Learn Algo Trading in 2023 With Some of the Best Algorithmic Trading Books


Knowledge and experience go hand in hand in all human endeavors, and while the two cannot be substituted for each other, both are imperative when trying to achieve success. For anyone interested in learning more about how successful is algorithmic trading, or just wanted to learn about trading algorithms in general, these books can help establish a baseline knowledge.

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  1. Algorithmic Trading: Winning Strategies and Their Rationale by Ernie Chan

Ernie Chan is a seasoned quantitative trader, and provides insights into the world of algorithmic trading. This book goes over algorithmic trading strategies and their rationales, offering practical examples and code snippets. It’s an excellent resource for both beginners and experienced traders hoping to enhance their algorithmic trading skills.

  1. Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest P. Chan

In this book, Chan guides readers through the process of building and implementing algorithmic trading strategies, covering everything from strategy development to risk management. The book is suitable for those interested in not just trading algorithms but also the business aspects of quantitative trading.

  1. Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson

Barry Johnson’s book is a comprehensive guide to algorithmic trading and Direct Market Access, or DMA. This book covers a variety of topics, from market microstructure, to algorithmic trading strategies, to the practical aspects and implementation of DMA.

  1. Python for Finance: Analyze Big Financial Data by Yves Hilpisch

Yves Hilpisch’s book focuses on using Python for financial analysis, making it an invaluable resource for algorithmic traders who prefer Python as their programming language. It covers topics such as time series analysis, risk management, and algorithmic trading strategies using Python for algorithmic trading.

  1. Machine Trading: Deploying Computer Algorithms to Conquer the Markets by Ernest P. Chan

Another work by Chan, “Machine Trading,” goes into the intersection of machine learning and algorithmic trading — two distinct technologies, and which not all trading algorithms employ. This book explores the application of machine learning, which is more to do with artificial intelligence, and its related techniques to financial markets, providing practical guidance on building and deploying machine learning-based trading strategies. Today, most trading algorithms are distinct from machine learning and artificial intelligence.

  1. Advances in Financial Machine Learning by Marcos Lopez de Prado

Marcos Lopez de Prado, a leading expert in the field, shares his insights into the application of machine learning in finance. Although the use of machine learning in algorithmic trading essentially turns this technology into artificial intelligence — and not algorithmic trading — this book provides interesting insight into what might be the future of algorithmic investing. However, for now, it is important to note that trading algorithms are not the same technology as artificial intelligence or machine learning, unless the algorithm specifically employs this novel AI technology. This book provides a deep dive into advanced machine learning techniques tailored for financial markets, making it an essential read for those looking to leverage novel and cutting-edge technology in their algorithmic trading strategies.

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Jeff Sekinger

Founder & CEO, Nurp LLC

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