Backtest Over-Optimization

Backtest over-optimization occurs when a strategy is excessively fine-tuned to historical data to achieve the best possible performance. This process often involves tweaking parameters like moving average lengths, stop-loss levels, or entry triggers until the backtest results look perfect.

While the backtest might show massive profits, the strategy is essentially "hard-coded" to past events. Because markets are dynamic and rarely repeat exactly, this over-optimized strategy will fail to adapt to new market conditions.

It is one of the most common reasons why traders lose money after a successful backtest. True robustness requires a balance between performance and simplicity, avoiding the trap of chasing the best historical fit.