Parameter Optimization

Parameter optimization is a crucial aspect of strategy development that helps you find the best configuration for your trading strategies. Planar provides sophisticated optimization tools including grid search, Bayesian optimization, and custom optimization algorithms.

Overview

Parameter optimization in Planar allows you to:

Systematically explore parameter spaces - Test multiple parameter combinations efficiently
Find optimal configurations - Identify parameter values that maximize your objective function
Validate strategy robustness - Ensure strategies perform well across different parameter ranges
Avoid overfitting - Use proper validation techniques to prevent curve fitting

Key Features

Multiple Algorithms - Grid search, random search, Bayesian optimization
Parallel Execution - Leverage multiple CPU cores for faster optimization
Custom Objectives - Define your own optimization metrics
Result Analysis - Comprehensive tools for analyzing optimization results
Visualization - Plot optimization surfaces and parameter relationships

Optimization Workflow

The typical optimization workflow in Planar follows these steps:

Define Parameters - Specify which strategy parameters to optimize
Set Parameter Ranges - Define the search space for each parameter
Choose Algorithm - Select optimization algorithm (grid search, Bayesian, etc.)
Define Objective - Specify the metric to optimize (Sharpe ratio, profit, etc.)
Run Optimization - Execute the optimization process
Analyze Results - Review and validate the optimal parameters

Parameter Definition

Basic Parameter Setup

Define optimizable parameters in your strategy:

Parameter Ranges

Define the search space for optimization:

Advanced Parameter Types

Support for different parameter types:

Optimization Algorithms

Grid Search

Exhaustive search testing all parameter combinations:

Bayesian Optimization

Efficient optimization using probabilistic models:

Random Search

Random sampling of parameter space:

Evolutionary Algorithms

Genetic algorithm-based optimization:

Objective Functions

Built-in Objectives

Planar provides several built-in objective functions:

Custom Objective Functions

Define your own optimization objectives:

Multi-Objective Optimization

Optimize multiple objectives simultaneously:

Optimization Configuration

Basic Configuration

Advanced Configuration

Result Analysis

Accessing Results

Result Visualization

Statistical Analysis

Validation and Overfitting Prevention

Cross-Validation

Walk-Forward Analysis

Out-of-Sample Testing

Performance Optimization

Parallel Processing

Memory Management

Early Stopping

Advanced Techniques

Hierarchical Optimization

Ensemble Optimization

Adaptive Optimization

Best Practices

Parameter Selection

Start Simple - Begin with a few key parameters
Domain Knowledge - Use reasonable parameter ranges based on market knowledge
Correlation Awareness - Avoid highly correlated parameters
Stability Testing - Ensure parameters are stable across different market conditions

Optimization Strategy

Coarse to Fine - Start with coarse grid search, then refine with Bayesian optimization
Multiple Objectives - Consider multiple metrics, not just returns
Robustness Testing - Test parameter sensitivity and stability
Out-of-Sample Validation - Always validate on unseen data

Avoiding Overfitting

Cross-Validation - Use proper time series cross-validation
Parameter Constraints - Apply reasonable bounds to parameters
Regularization - Penalize excessive complexity
Walk-Forward Testing - Simulate realistic trading conditions

Troubleshooting

Common Issues

Slow Optimization
- Enable parallel processing
- Reduce parameter space size
- Use more efficient algorithms (Bayesian vs grid search)
Poor Results
- Check parameter ranges are reasonable
- Verify objective function is appropriate
- Ensure sufficient data for optimization
Overfitting
- Use cross-validation
- Reduce parameter complexity
- Test on out-of-sample data
Memory Issues
- Enable memory-efficient mode
- Reduce batch size
- Use disk caching