Add CWR and ECWR optimization objective functions#548
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TheTiEr wants to merge 2 commits intojesse-ai:masterfrom
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Add CWR and ECWR optimization objective functions#548TheTiEr wants to merge 2 commits intojesse-ai:masterfrom
TheTiEr wants to merge 2 commits intojesse-ai:masterfrom
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Implemented CWR metric that combines annualized returns with R² (coefficient of determination) to measure both profitability and consistency of trading strategies. CWR uses linear regression without intercept to calculate R² from the equity curve, then multiplies it by annualized returns. Changes: - Add consistency_weighted_return() function in metrics.py - Include CWR in backtest metrics alongside existing ratios - Add CWR as selectable objective function in optimization mode - Update config.py to list CWR as available optimization option - Add CWR mapping in Optimize.py for best candidates display CWR is normalized between -0.5 and 2.0 for optimization fitness calculation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
…tive function Implemented ECWR metric that places stronger emphasis on linearity by squaring the R² coefficient. While CWR uses R², ECWR uses R²² to heavily penalize strategies with irregular equity curves, favoring only those with very consistent, linear growth patterns. Formula: ECWR = Annualized Returns × R²² Changes: - Add enhanced_consistency_weighted_return() function in metrics.py - Include ECWR in backtest metrics alongside existing ratios - Add ECWR as selectable objective function in optimization mode - Update config.py to list ECWR as available optimization option - Add ECWR mapping in Optimize.py for best candidates display ECWR is normalized between -0.5 and 1.0 for optimization fitness calculation. This stricter range reflects the squared R² penalty, resulting in lower absolute values compared to CWR. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Summary
This PR introduces two new optimization objective functions that measure both profitability and consistency of trading strategies through linear regression
analysis of the equity curve.
New Objective Functions
CWR (Consistency Weighted Return)
Annualized Returns × R²ECWR (Enhanced Consistency Weighted Return)
Annualized Returns × R²²Both metrics use linear regression without intercept to calculate R² from the equity curve, then multiply by annualized returns.
Changes
consistency_weighted_return()andenhanced_consistency_weighted_return()functions tometrics.pyconfig.pyto list new optionsOptimize.pyfor best candidates displayUse Cases