数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (1): 35-44.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.01.004

doi: 10.11871/jfdc.issn.2096-742X.2026.01.004

• 专刊：计算金融 • 上一篇    下一篇

基于强化学习方法的股票交易算法分析

廖禹铭1(),卢宇彤1,2,*()   

  1. 1.中山大学，计算机学院，广东 广州 510006
    2.国家超级计算广州中心，广东 广州 510006
  • 收稿日期:2025-02-28 出版日期:2026-02-20 发布日期:2026-02-02
  • 通讯作者: 卢宇彤
  • 作者简介:廖禹铭，中山大学，博士研究生，主要研究方向为高性能金融计算。
    本文中负责文献调研、文献分析与总结归纳。
    LIAO Yuming is a Ph.D. candidate at Sun Yat-sen University. Her current resear
    h focuses on high-performance financial computing.
    In this paper, she is responsible for conducting literature reviews, performing analyses, and summarizing the findings.
    E-mail: liaoym6@mail2.sysu.edu.cn|卢宇彤，中山大学，计算机学院教授、博导，国家超级计算广州中心主任，天河二号副总设计师。目前致力于超算和大数据、人工智能融合创新发展的技术、系统和应用的研究与实现。
    本文中负责把握总体方向与框架。
    LU Yutong is currently a professor and doctoral supervisor at the School of Computer Science and Engineering, Sun Yat-sen University. She is also the Director of the National Supercomputing Center in Guangzhou, and the Deputy Chief Designer of Tianhe-2. Her current research focuses on the integration of supercomputing with big data and artificial intelligence for innovative development.
    In this paper, she is responsible for the overall direction and framework.
    E-mail: yutong.lu@nscc-gz.cn
  • 基金资助:
    广东省基础与应用基础研究重大项目(2019B030302002)

An Analysis of Stock Trading Algorithms Based on Reinforcement Learning Methods

LIAO Yuming1(),LU Yutong1,2,*()   

  1. 1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
    2. National Supercomputer Center in Guangzhou, Guangzhou, Guangdong 510006, China
  • Received:2025-02-28 Online:2026-02-20 Published:2026-02-02
  • Contact: LU Yutong

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摘要：

【目的】股票市场作为计算金融研究的关键领域之一，强化学习技术在该场景中的应用是当今的重要研究课题。【文献范围】本文收集并分析了近年来股票强化学习算法相关的研究文献。【方法】本文系统梳理了智能计算在股票建模领域的核心挑战，讨论了传统统计方法与机器学习模型在股票领域的局限性，剖析了前沿股票强化学习算法。通过对比分析三种典型算法的性能表现，本文从收益稳定性、风险控制和计算效率三个维度进行了实证评估，并结合实验结果深入探讨了各算法的优势与不足。基于研究发现，本文提出了该方向未来的研究建议，为后续研究提供了理论依据和实践参考。【结论】尽管现阶段股票强化学习算法与LSTM、NLP等模型的融合在回测中表现良好，但模型复杂度的提升显著增加了计算的时间开销，降低了其在实时分析场景中的效率。未来研究需要在高效算法设计与硬件适配优化两方面协同推进，以解决当前存在的局限性。

关键词: 强化学习, 股票交易算法, 高性能计算

Abstract:

[Objective] Stock trading represents a critical topic within the field of finance and has garnered significant attention for the integration of reinforcement learning models into trading algorithms. [Coverage] This paper collects literature related to Reinforcement Learning algorithms designed for stock trading in recent years. [Methods] This paper systematically outlines the core challenges of intelligent computing in stock market modeling, discusses the limitations of traditional statistical methods and machine learning models in the stock domain, and analyzes state-of-the-art stock reinforcement learning algorithms. By comparing the performance of three representative algorithms, this study conducts an empirical evaluation from three dimensions: return stability, risk control, and computational efficiency. Based on the experimental results, the paper thoroughly examines the strengths and weaknesses of each algorithm. Building on these findings, it proposes future research directions for this field, providing theoretical foundations and practical references for further studies. [Conclusions] Although the integration of stock reinforcement learning algorithms with models such as LSTM and NLP has shown promising performance in backtesting, the increased model complexity has significantly raised computational time costs, reducing their efficiency in real-time analysis scenarios. Future research needs to advance both efficient algorithm design and hardware adaptation optimization to address current limitations.

Key words: reinforcement learning, stock trading, high-performance computing