Overfitting

Overfitting happens when a rule set is adjusted repeatedly against the same historical sample until it looks good on that specific data — often by fitting to noise rather than to any real, repeatable pattern. A sign of overfitting is a parameter sweep where performance is very good at one narrow setting and collapses everywhere nearby, or a strategy that performs far worse on out-of-sample data than on the period it was tuned on.