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Reinforcement Learning for Quantitative Trading

Author

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  • Shuo Sun
  • Rundong Wang
  • Bo An
Abstract
Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques' potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.

Suggested Citation

  • Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    • Handle: RePEc:arx:papers:2109.13851
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    Cited by:

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    2. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
    3. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.
    4. Tian Zhu & Wei Zhu, 2022. "Quantitative Trading through Random Perturbation Q-Network with Nonlinear Transaction Costs," Stats, MDPI, vol. 5(2), pages 1-15, June.
    5. Kim, Seil & Ogawa, Keiichi, 2024. "Who is able or unable to return to school? Exploring the short-term impact of the COVID-19 school closures on students' returning to school in Nigeria," International Journal of Educational Development, Elsevier, vol. 108(C).
    6. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    7. Eduardo C. Garrido-Merch'an & Sol Mora-Figueroa-Cruz-Guzm'an & Mar'ia Coronado-Vaca, 2023. "Deep Reinforcement Learning for ESG financial portfolio management," Papers 2307.09631, arXiv.org.
    8. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
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    10. Mao Guan & Xiao-Yang Liu, 2021. "Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach," Papers 2111.03995, arXiv.org, revised Dec 2021.
    11. Shuo Sun & Molei Qin & Xinrun Wang & Bo An, 2023. "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets," Papers 2302.00586, arXiv.org, revised Mar 2023.

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