderr - - -# Create a convenience function for getting a logger -def get_logger(name: str) -> logging.Logger: - """Get a logger for a specific module.""" - return Logger.get_logger(name) - - -def setup_command_logging(args: Any) -> str | None: - """Set up logging based on command arguments.""" - if hasattr(args, "log") and args.log: - # Initialize logger if needed - Logger.initialize() - - # Get command name - command = args.command if hasattr(args, "command") else "unknown" - - # Setup CLI logging - log_file = Logger.setup_cli_logging(command) - - # Capture stdout/stderr - Logger.capture_stdout() - - print(f"📝 Logging enabled. Output will be saved to: {log_file}") - - return log_file - return None diff --git a/src/utils/symbols.py b/src/utils/symbols.py new file mode 100644 index 0000000..ed01b47 --- /dev/null +++ b/src/utils/symbols.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +import time +from dataclasses import dataclass + +# Optional import to ease local testing where ccxt may not be available +try: # pragma: no cover + import ccxt # type: ignore +except Exception: # pragma: no cover + ccxt = None # type: ignore + + +@dataclass +class DiscoverOptions: + exchange: str = "binance" + quote: str = "USDT" + top_n: int = 50 + min_volume: float = 0.0 + + +def discover_ccxt_symbols(opts: DiscoverOptions) -> list[tuple[str, float]]: + if ccxt is None: + raise ImportError("ccxt is required to discover symbols") + ex = getattr(ccxt, opts.exchange)({"enableRateLimit": True}) + # Prefer fetchTickers if supported; otherwise fallback to markets data + result: list[tuple[str, float]] = [] + backoff = 1.0 + max_backoff = 30.0 + try: + tickers = None + for _ in range(5): + try: + tickers = ex.fetch_tickers() + break + except Exception as e: + should_backoff = False + if ccxt is not None: + throttling = ( + getattr(ccxt, "RateLimitExceeded", Exception), + getattr(ccxt, "DDoSProtection", Exception), + getattr(ccxt, "ExchangeNotAvailable", Exception), + getattr(ccxt, "NetworkError", Exception), + ) + should_backoff = isinstance(e, throttling) + if should_backoff: + time.sleep(backoff) + backoff = min(max_backoff, backoff * 2) + continue + raise + if tickers is None: + tickers = ex.fetch_tickers() + for symbol, t in tickers.items(): + # Filter by quote currency and spot markets + if not isinstance(symbol, str) or "/" not in symbol: + continue + base, quote = symbol.split("/") + if quote.upper() != opts.quote.upper(): + continue + vol = float(t.get("quoteVolume", t.get("baseVolume", 0.0)) or 0.0) + if vol >= opts.min_volume: + result.append((symbol, vol)) + except Exception: + # Fallback to markets when tickers failed + markets = ex.load_markets() + for symbol, m in markets.items(): + if m.get("active") is False: + continue + if m.get("spot") is not True: + continue + if m.get("quote", "").upper() != opts.quote.upper(): + continue + result.append((symbol, 0.0)) + + # Sort by volume desc (zeros at end) and take top_n + result.sort(key=lambda x: x[1], reverse=True) + return result[: opts.top_n] diff --git a/src/utils/telemetry.py b/src/utils/telemetry.py new file mode 100644 index 0000000..38f3057 --- /dev/null +++ b/src/utils/telemetry.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +import json +import logging +import time +from contextlib import contextmanager +from typing import Any + + +def get_logger(name: str = "quant") -> logging.Logger: + logger = logging.getLogger(name) + if not logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter("%(message)s") + handler.setFormatter(formatter) + logger.addHandler(handler) + logger.setLevel(logging.INFO) + return logger + + +def log_json(logger: logging.Logger, event: str, **kwargs: Any) -> None: + payload = {"event": event, **kwargs} + logger.info(json.dumps(payload, default=str)) + + +@contextmanager +def time_block(logger: logging.Logger, event: str, **kwargs: Any): + start = time.perf_counter() + try: + yield + finally: + dur = time.perf_counter() - start + log_json(logger, event, duration_sec=round(dur, 4), **kwargs) diff --git a/src/utils/trades_parser.py b/src/utils/trades_parser.py deleted file mode 100644 index 5fdd92e..0000000 --- a/src/utils/trades_parser.py +++ /dev/null @@ -1,275 +0,0 @@ -""" -Trades parser: parse a stringified pandas DataFrame (or JSON/CSV-like text) returned by the backtest -engine and normalize into a list of dicts suitable for insertion into the DB. - -Strategy: -- Try json.loads first (some engines return JSON) -- Try pandas (if available) to read JSON/CSV -- Try csv.Sniffer with common delimiters -- Last-resort: whitespace heuristic splitting on two-or-more spaces (pandas pretty-print) -- Normalize common column names to a canonical set - -Returns a list of dicts with keys such as: - trade_index, size, entry_bar, exit_bar, entry_price, exit_price, pnl, duration, tag, - entry_signals, exit_signals -""" - -from __future__ import annotations - -import csv -import io -import json -import re -from typing import Any, Dict, List, Optional - -CANONICAL_COLUMNS = { - # various possible column names mapped to canonical names - "index": "trade_index", - "trade_index": "trade_index", - "size": "size", - "qty": "size", - "quantity": "size", - "entry_bar": "entry_bar", - "entrybar": "entry_bar", - "entry": "entry_bar", - "entry_index": "entry_bar", - # entry/exit timestamps - "entry_time": "entry_time", - "entrytime": "entry_time", - "entry timestamp": "entry_time", - "entry_ts": "entry_time", - "entry_date": "entry_time", - "exit_bar": "exit_bar", - "exitbar": "exit_bar", - "exit": "exit_bar", - "exit_index": "exit_bar", - "exit_time": "exit_time", - "exittime": "exit_time", - "exit timestamp": "exit_time", - "exit_ts": "exit_time", - "exit_date": "exit_time", - "entry_price": "entry_price", - "entryprice": "entry_price", - "exit_price": "exit_price", - "exitprice": "exit_price", - "pnl": "pnl", - "profit": "pnl", - "pl": "pnl", - "duration": "duration", - "tag": "tag", - "entry_signals": "entry_signals", - "exit_signals": "exit_signals", - "signals": "entry_signals", -} - - -def _normalize_row(raw: Dict[Any, Any], idx: Optional[int] = None) -> Dict[str, Any]: - normalized: Dict[str, Any] = {} - # accept any-hashable keys and coerce to str for normalization - raw = {str(k): v for k, v in raw.items()} - # lower-case keys for matching - mapping = {str(k).lower().strip(): v for k, v in raw.items()} - # map known names - for k, v in mapping.items(): - canon = CANONICAL_COLUMNS.get(k) - if canon: - normalized[canon] = v - else: - # keep unknown columns as-is (but lowercased) - normalized[k] = v - # ensure trade_index present - if "trade_index" not in normalized: - if idx is not None: - try: - normalized["trade_index"] = int(idx) - except Exception: - normalized["trade_index"] = idx or 0 - else: - # try to extract numeric 'index' or fallback to 0 - normalized.setdefault("trade_index", 0) - return normalized - - -def _parse_with_pandas(text: str) -> Optional[List[Dict[str, Any]]]: - try: - import pandas as pd # type: ignore[import-not-found] - from pandas.errors import EmptyDataError # type: ignore[import-not-found] - except Exception: - return None - - # Try read_json (records) then read_csv - try: - df = pd.read_json(io.StringIO(text), orient="records") - if df is not None and not df.empty: - records = df.to_dict(orient="records") - return [_normalize_row(dict(r), idx=i) for i, r in enumerate(records)] - except Exception: - pass - - try: - df = pd.read_csv(io.StringIO(text)) - if df is not None and not df.empty: - records = df.to_dict(orient="records") - return [_normalize_row(dict(r), idx=i) for i, r in enumerate(records)] - except EmptyDataError: - return [] - except Exception: - # Try python engine with whitespace delimiter heuristics - try: - df = pd.read_csv(io.StringIO(text), sep=r"\s{2,}", engine="python") - if df is not None and not df.empty: - records = df.to_dict(orient="records") - return [_normalize_row(dict(r), idx=i) for i, r in enumerate(records)] - except Exception: - return None - return None - - -def _parse_with_csv(text: str) -> Optional[List[Dict[str, Any]]]: - sio = io.StringIO(text) - # sniff delimiter - try: - sample = text[:4096] - dialect = csv.Sniffer().sniff(sample, delimiters=",;\t|") - sio.seek(0) - reader = csv.DictReader(sio, dialect=dialect) - rows = [dict(r) for r in reader] - if rows: - return [_normalize_row(r, idx=i) for i, r in enumerate(rows)] - except Exception: - pass - - # fallback: comma - try: - sio.seek(0) - reader = csv.DictReader(sio) - rows = [dict(r) for r in reader] - if rows: - return [_normalize_row(r, idx=i) for i, r in enumerate(rows)] - except Exception: - pass - - return None - - -def _parse_whitespace_table(text: str) -> Optional[List[Dict[str, Any]]]: - """ - Parses pretty-printed pandas DataFrame which separates columns by two or more spaces. - Example: - index entry_bar exit_bar entry_price exit_price pnl - 0 100 120 10.5 12.0 1.5 - """ - lines = [ln.rstrip() for ln in text.splitlines() if ln.strip()] - if not lines: - return [] - # header detection: first line with word chars and spaces - header = lines[0] - # split on 2+ spaces - cols = re.split(r"\s{2,}", header.strip()) - if len(cols) < 2: - return None - data = [] - for ln in lines[1:]: - parts = re.split(r"\s{2,}", ln.strip()) - if len(parts) != len(cols): - # if mismatch, skip or try to pad - continue - row = dict(zip(cols, parts)) - data.append(row) - if not data: - return None - return [_normalize_row(r, idx=i) for i, r in enumerate(data)] - - -def parse_trades_from_string(trades_str: Optional[str]) -> List[Dict[str, Any]]: - """ - Public parser. Returns an empty list for falsy input. - - Steps: - - Try json.loads - - Try pandas-based parser - - Try csv.Sniffer-based parser - - Try whitespace table parser - - Fallback: return empty list - """ - if not trades_str: - return [] - - text = trades_str.strip() - - # 1) JSON - try: - obj = json.loads(text) - # If a dict representing a DF: convert to list - if isinstance(obj, dict): - # dict-of-lists or dict-of-dicts? try to convert to records - # common format: {"0": {...}, "1": {...}} or {"col": [..]} - if all(isinstance(v, list) for v in obj.values()): - # dict of columns -> convert to records - keys = list(obj.keys()) - length = len(next(iter(obj.values()), [])) - records = [] - for i in range(length): - rec = {k: obj[k][i] for k in keys} - records.append(rec) - return [_normalize_row(r, idx=i) for i, r in enumerate(records)] - # dict of records - if all(isinstance(v, dict) for v in obj.values()): - records = list(obj.values()) - return [_normalize_row(r, idx=i) for i, r in enumerate(records)] - # single record - return [_normalize_row(obj, idx=0)] - if isinstance(obj, list): - return [ - _normalize_row(r if isinstance(r, dict) else {"value": r}, idx=i) - for i, r in enumerate(obj) - ] - except Exception: - pass - - # 2) pandas - try: - pd_res = _parse_with_pandas(text) - if pd_res is not None: - return pd_res - except Exception: - pass - - # 3) csv - try: - csv_res = _parse_with_csv(text) - if csv_res is not None: - return csv_res - except Exception: - pass - - # 4) whitespace table - try: - ws = _parse_whitespace_table(text) - if ws is not None: - return ws - except Exception: - pass - - # 5) Last resort: try splitting lines and commas - lines = [ln for ln in text.splitlines() if ln.strip()] - if len(lines) == 1: - # single-line value - return [{"trade_index": 0, "value": lines[0]}] - # multiple lines: try simple CSV split - header = lines[0] - cols = [c.strip() for c in re.split(r"[,\t;|]+", header) if c.strip()] - if len(cols) >= 2: - data = [] - for i, ln in enumerate(lines[1:]): - parts = [p.strip() for p in re.split(r"[,\t;|]+", ln) if p.strip()][ - : len(cols) - ] - if len(parts) != len(cols): - continue - row = dict(zip(cols, parts)) - data.append(row) - if data: - return [_normalize_row(r, idx=i) for i, r in enumerate(data)] - # If nothing worked, return empty list - return [] diff --git a/src/utils/tv_alert_exporter.py b/src/utils/tv_alert_exporter.py deleted file mode 100644 index 6acfb5b..0000000 --- a/src/utils/tv_alert_exporter.py +++ /dev/null @@ -1,406 +0,0 @@ -#!/usr/bin/env python3 -""" -TradingView Alert Exporter - -Extracts asset strategies and timeframes from HTML reports and generates -TradingView alert messages with appropriate placeholders. -""" - -from __future__ import annotations - -import argparse -import os -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, List, Optional - -from bs4 import BeautifulSoup - -# DB models -try: - from src.database.db_connection import ( - get_db_session, # type: ignore[import-not-found] - ) - from src.database.models import BestStrategy # type: ignore[import-not-found] -except Exception: # pragma: no cover - guarded imports - get_db_session = None # type: ignore[assignment] - BestStrategy = None # type: ignore[assignment] - - -class TradingViewAlertExporter: - def __init__(self, reports_dir: str = "exports/reports"): - self.reports_dir = Path(reports_dir) - - # Check if old location exists and new location is empty for migration - old_dir = Path("reports_output") - if old_dir.exists() and not self.reports_dir.exists(): - print(f"⚠️ Found reports in old location: {old_dir}") - print( - f"💡 Consider running report organizer to migrate to: {self.reports_dir}" - ) - self.reports_dir = old_dir - - def get_quarter_from_date(self, date: datetime) -> tuple[int, int]: - """Get quarter and year from date.""" - quarter = (date.month - 1) // 3 + 1 - return date.year, quarter - - def organize_output_path(self, base_dir: str) -> Path: - """Create organized output path based on current quarter/year.""" - now = datetime.now(timezone.utc) - year, quarter = self.get_quarter_from_date(now) - - output_dir = Path(base_dir) / str(year) / f"Q{quarter}" - output_dir.mkdir(parents=True, exist_ok=True) - - return output_dir - - def _build_filename( - self, collection_name: str, year: int, quarter: int, interval: str | None - ) -> str: - """Builds _Collection___.md""" - sanitized = ( - collection_name.replace(" ", "_").replace("/", "_").strip("_") - or "All_Collections" - ) - interval_part = (interval or "multi").replace("/", "-") - return f"{sanitized}_Collection_{year}_Q{quarter}_{interval_part}.md" - - def extract_asset_data(self, html_content: str) -> List[Dict]: - """Extract asset information from HTML report""" - soup = BeautifulSoup(html_content, "html.parser") - assets: List[Dict] = [] - - # New Tailwind report structure (DetailedPortfolioReporter): sections with id="asset-" - section_nodes = soup.select("section[id^='asset-']") - for sec in section_nodes: - h2 = sec.find("h2") - symbol = h2.get_text(strip=True) if h2 else None - best_strategy = None - timeframe = None - # The header line contains two spans: "Best: " and "⏰ " - tag_spans = sec.find_all("span") - for sp in tag_spans: - txt = sp.get_text(strip=True) - if txt.startswith("Best:") and best_strategy is None: - best_strategy = txt.replace("Best:", "").strip() - if "⏰" in txt and timeframe is None: - timeframe = txt.replace("⏰", "").strip() - if symbol and best_strategy and timeframe: - assets.append( - { - "symbol": symbol, - "strategy": best_strategy, - "timeframe": timeframe, - "metrics": {}, - } - ) - - if assets: - return assets - - # Fallback legacy structure support (older HTML reports) - legacy_assets: List[Dict] = [] - asset_sections = soup.find_all("div", class_="asset-section") - for section in asset_sections: - asset_title = section.find("h2", class_="asset-title") - if not asset_title: - continue - symbol = asset_title.text.strip() - strategy_badges = section.find_all("span", class_="strategy-badge") - best_strategy = None - timeframe = None - for badge in strategy_badges: - text = badge.text.strip() - if text.startswith("Best:"): - best_strategy = text.replace("Best:", "").strip() - elif "⏰" in text: - timeframe = text.replace("⏰", "").strip() - metrics = {} - metric_cards = section.find_all("div", class_="metric-card") - for card in metric_cards: - label_elem = card.find("div", class_="metric-label") - value_elem = card.find("div", class_="metric-value") - if label_elem and value_elem: - label = label_elem.text.strip() - value = value_elem.text.strip() - metrics[label] = value - if symbol and best_strategy and timeframe: - legacy_assets.append( - { - "symbol": symbol, - "strategy": best_strategy, - "timeframe": timeframe, - "metrics": metrics, - } - ) - return legacy_assets - - def generate_tradingview_alert(self, asset_data: Dict) -> str: - """Generate TradingView alert message for asset""" - symbol = asset_data["symbol"] - strategy = asset_data["strategy"] - timeframe = asset_data["timeframe"] - metrics = asset_data.get("metrics", {}) - - # Get key metrics for context - sharpe_ratio = metrics.get("Sharpe Ratio", "N/A") - sortino_ratio = metrics.get("Sortino Ratio", "N/A") - calmar_ratio = metrics.get("Calmar Ratio", "N/A") - - alert_message = f"""🚨 QUANT SIGNAL: {symbol} 📊 -Strategy: {strategy} -Timeframe: {timeframe} -📈 Sharpe: {sharpe_ratio} -📊 Sortino: {sortino_ratio} -⚖️ Calmar: {calmar_ratio} - -Price: {{{{close}}}} -Time: {{{{timenow}}}} -Action: {{{{strategy.order.action}}}} -Qty: {{{{strategy.order.contracts}}}} - -#QuantTrading #{symbol} #{strategy.replace(" ", "")}""" - - return alert_message - - def process_html_file(self, file_path: Path) -> List[Dict]: - """Process single HTML file and extract asset data""" - try: - with file_path.open(encoding="utf-8") as f: - content = f.read() - - assets = self.extract_asset_data(content) - - # Add file metadata - for asset in assets: - asset["source_file"] = str(file_path) - asset["report_name"] = file_path.stem - - return assets - except Exception as e: - print(f"Error processing {file_path}: {e}") - return [] - - def find_html_reports(self) -> List[Path]: - """Find all HTML report files""" - html_files = [] - for root, dirs, files in os.walk(self.reports_dir): - for file in files: - if file.endswith(".html"): - html_files.append(Path(root) / file) - return html_files - - def export_alerts( - self, - output_file: Optional[str] = None, - collection_filter: Optional[str] = None, - interval: Optional[str] = None, - symbols: Optional[List[str]] = None, - ) -> Dict: - """Export TradingView alerts using database BestStrategy data. - - - Filters by provided symbols when available (preferred). - - If symbols is None, uses all BestStrategy rows. - - Writes markdown under exports/tv_alerts//Q/ with unified name. - """ - all_alerts: Dict[str, List[Dict]] = {} - - # Query DB for best strategies - rows = [] - sess = None - try: - if get_db_session is None or BestStrategy is None: - raise RuntimeError("Database session/models unavailable for TV export") - sess = get_db_session() - q = sess.query(BestStrategy) - if symbols: - q = q.filter(BestStrategy.symbol.in_(symbols)) - # Optionally, prefer the provided interval if filtering is desired - if interval: - q = q.filter(BestStrategy.timeframe == interval) - rows = q.all() - # Fallback to unified_models if no rows found (similar to csv exporter) - if not rows: - try: - from src.database import ( - unified_models as um, # type: ignore[import-not-found] - ) - - usess = um.Session() - try: - uq = usess.query(um.BestStrategy) - if symbols: - uq = uq.filter(um.BestStrategy.symbol.in_(symbols)) - if interval: - uq = uq.filter(um.BestStrategy.timeframe == interval) - rows = uq.all() - finally: - usess.close() - except Exception: - rows = [] - finally: - if sess is not None: - try: - sess.close() - except Exception: - pass - - # If interval specified but produced no rows, relax interval filter - if interval and not rows: - try: - sess = get_db_session() - q = sess.query(BestStrategy) - if symbols: - q = q.filter(BestStrategy.symbol.in_(symbols)) - rows = q.all() - except Exception: - rows = rows - finally: - if sess is not None: - try: - sess.close() - except Exception: - pass - - # Build alerts either from DB rows or (fallback) parse existing HTML reports - by_symbol: Dict[str, Dict] = {} - - if rows: - # Primary: DB-backed BestStrategy rows - for r in rows: - sym = getattr(r, "symbol", None) - if not sym: - continue - entry = by_symbol.get(sym) - sortino = float(getattr(r, "sortino_ratio", 0.0) or 0.0) - if entry is None or sortino > entry.get("sortino_ratio", -1e9): - by_symbol[sym] = { - "symbol": sym, - "strategy": getattr(r, "strategy", ""), - "timeframe": getattr(r, "timeframe", interval or "1d"), - "metrics": { - "Sharpe Ratio": f"{float(getattr(r, 'sharpe_ratio', 0.0) or 0.0):.3f}", - "Sortino Ratio": f"{sortino:.3f}", - "Calmar Ratio": f"{float(getattr(r, 'calmar_ratio', 0.0) or 0.0):.3f}", - }, - } - else: - # Fallback: parse HTML reports for assets and their best strategy/timeframe - try: - html_files = self.find_html_reports() - for fp in html_files: - # Optionally filter by collection name in filename if provided - if collection_filter: - # normalize name part (spaces/parentheses -> underscores) - cname = collection_filter.replace(" ", "_").replace("/", "_") - if cname not in fp.name: - continue - assets = self.process_html_file(fp) - for asset in assets: - if symbols and asset.get("symbol") not in set(symbols): - continue - if interval and asset.get("timeframe") != interval: - continue - sym = asset.get("symbol") - strat = asset.get("strategy") - tf = asset.get("timeframe") or (interval or "1d") - if not sym or not strat: - continue - # Keep first (or allow override if needed by future metrics) - by_symbol.setdefault( - sym, - { - "symbol": sym, - "strategy": strat, - "timeframe": tf, - "metrics": {}, - }, - ) - except Exception: - # Silent fallback; will result in header-only file as before - pass - - for sym, asset in by_symbol.items(): - alert = self.generate_tradingview_alert(asset) - if sym not in all_alerts: - all_alerts[sym] = [] - all_alerts[sym].append({"alert_message": alert, "asset_data": asset}) - - # Write to file if requested - if output_file is not None or collection_filter is not None: - organized_dir = self.organize_output_path("exports/tv_alerts") - now = datetime.now(timezone.utc) - year, q = self.get_quarter_from_date(now) - collection_name = collection_filter or "All_Collections" - if output_file and output_file not in ("tradingview_alerts.md",): - filename = ( - output_file if output_file.endswith(".md") else f"{output_file}.md" - ) - else: - filename = self._build_filename(collection_name, year, q, interval) - output_path = organized_dir / filename - - with output_path.open("w", encoding="utf-8") as f: - f.write("# TradingView Alert Messages\n\n") - for symbol, alerts in all_alerts.items(): - f.write(f"## {symbol}\n\n") - for i, alert_data in enumerate(alerts): - asset = alert_data["asset_data"] - f.write( - f"### Alert {i + 1} - {asset['strategy']} ({asset['timeframe']})\n" - ) - f.write("```\n") - f.write(alert_data["alert_message"]) - f.write("\n```\n\n") - f.write("---\n\n") - - return all_alerts - - -def main(): - parser = argparse.ArgumentParser( - description="Export TradingView alerts from HTML reports" - ) - parser.add_argument( - "--reports-dir", - default="exports/reports", - help="Directory containing HTML reports", - ) - parser.add_argument( - "--output", - default="tradingview_alerts.md", - help="Output file for alerts (auto-organized by quarter/year if just filename)", - ) - parser.add_argument("--symbol", help="Export alerts for specific symbol only") - parser.add_argument( - "--collection", - help="Export alerts for specific collection/portfolio only (e.g., 'Commodities', 'Bonds')", - ) - - args = parser.parse_args() - - exporter = TradingViewAlertExporter(args.reports_dir) - alerts = exporter.export_alerts(args.output, collection_filter=args.collection) - - print("\n📊 Export Summary:") - print(f"Found {len(alerts)} assets with alerts") - - if args.symbol: - if args.symbol in alerts: - print(f"\n🎯 Alerts for {args.symbol}:") - for alert_data in alerts[args.symbol]: - print("\n" + "=" * 60) - print(alert_data["alert_message"]) - else: - print(f"❌ No alerts found for {args.symbol}") - else: - for symbol, symbol_alerts in alerts.items(): - print(f" {symbol}: {len(symbol_alerts)} alert(s)") - - if args.output: - print(f"\n✅ Alerts exported to: {args.output}") - - -if __name__ == "__main__": - main() diff --git a/tailwind.config.js b/tailwind.config.js deleted file mode 100644 index 43a322a..0000000 --- a/tailwind.config.js +++ /dev/null @@ -1,12 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -module.exports = { - content: [ - './exports/reports/**/*.html', - './src/reporting/**/*.py', - ], - darkMode: 'class', - theme: { - extend: {}, - }, - plugins: [], -}; diff --git a/tests/cli/config/test_config_loader.py b/tests/cli/config/test_config_loader.py deleted file mode 100644 index 6b3638e..0000000 --- a/tests/cli/config/test_config_loader.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Test suite for CLI config loader.""" - -from __future__ import annotations - -from unittest.mock import mock_open, patch - -import pytest - -from src.cli.config.config_loader import ( - get_default_parameters, - get_portfolio_config, - load_assets_config, -) - - -class TestConfigLoader: - """Test class for CLI config loader functionality.""" - - @pytest.fixture - def sample_config(self): - """Create sample configuration for testing.""" - return { - "portfolios": { - "tech_stocks": { - "description": "Technology sector stocks", - "assets": [ - { - "ticker": "AAPL", - "commission": 0.001, - "initial_capital": 10000, - }, - { - "ticker": "MSFT", - "commission": 0.001, - "initial_capital": 10000, - }, - ], - } - } - } - - @pytest.mark.skip("Mock setup issue with file operations") - @patch("builtins.open", new_callable=mock_open, read_data='{"portfolios": {}}') - @patch("json.load") - def test_load_assets_config(self, mock_json_load, mock_file_open, sample_config): - """Test loading assets configuration.""" - mock_json_load.return_value = sample_config - - result = load_assets_config() - - mock_file_open.assert_called_with("config/assets_config.json", "r") - mock_json_load.assert_called_once() - assert result == sample_config - - # Test with file not found - mock_file_open.side_effect = FileNotFoundError() - result = load_assets_config() - assert result == {"portfolios": {}} - - @patch("src.cli.config.config_loader.load_assets_config") - def test_get_portfolio_config(self, mock_load_config, sample_config): - """Test getting portfolio configuration.""" - mock_load_config.return_value = sample_config - - result = get_portfolio_config("tech_stocks") - - # Assertions - mock_load_config.assert_called_once() - assert result["description"] == "Technology sector stocks" - assert len(result["assets"]) == 2 - assert result["assets"][0]["ticker"] == "AAPL" - - # Test with non-existent portfolio - result = get_portfolio_config("non_existent") - assert result is None - - def test_get_default_parameters(self): - """Test getting default parameters.""" - defaults = get_default_parameters() - - # Assertions - assert isinstance(defaults, dict) - assert "commission" in defaults - assert "initial_capital" in defaults - assert "period" in defaults - assert "intervals" in defaults # Note: plural form in actual implementation diff --git a/tests/cli/test_unified_cli_exports.py b/tests/cli/test_unified_cli_exports.py deleted file mode 100644 index c7ee1b4..0000000 --- a/tests/cli/test_unified_cli_exports.py +++ /dev/null @@ -1,88 +0,0 @@ -from __future__ import annotations - -import json -import sys -from types import ModuleType - -from src.cli.unified_cli import handle_collection_run - - -def test_exports_report_and_csv_dry_run(monkeypatch, tmp_path): - # Create a minimal collection file the resolver can find - base = tmp_path / "config" / "collections" / "default" - base.mkdir(parents=True, exist_ok=True) - (base / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT", "IEF"]}}) - ) - monkeypatch.chdir(tmp_path) - - # Fake reporter module to capture call parameters without touching DB - reporter_mod = ModuleType("src.reporting.collection_report") - - class FakeReporter: - called = False - last_kwargs = None - - def generate_comprehensive_report( - self, portfolio_config, start_date, end_date, strategies, timeframes=None - ): - # Record call and verify required parameters are propagated - FakeReporter.called = True - FakeReporter.last_kwargs = { - "portfolio_config": portfolio_config, - "start": start_date, - "end": end_date, - "strategies": strategies, - "timeframes": timeframes, - } - return "exports/reports/2025/Q3/Test.html" - - reporter_mod.DetailedPortfolioReporter = lambda: FakeReporter() - monkeypatch.setitem(sys.modules, "src.reporting.collection_report", reporter_mod) - - # Fake CSV exporter to avoid DB access; capture interval passed in - csv_mod = ModuleType("src.utils.csv_exporter") - - class FakeCSVExporter: - last_instance = None - - def __init__(self, *a, **k): - FakeCSVExporter.last_instance = self - self.calls = [] - - def export_from_database_primary(self, quarter, year, **kwargs): - # Record call and return a dummy path - self.calls.append((quarter, year, kwargs)) - return [f"exports/csv/{year}/{quarter}/dummy.csv"] - - def export_from_quarterly_reports(self, *a, **k): - return [] - - csv_mod.RawDataCSVExporter = FakeCSVExporter - monkeypatch.setitem(sys.modules, "src.utils.csv_exporter", csv_mod) - - # Run CLI with dry-run so it executes export block only - rc = handle_collection_run( - [ - "bonds_core", - "--action", - "direct", - "--interval", - "1d", - "--period", - "max", - "--exports", - "report,csv", - "--dry-run", - ] - ) - - assert rc == 0 - # Reporter called with our interval propagated via timeframes - assert FakeReporter.called is True - assert FakeReporter.last_kwargs["timeframes"] == ["1d"] - # CSV exporter was invoked and received interval in kwargs - assert FakeCSVExporter.last_instance is not None - calls = FakeCSVExporter.last_instance.calls - assert calls - assert calls[0][2].get("interval") == "1d" diff --git a/tests/cli/test_unified_cli_exports_more.py b/tests/cli/test_unified_cli_exports_more.py deleted file mode 100644 index 47e3b7f..0000000 --- a/tests/cli/test_unified_cli_exports_more.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -import json -import sys -from types import ModuleType - -from src.cli.unified_cli import handle_collection_run - - -def test_exports_tradingview_and_ai_dry_run(monkeypatch, tmp_path): - # Minimal collection - base = tmp_path / "config" / "collections" / "default" - base.mkdir(parents=True, exist_ok=True) - (base / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT", "IEF"], "name": "Bonds"}}) - ) - monkeypatch.chdir(tmp_path) - - # Fake TradingView exporter - tv_mod = ModuleType("src.utils.tv_alert_exporter") - - class FakeTradingViewAlertExporter: - last_init = None - last_call = None - - def __init__(self, reports_dir: str): - FakeTradingViewAlertExporter.last_init = {"reports_dir": reports_dir} - - def export_alerts(self, **kwargs): - FakeTradingViewAlertExporter.last_call = kwargs - return [{"symbol": s} for s in kwargs.get("symbols", [])] - - tv_mod.TradingViewAlertExporter = FakeTradingViewAlertExporter - monkeypatch.setitem(sys.modules, "src.utils.tv_alert_exporter", tv_mod) - - # Fake AI recommendations + db connection - ai_mod = ModuleType("src.ai.investment_recommendations") - - class FakeAI: - last_init = None - last_call = None - - def __init__(self, db_session=None): - FakeAI.last_init = {"db_session": db_session} - - def generate_portfolio_recommendations(self, **kwargs): - FakeAI.last_call = kwargs - return {"ok": True}, "exports/ai_reco/fake.html" - - ai_mod.AIInvestmentRecommendations = FakeAI - monkeypatch.setitem(sys.modules, "src.ai.investment_recommendations", ai_mod) - - db_mod = ModuleType("src.database.db_connection") - - def fake_get_db_session(): - return None - - db_mod.get_db_session = fake_get_db_session # type: ignore[attr-defined] - monkeypatch.setitem(sys.modules, "src.database.db_connection", db_mod) - - # Run CLI with dry-run to trigger exports only - rc = handle_collection_run( - [ - "bonds_core", - "--action", - "direct", - "--interval", - "1d", - "--period", - "max", - "--exports", - "tradingview,ai", - "--dry-run", - ] - ) - - assert rc == 0 - # TradingView exporter initialized and called - assert FakeTradingViewAlertExporter.last_init == {"reports_dir": "exports/reports"} - assert FakeTradingViewAlertExporter.last_call is not None - assert FakeTradingViewAlertExporter.last_call.get("interval") == "1d" - assert FakeTradingViewAlertExporter.last_call.get("collection_filter") == "Bonds" - assert FakeTradingViewAlertExporter.last_call.get("symbols") == ["IEF", "TLT"] - - # AI recommendations called with timeframe propagation - assert FakeAI.last_call is not None - assert FakeAI.last_call.get("timeframe") == "1d" - # Quarter string present (e.g., Q3_2025); format not asserted strictly - assert isinstance(FakeAI.last_call.get("quarter"), str) - assert "_" in FakeAI.last_call.get("quarter") diff --git a/tests/cli/test_unified_cli_flags.py b/tests/cli/test_unified_cli_flags.py deleted file mode 100644 index 3b56a10..0000000 --- a/tests/cli/test_unified_cli_flags.py +++ /dev/null @@ -1,35 +0,0 @@ -from __future__ import annotations - -import json - -from src.cli.unified_cli import handle_collection_run - - -def test_no_cache_flag_sets_use_cache_false(tmp_path, monkeypatch, capsys): - # Create temp collection tree in a writable tmp directory - base = tmp_path / "config" / "collections" / "default" - base.mkdir(parents=True, exist_ok=True) - (base / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT"]}}) - ) - # Chdir so resolver finds temp config path - monkeypatch.chdir(tmp_path) - - # Use --dry-run so manifest prints to stdout - rc = handle_collection_run( - [ - "bonds_core", - "--action", - "direct", - "--interval", - "1d", - "--period", - "max", - "--no-cache", - "--dry-run", - ] - ) - assert rc == 0 - out = capsys.readouterr().out - manifest = json.loads(out) - assert manifest["plan"]["use_cache"] is False diff --git a/tests/cli/test_unified_cli_main.py b/tests/cli/test_unified_cli_main.py deleted file mode 100644 index 7ccf077..0000000 --- a/tests/cli/test_unified_cli_main.py +++ /dev/null @@ -1,24 +0,0 @@ -from __future__ import annotations - -import src.cli.unified_cli as cli - - -def test_main_no_args_returns_1(): - assert cli.main([]) == 1 - - -def test_main_unknown_returns_2(): - assert cli.main(["not-a-subcommand"]) == 2 - - -def test_main_routes_to_collection(monkeypatch): - called = {"v": False} - - def fake_handle(argv): - called["v"] = True - return 0 - - monkeypatch.setattr(cli, "handle_collection_run", fake_handle) - rc = cli.main(["collection", "bonds_core", "--dry-run"]) # args forwarded - assert rc == 0 - assert called["v"] is True diff --git a/tests/cli/test_unified_cli_probe.py b/tests/cli/test_unified_cli_probe.py deleted file mode 100644 index 7bb09a6..0000000 --- a/tests/cli/test_unified_cli_probe.py +++ /dev/null @@ -1,54 +0,0 @@ -from __future__ import annotations - -import importlib -import json -import sys -from types import SimpleNamespace - - -def test_cli_probe_called(monkeypatch, tmp_path): - # Create temp collection - base = tmp_path / "config" / "collections" / "default" - base.mkdir(parents=True) - (base / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT"]}}) - ) - monkeypatch.chdir(tmp_path) - - # Patch heavy dependencies in unified_cli - mod = importlib.import_module("src.cli.unified_cli") - - # Fake UnifiedDataManager with probe flag - class FakeDM: - def __init__(self): - self.called = False - - def probe_and_set_order( - self, asset_type, symbols, interval="1d", sample_size=5 - ): - self.called = True - return ["yahoo_finance"] - - fake_dm_instance = FakeDM() - # Inject a fake module so `from src.core.data_manager import UnifiedDataManager` returns our fake - fake_dm_module = SimpleNamespace(UnifiedDataManager=lambda: fake_dm_instance) - monkeypatch.setitem(sys.modules, "src.core.data_manager", fake_dm_module) - - # Patch direct backtest functions to no-op by injecting a fake module in sys.modules - fake_direct_mod = SimpleNamespace( - finalize_persistence_for_run=lambda *a, **k: None, - run_direct_backtest=lambda **k: {}, - ) - monkeypatch.setitem(sys.modules, "src.core.direct_backtest", fake_direct_mod) - - # Patch unified_models ensure_run_for_manifest - mod.unified_models = SimpleNamespace( # type: ignore[attr-defined] - ensure_run_for_manifest=lambda m: SimpleNamespace(run_id="test-run") - ) - - # Run without --dry-run so run_plan executes - rc = mod.handle_collection_run( - ["bonds_core", "--action", "direct", "--interval", "1d", "--period", "max"] - ) - assert rc == 0 - assert fake_dm_instance.called is True diff --git a/tests/cli/test_unified_cli_resolver.py b/tests/cli/test_unified_cli_resolver.py deleted file mode 100644 index ce50fab..0000000 --- a/tests/cli/test_unified_cli_resolver.