We delve into the challenge of making an asset price series stationary (for reasons discussed below) and preserving as much memory/signal from the original series.
Posts
In this project we explore an example of applying meta labeling to high quality S&P500 EMini Futures data and create an open-source python package (mlfinlab) that is based on the work of Dr. Marcos Lopez de Prado in his book ‘Advances in Financial Machine Learning’.
This blog post investigates the idea of Meta Labeling and tries to help build an intuition for what is taking place.
MLFinLab is a “living and breathing” project in the sense that it is continually enhanced with new code from the chapters in the Advanced Financial Machine Learning book.
This weeks research was consumed by the concept of Meta-Labeling, how it works, and does it work out-of-sample? We have published a research report as well as an accompanying slide show.
First of all we want to thank everyone who has reached out to us with ideas and contributions to our package. Without all of your help, none of this would be possible.