Category:Overfitting

Generally speaking, overfitting occurs when out-of-sample (OOS) performance of some predictive system systematically differs from its performance in-sample (IS). It is clear why overfitting (both machine learning or trading definition) is dangerous in trading systems - it makes them unreliable and difficult to follow, not to speak about potential long-term losses. In papers section you may find research related to detection and prevention of overfitting when developing trading systems.
 * From standard machine learning perspective, overfitting happens at the level of complexity where OOS performance starts to degrade as model complexity increases.
 * In trading, they usually call overfitting any situation when OOS performance is worse than IS performance which is not sound theoretically.