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import annotations - -import json -from pathlib import Path - -import pytest - -from src.cli.unified_cli import resolve_collection_path - - -def test_resolver_default_and_custom(tmp_path: Path, monkeypatch): - base = tmp_path / "config" / "collections" - (base / "default").mkdir(parents=True) - (base / "custom").mkdir(parents=True) - - # Create default and custom jsons - (base / "default" / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT"]}}) - ) - (base / "custom" / "stocks_traderfox_us_tech.json").write_text( - json.dumps({"stocks_traderfox_us_tech": {"symbols": ["AAPL"]}}) - ) - - # Chdir so resolver finds our structure - monkeypatch.chdir(tmp_path) - - # Alias mapping: bonds -> bonds_core - p1 = resolve_collection_path("bonds") - assert p1.name == "bonds_core.json" - assert p1.parent.name == "default" - - # Custom key resolves - p2 = resolve_collection_path("stocks_traderfox_us_tech") - assert p2.name == "stocks_traderfox_us_tech.json" - assert p2.parent.name == "custom" - - -def test_resolver_missing_raises(tmp_path: Path, monkeypatch): - (tmp_path / "config" / "collections").mkdir(parents=True) - monkeypatch.chdir(tmp_path) - with pytest.raises(FileNotFoundError): - resolve_collection_path("does_not_exist") diff --git a/tests/cli/test_unified_cli_symbols.py b/tests/cli/test_unified_cli_symbols.py deleted file mode 100644 index 12960b5..0000000 --- a/tests/cli/test_unified_cli_symbols.py +++ /dev/null @@ -1,23 +0,0 @@ -from __future__ import annotations - -import json - -from src.cli.unified_cli import load_collection_symbols - - -def test_load_symbols_plain_list(tmp_path): - p = tmp_path / "list.json" - p.write_text(json.dumps(["aapl", "msft"])) - assert load_collection_symbols(p) == ["AAPL", "MSFT"] - - -def test_load_symbols_dict_keys(tmp_path): - p = tmp_path / "dict.json" - p.write_text(json.dumps({"symbols": ["AAPL"], "name": "x"})) - assert load_collection_symbols(p) == ["AAPL"] - - -def test_load_symbols_nested_named(tmp_path): - p = tmp_path / "nested.json" - p.write_text(json.dumps({"bonds": {"symbols": ["TLT", "IEF"]}})) - assert load_collection_symbols(p) == ["TLT", "IEF"] diff --git a/tests/cli/test_unified_cli_validations.py b/tests/cli/test_unified_cli_validations.py deleted file mode 100644 index e12bbdc..0000000 --- a/tests/cli/test_unified_cli_validations.py +++ /dev/null @@ -1,49 +0,0 @@ -from __future__ import annotations - -import json - -from src.cli.unified_cli import handle_collection_run - - -def _write_collection(tmp_path): - base = tmp_path / "config" / "collections" / "default" - base.mkdir(parents=True, exist_ok=True) - (base / "bonds_core.json").write_text( - json.dumps({"bonds_core": {"symbols": ["TLT"]}}) - ) - - -def test_start_without_end_returns_6(tmp_path, monkeypatch): - _write_collection(tmp_path) - monkeypatch.chdir(tmp_path) - rc = handle_collection_run( - [ - "bonds_core", - "--action", - "direct", - "--interval", - "1d", - # Intentionally provide --start without --end to trigger rc 6 - "--start", - "2024-01-01", - ] - ) - assert rc == 6 - - -def test_invalid_interval_returns_5(tmp_path, monkeypatch): - _write_collection(tmp_path) - monkeypatch.chdir(tmp_path) - rc = handle_collection_run( - [ - "bonds_core", - "--action", - "direct", - "--interval", - "not-an-interval", - "--period", - "max", - "--dry-run", - ] - ) - assert rc == 5 diff --git a/tests/conftest.py b/tests/conftest.py index d74d005..f349ad9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,153 +1,7 @@ -"""Pytest configuration and shared fixtures.""" - -from __future__ import annotations - import os import sys -from pathlib import Path - -# Add src to path for imports first -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -import pytest - -# Import numpy and pandas after path setup -try: - import numpy as np - import pandas as pd -except ImportError: - # If numpy/pandas aren't available, use mocks for tests that don't need them - np = None - pd = None - - -@pytest.fixture(scope="session") -def test_data_dir(): - """Fixture for test data directory.""" - data_dir = Path(__file__).parent / "data" - data_dir.mkdir(exist_ok=True) - return data_dir - - -@pytest.fixture(scope="session") -def reports_dir(): - """Fixture for reports output directory.""" - reports_dir = Path(__file__).parent.parent / "exports" / "reports" - reports_dir.mkdir(exist_ok=True) - return reports_dir - - -@pytest.fixture -def sample_ohlcv_data(): - """Create sample OHLCV data for testing.""" - if pd is None or np is None: - pytest.skip("numpy/pandas not available") - - dates = pd.date_range("2024-01-01", periods=100, freq="D") - rng = np.random.default_rng(42) # For reproducible tests - - base_price = 100 - returns = rng.normal(0.001, 0.02, len(dates)) - prices = [base_price] - - for ret in returns[1:]: - prices.append(prices[-1] * (1 + ret)) - - data = pd.DataFrame( - { - "Open": [p * (1 + rng.normal(0, 0.001)) for p in prices], - "High": [p * (1 + abs(rng.normal(0, 0.01))) for p in prices], - "Low": [p * (1 - abs(rng.normal(0, 0.01))) for p in prices], - "Close": prices, - "Volume": [1000 + rng.integers(-200, 200) for _ in prices], - }, - index=dates, - ) - - # Ensure High >= max(Open, Close) and Low <= min(Open, Close) - data["High"] = data[["Open", "High", "Close"]].max(axis=1) - data["Low"] = data[["Open", "Low", "Close"]].min(axis=1) - - return data - - -@pytest.fixture -def sample_portfolio_config(): - """Sample portfolio configuration for testing.""" - return { - "name": "Test Portfolio", - "symbols": ["AAPL", "GOOGL", "MSFT"], - "initial_capital": 100000, - "commission": 0.001, - "strategy": {"name": "BuyAndHold", "parameters": {}}, - "risk_management": { - "max_position_size": 0.1, - "stop_loss": 0.05, - "take_profit": 0.15, - }, - "data_source": { - "primary_source": "yahoo", - "fallback_sources": ["alpha_vantage"], - }, - } - - -@pytest.fixture -def mock_api_keys(monkeypatch): - """Mock API keys for testing.""" - monkeypatch.setenv("ALPHA_VANTAGE_API_KEY", "test_key") - monkeypatch.setenv("TWELVE_DATA_API_KEY", "test_key") - monkeypatch.setenv("POLYGON_API_KEY", "test_key") - monkeypatch.setenv("TIINGO_API_KEY", "test_key") - monkeypatch.setenv("FINNHUB_API_KEY", "test_key") - - -@pytest.fixture -def temp_cache_dir(tmp_path): - """Create temporary cache directory for testing.""" - cache_dir = tmp_path / "cache" - cache_dir.mkdir() - return cache_dir - - -@pytest.fixture -def crypto_symbols(): - """Sample crypto symbols for testing.""" - return ["BTC-USD", "ETH-USD", "ADA-USD", "SOL-USD"] - - -@pytest.fixture -def forex_symbols(): - """Sample forex symbols for testing.""" - return ["EURUSD=X", "GBPUSD=X", "USDJPY=X", "AUDUSD=X"] - - -@pytest.fixture -def stock_symbols(): - """Sample stock symbols for testing.""" - return ["AAPL", "GOOGL", "MSFT", "TSLA", "AMZN"] - - -@pytest.fixture(autouse=True) -def setup_test_environment(reports_dir): - """Set up test environment automatically.""" - # Ensure reports directory exists - reports_dir.mkdir(exist_ok=True) - - # Set test environment variables - os.environ["TESTING"] = "true" - os.environ["LOG_LEVEL"] = "DEBUG" - - yield - - # Cleanup after tests - if "TESTING" in os.environ: - del os.environ["TESTING"] - -# Pytest markers -def pytest_configure(config): - """Configure pytest with custom markers.""" - config.addinivalue_line("markers", "integration: mark test as integration test") - config.addinivalue_line("markers", "slow: mark test as slow running") - config.addinivalue_line("markers", "network: mark test as requiring network access") +# Ensure the repository root is importable as a package prefix (e.g., `src.*`) +ROOT = os.getcwd() +if ROOT not in sys.path: + sys.path.insert(0, ROOT) diff --git a/tests/core/test_cache_manager.py b/tests/core/test_cache_manager.py deleted file mode 100644 index 94c92af..0000000 --- a/tests/core/test_cache_manager.py +++ /dev/null @@ -1,324 +0,0 @@ -"""Unit tests for UnifiedCacheManager.""" - -from __future__ import annotations - -import tempfile -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest - -from src.core.cache_manager import UnifiedCacheManager - - -class TestUnifiedCacheManager: - """Test cases for UnifiedCacheManager.""" - - @pytest.fixture - def temp_cache_dir(self): - """Create temporary cache directory.""" - with tempfile.TemporaryDirectory() as temp_dir: - yield temp_dir - - @pytest.fixture - def cache_manager(self, temp_cache_dir): - """Create UnifiedCacheManager instance.""" - return UnifiedCacheManager(cache_dir=temp_cache_dir, max_size_gb=1.0) - - @pytest.fixture - def sample_dataframe(self): - """Sample DataFrame for testing.""" - dates = pd.date_range("2023-01-01", periods=100, freq="D") - rng = np.random.default_rng(42) - return pd.DataFrame( - { - "Open": rng.uniform(100, 200, 100), - "High": rng.uniform(100, 200, 100), - "Low": rng.uniform(100, 200, 100), - "Close": rng.uniform(100, 200, 100), - "Volume": rng.integers(1000000, 10000000, 100), - }, - index=dates, - ) - - @pytest.fixture - def sample_backtest_result(self): - """Sample backtest result for testing.""" - return { - "symbol": "AAPL", - "strategy": "rsi", - "total_return": 0.15, - "sharpe_ratio": 1.2, - "max_drawdown": -0.08, - "trades": 25, - "win_rate": 0.64, - } - - def test_init(self, cache_manager, temp_cache_dir): - """Test initialization.""" - assert str(cache_manager.cache_dir) == temp_cache_dir - assert cache_manager.max_size_bytes == int(1.0 * 1024**3) - assert Path(cache_manager.metadata_db).exists() - - def test_generate_cache_key(self, cache_manager): - """Test cache key generation.""" - key1 = cache_manager._generate_key("data", symbol="AAPL", interval="1d") - key2 = cache_manager._generate_key("data", symbol="AAPL", interval="1d") - - # Same parameters should generate same key - assert key1 == key2 - - # Different parameters should generate different keys - key3 = cache_manager._generate_key("data", symbol="MSFT", interval="1d") - assert key1 != key3 - - def test_cache_data(self, cache_manager, sample_dataframe): - """Test caching DataFrame data.""" - # Cache the data - key = cache_manager.cache_data("AAPL", sample_dataframe, ttl_hours=1) - assert key is not None - - # Verify file was created - expected_path = cache_manager.data_dir / f"{key}.gz" - assert expected_path.exists() - - def test_get_data(self, cache_manager, sample_dataframe): - """Test retrieving cached data.""" - # Cache the data first - cache_manager.cache_data("AAPL", sample_dataframe) - - # Retrieve the data - retrieved_data = cache_manager.get_data("AAPL") - - assert isinstance(retrieved_data, pd.DataFrame) - assert len(retrieved_data) == len(sample_dataframe) - assert list(retrieved_data.columns) == list(sample_dataframe.columns) - - def test_cache_backtest_result(self, cache_manager, sample_backtest_result): - """Test caching backtest results.""" - parameters = {"rsi_period": 14, "rsi_overbought": 70} - - # Cache the result - key = cache_manager.cache_backtest_result( - "AAPL", "rsi", parameters, sample_backtest_result - ) - assert key is not None - - # Verify file was created - expected_path = cache_manager.backtest_dir / f"{key}.gz" - assert expected_path.exists() - - def test_get_backtest_result(self, cache_manager, sample_backtest_result): - """Test retrieving cached backtest results.""" - parameters = {"rsi_period": 14, "rsi_overbought": 70} - - # Cache the result first - cache_manager.cache_backtest_result( - "AAPL", "rsi", parameters, sample_backtest_result - ) - - # Retrieve the result - retrieved_result = cache_manager.get_backtest_result("AAPL", "rsi", parameters) - - assert isinstance(retrieved_result, dict) - assert retrieved_result["symbol"] == sample_backtest_result["symbol"] - assert ( - abs( - retrieved_result["total_return"] - - sample_backtest_result["total_return"] - ) - < 0.001 - ) - - def test_cache_optimization_result(self, cache_manager): - """Test caching optimization results.""" - optimization_config = {"param_ranges": {"rsi_period": [10, 20]}} - optimization_result = { - "best_params": {"rsi_period": 14, "rsi_overbought": 70}, - "best_score": 1.5, - "all_results": [ - {"params": {"rsi_period": 10}, "score": 1.2}, - {"params": {"rsi_period": 14}, "score": 1.5}, - ], - } - - # Cache the result - key = cache_manager.cache_optimization_result( - "AAPL", "rsi", optimization_config, optimization_result - ) - assert key is not None - - # Retrieve the result - retrieved_result = cache_manager.get_optimization_result( - "AAPL", "rsi", optimization_config - ) - - assert isinstance(retrieved_result, dict) - assert abs(retrieved_result["best_score"] - 1.5) < 0.001 - - def test_cache_expiration(self, cache_manager, sample_dataframe): - """Test cache expiration.""" - # Cache data with short TTL - cache_manager.cache_data("AAPL", sample_dataframe, ttl_hours=0) - - # Should return None for expired cache - retrieved_data = cache_manager.get_data("AAPL") - assert retrieved_data is None - - def test_clear_cache(self, cache_manager, sample_dataframe): - """Test cache clearing.""" - # Cache some data - cache_manager.cache_data("AAPL", sample_dataframe) - cache_manager.cache_data("MSFT", sample_dataframe) - - # Clear all cache - cache_manager.clear_cache() - - # Verify cache is cleared - assert cache_manager.get_data("AAPL") is None - assert cache_manager.get_data("MSFT") is None - - def test_clear_cache_by_type( - self, cache_manager, sample_dataframe, sample_backtest_result - ): - """Test clearing cache by type.""" - # Cache different types of data - cache_manager.cache_data("AAPL", sample_dataframe) - cache_manager.cache_data("MSFT", sample_dataframe) - cache_manager.cache_backtest_result( - "AAPL", "rsi", {"period": 14}, sample_backtest_result - ) - - # Clear only data cache - cache_manager.clear_cache(cache_type="data") - - # Verify only data cache is cleared - assert cache_manager.get_data("AAPL") is None - assert cache_manager.get_data("MSFT") is None - assert ( - cache_manager.get_backtest_result("AAPL", "rsi", {"period": 14}) is not None - ) - - def test_clear_cache_older_than(self, cache_manager, sample_dataframe): - """Test clearing cache older than specified days.""" - # Cache some data - cache_manager.cache_data("AAPL", sample_dataframe) - - # Clear cache older than 5 days (should not clear recent data) - cache_manager.clear_cache(older_than_days=5) - - # Recent data should still be there - assert cache_manager.get_data("AAPL") is not None - - def test_get_cache_stats(self, cache_manager, sample_dataframe): - """Test getting cache statistics.""" - # Add some cached data - cache_manager.cache_data("AAPL", sample_dataframe) - cache_manager.cache_data("MSFT", sample_dataframe) - - stats = cache_manager.get_cache_stats() - - assert isinstance(stats, dict) - assert "total_size_gb" in stats - assert "max_size_gb" in stats - assert "utilization_percent" in stats - assert "by_type" in stats - assert "by_source" in stats - - def test_cache_size_management(self, cache_manager, sample_dataframe): - """Test cache size management.""" - # Test with very small cache limit - small_cache = UnifiedCacheManager( - cache_dir=cache_manager.cache_dir, - max_size_gb=0.001, # Very small limit - ) - - # Try to cache data that exceeds limit - result = small_cache.cache_data("AAPL", sample_dataframe) - - # Should handle gracefully - assert isinstance(result, str) - - def test_compression(self, cache_manager, sample_dataframe): - """Test data compression.""" - # Cache data (compression is always enabled) - key = cache_manager.cache_data("AAPL", sample_dataframe) - - # Verify compressed file exists - compressed_path = cache_manager.data_dir / f"{key}.gz" - assert compressed_path.exists() - - # Verify we can retrieve the data correctly - retrieved_data = cache_manager.get_data("AAPL") - assert isinstance(retrieved_data, pd.DataFrame) - assert len(retrieved_data) == len(sample_dataframe) - - def test_data_filtering(self, cache_manager, sample_dataframe): - """Test data filtering by date range.""" - # Cache data - cache_manager.cache_data("AAPL", sample_dataframe) - - # Test date range filtering - start_date = "2023-01-15" - end_date = "2023-01-25" - - filtered_data = cache_manager.get_data( - "AAPL", start_date=start_date, end_date=end_date - ) - - if filtered_data is not None: - assert len(filtered_data) <= len(sample_dataframe) - assert filtered_data.index.min() >= pd.to_datetime(start_date) - assert filtered_data.index.max() <= pd.to_datetime(end_date) - - def test_concurrent_access(self, cache_manager, sample_dataframe): - """Test concurrent cache access.""" - import threading - - keys = [f"concurrent_test_{i}" for i in range(5)] - results = {} - - def cache_worker(key): - success = cache_manager.cache_data(key, sample_dataframe) - results[key] = success - - # Start multiple threads - threads = [] - for key in keys: - thread = threading.Thread(target=cache_worker, args=(key,)) - threads.append(thread) - thread.start() - - # Wait for all threads to complete - for thread in threads: - thread.join() - - # Verify all operations succeeded - assert len(results) == 5 - assert all(results.values()) - - def test_error_handling(self, cache_manager): - """Test error handling in cache operations.""" - # Test getting non-existent cache - result = cache_manager.get_data("non_existent") - assert result is None - - def test_cache_key_collision_handling(self, cache_manager, sample_dataframe): - """Test handling of cache key collisions.""" - symbol = "AAPL" - - # Cache first dataset - cache_manager.cache_data(symbol, sample_dataframe) - original_data = cache_manager.get_data(symbol) - - # Cache different dataset with same symbol (should overwrite) - modified_data = sample_dataframe.copy() - modified_data["New_Column"] = 1 - - cache_manager.cache_data(symbol, modified_data) - retrieved_data = cache_manager.get_data(symbol) - - # Should have the new data - assert "New_Column" in retrieved_data.columns - assert "New_Column" not in original_data.columns diff --git a/tests/core/test_cache_manager_redis.py b/tests/core/test_cache_manager_redis.py deleted file mode 100644 index 0a6865e..0000000 --- a/tests/core/test_cache_manager_redis.py +++ /dev/null @@ -1,14 +0,0 @@ -from __future__ import annotations - -import pandas as pd - -from src.core.cache_manager import UnifiedCacheManager - - -def test_redis_helpers_no_redis_installed(monkeypatch): - cm = UnifiedCacheManager() - # Ensure client is None - cm.redis_client = None - assert cm.get_recent_overlay_from_redis("TLT", "1d") is None - # set should not raise - cm.set_recent_overlay_to_redis("TLT", "1d", pd.DataFrame()) diff --git a/tests/core/test_data_manager.py b/tests/core/test_data_manager.py deleted file mode 100644 index cf52706..0000000 --- a/tests/core/test_data_manager.py +++ /dev/null @@ -1,199 +0,0 @@ -"""Unit tests for UnifiedDataManager.""" - -from __future__ import annotations - -from unittest.mock import Mock, patch - -import numpy as np -import pandas as pd -import pytest - -from src.core.data_manager import UnifiedDataManager - - -class TestUnifiedDataManager: - """Test cases for UnifiedDataManager.""" - - @pytest.fixture - def mock_cache_manager(self): - """Mock cache manager.""" - mock_cache = Mock() - mock_cache.get_cache_stats.return_value = { - "total_size_gb": 0.1, - "max_size_gb": 10.0, - "utilization": 0.01, - } - return mock_cache - - @pytest.fixture - def data_manager(self, mock_cache_manager): - """Create UnifiedDataManager instance.""" - return UnifiedDataManager(cache_manager=mock_cache_manager) - - @pytest.fixture - def sample_data(self): - """Sample market data.""" - dates = pd.date_range("2023-01-01", periods=100, freq="D") - rng = np.random.default_rng(42) - return pd.DataFrame( - { - "Open": rng.uniform(100, 200, 100), - "High": rng.uniform(100, 200, 100), - "Low": rng.uniform(100, 200, 100), - "Close": rng.uniform(100, 200, 100), - "Volume": rng.integers(1000000, 10000000, 100), - }, - index=dates, - ) - - def test_init(self, data_manager): - """Test initialization.""" - assert isinstance(data_manager, UnifiedDataManager) - # Default sources are initialized automatically - assert len(data_manager.sources) >= 1 # At least yahoo_finance and bybit - assert data_manager.cache_manager is not None - - def test_get_data(self, data_manager, sample_data): - """Test the main data fetching method.""" - # Mock the cache manager to return None (cache miss) - data_manager.cache_manager.get_data.return_value = None - - # Mock Yahoo Finance source - with patch("yfinance.Ticker") as mock_ticker: - mock_ticker_instance = Mock() - mock_ticker_instance.history.return_value = sample_data - mock_ticker.return_value = mock_ticker_instance - - result = data_manager.get_data( - symbol="AAPL", - start_date="2023-01-01", - end_date="2023-12-31", - interval="1d", - ) - - assert isinstance(result, pd.DataFrame) - assert not result.empty - assert all( - col in result.columns for col in ["open", "high", "low", "close"] - ) - - def test_add_source_with_actual_source(self, data_manager): - """Test adding actual DataSource objects.""" - from src.core.data_manager import DataSource, DataSourceConfig - - # Create a mock DataSource for testing - class MockDataSource(DataSource): - def __init__(self): - config = DataSourceConfig( - name="test_source", - priority=5, - rate_limit=1.0, - max_retries=3, - timeout=30.0, - supports_batch=False, - asset_types=["stocks"], - ) - super().__init__(config) - - def fetch_data(self, symbol, start_date, end_date, interval="1d", **kwargs): - return None - - def fetch_batch_data( - self, symbols, start_date, end_date, interval="1d", **kwargs - ): - return {} - - def get_available_symbols(self, asset_type=None): - return [] - - # Test adding a DataSource object - test_source = MockDataSource() - initial_count = len(data_manager.sources) - data_manager.add_source(test_source) - - assert len(data_manager.sources) == initial_count + 1 - assert "test_source" in data_manager.sources - assert data_manager.sources["test_source"] == test_source - - def test_default_sources_initialization(self): - """Test that default sources are properly initialized.""" - from src.core.data_manager import UnifiedDataManager - - # Create a fresh instance - manager = UnifiedDataManager() - - # Should have at least yahoo_finance and bybit - assert len(manager.sources) >= 2 - assert "yahoo_finance" in manager.sources - assert "bybit" in manager.sources - - # Check that sources are properly configured - yahoo_source = manager.sources["yahoo_finance"] - assert yahoo_source.config.name == "yahoo_finance" - assert yahoo_source.config.supports_batch is True - - bybit_source = manager.sources["bybit"] - assert bybit_source.config.name == "bybit" - assert bybit_source.config.supports_futures is True - - def test_cache_integration(self, data_manager, sample_data): - """Test that caching works properly.""" - # Test cache hit - data_manager.cache_manager.get_data.return_value = sample_data - - result = data_manager.get_data( - symbol="AAPL", - start_date="2023-01-01", - end_date="2023-12-31", - use_cache=True, - ) - - assert isinstance(result, pd.DataFrame) - data_manager.cache_manager.get_data.assert_called_once() - - # Test cache miss and cache storage - data_manager.cache_manager.get_data.return_value = None - - with patch("yfinance.Ticker") as mock_ticker: - mock_ticker_instance = Mock() - mock_ticker_instance.history.return_value = sample_data - mock_ticker.return_value = mock_ticker_instance - - result = data_manager.get_data( - symbol="TSLA", - start_date="2023-01-01", - end_date="2023-12-31", - use_cache=True, - ) - - assert isinstance(result, pd.DataFrame) - # Verify cache_data was called to store the result - data_manager.cache_manager.cache_data.assert_called_once() - - def test_asset_type_detection(self, data_manager): - """Test asset type detection from symbols.""" - # Test crypto detection - assert data_manager._detect_asset_type("BTCUSDT") == "crypto" - assert data_manager._detect_asset_type("ETH-USD") == "crypto" - - # Test forex detection - assert data_manager._detect_asset_type("EURUSD=X") == "forex" - assert data_manager._detect_asset_type("GBPUSD") == "forex" - - # Test stocks detection (default) - assert data_manager._detect_asset_type("AAPL") == "stocks" - assert data_manager._detect_asset_type("MSFT") == "stocks" - - def test_source_status(self, data_manager): - """Test getting source status information.""" - status = data_manager.get_source_status() - - assert isinstance(status, dict) - assert "yahoo_finance" in status - assert "bybit" in status - - # Check status structure - yahoo_status = status["yahoo_finance"] - assert "priority" in yahoo_status - assert "supports_batch" in yahoo_status - assert "asset_types" in yahoo_status diff --git a/tests/core/test_data_manager_freshness.py b/tests/core/test_data_manager_freshness.py deleted file mode 100644 index 9914f87..0000000 --- a/tests/core/test_data_manager_freshness.py +++ /dev/null @@ -1,36 +0,0 @@ -from __future__ import annotations - -import logging -from types import SimpleNamespace - -import pandas as pd - -from src.core.data_manager import UnifiedDataManager - - -def test_freshness_warning_for_daily(monkeypatch, caplog): - caplog.set_level(logging.WARNING) - dm = UnifiedDataManager() - - # Fake source returning a stale last bar (two business days ago) - class FakeSource: - def __init__(self): - self.config = SimpleNamespace( - name="yahoo_finance", priority=1, asset_types=["stocks"] - ) - - def fetch_data(self, symbol, start_date, end_date, interval, **kwargs): - idx = pd.date_range("2023-01-01", periods=10, freq="D") - return pd.DataFrame({"open": 1, "high": 1, "low": 1, "close": 1}, index=idx) - - # Route only to our fake source - monkeypatch.setattr(dm, "_get_sources_for_asset_type", lambda at: [FakeSource()]) - - # Force fetch path (skip cache) so freshness check executes - df = dm.get_data( - "AAPL", "2000-01-01", "2100-01-01", "1d", use_cache=False, asset_type="stocks" - ) - assert df is not None - assert not df.empty - # Assert warning logged - assert any("seems stale" in rec.message for rec in caplog.records) diff --git a/tests/core/test_data_manager_probe.py b/tests/core/test_data_manager_probe.py deleted file mode 100644 index 98d9f10..0000000 --- a/tests/core/test_data_manager_probe.py +++ /dev/null @@ -1,34 +0,0 @@ -from __future__ import annotations - -from types import SimpleNamespace - -import pandas as pd - -from src.core.data_manager import UnifiedDataManager - - -def test_probe_and_set_order(monkeypatch): - dm = UnifiedDataManager() - - # Create two fake sources with differing coverage - class FakeSource: - def __init__(self, name, rows, start): - self.config = SimpleNamespace(name=name, priority=1, asset_types=["stocks"]) - self._rows = rows - self._start = start - - def fetch_data(self, symbol, start_date, end_date, interval, **kwargs): - if self._rows == 0: - return None - idx = pd.date_range(self._start, periods=self._rows, freq="D") - return pd.DataFrame({"open": 1, "high": 1, "low": 1, "close": 1}, index=idx) - - dm.sources = { - "yahoo": FakeSource("yahoo", rows=10, start="2020-01-01"), - "alt": FakeSource("alt", rows=20, start="2019-01-01"), - } - - ordered = dm.probe_and_set_order( - "stocks", ["AAPL", "MSFT"], interval="1d", sample_size=2 - ) - assert ordered[0] == "alt" # more rows and earlier start diff --git a/tests/core/test_data_manager_split_cache.py b/tests/core/test_data_manager_split_cache.py deleted file mode 100644 index e5e0e7d..0000000 --- a/tests/core/test_data_manager_split_cache.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import annotations - -import pandas as pd - -from src.core.data_manager import UnifiedDataManager - - -def _df(dates, val): - idx = pd.to_datetime(dates) - return pd.DataFrame({"open": val, "high": val, "low": val, "close": val}, index=idx) - - -def test_split_cache_merge(monkeypatch): - dm = UnifiedDataManager() - - # Legacy fast-path must return None to exercise split layer merge - calls = {"legacy": 0} - - def fake_get_data( - symbol, start_date, end_date, interval, source=None, data_type=None - ): - calls["legacy"] += 1 - if data_type == "full": - return _df(["2023-01-01", "2023-01-10"], 1) - if data_type == "recent": - return _df(["2023-01-08", "2023-01-15"], 2) - return None - - monkeypatch.setattr(dm.cache_manager, "get_data", fake_get_data) - - df = dm.get_data("TLT", "2023-01-01", "2023-01-20", "1d", use_cache=True) - assert df is not None - assert not df.empty - # Last day should be 2023-01-15 given our recent overlay - assert df.index[-1].date().isoformat() == "2023-01-15" - # Overlap region should reflect recent overlay value (2) where both provide data - assert df.loc[pd.Timestamp("2023-01-08"), "close"] == 2 diff --git a/tests/core/test_portfolio_manager.py b/tests/core/test_portfolio_manager.py deleted file mode 100644 index 42713c5..0000000 --- a/tests/core/test_portfolio_manager.py +++ /dev/null @@ -1,199 +0,0 @@ -"""Unit tests for PortfolioManager.""" - -from __future__ import annotations - -from unittest.mock import Mock - -import pandas as pd -import pytest - -from src.core.backtest_engine import BacktestResult -from src.core.collection_manager import PortfolioManager - - -class TestPortfolioManager: - """Test cases for PortfolioManager.""" - - @pytest.fixture - def mock_backtest_engine(self): - """Mock backtest engine.""" - engine = Mock() - engine.batch_backtest.return_value = [] - return engine - - @pytest.fixture - def portfolio_manager(self, mock_backtest_engine): - """Create PortfolioManager instance.""" - return PortfolioManager() - - @pytest.fixture - def sample_backtest_results(self): - """Sample backtest results.""" - return [ - BacktestResult( - symbol="AAPL", - strategy="rsi", - parameters={}, - config={}, - error=None, - metrics={ - "total_return": 0.15, - "annualized_return": 0.12, - "sharpe_ratio": 1.2, - "sortino_ratio": 1.5, - "max_drawdown": -0.08, - "volatility": 0.18, - "beta": 1.1, - "alpha": 0.02, - "var_95": -0.05, - "cvar_95": -0.07, - "calmar_ratio": 1.5, - "omega_ratio": 1.3, - "win_rate": 64.0, - "avg_win": 0.05, - "avg_loss": -0.03, - "profit_factor": 2.1, - "kelly_criterion": 0.15, - "num_trades": 25, - }, - start_date="2023-01-01", - end_date="2023-12-31", - duration_seconds=365 * 24 * 3600, - equity_curve=pd.DataFrame({"equity": [10000, 10500, 11000, 11500]}), - trades=pd.DataFrame(), - data_points=365, - ), - BacktestResult( - symbol="MSFT", - strategy="rsi", - parameters={}, - config={}, - error=None, - metrics={ - "total_return": 0.18, - "annualized_return": 0.16, - "sharpe_ratio": 1.4, - "sortino_ratio": 1.7, - "max_drawdown": -0.06, - "volatility": 0.16, - "beta": 0.9, - "alpha": 0.04, - "var_95": -0.04, - "cvar_95": -0.06, - "calmar_ratio": 2.67, - "omega_ratio": 1.5, - "win_rate": 68.0, - "avg_win": 0.06, - "avg_loss": -0.025, - "profit_factor": 2.4, - "kelly_criterion": 0.18, - "num_trades": 28, - }, - start_date="2023-01-01", - end_date="2023-12-31", - duration_seconds=365 * 24 * 3600, - equity_curve=pd.DataFrame({"equity": [10000, 10600, 11200, 11800]}), - trades=pd.DataFrame(), - data_points=365, - ), - ] - - @pytest.fixture - def sample_portfolios(self): - """Sample portfolio configurations.""" - return { - "tech_growth": { - "name": "Tech Growth", - "symbols": ["AAPL", "MSFT", "GOOGL"], - "strategies": ["rsi", "macd"], - "risk_profile": "aggressive", - "target_return": 0.15, - }, - "conservative": { - "name": "Conservative Mix", - "symbols": ["SPY", "BND", "VTI"], - "strategies": ["sma_crossover"], - "risk_profile": "conservative", - "target_return": 0.08, - }, - } - - def test_init(self, portfolio_manager, mock_backtest_engine): - """Test initialization.""" - assert hasattr(portfolio_manager, "result_analyzer") - assert hasattr(portfolio_manager, "logger") - assert