A lot has been written about Deep Learning in Quantitative Finance, Trading, and Asset Management. Don’t waste your time with fluff, however: these are the books to read.

Advances in Financial Machine Learning by Marcos López de Prado

The more technical of the two books by Lopez de Prado.

Target audience

• The author states that the target audience is investment professionals with a strong machine learning background.
• In addition, it’s written for data scientists with experience implementing machine learning outside finance.

Why this book

• The book delves into the application of machine learning in the finance industry, presenting the reader with a new approach to financial markets and modelling.
• It provides insights into the use of machine learning algorithms for trading, as well as understanding the complexities of financial markets.
• It covers financial-specific machine learning techniques, including labelling financial data, feature engineering, and methods to prevent overfitting in financial models.
• The author promotes a research-driven approach to develop financial strategies using machine learning, providing real-world examples and practical methods to develop and test predictive models.
• The book also explores how advanced machine learning can help mitigate the issues of data snooping, non-stationary environments, and other unique characteristics of financial markets.

Machine Learning for Asset Managers by Marcos López de Prado

Much shorter and less technical than Lopez de Prado’s first book (see above).

Target Audience

• The book is intended for people working in asset management.
• It’s a very practical guide, meant to ultimately enhance returns from whatever trading strategies they use.

Why this book

• This book is relatively easy to read, i.e. not overly technical
• It gives you a comprehensive introduction to machine learning techniques, including deep learning
• It has a very practical focus

Deep Learning in Finance by Paul Bilokon, Matthew F. Dixon, and Igor Halperin

• A very solid overview of the application of Deep Learning in Quantitative Finance.
• This is a technical book, with lots of detail.

Target Audience

This book is for both advanced graduate students and researchers, as well as practitioners with the prerequisite skills.

Why this book

• This book is intended as a bridge from “traditional” econometrics into the field of quantitative finance
• it’s written for use in Quantitative Finance, keeping in mind the specific requirements within this field
• There is Python code for each chapter: https://github.com/mfrdixon/MLFinanceCodes

Hands-On Machine Learning for Algorithmic Trading by Stefan Jansen.

• This book is next on my list. I cannot give you hands-on comments, but it seems promising.
• This is a practical book, with code examples available here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading
• The author was recently featured on Macro Hive, one of my favourite podcasts:
• https://macrohive.com/hive-podcasts/ep-164-stefan-jansen-on-how-to-use-chatgpt-and-ai-in-finance/

Summary

I very much like and appreciate the books listed above. They are well worth the time and effort for anyone interested in applying Deep Learning in Quantitative Finance.