Название: Reinforcement Learning for Finance: A Python-Based Introduction (Early Release)
Автор: Yves J. Hilpisch
Издательство: O’Reilly Media, Inc.
Год: 2024-03-27
Страниц: 153
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

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.

“Bayesian Learning” discusses Bayesian learning as an example of learning through interaction. “Reinforcement Learning” presents breakthroughs in artificial intelligence that were made possible through reinforcement learning. It also describes the major building blocks of reinforcement learning. “Deep Q-Learning” explains the two major characteristics of deep Q-learning which is the most important algorithm for the remainder of the book. A modern and Python-based discussion of Bayesian statistics is found in Downey.

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.