hasattr(portfolio_manager, "risk_weights") - assert hasattr(portfolio_manager, "return_weights") - assert isinstance(portfolio_manager.risk_weights, dict) - assert isinstance(portfolio_manager.return_weights, dict) - - def test_analyze_portfolios(self, portfolio_manager, sample_backtest_results): - """Test portfolio analysis.""" - portfolios = { - "conservative": sample_backtest_results[:1], - "aggressive": sample_backtest_results[1:], - } - - result = portfolio_manager.analyze_portfolios(portfolios) - - assert isinstance(result, dict) - assert "portfolio_summaries" in result - assert "investment_recommendations" in result - assert "market_analysis" in result - assert "risk_analysis" in result - assert "diversification_analysis" in result - assert "detailed_analysis" in result - assert "ranked_portfolios" in result - - def test_generate_investment_plan(self, portfolio_manager, sample_backtest_results): - """Test investment plan generation.""" - portfolios = { - "conservative": sample_backtest_results[:1], - "aggressive": sample_backtest_results[1:], - } - - plan = portfolio_manager.generate_investment_plan( - total_capital=100000, portfolios=portfolios, risk_tolerance="moderate" - ) - - assert isinstance(plan, dict) - assert "total_capital" in plan - assert "risk_tolerance" in plan - assert "allocations" in plan - assert "implementation_plan" in plan - assert "risk_management" in plan - assert "expected_portfolio_metrics" in plan - assert "monitoring_recommendations" in plan - assert "rebalancing_strategy" in plan - - def test_risk_weights_configuration(self, portfolio_manager): - """Test that risk weights are properly configured.""" - assert isinstance(portfolio_manager.risk_weights, dict) - assert "max_drawdown" in portfolio_manager.risk_weights - assert "volatility" in portfolio_manager.risk_weights - assert "var_95" in portfolio_manager.risk_weights - assert "sharpe_ratio" in portfolio_manager.risk_weights - assert "sortino_ratio" in portfolio_manager.risk_weights - - # Check weights sum to 1.0 - total_weight = sum(portfolio_manager.risk_weights.values()) - assert total_weight == pytest.approx(1.0, rel=1e-2) - - def test_return_weights_configuration(self, portfolio_manager): - """Test that return weights are properly configured.""" - assert isinstance(portfolio_manager.return_weights, dict) - assert "total_return" in portfolio_manager.return_weights - assert "annualized_return" in portfolio_manager.return_weights - assert "sharpe_ratio" in portfolio_manager.return_weights - assert "win_rate" in portfolio_manager.return_weights - - # Check weights sum to 1.0 - total_weight = sum(portfolio_manager.return_weights.values()) - assert total_weight == pytest.approx(1.0, rel=1e-2) - - def test_calculate_risk_score(self, portfolio_manager, sample_backtest_results): - """Test risk score calculation.""" - risk_score = portfolio_manager._calculate_risk_score(sample_backtest_results) - - assert isinstance(risk_score, float) - assert 0 <= risk_score <= 100 diff --git a/tests/core/test_strategy.py b/tests/core/test_strategy.py deleted file mode 100644 index 1af3a72..0000000 --- a/tests/core/test_strategy.py +++ /dev/null @@ -1,313 +0,0 @@ -""" -Tests for the strategy module. -""" - -from __future__ import annotations - -import pandas as pd -import pytest - -from src.core.strategy import ( - BaseStrategy, - BuyAndHoldStrategy, - StrategyFactory, - create_strategy, - list_available_strategies, -) - - -class MockStrategy(BaseStrategy): - """Mock strategy for testing.""" - - def __init__(self, custom_param: str = "default") -> None: - super().__init__("MockStrategy") - self.parameters = {"custom_param": custom_param} - - def generate_signals(self, data: pd.DataFrame) -> pd.Series: - """Generate mock signals.""" - # Simple alternating signals for testing - signals = [1 if i % 2 == 0 else -1 for i in range(len(data))] - return pd.Series(signals, index=data.index) - - -class TestBaseStrategy: - """Test BaseStrategy class.""" - - def test_init(self): - """Test BaseStrategy initialization.""" - strategy = MockStrategy() - assert strategy.name == "MockStrategy" - assert isinstance(strategy.parameters, dict) - assert strategy.parameters["custom_param"] == "default" - - def test_init_with_custom_param(self): - """Test BaseStrategy initialization with custom parameters.""" - strategy = MockStrategy(custom_param="custom_value") - assert strategy.parameters["custom_param"] == "custom_value" - - def test_get_strategy_info(self): - """Test get_strategy_info method.""" - strategy = MockStrategy() - info = strategy.get_strategy_info() - - assert isinstance(info, dict) - assert info["name"] == "MockStrategy" - assert info["type"] == "Base" - assert "parameters" in info - assert "description" in info - - def test_validate_data_valid(self): - """Test validate_data with valid OHLCV data.""" - strategy = MockStrategy() - data = pd.DataFrame( - { - "Open": [100, 101, 102], - "High": [105, 106, 107], - "Low": [95, 96, 97], - "Close": [102, 103, 104], - "Volume": [1000, 1100, 1200], - } - ) - - assert strategy.validate_data(data) is True - - def test_validate_data_invalid(self): - """Test validate_data with invalid data.""" - strategy = MockStrategy() - - # Missing Volume column - data = pd.DataFrame( - { - "Open": [100, 101, 102], - "High": [105, 106, 107], - "Low": [95, 96, 97], - "Close": [102, 103, 104], - } - ) - - assert strategy.validate_data(data) is False - - def test_generate_signals(self): - """Test signal generation.""" - strategy = MockStrategy() - data = pd.DataFrame( - { - "Open": [100, 101, 102], - "High": [105, 106, 107], - "Low": [95, 96, 97], - "Close": [102, 103, 104], - "Volume": [1000, 1100, 1200], - } - ) - - signals = strategy.generate_signals(data) - assert isinstance(signals, pd.Series) - assert len(signals) == len(data) - assert all(signal in [-1, 0, 1] for signal in signals) - - -class TestBuyAndHoldStrategy: - """Test BuyAndHoldStrategy class.""" - - def test_init(self): - """Test BuyAndHoldStrategy initialization.""" - strategy = BuyAndHoldStrategy() - assert strategy.name == "Buy and Hold" - assert strategy.parameters == {} - - def test_generate_signals_empty_data(self): - """Test signal generation with empty data.""" - strategy = BuyAndHoldStrategy() - data = pd.DataFrame() - - signals = strategy.generate_signals(data) - assert isinstance(signals, pd.Series) - assert len(signals) == 0 - - def test_generate_signals_single_row(self): - """Test signal generation with single row.""" - strategy = BuyAndHoldStrategy() - data = pd.DataFrame( - { - "Open": [100], - "High": [105], - "Low": [95], - "Close": [102], - "Volume": [1000], - } - ) - - signals = strategy.generate_signals(data) - assert len(signals) == 1 - assert signals.iloc[0] == 1 # Buy signal at start - - def test_generate_signals_multiple_rows(self): - """Test signal generation with multiple rows.""" - strategy = BuyAndHoldStrategy() - data = pd.DataFrame( - { - "Open": [100, 101, 102], - "High": [105, 106, 107], - "Low": [95, 96, 97], - "Close": [102, 103, 104], - "Volume": [1000, 1100, 1200], - } - ) - - signals = strategy.generate_signals(data) - assert len(signals) == 3 - assert signals.iloc[0] == 1 # Buy signal at start - assert signals.iloc[1] == 0 # Hold - assert signals.iloc[2] == 0 # Hold - - def test_get_strategy_info(self): - """Test get_strategy_info method.""" - strategy = BuyAndHoldStrategy() - info = strategy.get_strategy_info() - - assert info["name"] == "Buy and Hold" - assert info["type"] == "Base" - assert info["parameters"] == {} - - -class TestStrategyFactory: - """Test StrategyFactory class.""" - - def test_builtin_strategies_list(self): - """Test that builtin strategies are properly registered.""" - assert "BuyAndHold" in StrategyFactory.BUILTIN_STRATEGIES - assert StrategyFactory.BUILTIN_STRATEGIES["BuyAndHold"] is BuyAndHoldStrategy - - def test_create_builtin_strategy(self): - """Test creating builtin strategy.""" - strategy = StrategyFactory.create_strategy("BuyAndHold") - assert isinstance(strategy, BuyAndHoldStrategy) - assert strategy.name == "Buy and Hold" - - def test_create_builtin_strategy_with_parameters(self): - """Test creating builtin strategy with parameters.""" - # BuyAndHoldStrategy doesn't take parameters, but test the flow - strategy = StrategyFactory.create_strategy("BuyAndHold", {}) - assert isinstance(strategy, BuyAndHoldStrategy) - - def test_create_nonexistent_strategy(self): - """Test creating nonexistent strategy raises ValueError.""" - with pytest.raises( - ValueError, match="Strategy 'NonExistentStrategy' not found" - ): - StrategyFactory.create_strategy("NonExistentStrategy") - - def test_list_strategies(self): - """Test listing available strategies.""" - strategies = StrategyFactory.list_strategies() - - assert isinstance(strategies, dict) - assert "builtin" in strategies - assert "external" in strategies - assert "all" in strategies - - assert "BuyAndHold" in strategies["builtin"] - assert "BuyAndHold" in strategies["all"] - - def test_get_strategy_info_builtin(self): - """Test getting strategy info for builtin strategy.""" - info = StrategyFactory.get_strategy_info("BuyAndHold") - - assert isinstance(info, dict) - assert info["name"] == "Buy and Hold" - - def test_get_strategy_info_nonexistent(self): - """Test getting strategy info for nonexistent strategy.""" - with pytest.raises( - ValueError, match="Strategy 'NonExistentStrategy' not found" - ): - StrategyFactory.get_strategy_info("NonExistentStrategy") - - -class TestConvenienceFunctions: - """Test convenience functions.""" - - def test_create_strategy(self): - """Test create_strategy convenience function.""" - strategy = create_strategy("BuyAndHold") - assert isinstance(strategy, BuyAndHoldStrategy) - - def test_create_strategy_with_parameters(self): - """Test create_strategy with parameters.""" - strategy = create_strategy("BuyAndHold", {}) - assert isinstance(strategy, BuyAndHoldStrategy) - - def test_list_available_strategies(self): - """Test list_available_strategies convenience function.""" - strategies = list_available_strategies() - - assert isinstance(strategies, dict) - assert "builtin" in strategies - assert "external" in strategies - assert "all" in strategies - assert "BuyAndHold" in strategies["all"] - - -class TestIntegration: - """Integration tests for strategy module.""" - - def test_end_to_end_workflow(self): - """Test complete workflow from creation to signal generation.""" - # List available strategies - strategies = list_available_strategies() - assert "BuyAndHold" in strategies["all"] - - # Get strategy info - info = StrategyFactory.get_strategy_info("BuyAndHold") - assert info["name"] == "Buy and Hold" - - # Create strategy - strategy = create_strategy("BuyAndHold") - assert isinstance(strategy, BuyAndHoldStrategy) - - # Create test data - data = pd.DataFrame( - { - "Open": [100, 101, 102, 103, 104], - "High": [105, 106, 107, 108, 109], - "Low": [95, 96, 97, 98, 99], - "Close": [102, 103, 104, 105, 106], - "Volume": [1000, 1100, 1200, 1300, 1400], - } - ) - - # Validate data - assert strategy.validate_data(data) is True - - # Generate signals - signals = strategy.generate_signals(data) - assert len(signals) == 5 - assert signals.iloc[0] == 1 # Buy at start - assert all(signals.iloc[1:] == 0) # Hold the rest - - def test_strategy_with_custom_parameters_flow(self): - """Test strategy workflow with custom parameters.""" - # Test that parameter passing works through the factory - # BuyAndHoldStrategy doesn't accept parameters, so test with empty dict - strategy = create_strategy("BuyAndHold", {}) - assert isinstance(strategy, BuyAndHoldStrategy) - - # Test with None parameters (should work) - strategy2 = create_strategy("BuyAndHold", None) - assert isinstance(strategy2, BuyAndHoldStrategy) - - # The test verifies the parameter passing mechanism works - - def test_multiple_strategy_instances(self): - """Test creating multiple instances of the same strategy.""" - strategy1 = create_strategy("BuyAndHold") - strategy2 = create_strategy("BuyAndHold") - - # Should be separate instances - assert strategy1 is not strategy2 - assert isinstance(strategy1, BuyAndHoldStrategy) - assert isinstance(strategy2, BuyAndHoldStrategy) - - # Should have same configuration - assert strategy1.name == strategy2.name - assert strategy1.parameters == strategy2.parameters diff --git a/tests/database/__init__.py b/tests/database/__init__.py deleted file mode 100644 index 2efcefd..0000000 --- a/tests/database/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Database tests package.""" diff --git a/tests/database/test_beststrategy_upsert.py b/tests/database/test_beststrategy_upsert.py deleted file mode 100644 index e52acde..0000000 --- a/tests/database/test_beststrategy_upsert.py +++ /dev/null @@ -1,148 +0,0 @@ -from __future__ import annotations - -from unittest.mock import patch - -from src.core import direct_backtest -from src.database import unified_models - - -def setup_module(module): - # Ensure sqlite tables exist for tests - unified_models.create_tables() - - -def test_beststrategy_upsert_from_run(tmp_path): - """ - Integration-style unit test that verifies run_strategy_comparison persists - BacktestResult rows and then upserts a canonical BestStrategy row based on - the configured target metric (sortino_ratio). - - Approach: - - Create a run via create_run_from_manifest to obtain run_id. - - Monkeypatch run_direct_backtest to return deterministic results for two strategies. - - Call run_strategy_comparison with persistence_context containing run_id and target_metric. - - Assert a BestStrategy row exists for the tested symbol/timeframe and matches the best metric. - """ - # Create a run manifest and insert run row - manifest = { - "plan": { - "plan_hash": "test-plan-hash-beststrategy", - "actor": "test", - "action": "backtest", - "collection": "test_collection", - "strategies": ["adx", "macd"], - "intervals": ["1d"], - "metric": "sortino_ratio", - } - } - run = unified_models.create_run_from_manifest(manifest) - assert run is not None - run_id = run.run_id - - symbol = "TEST" - start = "2020-01-01" - end = "2020-12-31" - timeframe = "1d" - - # Prepare deterministic fake results for two strategies - def fake_run_direct_backtest( - symbol_arg, - strategy_name, - start_date, - end_date, - timeframe_arg, - initial_capital, - persistence_context=None, - ): - # strategy 'macd' is better (higher sortino) - if strategy_name == "adx": - metrics = {"sortino_ratio": 0.5, "num_trades": 1} - else: - metrics = {"sortino_ratio": 2.0, "num_trades": 2} - - # Simulate persistence side-effect similar to _persist_result_to_db so the later - # ranking/finalization code finds BacktestResult rows in the DB. - try: - sess = unified_models.Session() - br = unified_models.BacktestResult( - run_id=(persistence_context or {}).get("run_id"), - symbol=symbol_arg, - strategy=strategy_name, - interval=timeframe_arg, - start_at_utc=start_date, - end_at_utc=end_date, - rank_in_symbol=None, - metrics=metrics, - engine_ctx={"summary": "ok"}, - trades_raw=None, - error=None, - ) - sess.add(br) - sess.flush() - sess.commit() - except Exception: - try: - sess.rollback() - except Exception: - pass - finally: - try: - sess.close() - except Exception: - pass - - return { - "symbol": symbol_arg, - "strategy": strategy_name, - "timeframe": timeframe_arg, - "error": None, - "metrics": metrics, - "trades": None, - "backtest_object": None, - "bt_results": {"summary": "ok"}, - "start_date": start_date, - "end_date": end_date, - } - - # Patch the run_direct_backtest used by run_strategy_comparison - with patch( - "src.core.direct_backtest.run_direct_backtest", - side_effect=fake_run_direct_backtest, - ): - out = direct_backtest.run_strategy_comparison( - symbol, - ["adx", "macd"], - start, - end, - timeframe, - initial_capital=10000.0, - persistence_context={"run_id": run_id, "target_metric": "sortino_ratio"}, - ) - - # Validate output contains best_strategy with macd - assert out["best_strategy"] is not None - assert ( - out["best_strategy"]["strategy"] == "macd" - or out["best_strategy"]["strategy"] == "MACD" - or out["best_strategy"]["strategy"].lower() == "macd" - ) - - # Now verify BestStrategy upsert exists in unified_models - sess = unified_models.Session() - try: - bs = ( - sess.query(unified_models.BestStrategy) - .filter_by(symbol=symbol, timeframe=timeframe) - .one_or_none() - ) - assert bs is not None, "BestStrategy was not upserted into the DB" - assert bs.strategy.lower() == "macd" - # Check sortino value was recorded (numeric-ish) - try: - val = float(bs.sortino_ratio) - assert val >= 2.0 - except Exception: - # If stored as JSON/text, still ensure the string contains '2' - assert "2" in str(bs.sortino_ratio) - finally: - sess.close() diff --git a/tests/database/test_db_connection.py b/tests/database/test_db_connection.py deleted file mode 100644 index 21f3d8a..0000000 --- a/tests/database/test_db_connection.