Overfitting (Curve-Fitting)

Also known as: curve fitting, curve-fitting

Overfitting is fitting a strategy to the noise in historical data rather than a real signal — so it looks perfect in-sample and falls apart out-of-sample. The more parameters and rules you add, the worse it gets. Defences: simplicity, out-of-sample testing, and a real economic reason the edge works.