
Machine Learning for Algorithmic Trading
by Stefan Jansen (2020)
Like 'Advances in Financial Machine Learning', this book offers practical ML applications for trading strategies.

by Marcos López de Prado (2018)
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
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by Stefan Jansen (2020)
Like 'Advances in Financial Machine Learning', this book offers practical ML applications for trading strategies.

by Max Kuhn & Kjell Johnson (2019)
This book delves into feature engineering, a core challenge also emphasized in 'Advances in Financial Machine Learning'.

by Ernest P. Chan (2008)
Like 'Advances in Financial Machine Learning', Chan explains how to think about predictive features in trading.

by Marcos López de Prado (2020)
This companion to 'Advances in Financial Machine Learning' further explores feature importance and validation.

by Timothy Masters (2013)
This book, like 'Advances in Financial Machine Learning', focuses on statistically robust methods for financial analysis.
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