Hands-On Machine Learning for Algorithmic Trading

Challenges with cross-validation in finance

A key assumption for the cross-validation methods discussed so far is the independent and identical (iid) distribution of the samples available for training.

For financial data, this is often not the case. On the contrary, financial data is neither independently nor identically distributed because of serial correlation and time-varying standard deviation, also known as heteroskedasticity (see the next two chapters for more details). The TimeSeriesSplit in the sklearn.model_selection module aims to address the linear order of time-series data.