Trading in financial markets can be a challenging and complex endeavour, with ever-changing conditions and numerous factors to consider. With markets becoming increasingly competitive all the time, it is a never ending struggle to stay ahead of the curve. Machine learning (ML) has made several advances in recent years, particularly by becoming more accessible. One might think then why not use ML models in markets to challenge more traditional ways of trading? Well the answer is, unfortunately, that it is not so simple.

Recently, I got my copy of Advances in Financial Machine Learning by Marcos Lopez de Prado. Lopez de Prado is a renowned quant researcher who has managed billions throughout his career. The book is an amazing resource to anyone interested in data science and finance, and it offers valuable insights into how advanced predictive techniques are applied to financial problems. This post is the first of a series dedicated to applying the approaches introduced by Lopez de Prado to real (and occasionally, synthetic) datasets. My hope is that by writing these posts I can solidify my understanding of the material and share some lessons learned along the way. Without further ado, let's proceed to the main subject of this post: bars.

The most challenging part of trading is to decide when to exit a position. This EPAT Project could help you predict when to exit a BUY/ SELL position ie. in predicting SL/TP signal without human intervention, by using Machine Learning. This article is the final project submitted by the authors as a part of their coursework in the Executive Programme in Algorithmic Trading (EPAT) at QuantInsti . Do check our Projects page and have a look at what our students are building. Sunanda Balla is a senior data scientist building algorithmic trading models using machine learning.