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132 lines
5.0 KiB
Markdown
132 lines
5.0 KiB
Markdown
# Backtesting Optimization
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The Backtesting Optimization page provides powerful tools to find optimal trading strategy parameters through systematic testing and analysis.
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## Features
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### 🔧 Parameter Optimization
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- **Grid Search**: Test all combinations of parameter values systematically
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- **Random Search**: Efficiently explore large parameter spaces
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- **Genetic Algorithms**: Evolve parameters using natural selection principles
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- **Bayesian Optimization**: Smart parameter search using probabilistic models
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### 📊 Optimization Targets
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- **Maximize Sharpe Ratio**: Optimize for risk-adjusted returns
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- **Maximize Total P&L**: Focus on absolute profit maximization
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- **Minimize Drawdown**: Prioritize capital preservation
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- **Custom Objectives**: Define multi-objective optimization functions
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### 🎯 Parameter Configuration
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- **Range Definition**: Set min/max values for each parameter
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- **Step Sizes**: Define granularity of parameter search
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- **Constraints**: Apply realistic bounds and relationships
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- **Parameter Groups**: Test correlated parameters together
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### 📈 Results Analysis
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- **3D Surface Plots**: Visualize parameter interactions
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- **Heatmaps**: Identify optimal parameter regions
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- **Parallel Coordinates**: Explore high-dimensional results
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- **Performance Rankings**: Compare top parameter combinations
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## Usage Instructions
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### 1. Select Base Strategy
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- Choose the strategy to optimize from available backtests
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- Load the baseline configuration as starting point
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- Review historical performance metrics
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### 2. Define Parameter Space
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- **Select Parameters**: Choose which parameters to optimize
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- **Set Ranges**: Define minimum and maximum values
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- Spreads: 0.1% - 5.0%
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- Order amounts: 10% - 100%
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- Risk limits: 0.5% - 10%
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- **Configure Steps**: Set increment sizes for each parameter
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### 3. Configure Optimization
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- **Algorithm**: Select optimization method
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- Grid Search: Complete but computationally intensive
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- Random Search: Good for initial exploration
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- Bayesian: Efficient for expensive evaluations
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- **Objective Function**: Choose what to optimize
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- **Constraints**: Set practical limitations
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- **Iterations**: Define search budget
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### 4. Run Optimization
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- Review estimated runtime and resource usage
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- Start optimization process
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- Monitor real-time progress and intermediate results
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- Pause/resume long-running optimizations
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### 5. Analyze Results
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- View top performing parameter sets
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- Explore parameter sensitivity analysis
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- Export optimal configurations
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- Create ensemble strategies from top performers
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## Technical Notes
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### Optimization Engine
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- **Parallel Processing**: Multiple backtests run simultaneously
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- **Distributed Computing**: Leverage multiple CPU cores
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- **Memory Management**: Efficient handling of large result sets
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- **Checkpointing**: Save progress for long optimizations
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### Search Algorithms
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- **Grid Search**: Exhaustive search with deterministic coverage
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- **Random Search**: Monte Carlo sampling with proven efficiency
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- **Bayesian Optimization**: Gaussian Process regression for smart search
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- **Genetic Algorithms**: Population-based evolutionary optimization
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### Performance Metrics
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- **Primary Metrics**: Sharpe ratio, total return, maximum drawdown
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- **Risk Metrics**: VaR, CVaR, Sortino ratio, Calmar ratio
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- **Trade Metrics**: Win rate, profit factor, average trade P&L
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- **Stability Metrics**: Return consistency, strategy robustness
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## Component Structure
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```
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optimize/
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├── optimize.py # Main optimization interface
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├── engines/
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│ ├── grid_search.py # Grid search implementation
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│ ├── random_search.py # Random search algorithm
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│ ├── bayesian.py # Bayesian optimization
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│ └── genetic.py # Genetic algorithm
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├── objectives/
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│ ├── metrics.py # Objective function definitions
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│ └── constraints.py # Constraint handling
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└── visualization/
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├── surfaces.py # 3D parameter surfaces
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├── heatmaps.py # 2D optimization heatmaps
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└── parallel_coords.py # Multi-dimensional plots
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```
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## Best Practices
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### Parameter Selection
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- Start with 2-3 most impactful parameters
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- Use domain knowledge to set reasonable ranges
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- Consider parameter interactions and dependencies
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- Validate results with out-of-sample data
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### Optimization Strategy
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- Begin with coarse grid search for exploration
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- Refine with Bayesian optimization
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- Validate top results with extended backtests
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- Test robustness with walk-forward analysis
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### Resource Management
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- Estimate computational requirements upfront
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- Use random search for high-dimensional spaces
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- Implement early stopping for poor performers
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- Save intermediate results frequently
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## Error Handling
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The optimization page includes comprehensive error handling:
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- **Parameter Validation**: Ensures valid parameter ranges and relationships
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- **Resource Limits**: Prevents system overload with job queuing
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- **Convergence Detection**: Identifies when optimization plateaus
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- **Result Validation**: Checks for numerical stability and outliers |