Complete AGI Trading Platform: trading engine, DataOps, infrastructure, tests, CI/CD, docs

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PR #62 shipped the AI layer (AGI orchestrator, vertical/synthetic/quantum AI, ops frameworks) but left the execution and data plumbing unimplemented. This PR adds everything needed to run the platform end-to-end.

Shared layer (shared/)

• Pydantic v2 models: Order, Position, Portfolio, OHLCV, OrderBook, Ticker, Trade, TradingSignal, RiskAssessment, AGIDecision
• config.py: pydantic-settings with TRADING_* env vars + YAML overlay via get_config()
• logger.py: loguru with rotating file sink and async correlation ID propagation
• exceptions.py: domain exception hierarchy (OrderRejectedError, RiskLimitError, etc.)
• utils.py: retry decorator, RateLimiter (token bucket), Timer

Trading engine (trading_engine/)

• BaseConnector ABC — uniform interface across exchanges
• AlpacaConnector (paper mode default) and BinanceConnector (testnet default)
• OrderManager — async order registry; tracks PENDING → SUBMITTED → ACCEPTED → FILLED/CANCELLED
• RiskEngine — pre-trade checks: order notional, position concentration, portfolio drawdown, daily loss limit
• PortfolioManager — asyncio.Lock-protected fill application and mark-to-market P&L

assessment = await risk_engine.evaluate(order, portfolio)
if assessment.is_approved:
    exchange_id = await order_manager.submit_order(order)

DataOps (dataops/)

• MarketDataCollector — WebSocket feed → Redis pub/sub, exponential-backoff reconnect
• TimeSeriesDB — asyncpg wrapper for TimescaleDB (OHLCV + trade storage)
• StreamProcessor — aiokafka producer/consumer with per-topic handler dispatch

Infrastructure

• docker-compose.yml: TimescaleDB, Redis, Kafka/ZooKeeper, Prometheus, Grafana
• docker/Dockerfile.trading-engine: Python 3.12-slim, non-root user
• Kubernetes manifests: Deployment + StatefulSet for Postgres, ServiceAccount
• infrastructure/prometheus/prometheus.yml

Root files

• requirements.txt, main.py (FastAPI + /health + /metrics), config.yaml, .env.example, .gitignore

Tests

• 26 unit + integration tests covering OrderManager, RiskEngine, PortfolioManager, and full trade lifecycle

CI/CD

• ci.yml: ruff lint → pytest → Docker build
• cd.yml: staging deploy on main push; production deploy on semver tag

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Original prompt

Complete the AGI Trading Platform by adding the missing critical components from PR #62.

What's Missing from PR #62

The foundation PR #62 implemented the AI components but is missing:

• Trading Engine (exchange connectors, order management)
• DataOps (data ingestion, storage)
• Infrastructure (Docker, Kubernetes)
• Configuration files (main.py, requirements.txt, config.yaml)
• Tests
• Documentation

Implement These Components

1. Trading Engine

Create trading-engine/ directory with:

trading-engine/connectors/base_connector.py - Abstract base class:

from abc import ABC, abstractmethod
from typing import List, Optional
from shared.models.trading_models import Order, Position

class BaseConnector(ABC):
    @abstractmethod
    async def connect(self) -> bool:
        pass
    
    @abstractmethod
    async def place_order(self, order: Order) -> str:
        pass
    
    @abstractmethod
    async def get_positions(self) -> List[Position]:
        pass

trading-engine/connectors/alpaca_connector.py - Alpaca integration with alpaca-py library
trading-engine/connectors/binance_connector.py - Binance integration with python-binance

trading-engine/execution/order_manager.py - Order lifecycle management
trading-engine/risk_management/risk_engine.py - Pre-trade risk checks
trading-engine/portfolio/portfolio_manager.py - Position tracking and P&L

2. DataOps

Create dataops/ directory with:

dataops/ingestion/market_data_collector.py - Real-time market data collection
dataops/storage/time_series_db.py - TimescaleDB wrapper
dataops/processing/stream_processor.py - Kafka stream processing

3. Infrastructure

docker-compose.yml - Full stack with Redis, Postgres, Kafka, Prometheus, Grafana
docker/Dockerfile.trading-engine - Trading engine container
infrastructure/kubernetes/deployments/ - K8s manifests

4. Root Files

requirements.txt - All Python dependencies (torch, transformers, alpaca-py, etc.)
main.py - FastAPI application entry point
config.yaml - Application configuration
.env.example - Environment variables template

5. Tests

tests/unit/test_trading_engine.py - Trading engine unit tests
tests/integration/test_end_to_end.py - Full workflow tests
tests/conftest.py - Pytest fixtures

6. CI/CD

.github/workflows/ci.yml - Lint, test, build pipeline
.github/workflows/cd.yml - Deploy pipeline

7. Documentation

README.md - Comprehensive getting started guide with architecture overview
docs/DEPLOYMENT.md - Deployment instructions
docs/API.md - API documentation

Implementation Guidelines

• Use async/await patterns for I/O operations
• Integrate with existing shared models from PR Implement AGI Trading Platform foundation (partial) #62
• Add comprehensive error handling
• Include type hints and docstrings
• Paper trading mode by default
• Follow existing code style from PR Implement AGI Trading Platform foundation (partial) #62

This pull request was created from Copilot chat.

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[Stacey77]

#63

[Stacey77]

OK

[Stacey77]

DONE