Resources

Machine Learning for Algorithmic Trading - Stefan Jansen

This book explains how different machine learning techniques can be applied to algo-trading. It doesn’t focus on only a single technique like deep learning, but touches many techniques instead. For example, it has some good content for Bayesian machine learning.

Because of the all the different techniques described, this book is over 800 pages long. If you want to start using machine learning in your strategies, this book is a must-have in my honest opinion. One thing to keep in mind is that because machine learning is progressing so fast, some libraries and examples are outdated.

Advances in Financial Machine Learning - Marcos Lopez de Prado

This book has several innovative ideas about different approaches to algo-trading. And although the code-snippets that are included are written in Python, the underlying theory is applicable to any implementation. Also, this book contains good information about how easy it is for your approach to developing look-ahead bias without realizing it.

Marcos Lopez de Prado also has some good YouTube videos where he explains much of the same theory. So check those out first if you are not sure yet about buying this book.

Trading Systems - Emilio Tomasini

A good book for a beginner that has plenty of pragmatic tips how to build, test, optimise and implement robustness trading systems. There is some AmiBroker code included, but even without understanding this, you can still follow along.

If you are new to statistics, this book also has some good introduction on how to deal with forecasts in the context of uncertainty.

Automated Stock Trading Systems - Laurens Bensdorp

Easy to understand and follow with extra attention on how to diversify your portfolio using different strategies. The book also describes 7 concrete strategies and the rules that need to be implemented for each of these strategies. The strategies are not hard to understand and are a mix of trend-following and mean-reversion.

This book doesn’t contain any code snippets. But I would argue implementing some strategies yourself from scratch, is a great learning experience.

Like many other "Amazon printed" books, the images are not great. But in this case fortunately they are not that important to understand the content.

Systematic Trading - Robert Carver

Robert Carver is a regular guest at the podcast Top Traders Unplugged and has written several good books, of which this is one.

He develops his own algo-trading software (available under open-source) and that shows. There are enough details in the book to actual implement the strategies in your own code. If anything, due to the high density of information, some chapters require multiple reads before grasping everything.

Algorithmic Trading - Ernest P. Chan

This book does not try to sell you trading strategies, but teaches you the methods and underlying discipline to achieve good strategies.

The code snippets included are written in MATLAB. But don’t let that put you off, they are easy to understand, especially if you have some background in math or another computer language. The chapters are divided between mean-reversion and momentum strategies and the last chapter has useful tips on how to approach risk management.

This book is more theoretical than some others books on this list. But that is also a major bonus; it means it will stay relevant longer, and not be outdated as quick as books that focus more on programming using third-party libraries.

Applied Time Series Econometrics - Helmut Lütkepohl

This book provides a great foundation for understanding much of the theory behind time series analysis. It clearly explains commonly used models and important concepts like stationary processes. However, it requires a good knowledge of math at least at an undergraduate level. The examples used in this book cover both macro-economical and investment oriented use-cases.

Besides the great content, there is also a Java GUI project provided at http://jmulti.de that implements various models explained in this book, like an ARIMA model. This Java project is however a bit outdated since the code-base pre-dates 2010.