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