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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