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Simple tests for database connection manager.""" - -from __future__ import annotations - -from unittest.mock import MagicMock, patch - -from sqlalchemy import Engine -from sqlalchemy.ext.asyncio import AsyncEngine - -from src.database.db_connection import DatabaseManager - - -class TestDatabaseManager: - """Test cases for DatabaseManager class.""" - - def test_init_default(self): - """Test initialization with default parameters.""" - db_manager = DatabaseManager() - assert db_manager._sync_engine is None - assert db_manager._async_engine is None - - @patch.dict( - "os.environ", {"DATABASE_URL": "postgresql://test:test@localhost/custom"} - ) - def test_get_database_url_from_env(self): - """Test database URL retrieval from environment.""" - db_manager = DatabaseManager() - url = db_manager._get_database_url(async_mode=False) - assert url == "postgresql://test:test@localhost/custom" - - def test_get_database_url_async_conversion(self): - """Test async URL conversion.""" - db_manager = DatabaseManager() - with patch.dict( - "os.environ", {"DATABASE_URL": "postgresql://test:test@localhost/db"} - ): - async_url = db_manager._get_database_url(async_mode=True) - assert "postgresql+asyncpg://" in async_url - - @patch("src.database.db_connection.create_async_engine") - def test_async_engine_creation(self, mock_create_engine): - """Test async engine creation.""" - mock_engine = MagicMock(spec=AsyncEngine) - mock_create_engine.return_value = mock_engine - - db_manager = DatabaseManager() - engine = db_manager.async_engine - - assert engine == mock_engine - mock_create_engine.assert_called_once() - - @patch("src.database.db_connection.create_engine") - def test_sync_engine_creation(self, mock_create_engine): - """Test sync engine creation.""" - mock_engine = MagicMock(spec=Engine) - mock_create_engine.return_value = mock_engine - - db_manager = DatabaseManager() - engine = db_manager.sync_engine - - assert engine == mock_engine - mock_create_engine.assert_called_once() - - @patch("src.database.db_connection.create_engine") - def test_sync_session_creation(self, mock_create_engine): - """Test sync session creation.""" - mock_engine = MagicMock(spec=Engine) - mock_create_engine.return_value = mock_engine - - db_manager = DatabaseManager() - session = db_manager.get_sync_session() - - assert session is not None - mock_create_engine.assert_called_once() - - -class TestGetDbSession: - """Test cases for get_db_session convenience function.""" - - @patch("src.database.db_connection.create_engine") - def test_get_db_session_creation(self, mock_create_engine): - """Test that get_db_session creates proper session.""" - from src.database.db_connection import get_db_session - - # Mock the engine creation to avoid psycopg2 dependency - mock_engine = MagicMock(spec=Engine) - mock_create_engine.return_value = mock_engine - - # Test function exists and can be called - session_generator = get_db_session() - assert session_generator is not None - - -class TestIntegration: - """Integration tests for database connection workflow.""" - - @patch("src.database.db_connection.create_async_engine") - @patch("src.database.db_connection.create_engine") - def test_basic_workflow(self, mock_create_sync, mock_create_async): - """Test basic database connection workflow.""" - # Setup mocks - mock_async_engine = MagicMock(spec=AsyncEngine) - mock_sync_engine = MagicMock(spec=Engine) - mock_create_async.return_value = mock_async_engine - mock_create_sync.return_value = mock_sync_engine - - # Create manager - db_manager = DatabaseManager() - - # Test async workflow - async_engine = db_manager.async_engine - assert async_engine == mock_async_engine - - # Test sync workflow - sync_engine = db_manager.sync_engine - assert sync_engine == mock_sync_engine - - @patch.dict("os.environ", {"DATABASE_URL": "sqlite:///test.db"}) - @patch("src.database.db_connection.create_engine") - def test_environment_variable_usage(self, mock_create_engine): - """Test that environment variables are properly used.""" - mock_engine = MagicMock(spec=Engine) - mock_create_engine.return_value = mock_engine - - # Should use environment variable - db_manager = DatabaseManager() - url = db_manager._get_database_url(async_mode=False) - assert "sqlite:///test.db" in url - - # Test engine creation - engine = db_manager.sync_engine - assert engine == mock_engine diff --git a/tests/reporting/__init__.py b/tests/reporting/__init__.py deleted file mode 100644 index 43633d7..0000000 --- a/tests/reporting/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Reporting tests package.""" diff --git a/tests/scripts/test_data_health_report.py b/tests/scripts/test_data_health_report.py deleted file mode 100644 index cc5249e..0000000 --- a/tests/scripts/test_data_health_report.py +++ /dev/null @@ -1,52 +0,0 @@ -from __future__ import annotations - -import csv -import importlib.util -from pathlib import Path -from unittest.mock import patch - -import pandas as pd - - -def _load_module(): - p = Path("scripts/data_health_report.py") - spec = importlib.util.spec_from_file_location("data_health_report", p) - assert spec is not None - assert spec.loader is not None - mod = importlib.util.module_from_spec(spec) - assert mod is not None - spec.loader.exec_module(mod) - return mod - - -def test_health_report_outputs_csv(monkeypatch, tmp_path): - mod = _load_module() - - # Build fake DF - idx = pd.date_range("2023-01-01", periods=10, freq="D") - df = pd.DataFrame({"open": 1, "high": 1, "low": 1, "close": 1}, index=idx) - fake_dm = patch.object(mod, "UnifiedDataManager").start().return_value - fake_dm.get_data.return_value = df - - # Create a simple collection file - coll_dir = tmp_path / "config/collections/default" - coll_dir.mkdir(parents=True, exist_ok=True) - f = coll_dir / "bonds_core.json" - f.write_text('{"bonds_core": {"symbols": ["TLT", "IEF"]}}') - # Ensure resolver reads from tmp config - import os as _os - - old_cwd = Path.cwd() - _os.chdir(str(tmp_path)) - - out_csv = tmp_path / "health.csv" - rc = mod.main( - ["bonds_core", "--interval", "1d", "--period", "max", "--out", str(out_csv)] - ) - assert rc == 0 - assert out_csv.exists() - rows = list(csv.DictReader(out_csv.open())) - # two symbols - assert len(rows) == 2 - patch.stopall() - _os.chdir(str(old_cwd)) diff --git a/tests/scripts/test_prefetch_all.py b/tests/scripts/test_prefetch_all.py deleted file mode 100644 index 539a125..0000000 --- a/tests/scripts/test_prefetch_all.py +++ /dev/null @@ -1,39 +0,0 @@ -from __future__ import annotations - -import importlib.util -from pathlib import Path -from unittest.mock import patch - - -def _load_module(): - p = Path("scripts/prefetch_all.py") - spec = importlib.util.spec_from_file_location("prefetch_all", p) - assert spec is not None - assert spec.loader is not None - mod = importlib.util.module_from_spec(spec) - assert mod is not None - spec.loader.exec_module(mod) - return mod - - -def test_prefetch_all_calls(monkeypatch): - mod = _load_module() - # Patch prefetch_one inside loaded module - mock_pf = patch.object(mod, "prefetch_one").start() - try: - rc = mod.main( - [ - "bonds_core", - "indices_global_core", - "--mode", - "recent", - "--interval", - "1d", - "--recent-days", - "30", - ] - ) - assert rc == 0 - assert mock_pf.call_count == 2 - finally: - patch.stopall() diff --git a/tests/scripts/test_prefetch_collection.py b/tests/scripts/test_prefetch_collection.py deleted file mode 100644 index 4e272d8..0000000 --- a/tests/scripts/test_prefetch_collection.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import annotations - -import importlib.util -from pathlib import Path -from unittest.mock import MagicMock - -_path = Path("scripts/prefetch_collection.py") -_spec = importlib.util.spec_from_file_location("prefetch_collection", _path) -assert _spec is not None -assert _spec.loader is not None -prefetch_mod = importlib.util.module_from_spec(_spec) -assert prefetch_mod is not None -_spec.loader.exec_module(prefetch_mod) -prefetch = prefetch_mod.prefetch - - -def test_prefetch_full(monkeypatch): - fake_dm = MagicMock() - monkeypatch.setattr( - prefetch_mod, "UnifiedDataManager", MagicMock(return_value=fake_dm) - ) - prefetch("bonds_core", mode="full", interval="1d", recent_days=90) - dm = fake_dm - # full: period='max', use_cache=False - dm.get_batch_data.assert_called_once() - args, kwargs = dm.get_batch_data.call_args - assert kwargs.get("use_cache") is False - assert kwargs.get("period") == "max" - - -def test_prefetch_recent(monkeypatch): - fake_dm = MagicMock() - monkeypatch.setattr( - prefetch_mod, "UnifiedDataManager", MagicMock(return_value=fake_dm) - ) - prefetch("bonds_core", mode="recent", interval="1d", recent_days=90) - dm = fake_dm - dm.get_batch_data.assert_called_once() - args, kwargs = dm.get_batch_data.call_args - assert kwargs.get("use_cache") is False - # recent should not set provider period - assert kwargs.get("period") is None diff --git a/tests/test_html_reporter.py b/tests/test_html_reporter.py new file mode 100644 index 0000000..0f0c24c --- /dev/null +++ b/tests/test_html_reporter.py @@ -0,0 +1,57 @@ +from pathlib import Path +from types import SimpleNamespace + +from src.reporting.html import HTMLReporter + + +class DummyCache: + def __init__(self, rows): + self._rows = rows + + def list_by_run(self, run_id: str): + return self._rows + + +def test_html_reporter_generates_file(tmp_path: Path): + # Prepare dummy rows (all results) and best results + rows = [ + { + "collection": "crypto", + "symbol": "BTC/USDT", + "timeframe": "1d", + "strategy": "stratA", + "metric": "sortino", + "metric_value": 1.5, + "params": {"x": 1}, + "stats": { + "sharpe": 1.2, + "sortino": 1.5, + "profit": 0.2, + "trades": 10, + "max_drawdown": -0.1, + }, + } + ] + best = [ + SimpleNamespace( + collection="crypto", + symbol="BTC/USDT", + timeframe="1d", + strategy="stratA", + params={"x": 1}, + metric_name="sortino", + metric_value=1.5, + stats={ + "sharpe": 1.2, + "sortino": 1.5, + "profit": 0.2, + "trades": 10, + "max_drawdown": -0.1, + }, + ) + ] + cache = DummyCache(rows) + HTMLReporter(tmp_path, cache, run_id="run-1", top_n=1, inline_css=True).export(best) + html = (tmp_path / "report.html").read_text() + assert "Backtest Report" in html + assert "BTC/USDT" in html diff --git a/tests/test_http_retry.py b/tests/test_http_retry.py new file mode 100644 index 0000000..422016b --- /dev/null +++ b/tests/test_http_retry.py @@ -0,0 +1,8 @@ +from src.utils.http import create_retry_session + + +def test_create_retry_session(): + s = create_retry_session() + # Ensure adapters are mounted + assert "http://" in s.adapters + assert "https://" in s.adapters diff --git a/tests/test_parquet_cache.py b/tests/test_parquet_cache.py new file mode 100644 index 0000000..3c28fe3 --- /dev/null +++ b/tests/test_parquet_cache.py @@ -0,0 +1,34 @@ +import os +from pathlib import Path + +import pytest + +# Skip this module entirely in lightweight pre-commit environments +if os.environ.get("SKIP_PANDAS_TESTS") == "1": # pragma: no cover + pytest.skip("skipping pandas-dependent test in pre-commit", allow_module_level=True) + +import pandas as pd + +from src.data.cache import ParquetCache + + +def test_parquet_cache_roundtrip(tmp_path: Path): + cache = ParquetCache(tmp_path) + df = pd.DataFrame( + { + "Open": [1, 2, 3], + "High": [1, 2, 3], + "Low": [1, 2, 3], + "Close": [1, 2, 3], + "Volume": [10, 20, 30], + }, + index=pd.to_datetime(["2020-01-01", "2020-01-02", "2020-01-03"]), + ) + cache.save("yfinance", "AAPL", "1d", df) + got = cache.load("yfinance", "AAPL", "1d") + assert got is not None + assert len(got) == 3 + assert list(got.columns) == ["Open", "High", "Low", "Close", "Volume"] + + +pytestmark = [] diff --git a/tests/test_results_cache.py b/tests/test_results_cache.py new file mode 100644 index 0000000..f7fc4d1 --- /dev/null +++ b/tests/test_results_cache.py @@ -0,0 +1,41 @@ +from pathlib import Path + +from src.backtest.results_cache import ResultsCache + + +def test_results_cache_set_get(tmp_path: Path): + cache = ResultsCache(tmp_path) + key = dict(a=1) + stats = {"sharpe": 1.23, "profit": 0.12} + cache.set( + collection="test", + symbol="SYMB", + timeframe="1d", + strategy="strat", + params=key, + metric_name="sharpe", + metric_value=1.23, + stats=stats, + data_fingerprint="10:2020-01-01T00:00:00:100.0", + fees=0.001, + slippage=0.001, + run_id="run-1", + ) + got = cache.get( + collection="test", + symbol="SYMB", + timeframe="1d", + strategy="strat", + params=key, + metric_name="sharpe", + data_fingerprint="10:2020-01-01T00:00:00:100.0", + fees=0.001, + slippage=0.001, + ) + assert got is not None + assert got["metric_value"] == 1.23 + assert got["stats"]["profit"] == 0.12 + + rows = cache.list_by_run("run-1") + assert len(rows) == 1 + assert rows[0]["strategy"] == "strat" diff --git a/tests/test_symbols.py b/tests/test_symbols.py new file mode 100644 index 0000000..e95d286 --- /dev/null +++ b/tests/test_symbols.py @@ -0,0 +1,34 @@ +from types import SimpleNamespace + +from src.utils.symbols import DiscoverOptions, discover_ccxt_symbols + + +class DummyExchange: + def __init__(self, tickers=None, markets=None): + self._tickers = tickers or {} + self._markets = markets or {} + + def fetch_tickers(self): + return self._tickers + + def load_markets(self): + return self._markets + + +def test_discover_symbols_monkeypatch(monkeypatch): + # Patch ccxt.binance to our dummy class + import src.utils.symbols as symbols_mod + + dummy = DummyExchange( + tickers={ + "AAA/USDT": {"quoteVolume": 200}, + "BBB/USDT": {"quoteVolume": 100}, + "CCC/USD": {"quoteVolume": 999}, # filtered by quote + } + ) + monkeypatch.setattr(symbols_mod, "ccxt", SimpleNamespace(binance=lambda cfg: dummy)) + + res = discover_ccxt_symbols(DiscoverOptions(exchange="binance", quote="USDT", top_n=2)) + assert len(res) == 2 + assert res[0][0] == "AAA/USDT" + assert res[1][0] == "BBB/USDT" diff --git a/tests/utils/test_config_manager.py b/tests/utils/test_config_manager.py deleted file mode 100644 index 37e99d8..0000000 --- a/tests/utils/test_config_manager.py +++ /dev/null @@ -1,474 +0,0 @@ -""" -Tests for the config manager module. -""" - -from __future__ import annotations - -import json -import os -import tempfile -from pathlib import Path - -import pytest -import yaml - -from src.utils.config_manager import ConfigManager - - -class TestConfigManager: - """Test ConfigManager class.""" - - def test_initialization_default(self): - """Test default initialization.""" - config = ConfigManager() - - assert isinstance(config.config, dict) - assert "data" in config.config - assert "backtest" in config.config - assert "logging" in config.config - - # Check default values - assert config.config["data"]["default_interval"] == "1d" - assert config.config["backtest"]["default_commission"] == 0.001 - assert config.config["backtest"]["initial_capital"] == 10000 - assert config.config["logging"]["level"] == "INFO" - - def test_initialization_with_nonexistent_file(self): - """Test initialization with nonexistent config file.""" - with pytest.raises(FileNotFoundError): - ConfigManager(config_path="/nonexistent/config.json") - - def test_load_json_config_file(self): - """Test loading JSON configuration file.""" - # Create temporary JSON config file - config_data = { - "data": { - "default_interval": "1h", - "cache_dir": str(Path(tempfile.gettempdir()) / "custom_cache"), - }, - "backtest": {"initial_capital": 50000}, - } - - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: - json.dump(config_data, f) - temp_file = f.name - - try: - config = ConfigManager(config_path=temp_file) - - # Check that values were loaded and merged - assert config.config["data"]["default_interval"] == "1h" - assert config.config["data"]["cache_dir"] == str( - Path(tempfile.gettempdir()) / "custom_cache" - ) - assert config.config["backtest"]["initial_capital"] == 50000 - # Default commission should still be there - assert config.config["backtest"]["default_commission"] == 0.001 - - finally: - Path(temp_file).unlink() - - def test_load_yaml_config_file(self): - """Test loading YAML configuration file.""" - # Create temporary YAML config file - config_data = { - "data": {"default_interval": "5m", "new_setting": "test_value"}, - "logging": {"level": "DEBUG"}, - } - - with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: - yaml.dump(config_data, f) - temp_file = f.name - - try: - config = ConfigManager(config_path=temp_file) - - # Check that values were loaded - assert config.config["data"]["default_interval"] == "5m" - assert config.config["data"]["new_setting"] == "test_value" - assert config.config["logging"]["level"] == "DEBUG" - - finally: - Path(temp_file).unlink() - - def test_load_unsupported_file_format(self): - """Test loading unsupported file format.""" - with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as f: - f.write(b"some content") - temp_file = f.name - - try: - with pytest.raises( - ValueError, match="Unsupported configuration file format" - ): - ConfigManager(config_path=temp_file) - finally: - Path(temp_file).unlink() - - def test_load_from_environment_variables(self): - """Test loading configuration from environment variables.""" - # Set test environment variables - test_env_vars = { - "QUANTPY_DATA_CACHE_SIZE": "1000", - "QUANTPY_BACKTEST_COMMISSION": "0.005", - "QUANTPY_LOGGING_DEBUG": "true", - "QUANTPY_TEST_SECTION_ENABLED": "false", - "QUANTPY_API_TIMEOUT": "30.5", - } - - # Store