Overfitting (curve fitting)

Overfitting is tuning a strategy so closely to historical data that it captures noise rather than a real edge, and fails on new data.

Symptoms include too many parameters, perfect-looking backtests and results that collapse out-of-sample. Guarding against it means simpler rules, out-of-sample testing and walk-forward analysis. A robust strategy works across many instruments and periods.