Recommended Reinforcement Learning Resources for Finance and Trading

Summary

This Chinese-language article curates research papers and books on reinforcement learning (RL) in finance. Its examples span trading and market applications, Q-learning combined with Black–Scholes, option hedging and dynamic replication, portfolio allocation, wealth management, and optimal order execution. It also points readers to foundational and advanced RL texts, practical deep learning books, and broader machine learning resources for finance.

The article is a reading list rather than a technical tutorial or evaluation of methods. It gives brief descriptions of the listed works but provides no experiments, comparative evidence, or implementation guidance for judging whether an RL approach will work in a particular market. Some entries appear duplicated between the research and book sections, and its general claims about RL should not be treated as demonstrated trading results. The list can help readers map topics and find starting points for further study.

Key ideas

• The suggested research covers RL applications in trading, derivatives, portfolio management, and execution.
• Several listed works connect Q-learning or deep learning with option pricing and hedging.
• The recommended books range from RL fundamentals to finance-focused machine learning and practical deep RL.
• The article summarizes resources but does not test or compare their methods.
• The descriptions are brief, and some recommendations are repeated across sections.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.