original values to restore later - original_values = {} - for key in test_env_vars: - original_values[key] = os.environ.get(key) - os.environ[key] = test_env_vars[key] - - try: - config = ConfigManager() - - # Check that environment variables were loaded - assert ( - config.config["data"]["cache_size"] == 1000 - ) # String converted to int - assert ( - config.config["backtest"]["commission"] == 0.005 - ) # String converted to float - assert config.config["logging"]["debug"] is True # String converted to bool - assert ( - config.config["test"]["section_enabled"] is False - ) # String converted to bool - assert config.config["api"]["timeout"] == 30.5 # String converted to float - - finally: - # Restore original environment - for key, original_value in original_values.items(): - if original_value is None: - os.environ.pop(key, None) - else: - os.environ[key] = original_value - - def test_get_method_dot_notation(self): - """Test get method with dot notation.""" - config = ConfigManager() - - # Test existing values - assert config.get("data.default_interval") == "1d" - assert config.get("backtest.initial_capital") == 10000 - assert config.get("logging.level") == "INFO" - - # Test nonexistent values - assert config.get("nonexistent.key") is None - assert config.get("data.nonexistent") is None - - # Test with default values - assert config.get("nonexistent.key", "default_value") == "default_value" - assert config.get("data.nonexistent", 42) == 42 - - def test_get_method_root_level(self): - """Test get method for root level keys.""" - config = ConfigManager() - - # Test getting entire sections - data_section = config.get("data") - assert isinstance(data_section, dict) - assert "default_interval" in data_section - - def test_set_method(self): - """Test set method.""" - config = ConfigManager() - - # Set new value in existing section - config.set("data.new_key", "new_value") - assert config.config["data"]["new_key"] == "new_value" - - # Set value in new section - config.set("new_section.test_key", 123) - assert config.config["new_section"]["test_key"] == 123 - - def test_save_to_json_file(self): - """Test saving configuration to JSON file.""" - config = ConfigManager() - config.set("test.value", "test_data") - - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: - temp_file = f.name - - try: - config.save_to_file(temp_file) - - # Verify file was saved correctly - with Path(temp_file).open() as f: - saved_config = json.load(f) - - assert saved_config["test"]["value"] == "test_data" - assert "data" in saved_config - assert "backtest" in saved_config - - finally: - Path(temp_file).unlink() - - def test_save_to_yaml_file(self): - """Test saving configuration to YAML file.""" - config = ConfigManager() - config.set("test.yaml_value", "yaml_data") - - with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: - temp_file = f.name - - try: - config.save_to_file(temp_file) - - # Verify file was saved correctly - with Path(temp_file).open() as f: - saved_config = yaml.safe_load(f) - - assert saved_config["test"]["yaml_value"] == "yaml_data" - assert "data" in saved_config - - finally: - Path(temp_file).unlink() - - def test_save_unsupported_format(self): - """Test saving to unsupported file format.""" - config = ConfigManager() - - with pytest.raises(ValueError, match="Unsupported file format"): - config.save_to_file("config.txt") - - def test_nested_dict_update(self): - """Test nested dictionary update functionality.""" - config = ConfigManager() - - # Create test configuration with nested structure - test_config = { - "data": { - "cache_dir": "/new/cache/dir", - "new_nested": {"deep_value": "test"}, - }, - "completely_new": {"setting": "value"}, - } - - config._update_nested_dict(config.config, test_config) - - # Check that nested values were updated correctly - assert config.config["data"]["cache_dir"] == "/new/cache/dir" - assert config.config["data"]["new_nested"]["deep_value"] == "test" - assert config.config["completely_new"]["setting"] == "value" - - # Check that existing values not in update dict are preserved - assert config.config["data"]["default_interval"] == "1d" - assert config.config["backtest"]["initial_capital"] == 10000 - - def test_environment_variable_type_conversion(self): - """Test type conversion for environment variables.""" - test_cases = [ - ("QUANTPY_TEST_BOOL_TRUE", "true", True), - ("QUANTPY_TEST_BOOL_YES", "yes", True), - ("QUANTPY_TEST_BOOL_1", "1", True), - ("QUANTPY_TEST_BOOL_FALSE", "false", False), - ("QUANTPY_TEST_BOOL_NO", "no", False), - ("QUANTPY_TEST_BOOL_0", "0", False), - ("QUANTPY_TEST_INT", "42", 42), - ("QUANTPY_TEST_FLOAT", "3.14", 3.14), - ("QUANTPY_TEST_STRING", "hello", "hello"), - ] - - # Store original values - original_values = {} - for env_var, value, _ in test_cases: - original_values[env_var] = os.environ.get(env_var) - os.environ[env_var] = value - - try: - config = ConfigManager() - - # Check type conversions - assert config.config["test"]["bool_true"] is True - assert config.config["test"]["bool_yes"] is True - assert config.config["test"]["bool_1"] is True - assert config.config["test"]["bool_false"] is False - assert config.config["test"]["bool_no"] is False - assert config.config["test"]["bool_0"] is False - assert config.config["test"]["int"] == 42 - assert config.config["test"]["float"] == 3.14 - assert config.config["test"]["string"] == "hello" - - finally: - # Restore environment - for env_var, _, _ in test_cases: - original_value = original_values[env_var] - if original_value is None: - os.environ.pop(env_var, None) - else: - os.environ[env_var] = original_value - - def test_directory_creation(self): - """Test that necessary directories are created.""" - config = ConfigManager() - - # Check that cache directory path exists - cache_dir = Path(config.config["data"]["cache_dir"]) - assert cache_dir.exists() - - # Check that log directory path exists - log_file = Path(config.config["logging"]["log_file"]) - assert log_file.parent.exists() - - def test_get_with_invalid_path(self): - """Test get method with invalid path structures.""" - config = ConfigManager() - - # Test empty path - assert config.get("") is None - - # Test path that goes through non-dict value - config.set("test.simple_value", "not_a_dict") - assert config.get("test.simple_value.nonexistent") is None - - def test_configuration_persistence(self): - """Test that configuration changes persist through save/load cycle.""" - # Create initial config - config1 = ConfigManager() - config1.set("test.persist_value", "original") - config1.set("new_section.new_key", 999) - - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: - temp_file = f.name - - try: - # Save configuration - config1.save_to_file(temp_file) - - # Load configuration in new instance - config2 = ConfigManager(config_path=temp_file) - - # Verify values persisted - assert config2.config["test"]["persist_value"] == "original" - assert config2.config["new_section"]["new_key"] == 999 - - # Verify defaults are still there - assert config2.config["data"]["default_interval"] == "1d" - - finally: - Path(temp_file).unlink() - - -class TestIntegration: - """Integration tests for ConfigManager.""" - - def test_complete_workflow(self): - """Test complete configuration workflow.""" - # Set up environment variables - os.environ["QUANTPY_API_KEY"] = "test_key_123" - os.environ["QUANTPY_DATA_PROVIDER"] = "yahoo" - - # Create config file - config_data = { - "backtest": {"initial_capital": 100000, "commission": 0.002}, - "strategies": { - "buy_and_hold": {"enabled": True}, - "mean_reversion": {"enabled": False, "window": 20}, - }, - } - - with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: - yaml.dump(config_data, f) - config_file = f.name - - try: - # Initialize with all sources - config = ConfigManager(config_path=config_file) - - # Test that all sources were loaded correctly - - # Defaults - assert config.get("data.default_interval") == "1d" - assert config.get("logging.level") == "INFO" - - # File config - assert config.get("backtest.initial_capital") == 100000 - assert config.get("strategies.buy_and_hold.enabled") is True - assert config.get("strategies.mean_reversion.window") == 20 - - # Environment variables - assert config.get("api.key") == "test_key_123" - assert config.get("data.provider") == "yahoo" - - # Test precedence (env vars should override file config) - os.environ["QUANTPY_BACKTEST_INITIAL_CAPITAL"] = "75000" - config = ConfigManager(config_path=config_file) - assert config.get("backtest.initial_capital") == 75000 - - # Test modification and persistence - config.set("runtime.test_run", True) - - with tempfile.NamedTemporaryFile( - mode="w", suffix=".json", delete=False - ) as f: - output_file = f.name - - config.save_to_file(output_file) - - # Load saved config - config2 = ConfigManager(config_path=output_file) - assert config2.get("runtime.test_run") is True - - Path(output_file).unlink() - - finally: - Path(config_file).unlink() - # Clean up environment - os.environ.pop("QUANTPY_API_KEY", None) - os.environ.pop("QUANTPY_DATA_PROVIDER", None) - os.environ.pop("QUANTPY_BACKTEST_INITIAL_CAPITAL", None) - - def test_complex_nested_configuration(self): - """Test handling of complex nested configurations.""" - complex_config = { - "data_sources": { - "primary": { - "type": "yahoo", - "settings": { - "timeout": 30, - "retries": 3, - "cache": {"enabled": True, "ttl": 3600}, - }, - }, - "secondary": { - "type": "alpha_vantage", - "settings": {"api_key": "demo", "premium": False}, - }, - }, - "strategies": { - "momentum": { - "params": { - "lookback": 252, - "top_n": 10, - "rebalance_freq": "monthly", - } - } - }, - } - - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: - json.dump(complex_config, f) - config_file = f.name - - try: - config = ConfigManager(config_path=config_file) - - # Test deep nested access - assert config.get("data_sources.primary.type") == "yahoo" - assert config.get("data_sources.primary.settings.timeout") == 30 - assert config.get("data_sources.primary.settings.cache.enabled") is True - assert config.get("data_sources.secondary.settings.premium") is False - assert config.get("strategies.momentum.params.lookback") == 252 - - # Test nonexistent deep paths - assert config.get("data_sources.primary.settings.cache.nonexistent") is None - assert ( - config.get("strategies.nonexistent.params.value", "default") - == "default" - ) - - finally: - Path(config_file).unlink() diff --git a/tests/utils/test_config_manager_basic.py b/tests/utils/test_config_manager_basic.py deleted file mode 100644 index bfad933..0000000 --- a/tests/utils/test_config_manager_basic.py +++ /dev/null @@ -1,166 +0,0 @@ -"""Basic tests for config manager.""" - -from __future__ import annotations - -from unittest.mock import mock_open, patch - -import pytest - -from src.utils.config_manager import ConfigManager - - -class TestConfigManagerBasic: - """Basic test cases for ConfigManager class.""" - - def test_init_default(self): - """Test initialization with default parameters.""" - config = ConfigManager() - assert hasattr(config, "config") - assert isinstance(config.config, dict) - - @patch( - "src.utils.config_manager.Path.open", - new_callable=mock_open, - read_data='{"test": "value"}', - ) - @patch("src.utils.config_manager.Path.exists") - def test_load_json_config(self, mock_exists, mock_file): - """Test loading JSON configuration.""" - mock_exists.return_value = True - - config = ConfigManager("test_config.json") - assert config.config.get("test") == "value" - - @patch( - "src.utils.config_manager.Path.open", - new_callable=mock_open, - read_data="test: value\nother: data", - ) - @patch("src.utils.config_manager.Path.exists") - def test_load_yaml_config(self, mock_exists, mock_file): - """Test loading YAML configuration.""" - mock_exists.return_value = True - - with patch("src.utils.config_manager.yaml.safe_load") as mock_yaml: - mock_yaml.return_value = {"test": "value", "other": "data"} - config = ConfigManager("test_config.yaml") - assert config.config.get("test") == "value" - - def test_get_method_basic(self): - """Test basic get method functionality.""" - config = ConfigManager() - config.config = {"test": {"nested": "value"}} - - assert config.get("test.nested") == "value" - assert config.get("nonexistent", "default") == "default" - - def test_set_method_basic(self): - """Test basic set method functionality.""" - config = ConfigManager() - - config.set("test.nested", "value") - assert config.get("test.nested") == "value" - - @patch("src.utils.config_manager.Path.open", new_callable=mock_open) - @patch("src.utils.config_manager.Path.mkdir") - def test_save_json_format(self, mock_mkdir, mock_file): - """Test saving configuration in JSON format.""" - config = ConfigManager() - config.config = {"test": "value"} - - config.save_to_file("test_output.json") - mock_file.assert_called_once() - - @patch("src.utils.config_manager.Path.open", new_callable=mock_open) - @patch("src.utils.config_manager.Path.mkdir") - def test_save_yaml_format(self, mock_mkdir, mock_file): - """Test saving configuration in YAML format.""" - config = ConfigManager() - config.config = {"test": "value"} - - with patch("src.utils.config_manager.yaml.dump") as mock_yaml_dump: - config.save_to_file("test_output.yaml") - mock_yaml_dump.assert_called_once() - - def test_environment_variable_loading(self): - """Test loading configuration from environment variables.""" - with patch.dict("os.environ", {"TEST_CONFIG_VAR": "test_value"}): - config = ConfigManager() - config._load_from_env() - - # Check that environment variables were processed - assert hasattr(config, "config") - - def test_unsupported_file_format(self): - """Test handling of unsupported file formats.""" - config = ConfigManager() - with pytest.raises(ValueError, match="Unsupported file format"): - config.save_to_file("test.txt") - - def test_nested_dict_operations(self): - """Test nested dictionary operations.""" - config = ConfigManager() - - # Test deep nesting - config.set("level1.level2.level3", "deep_value") - assert config.get("level1.level2.level3") == "deep_value" - - # Test overwriting - config.set("level1.level2.level3", "new_value") - assert config.get("level1.level2.level3") == "new_value" - - -class TestConfigManagerIntegration: - """Integration tests for ConfigManager.""" - - @patch( - "src.utils.config_manager.Path.open", - new_callable=mock_open, - read_data='{"app": {"name": "test", "version": "1.0"}}', - ) - @patch("src.utils.config_manager.Path.exists") - def test_complete_workflow(self, mock_exists, mock_file): - """Test complete configuration workflow.""" - mock_exists.return_value = True - - # Load configuration - config = ConfigManager("app_config.json") - - # Read values - app_name = config.get("app.name") - assert app_name == "test" - - # Modify values - config.set("app.version", "2.0") - assert config.get("app.version") == "2.0" - - # Configuration should be ready for saving - assert config.config is not None - - def test_error_handling(self): - """Test error handling in configuration operations.""" - config = ConfigManager() - - # Test accessing non-existent key without default - result = config.get("nonexistent") - assert result is None - - # Test with default value - result = config.get("nonexistent", "default") - assert result == "default" - - def test_type_conversion(self): - """Test automatic type conversion in configuration.""" - config = ConfigManager() - - # Set different types - config.set("string_val", "text") - config.set("int_val", 42) - config.set("bool_val", True) - config.set("list_val", [1, 2, 3]) - - # Verify types are preserved - assert isinstance(config.get("string_val"), str) - assert isinstance(config.get("int_val"), int) - assert isinstance(config.get("bool_val"), bool) - assert isinstance(config.get("list_val"), list) diff --git a/tests/utils/test_csv_exporter_db_fallback.py b/tests/utils/test_csv_exporter_db_fallback.py deleted file mode 100644 index 1f65756..0000000 --- a/tests/utils/test_csv_exporter_db_fallback.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -import types - -from src.utils.csv_exporter import RawDataCSVExporter - - -class _FakeQuery: - def __init__(self, rows): - self._rows = rows - - # chainable API - def filter(self, *a, **k): - return self - - def all(self): - return list(self._rows) - - -def test_db_primary_empty_falls_back_to_unified_models(monkeypatch, tmp_path): - # Chdir for output paths - monkeypatch.chdir(tmp_path) - - # Fake primary DB session returning empty - class _PrimarySession: - def query(self, *_): - return _FakeQuery([]) - - def close(self): - pass - - def fake_get_db_session(): - return _PrimarySession() - - monkeypatch.setattr("src.utils.csv_exporter.get_db_session", fake_get_db_session) - - # Fake unified_models module with one BestStrategy row - class Row: - symbol = "AAPL" - strategy = "BuyHold" - timeframe = "1d" - sortino_ratio = 1.1 - sharpe_ratio = 0.9 - calmar_ratio = 0.8 - total_return = 12.3 - max_drawdown = 5.0 - updated_at = None - - class _UnifiedSession: - def query(self, *_): - return _FakeQuery([Row()]) - - def close(self): - pass - - fake_um = types.SimpleNamespace(Session=lambda: _UnifiedSession(), BestStrategy=Row) - monkeypatch.setitem( - __import__("sys").modules, "src.database.unified_models", fake_um - ) - - exp = RawDataCSVExporter() - files = exp.export_from_database_primary( - quarter="Q3", - year="2025", - export_format="best-strategies", - portfolio_name="Test", - portfolio_path=None, - interval="1d", - ) - - # Should produce a CSV via the fallback path - assert files - assert files[0].endswith(".csv") diff --git a/tests/utils/test_csv_exporter_misc.py b/tests/utils/test_csv_exporter_misc.py deleted file mode 100644 index 510b89b..0000000 --- a/tests/utils/test_csv_exporter_misc.py +++ /dev/null @@ -1,68 +0,0 @@ -from __future__ import annotations - -import csv -from pathlib import Path - -from src.utils.csv_exporter import RawDataCSVExporter - - -def _write_sample_report_html(target: Path) -> None: - target.parent.mkdir(parents=True, exist_ok=True) - html = """ - - - - - - - - -
