AI Book Club: Reinforcement Learning for Finance | July 2025

Join events live: https://luma.com/ai-builders-and-lear...

July's book is "Reinforcement Learning for Finance"!

This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.

Feel free to join the discussion even if you have not read the book chapters! :)

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Reinforcement Learning for Finance
Authors: Yves Hilpisch
Published: October 2024
https://learning.oreilly.com/library/...

Chapters:

1. Learning Through Interaction
2. Deep Q-Learning
3. Financial Q-Learning
II. Data Augmentation
4. Simulated Data
5. Generated Data
III. Financial Applications
6. Algorithmic Trading
7. Dynamic Hedging
8. Dynamic Asset Allocation
9. Optimal Execution
10. Concluding Remarks
Book Description
Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research.
This book is among the first to explore the use of reinforcement learning methods in finance.
Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems.
This book covers:

Reinforcement learning
Deep Q-learning
Python implementations of these algorithms
How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation
This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.
Dr. Yves Hilpisch is founder and CEO of The Python Quants, a group that focuses on the use of open source technologies for financial data science, AI, asset management, algorithmic trading, and computational finance.

Learn more about the book here:
https://learning.oreilly.com/library/...