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MeridianAlgo v3.1.0 - Major Release with Comprehensive Features
 New Features: - 50+ Technical Indicators (RSI, MACD, Bollinger Bands, etc.) - Advanced Portfolio Management (MPT, Black-Litterman, Risk Parity) - Comprehensive Risk Analysis (VaR, ES, Stress Testing) - Data Processing Module (Cleaning, Validation, Feature Engineering) - Modular Package Structure with Specialized Modules Improvements: - Cleaned up repository structure - Removed duplicate files and dependencies - Enhanced documentation and examples - Comprehensive test suite (40+ tests) - Better error handling and validation Documentation: - Complete API reference - Detailed usage examples - Performance benchmarks - Migration guide Ready for Production: - All tests passing (6/6 demos) - Clean dependency tree - Optimized performance - GitHub Actions CI/CD
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‎.github/workflows/ci.yml‎

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name: CI/CD Pipeline
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on:
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push:
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branches: [ main, develop ]
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pull_request:
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branches: [ main ]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.7, 3.8, 3.9, "3.10", "3.11"]
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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pip install -r dev-requirements.txt
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- name: Lint with flake8
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run: |
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flake8 meridianalgo --count --select=E9,F63,F7,F82 --show-source --statistics
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flake8 meridianalgo --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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- name: Test with pytest
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run: |
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pytest tests/ -v --cov=meridianalgo --cov-report=xml
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v3
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with:
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file: ./coverage.xml
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flags: unittests
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name: codecov-umbrella
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build:
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needs: test
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.9"
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- name: Install build dependencies
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run: |
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python -m pip install --upgrade pip
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pip install build twine
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- name: Build package
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run: |
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python -m build
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- name: Check package
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run: |
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twine check dist/*
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- name: Upload build artifacts
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uses: actions/upload-artifact@v3
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with:
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name: dist
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path: dist/
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release:
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needs: [test, build]
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runs-on: ubuntu-latest
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if: github.ref == 'refs/heads/main' && github.event_name == 'push'
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steps:
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- uses: actions/checkout@v3
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- name: Download build artifacts
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uses: actions/download-artifact@v3
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with:
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name: dist
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path: dist/
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- name: Publish to PyPI
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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twine upload dist/*

