This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado.  In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and  also meta-labeling which is a technique designed to address several issues.