What is overfitting in a trading strategy?

What it means

The mechanism is straightforward. Given enough parameters and enough attempts, any dataset can be fitted almost perfectly — and the better the fit to one period, the less likely it is to describe anything general. The resulting backtest looks superb precisely because it is wrong.

Why it matters

It is rarely deliberate. It happens through ordinary iteration: adjust a threshold, the result improves, keep the change. Each individual decision is reasonable and the cumulative effect is a system shaped entirely by one sample of history.

What this changes in practice

Two defences actually work. Hold data back and test once, and prefer fewer parameters over more. We hit this directly — an earlier version scaled reward-to-risk by market regime, which improved in-sample results and turned out to be fitting noise. Removing the feature and using a single 2.0 ratio everywhere made the system better by making it simpler.