深度强化学习应用于金融市场量化交易研究综述

许波; 贺一峻; 温健城; 李祥霞

doi:10.11959/j.issn.2096-6652.202439

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深度强化学习应用于金融市场量化交易研究综述

综述与展望 | 更新时间：2025-01-24

- 深度强化学习应用于金融市场量化交易研究综述
- Review: the application of deep reinforcement learning to quantitative trading in financial market
- 智能科学与技术学报 2024年6卷第4期页码：416-428
- 作者机构：
  
  1.广东财经大学信息学院，广东广州 510320
  2.广东省智能商务工程技术研究中心，广东广州 510320
- 作者简介：
  
  [ "许波（1982- ），男，博士，广东财经大学信息学院副教授，CCF高级会员，计算机应用专委会执行委员，主要研究方向为机器学习、多智能体等。" ]
  [ "贺一峻（1998- ），男，广东财经大学信息学院硕士生，CCF学生会员，主要研究方向为机器学习、多智能体。" ]
  [ "温健城（2001- ），男，广东财经大学信息学院硕士生，CCF学生会员，主要研究方向为机器学习、多智能体。" ]
  [ "李祥霞（1988- ），女，博士，广东财经大学信息学院副教授，主要研究方向为机器学习、深度学习等。" ]
- 基金信息：
  
  全国统计科学研究项目(2024LY075);教育部人文社科项目(24YJAZH187);广东省哲学社会科学规划项目(GD24CGL08)
- DOI：10.11959/j.issn.2096-6652.202439
  中图分类号： TP39
- 收稿日期：2024-08-07，
  
  修回日期：2024-10-11，
  
  纸质出版日期：2024-12-15
- 稿件说明：
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许波,贺一峻,温健城等.深度强化学习应用于金融市场量化交易研究综述[J].智能科学与技术学报,2024,06(04):416-428.

XU Bo,HE Yijun,WEN Jiancheng,et al.Review: the application of deep reinforcement learning to quantitative trading in financial market[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):416-428.
许波,贺一峻,温健城等.深度强化学习应用于金融市场量化交易研究综述[J].智能科学与技术学报,2024,06(04):416-428. DOI： 10.11959/j.issn.2096-6652.202439.

XU Bo,HE Yijun,WEN Jiancheng,et al.Review: the application of deep reinforcement learning to quantitative trading in financial market[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):416-428. DOI： 10.11959/j.issn.2096-6652.202439.

摘要

深度强化学习（deep reinforcement learning，DRL）作为一种实现通用人工智能的有效学习范式，已在一系列实际金融市场量化交易应用中取得了显著成果，成为该领域的主流方法。首先，对深度强化学习的基本概念和原理进行了详细介绍，在此基础上，系统地综述了DRL在金融市场量化交易中的应用和实践进展，涵盖了基于策略的算法模型、基于价值的算法模型，以及基于演员—评论家的算法模型等不同类型的DRL在金融市场量化交易中的应用。进一步探讨了DRL在金融市场量化交易中的优势，指出其能够根据市场环境的动态变化调整交易策略，以适应不同的市场环境。其次，指出了DRL在金融市场量化交易中面临的挑战，包括数据质量问题、模型稳定性问题、过拟合问题等。最后，对DRL在金融市场量化交易领域未来的发展趋势进行了展望。随着算法的不断优化和计算能力的提升，DRL将在金融市场量化交易领域发挥更加重要的作用，为投资决策提供更加准确和可靠的支持。

Abstract

As an effective learning paradigm to realize general artificial intelligence

deep reinforcement learning (DRL) has achieved significant results in a series of practical quantitative trading applications in financial market

becoming the mainstream method in this field. Firstly

a detailed introduction to the basic concepts and principles of deep reinforcement learning were provided. On this basis

a systematic review was conducted on the application and practical progress of DRL in quantitative trading in financial market

covering the application of different types of DRL

such as policy-based algorithm models

value-based algorithm models

and actor-critic algorithm models in quantitative trading in financial market. The advantages of DRL in quantitative trading in financial market were further explored

pointing out DRL could adjust trading strategies based on dynamic changes in the market environments to adapt to different market environments. Secondly

the challenges faced by DRL in quantitative trading in financial market were pointed out

including data quality issues

model stability issues

overfitting issues

etc. Finally

we outlooked the future development trend of DRL in the field of quantitative trading in financial market. It is believed that with the continuous optimization of algorithms and the improvement of computing power

DRL will play a more important role in the field of quantitative trading in financial market

providing more accurate and reliable support for investment decisions.

关键词

Keywords

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