Advanced techniques for parameter tuning, genetic algorithms, and walk-forward analysis to maximize strategy performance while avoiding overfitting.
Test all combinations of parameters in defined ranges. Exhaustive but time-consuming.
Optimize on training period, test on following period, roll forward. Prevents overfitting.
Randomly shuffle trades to test if results are due to luck or skill. Establishes confidence intervals.
Risk-adjusted returns matter more than raw returns. A 20% return with 1.5 Sharpe beats 30% return with 0.8 Sharpe.
Fewer parameters = more robust. A 2-parameter strategy that works across symbols beats a 10-parameter strategy that's perfect on one symbol.
Optimize on 2020-2022, test on 2023. Then optimize on 2021-2023, test on 2024. Consistency across periods proves robustness.
