Avoid Overfitting in Optimization

Optimization can destroy a strategy if you chase perfect historical results. Focus on robustness instead.

Limit parameter ranges

Keep parameter grids tight and based on market intuition, not random search.

Use out-of-sample tests

Reserve recent data for final validation. Never optimize on the entire dataset.

Evaluate stability

A good strategy performs well across nearby parameter values, not a single sweet spot.

Prefer simpler rules

Complex models tend to overfit. If two variants perform similarly, choose the simpler one.

Monitor live drift

Once deployed, compare expected vs. actual performance and adjust carefully.
Optimization should improve resilience, not just historical profit curves.