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85 lines
3.6 KiB
Markdown
85 lines
3.6 KiB
Markdown
# Backtesting Analysis
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The Backtesting Analysis page provides comprehensive tools for analyzing and comparing the performance of your trading strategy backtests.
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## Features
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### 📊 Performance Analysis
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- **Strategy Performance Metrics**: View detailed metrics including total P&L, win rate, Sharpe ratio, and maximum drawdown
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- **Trade-by-Trade Analysis**: Examine individual trades with entry/exit times, prices, and P&L
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- **Performance Visualization**: Interactive charts showing cumulative returns, drawdown periods, and trade distribution
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- **Multi-Backtest Comparison**: Compare performance across multiple backtests side-by-side
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### 📈 Advanced Analytics
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- **Statistical Analysis**: Distribution plots for returns, trade duration, and P&L
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- **Risk Metrics**: Comprehensive risk analysis including VaR, CVaR, and risk-adjusted returns
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- **Market Correlation**: Analyze strategy performance relative to market conditions
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- **Time-based Analysis**: Performance breakdown by hour, day, and month
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### 🔍 Trade Insights
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- **Trade Clustering**: Identify patterns in winning and losing trades
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- **Entry/Exit Analysis**: Evaluate the effectiveness of entry and exit signals
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- **Position Sizing**: Analyze the impact of position sizes on overall performance
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- **Fee Impact**: Understand how trading fees affect profitability
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## Usage Instructions
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### 1. Select Backtests
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- Choose one or more completed backtests from the dropdown menu
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- Filter backtests by date range, strategy type, or performance metrics
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- Load historical backtests from saved results
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### 2. Configure Analysis
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- Select the metrics and visualizations you want to display
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- Set date ranges for focused analysis
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- Choose comparison benchmarks (e.g., buy-and-hold, market indices)
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### 3. Analyze Results
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- Review performance summary cards showing key metrics
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- Explore interactive charts by zooming, panning, and hovering for details
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- Export analysis results as reports (PDF/CSV)
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- Save analysis configurations for future use
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### 4. Compare Strategies
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- Add multiple backtests to the comparison view
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- Align backtests by date for fair comparison
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- Identify which strategies perform best under different market conditions
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## Technical Notes
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### Data Processing
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- Backtesting results are loaded from the backend storage system
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- Large datasets are processed incrementally for optimal performance
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- Caching is implemented for frequently accessed analysis results
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### Visualization Components
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- **Plotly**: Interactive charts with zoom, pan, and export capabilities
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- **Pandas**: Efficient data manipulation and statistical calculations
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- **NumPy**: High-performance numerical computations
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### Performance Considerations
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- Analysis of large backtests (>10,000 trades) may take several seconds
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- Charts are rendered progressively to maintain UI responsiveness
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- Memory usage is optimized through data chunking
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## Component Structure
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```
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analyze/
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├── analyze.py # Main page application
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├── components/
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│ ├── metrics.py # Performance metric calculations
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│ ├── charts.py # Visualization components
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│ └── comparison.py # Multi-backtest comparison tools
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└── utils/
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├── data_loader.py # Backtest data loading utilities
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└── statistics.py # Statistical analysis functions
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```
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## Error Handling
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The analysis page includes robust error handling for:
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- **Missing Data**: Graceful handling when backtest data is incomplete
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- **Calculation Errors**: Safe fallbacks for metric calculations
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- **Memory Limits**: Automatic data sampling for very large datasets
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- **Visualization Errors**: Alternative displays when charts fail to render |