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docs/intelligence-services/backtest-engine/README.md

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+# Backtest Engine
+
+## Overview
+The Backtest Engine service provides comprehensive historical simulation capabilities for trading strategies within the stock-bot platform. It enables strategy developers to evaluate performance, risk, and robustness of trading algorithms using historical market data before deploying them to production.
+
+## Key Features
+
+### Simulation Framework
+- **Event-based Processing**: True event-driven simulation of market activities
+- **Vectorized Processing**: High-performance batch processing for speed
+- **Multi-asset Support**: Simultaneous testing across multiple instruments
+- **Historical Market Data**: Access to comprehensive price and volume history
+
+### Performance Analytics
+- **Return Metrics**: CAGR, absolute return, risk-adjusted metrics
+- **Risk Metrics**: Drawdown, volatility, VaR, expected shortfall
+- **Transaction Analysis**: Slippage modeling, fee impact, market impact
+- **Statistical Analysis**: Win rate, profit factor, Sharpe/Sortino ratios
+
+### Realistic Simulation
+- **Order Book Simulation**: Realistic market depth modeling
+- **Latency Modeling**: Simulates execution and market data delays
+- **Fill Probability Models**: Realistic order execution simulation
+- **Market Impact Models**: Adjusts prices based on order sizes
+
+### Development Tools
+- **Parameter Optimization**: Grid search and genetic algorithm optimization
+- **Walk-forward Testing**: Time-based validation with parameter stability
+- **Monte Carlo Analysis**: Probability distribution of outcomes
+- **Sensitivity Analysis**: Impact of parameter changes on performance
+
+## Integration Points
+
+### Upstream Connections
+- Market Data Gateway (for historical data)
+- Feature Store (for historical feature values)
+- Strategy Repository (for strategy definitions)
+
+### Downstream Consumers
+- Strategy Orchestrator (for optimized parameters)
+- Risk Guardian (for risk model validation)
+- Trading Dashboard (for backtest visualization)
+- Strategy Development Environment
+
+## Technical Implementation
+
+### Technology Stack
+- **Runtime**: Node.js with TypeScript
+- **Computation Engine**: Optimized numerical libraries
+- **Storage**: Time-series database for results
+- **Visualization**: Interactive performance charts
+- **Distribution**: Parallel processing for large backtests
+
+### Architecture Pattern
+- Pipeline architecture for data flow
+- Plugin system for custom components
+- Separation of strategy logic from simulation engine
+- Reproducible random state management
+
+## Development Guidelines
+
+### Strategy Development
+- Strategy interface definition
+- Testing harness documentation
+- Performance optimization guidelines
+- Validation requirements
+
+### Simulation Configuration
+- Parameter specification format
+- Simulation control options
+- Market assumption configuration
+- Execution model settings
+
+### Results Analysis
+- Standard metrics calculation
+- Custom metric development
+- Visualization best practices
+- Comparative analysis techniques
+
+## Future Enhancements
+- Agent-based simulation for market microstructure
+- Cloud-based distributed backtesting
+- Real market data replay with tick data
+- Machine learning for parameter optimization
+- Strategy combination and portfolio optimization
+- Enhanced visualization and reporting capabilities