deploy/pages/backtesting/optimize/README.md

Backtesting Optimization

The Backtesting Optimization page provides powerful tools to find optimal trading strategy parameters through systematic testing and analysis.

Features

🔧 Parameter Optimization

• Grid Search: Test all combinations of parameter values systematically
• Random Search: Efficiently explore large parameter spaces
• Genetic Algorithms: Evolve parameters using natural selection principles
• Bayesian Optimization: Smart parameter search using probabilistic models

📊 Optimization Targets

• Maximize Sharpe Ratio: Optimize for risk-adjusted returns
• Maximize Total P&L: Focus on absolute profit maximization
• Minimize Drawdown: Prioritize capital preservation
• Custom Objectives: Define multi-objective optimization functions

🎯 Parameter Configuration

• Range Definition: Set min/max values for each parameter
• Step Sizes: Define granularity of parameter search
• Constraints: Apply realistic bounds and relationships
• Parameter Groups: Test correlated parameters together

📈 Results Analysis

• 3D Surface Plots: Visualize parameter interactions
• Heatmaps: Identify optimal parameter regions
• Parallel Coordinates: Explore high-dimensional results
• Performance Rankings: Compare top parameter combinations

Usage Instructions

1. Select Base Strategy

• Choose the strategy to optimize from available backtests
• Load the baseline configuration as starting point
• Review historical performance metrics

2. Define Parameter Space

• Select Parameters: Choose which parameters to optimize
• Set Ranges: Define minimum and maximum values
  • Spreads: 0.1% - 5.0%
  • Order amounts: 10% - 100%
  • Risk limits: 0.5% - 10%
• Configure Steps: Set increment sizes for each parameter

3. Configure Optimization

• Algorithm: Select optimization method
  • Grid Search: Complete but computationally intensive
  • Random Search: Good for initial exploration
  • Bayesian: Efficient for expensive evaluations
• Objective Function: Choose what to optimize
• Constraints: Set practical limitations
• Iterations: Define search budget

4. Run Optimization

• Review estimated runtime and resource usage
• Start optimization process
• Monitor real-time progress and intermediate results
• Pause/resume long-running optimizations

5. Analyze Results

• View top performing parameter sets
• Explore parameter sensitivity analysis
• Export optimal configurations
• Create ensemble strategies from top performers

Technical Notes

Optimization Engine

• Parallel Processing: Multiple backtests run simultaneously
• Distributed Computing: Leverage multiple CPU cores
• Memory Management: Efficient handling of large result sets
• Checkpointing: Save progress for long optimizations

Search Algorithms

• Grid Search: Exhaustive search with deterministic coverage
• Random Search: Monte Carlo sampling with proven efficiency
• Bayesian Optimization: Gaussian Process regression for smart search
• Genetic Algorithms: Population-based evolutionary optimization

Performance Metrics

• Primary Metrics: Sharpe ratio, total return, maximum drawdown
• Risk Metrics: VaR, CVaR, Sortino ratio, Calmar ratio
• Trade Metrics: Win rate, profit factor, average trade P&L
• Stability Metrics: Return consistency, strategy robustness

Component Structure

optimize/
├── optimize.py              # Main optimization interface
├── engines/
│   ├── grid_search.py      # Grid search implementation
│   ├── random_search.py    # Random search algorithm
│   ├── bayesian.py         # Bayesian optimization
│   └── genetic.py          # Genetic algorithm
├── objectives/
│   ├── metrics.py          # Objective function definitions
│   └── constraints.py      # Constraint handling
└── visualization/
    ├── surfaces.py         # 3D parameter surfaces
    ├── heatmaps.py         # 2D optimization heatmaps
    └── parallel_coords.py  # Multi-dimensional plots

Best Practices

Parameter Selection

• Start with 2-3 most impactful parameters
• Use domain knowledge to set reasonable ranges
• Consider parameter interactions and dependencies
• Validate results with out-of-sample data

Optimization Strategy

• Begin with coarse grid search for exploration
• Refine with Bayesian optimization
• Validate top results with extended backtests
• Test robustness with walk-forward analysis

Resource Management

• Estimate computational requirements upfront
• Use random search for high-dimensional spaces
• Implement early stopping for poor performers
• Save intermediate results frequently

Error Handling

The optimization page includes comprehensive error handling:

• Parameter Validation: Ensures valid parameter ranges and relationships
• Resource Limits: Prevents system overload with job queuing
• Convergence Detection: Identifies when optimization plateaus
• Result Validation: Checks for numerical stability and outliers