Reinforcement Learning for Finance, A Python-Based Introductio, Hilpisch Y., 2025

Reinforcement Learning for Finance, A Python-Based Introductio, Hilpisch Y., 2025.
     
   This book is intended as a concise, Python-based introduction to the major ideas and elements of RL and DQL as applied to finance. It should be useful to both students and academics as well as to practitioners in search of alternatives to existing financial theories and algorithms. The book expects basic knowledge of the Python programming language, object-oriented programming, and the major Python packages used in data science and machine learning, such as NumPy, pandas, matplotlib, scikit-learn, and TensorFlow.




Probability Matching.
Koehler and James (2014) report results from studies analyzing probability matching, utility maximization, and other types of decision strategies. The studies include a total of 1,557 university students. The researchers find that probability matching is the most frequent strategy chosen or a close second to the utility maximizing strategy.

The researchers also find that the utility maximizing strategy is chosen in general by the “most cognitively able participants.” They approximate cognitive ability through Scholastic Aptitude Test (SAT) scores, Mathematics Experience Composite scores, and the number of university statistics courses taken.

As is often the case in decision making, human beings might need formal training and experience to overcome urges and behaviors that feel natural to achieve optimal results.

CONTENTS.
Preface.
I. The Basics.
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.
Index.
About the Author.



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