《金融强化学习：以Python为基础的入门指南》(真/零售EPUB)

English | November 19th, 2024 | ISBN: 109816914X | 212 pages | True EPUB (Retail Copy) | 9.58 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.

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.

强化学习（RL）已导致人工智能领域取得多项突破。仅使用Q-learning（DQL）算法，就帮助人们开发出了能以超人类水平玩游戏机游戏和棋盘游戏的代理。最近，RL、DQL以及类似的方法在与金融研究相关的出版物中受到了欢迎。 这本书是首次探索强化学习方法在金融领域的应用的书籍。 作者Yves Hilpisch是The Python Quants的创始人兼CEO，在简洁的方式下提供了你所需的基础知识。机器学习从业者、金融交易者、投资经理、战略家和分析人员将重点关注这些算法的具体实现形式，即以独立的Python代码的形式进行，并应用于重要的金融问题上。 这本书涵盖的内容包括： • 强化学习 • 深度Q-learning • 这些算法的Python实现 • 如何应用这些算法来解决诸如算法交易、动态对冲和动态资产配置等金融问题 这本书是这一主题的最佳参考书。你可以阅读一次，根据自己的需求或想法进行修改，并在处理金融领域中的强化学习时随时查阅它。 Yves Hilpisch博士是The Python Quants的创始人兼CEO，该组织专注于使用开源技术来进行金融数据科学、AI、资产管理、算法交易和计算金融。

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