    SymbolStrategyTimeframeSortino_RatioTotal_Return_Pct
    BTCUSDTMomentum1d1.645.0
    ETHUSDTBuyAndHold1d1.130.0
    - - - """ - target.write_text(html, encoding="utf-8") - - -def test_get_available_columns_contains_core_fields(): - exp = RawDataCSVExporter() - cols = exp.get_available_columns() - # Core identifiers and metrics should be present - assert "Symbol" in cols - assert "Strategy" in cols - assert "Timeframe" in cols - assert "Sortino_Ratio" in cols - assert "Total_Return_Pct" in cols - - -def test_export_from_quarterly_reports_full(tmp_path, monkeypatch): - # Anchor repo CWD and create an example HTML report - monkeypatch.chdir(tmp_path) - report_path = Path("exports") / "reports" / "2025" / "Q2" / "Crypto.html" - _write_sample_report_html(report_path) - - exp = RawDataCSVExporter() - files = exp.export_from_quarterly_reports( - quarter="Q2", - year="2025", - export_format="full", - collection_name="Crypto", - interval="1d", - ) - - assert len(files) == 1 - out = Path(files[0]) - assert out.exists() - # CSV should contain added metadata columns and original metrics - with out.open(newline="") as f: - reader = csv.DictReader(f) - cols = reader.fieldnames or [] - assert "Quarter" in cols - assert "Year" in cols - assert "Export_Date" in cols - assert "Symbol" in cols - assert "Strategy" in cols - assert "Timeframe" in cols - # At least two rows written from our HTML table - rows = list(reader) - assert len(rows) >= 2 diff --git a/tests/utils/test_csv_exporter_parsers.py b/tests/utils/test_csv_exporter_parsers.py deleted file mode 100644 index 8dce34f..0000000 --- a/tests/utils/test_csv_exporter_parsers.py +++ /dev/null @@ -1,31 +0,0 @@ -from __future__ import annotations - -from bs4 import BeautifulSoup - -from src.utils.csv_exporter import RawDataCSVExporter - - -def test_parse_table_row_maps_headers(): - exp = RawDataCSVExporter() - headers = ["Symbol", "Strategy", "Timeframe", "Sortino Ratio", "Total Return %"] - cells = ["AAPL", "BuyHold", "1d", "1.2", "34.5"] - row = exp._parse_table_row(headers, cells) - assert row is not None - assert row["Symbol"] == "AAPL" - assert row["Strategy"] == "BuyHold" - assert row["Timeframe"] == "1d" - assert row["Sortino_Ratio"] == 1.2 - assert row["Total_Return_Pct"] == 34.5 - - -def test_parse_metric_card_extracts_data(): - exp = RawDataCSVExporter() - # Use a symbol that matches the regex in _parse_metric_card (e.g., BTCUSDT) - html = '
    BTCUSDT: 12.3% Strategy: Momentum
    ' - soup = BeautifulSoup(html, "html.parser") - card = soup.find("div") - data = exp._parse_metric_card(card) - assert data is not None - assert data["Symbol"] == "BTCUSDT" - assert data["Strategy"] == "Momentum" - assert data["Total_Return_Pct"] == 12.3 diff --git a/tests/utils/test_csv_exporter_paths.py b/tests/utils/test_csv_exporter_paths.py deleted file mode 100644 index 5942910..0000000 --- a/tests/utils/test_csv_exporter_paths.py +++ /dev/null @@ -1,8 +0,0 @@ -from __future__ import annotations - -from src.utils.csv_exporter import RawDataCSVExporter - - -def test_csv_exporter_default_output_dir(): - exp = RawDataCSVExporter() - assert str(exp.output_dir).endswith("exports/csv") diff --git a/tests/utils/test_csv_exporter_quarterly.py b/tests/utils/test_csv_exporter_quarterly.py deleted file mode 100644 index 4d1df6c..0000000 --- a/tests/utils/test_csv_exporter_quarterly.py +++ /dev/null @@ -1,54 +0,0 @@ -from __future__ import annotations - -import csv -from pathlib import Path - -from src.utils.csv_exporter import RawDataCSVExporter - - -def _write_sample_report_html(target: Path) -> None: - target.parent.mkdir(parents=True, exist_ok=True) - html = """ - - - - - - - - -
    SymbolStrategyTimeframeSortino_RatioTotal_Return_Pct
    AAPLBuyAndHold1d1.225.0
    MSFTMeanRevert1d0.818.0
    - - - """ - target.write_text(html, encoding="utf-8") - - -def test_export_from_quarterly_reports_best_strategies(tmp_path, monkeypatch): - # Ensure exporter reads from repo-relative exports/reports - monkeypatch.chdir(tmp_path) - reports_root = Path("exports") / "reports" / "2025" / "Q3" - _write_sample_report_html(reports_root / "Tech_Portfolio.html") - - exp = RawDataCSVExporter() - files = exp.export_from_quarterly_reports( - quarter="Q3", - year="2025", - export_format="best-strategies", - collection_name="Tech Portfolio", - interval="1d", - ) - - # One CSV file generated in standard location - assert len(files) == 1 - out = Path(files[0]) - assert out.exists() - assert str(out).endswith( - "exports/csv/2025/Q3/Tech_Portfolio_Collection_2025_Q3_1d.csv" - ) - - # Validate basic columns present - with out.open(newline="") as f: - reader = csv.DictReader(f) - cols = reader.fieldnames or [] - assert set(["Asset", "Best Strategy", "Resolution"]).issubset(cols) diff --git a/tests/utils/test_logger.py b/tests/utils/test_logger.py deleted file mode 100644 index 2b89db0..0000000 --- a/tests/utils/test_logger.py +++ /dev/null @@ -1,206 +0,0 @@ -"""Basic tests for logger utility.""" - -from __future__ import annotations - -import logging -from pathlib import Path -from unittest.mock import MagicMock, patch - -import pytest - - -class TestLoggerConfiguration: - """Test cases for logger configuration functions.""" - - def test_setup_logging_function_exists(self): - """Test that setup_command_logging function exists.""" - # Check if the function is available in the module - from src.utils.logger import setup_command_logging - - assert callable(setup_command_logging) - - def test_get_logger_function_exists(self): - """Test that get_logger function exists.""" - # Check if the function is available in the module - from src.utils.logger import get_logger - - assert callable(get_logger) - - @patch("src.utils.logger.logging.basicConfig") - def test_basic_logging_setup(self, mock_basic_config): - """Test basic logging configuration.""" - # Test that logging configuration can be called - logger = logging.getLogger("test") - assert logger is not None - - def test_logger_creation(self): - """Test logger creation.""" - logger = logging.getLogger("test_logger") - assert logger is not None - assert logger.name == "test_logger" - - def test_logger_hierarchy(self): - """Test logger hierarchy.""" - parent_logger = logging.getLogger("parent") - child_logger = logging.getLogger("parent.child") - - assert parent_logger is not None - assert child_logger is not None - assert child_logger.parent == parent_logger - - @patch("src.utils.logger.logging.FileHandler") - def test_file_handler_creation(self, mock_file_handler): - """Test file handler creation.""" - mock_handler = MagicMock() - mock_file_handler.return_value = mock_handler - - # Test basic file handler creation - handler = logging.FileHandler("test.log") - assert handler is not None - - def test_log_levels(self): - """Test different log levels.""" - # Test that all log levels are available - assert hasattr(logging, "DEBUG") - assert hasattr(logging, "INFO") - assert hasattr(logging, "WARNING") - assert hasattr(logging, "ERROR") - assert hasattr(logging, "CRITICAL") - - @patch("src.utils.logger.logging.StreamHandler") - def test_console_handler(self, mock_stream_handler): - """Test console handler creation.""" - mock_handler = MagicMock() - mock_stream_handler.return_value = mock_handler - - # Test console handler - handler = logging.StreamHandler() - assert handler is not None - - def test_logger_formatting(self): - """Test logger formatting.""" - formatter = logging.Formatter( - "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - ) - assert formatter is not None - - # Test that formatter can format a record - record = logging.LogRecord( - name="test", - level=logging.INFO, - pathname="", - lineno=0, - msg="Test message", - args=(), - exc_info=None, - ) - formatted = formatter.format(record) - assert "Test message" in formatted - - def test_logger_filters(self): - """Test logger filters functionality.""" - logger = logging.getLogger("test_filter") - - # Test adding and removing filters - original_filter_count = len(logger.filters) - - class TestFilter: - def filter(self, record): - return True - - test_filter = TestFilter() - logger.addFilter(test_filter) - assert len(logger.filters) == original_filter_count + 1 - - logger.removeFilter(test_filter) - assert len(logger.filters) == original_filter_count - - -class TestLoggerIntegration: - """Integration tests for logger functionality.""" - - def test_complete_logging_workflow(self): - """Test complete logging workflow.""" - # Create logger - logger = logging.getLogger("integration_test") - - # Set level - logger.setLevel(logging.DEBUG) - - # Create handler - handler = logging.StreamHandler() - handler.setLevel(logging.INFO) - - # Create formatter - formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s") - handler.setFormatter(formatter) - - # Add handler to logger - logger.addHandler(handler) - - # Test logging (should not raise exceptions) - try: - logger.debug("Debug message") - logger.info("Info message") - logger.warning("Warning message") - logger.error("Error message") - logger.critical("Critical message") - except Exception as e: - pytest.fail(f"Logging workflow failed: {e}") - - # Clean up - logger.removeHandler(handler) - - def test_multiple_handlers(self): - """Test logger with multiple handlers.""" - logger = logging.getLogger("multi_handler_test") - - # Add multiple handlers - handler1 = logging.StreamHandler() - handler2 = logging.StreamHandler() - - logger.addHandler(handler1) - logger.addHandler(handler2) - - assert len(logger.handlers) >= 2 - - # Clean up - logger.removeHandler(handler1) - logger.removeHandler(handler2) - - @patch("src.utils.logger.Path.mkdir") - def test_log_directory_creation(self, mock_mkdir): - """Test log directory creation.""" - # Test basic path operations - log_path = Path("logs/test.log") - parent_dir = log_path.parent - assert parent_dir.name == "logs" - - def test_configuration_persistence(self): - """Test that logger configuration persists.""" - logger_name = "persistence_test" - - # Configure logger - logger1 = logging.getLogger(logger_name) - logger1.setLevel(logging.WARNING) - - # Get same logger again - logger2 = logging.getLogger(logger_name) - - # Should be the same instance - assert logger1 is logger2 - assert logger2.level == logging.WARNING - - def test_exception_logging(self): - """Test exception logging functionality.""" - logger = logging.getLogger("exception_test") - - try: - # Create an exception for testing - raise ValueError("Test exception") - except ValueError: - # Should not raise an exception when logging - try: - logger.exception("Exception occurred") - except Exception as e: - pytest.fail(f"Exception logging failed: {e}") diff --git a/tests/utils/test_tradingview_alert_exporter.py b/tests/utils/test_tradingview_alert_exporter.py deleted file mode 100644 index c6c07d0..0000000 --- a/tests/utils/test_tradingview_alert_exporter.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Tests for TradingView Alert Exporter utility.""" - -from __future__ import annotations - -from datetime import datetime -from pathlib import Path -from unittest.mock import mock_open, patch - -from src.utils.tv_alert_exporter import TradingViewAlertExporter - - -class TestTradingViewAlertExporter: - """Test cases for TradingViewAlertExporter class.""" - - def test_init_default(self): - """Test initialization with default parameters.""" - exporter = TradingViewAlertExporter() - assert exporter.reports_dir == Path("exports/reports") - - def test_init_custom_reports_dir(self): - """Test initialization with custom reports directory.""" - custom_dir = "custom/reports" - exporter = TradingViewAlertExporter(reports_dir=custom_dir) - assert exporter.reports_dir == Path(custom_dir) - - def test_get_quarter_from_date(self): - """Test quarter calculation from date.""" - exporter = TradingViewAlertExporter() - - # Test Q1 - jan_date = datetime(2023, 1, 15) - year, quarter = exporter.get_quarter_from_date(jan_date) - assert year == 2023 - assert quarter == 1 - - # Test Q2 - may_date = datetime(2023, 5, 10) - year, quarter = exporter.get_quarter_from_date(may_date) - assert year == 2023 - assert quarter == 2 - - @patch("src.utils.tv_alert_exporter.Path.mkdir") - def test_organize_output_path(self, mock_mkdir): - """Test organized output path creation.""" - exporter = TradingViewAlertExporter() - - with patch("src.utils.tv_alert_exporter.datetime") as mock_datetime: - mock_datetime.now.return_value = datetime(2023, 6, 15) - mock_datetime.timezone.utc = datetime.now().tzinfo - - output_path = exporter.organize_output_path("test_base") - - assert "2023" in str(output_path) - assert "Q2" in str(output_path) - mock_mkdir.assert_called_once() - - def test_extract_asset_data_basic(self): - """Test basic asset data extraction.""" - exporter = TradingViewAlertExporter() - - html_content = """ - - - - - -
    SymbolStrategyTimeframe
    AAPLBuyAndHold1D
    - - - """ - - assets = exporter.extract_asset_data(html_content) - assert isinstance(assets, list) - - def test_generate_alert_message(self): - """Test alert message generation.""" - exporter = TradingViewAlertExporter() - - asset_data = { - "symbol": "AAPL", - "strategy": "BuyAndHold", - "timeframe": "1D", - "metrics": {"Sharpe Ratio": "1.2", "Net Profit": "15%", "Win Rate": "65%"}, - } - - alert = exporter.generate_tradingview_alert(asset_data) - - assert isinstance(alert, str) - assert "AAPL" in alert - assert "BuyAndHold" in alert - assert "1D" in alert - - @patch("builtins.open", new_callable=mock_open, read_data="") - @patch("src.utils.tv_alert_exporter.Path.exists") - def test_process_html_file(self, mock_exists, mock_file): - """Test HTML file processing.""" - mock_exists.return_value = True - - exporter = TradingViewAlertExporter() - test_file = Path("test_report.html") - - with patch.object(exporter, "extract_asset_data", return_value=[]): - result = exporter.process_html_file(test_file) - - assert isinstance(result, list) - - @patch("os.walk") - def test_find_html_reports(self, mock_walk): - """Test finding HTML report files.""" - mock_walk.return_value = [ - ("/exports/reports", [], ["report1.html", "report2.html", "data.json"]) - ] - - exporter = TradingViewAlertExporter() - html_files = exporter.find_html_reports() - - assert len(html_files) == 2 - assert all(str(f).endswith(".html") for f in html_files) - - -class TestIntegration: - """Integration tests for the complete workflow.""" - - @patch("os.walk") - @patch("builtins.open", new_callable=mock_open, read_data="") - def test_complete_workflow(self, mock_file, mock_walk): - """Test complete workflow from finding files to processing.""" - mock_walk.return_value = [("/exports/reports", [], ["test_report.html"])] - - exporter = TradingViewAlertExporter() - - # Find HTML files - html_files = exporter.find_html_reports() - assert len(html_files) > 0 - - # Process first file - with patch.object(exporter, "extract_asset_data", return_value=[]): - result = exporter.process_html_file(html_files[0]) - assert isinstance(result, list) - - -# Test comment diff --git a/tools/diagnose_cache.py b/tools/diagnose_cache.py deleted file mode 100644 index f626791..0000000 --- a/tools/diagnose_cache.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python3 -""" -Diagnostic helper to inspect UnifiedCacheManager stats and recent cache entries. -Run inside project root (or inside Docker) to get quick visibility into cache state. -""" - -from __future__ import annotations - -import json - -from src.core.cache_manager import UnifiedCacheManager - - -def main(): - cm = UnifiedCacheManager() - stats = cm.get_cache_stats() - print("Cache stats:") - print(json.dumps(stats, indent=2)) - - # List recent data cache entries - print("\nRecent data cache entries (up to 20):") - entries = cm._find_entries("data") # internal helper - entries_sorted = sorted(entries, key=lambda e: e.last_accessed, reverse=True) - for e in entries_sorted[:20]: - print( - f"- key={e.key} source={e.source} symbol={e.symbol} interval={e.interval} created_at={e.created_at.isoformat()} size_bytes={e.size_bytes}" - ) - - -if __name__ == "__main__": - main()