Introduction to Sampling¶

In financial machine learning, samples are not independent. For the most part, traditional machine learning algorithms assume that samples are IID, in the case of financial machine learning samples are neither identically distributed nor independent. In this section we will tackle the problem of samples dependency.

As you will remember, we mostly label our data sets using the triple-barrier method, as per Advances in Financial Machine Learning. Each label in a triple-barrier event has a label index and a label end time (t1) which corresponds to time when one of barriers were touched.

Note

Underlying Literature

The following sources describe this method in more detail:

Advances in Financial Machine Learning, Chapter 4 by Marcos Lopez de Prado.

This module explores those ideas further and provides implementations for researchers.