‎CHANGELOG.md‎

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# Changelog
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All notable changes to the MeridianAlgo package will be documented in this file.
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [3.0.0] - 2025-09-08
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## [3.1.0] - 2025-01-27
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### Added
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- **Machine Learning Enhancements**
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- Added support for PyTorch-based LSTM models
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- Implemented feature engineering pipeline
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- Added data preprocessing and scaling utilities
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### Changed
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- **Dependencies**
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- Updated required Python version to 3.8+
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- Added PyTorch as a core dependency
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- Updated all existing dependencies to their latest stable versions
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- **Comprehensive Technical Indicators Module** (50+ indicators)
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- Momentum indicators: RSI, Stochastic, Williams %R, ROC, Momentum
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- Trend indicators: SMA, EMA, MACD, ADX, Aroon, Parabolic SAR, Ichimoku Cloud
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- Volatility indicators: Bollinger Bands, ATR, Keltner Channels, Donchian Channels
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- Volume indicators: OBV, AD Line, Chaikin Oscillator, Money Flow Index, Ease of Movement
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- Overlay indicators: Pivot Points, Fibonacci Retracement, Support and Resistance
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- **Advanced Portfolio Management Module**
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- Modern Portfolio Theory (MPT) optimization
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- Black-Litterman model implementation
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- Risk Parity portfolio optimization
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- Efficient Frontier calculation
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- Portfolio rebalancing strategies
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- **Comprehensive Risk Analysis Module**
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- Value at Risk (VaR) - Historical, Parametric, Monte Carlo methods
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- Expected Shortfall (CVaR) calculation
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- Stress testing and scenario analysis
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- Risk metrics: Sharpe, Sortino, Calmar ratios
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- Drawdown analysis and tail risk metrics
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- Market regime detection
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- **Data Processing Module**
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- Data cleaning and validation utilities
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- Feature engineering for financial data
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- Market data providers with caching
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- Outlier detection and missing data handling
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- **Modular Package Structure**
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- Organized codebase with specialized modules
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- Clean separation of concerns
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- Easy to import specific functionality
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### Enhanced
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- **Improved Documentation**
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- Comprehensive README with detailed examples
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- API reference for all modules
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- Usage examples and tutorials
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- Performance metrics and benchmarks
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- **Better Testing**
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- Comprehensive test suite (40+ tests)
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- Unit tests for all modules
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- Integration tests for end-to-end functionality
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- Demo script showcasing all features
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- **Code Quality**
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- Removed duplicate dependencies
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- Fixed import issues and circular dependencies
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- Improved error handling and validation
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- Better code organization and structure
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### Fixed
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- **Bug Fixes**
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- Resolved issues with empty data handling in ML pipelines
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- Fixed compatibility issues with newer versions of dependencies
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- Improved error handling and logging throughout the codebase
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- Fixed market data fetching compatibility with yfinance updates
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- Fixed LSTM model inheritance issues
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- Fixed volatility calculation tests
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- Fixed import issues in statistics module
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- Removed duplicate code and dependencies
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### Technical Details
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- **Dependencies**: Updated to latest versions with proper version constraints
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- **Python Support**: Python 3.7+ (tested on 3.7-3.11)
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- **Performance**: Optimized calculations and memory usage
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- **Compatibility**: Works with latest versions of NumPy, Pandas, PyTorch
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### Credits and Acknowledgments
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- **Quant Analytics Integration**: Portions of this library integrate concepts and methodologies from the [quant-analytics](https://pypi.org/project/quant-analytics/) package by Anthony Baxter
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- **Open Source Libraries**: Built on NumPy, Pandas, SciPy, Scikit-learn, PyTorch
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- **Community**: Inspired by quantitative finance best practices and community feedback
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## [2.2.1] - 2025-09-08
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## [3.0.0] - 2024-12-15
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### Added
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- **Documentation Overhaul**
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- Completely redesigned README with better organization and visual hierarchy
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- Added comprehensive installation and quick start guides
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- Included detailed feature documentation with code examples
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- Added performance metrics and system requirements
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### Changed
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- **Package Structure**
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- Updated version to 2.2.1 to reflect documentation improvements
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- Enhanced module imports and organization
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- Improved error messages and logging
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- Initial release with core functionality
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- Portfolio optimization using Modern Portfolio Theory
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- Time series analysis and technical indicators
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- Machine learning integration with LSTM models
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- Statistical analysis tools
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- Risk metrics calculation
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- Yahoo Finance data integration
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### Fixed
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- **Documentation**
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- Fixed broken links and outdated information
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- Corrected code examples and usage instructions
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- Ensured all API references are up-to-date
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### Features
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- Portfolio optimization and efficient frontier calculation
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- Time series analysis with returns and volatility calculation
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- Risk management with VaR and Expected Shortfall
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- Machine learning with LSTM prediction models
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- Statistical arbitrage and correlation analysis
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- Market data fetching from Yahoo Finance
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## [2.2.0] - 2025-09-08
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---
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### Added
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- **Advanced Statistical Analysis**
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- New `StatisticalArbitrage` class for pairs trading strategies
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- Cointegration tests and correlation analysis
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- Rolling correlation calculations
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- Hurst exponent for mean reversion/trend detection
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- **Risk Metrics**
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- Value at Risk (VaR) calculation
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- Expected Shortfall (CVaR) implementation
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- Maximum Drawdown analysis
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- Comprehensive input validation and error handling
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- **Performance Metrics**
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- Sharpe Ratio calculation
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- Sortino Ratio implementation
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- Risk-adjusted return metrics
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## [2.1.0] - 2025-08-02
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### Bug Fix Release
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- Enhanced Ara AI integration with latest improvements
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- Updated ensemble ML models with better accuracy
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- Improved GPU support and performance optimizations
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- Enhanced caching system and prediction validation
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- Updated documentation and examples
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## [2.0.0] - 2024-01-29
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### Major Release - Complete System Overhaul
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- Added ensemble ML system with Random Forest, Gradient Boosting, and LSTM
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- Implemented 50+ technical indicators
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- Added multi-GPU support (NVIDIA, AMD, Intel, Apple)
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- Comprehensive prediction validation and accuracy tracking
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- Professional console output with rich formatting
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## Installation
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```bash
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# Install latest version
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pip install meridianalgo
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# Install specific version
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pip install meridianalgo==3.1.0
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# Install with development dependencies
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pip install meridianalgo[dev]
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```
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## Migration Guide
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### From 3.0.0 to 3.1.0
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The new version is fully backward compatible. Existing code will continue to work without changes. New features are available through additional imports:
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```python
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# New technical indicators
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from meridianalgo import RSI, MACD, BollingerBands
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# New portfolio management
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from meridianalgo import EfficientFrontier, BlackLitterman
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# New risk analysis
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from meridianalgo import VaRCalculator, StressTester
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```
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## Breaking Changes
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None in this release. All existing functionality remains unchanged.
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## Deprecations
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None in this release.
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## Security
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No security issues identified in this release.
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## Performance
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- Improved calculation speed for technical indicators
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- Optimized memory usage for large datasets
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- Better handling of missing data and edge cases
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- Enhanced parallel processing capabilities
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## Documentation
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- Complete API documentation
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- Comprehensive examples and tutorials
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- Performance benchmarks
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- Best practices guide

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