数据与计算发展前沿2026，Vol.8Issue(1)：35-44,10.DOI:10.11871/jfdc.issn.2096-742X.2026.01.004
基于强化学习方法的股票交易算法分析
An Analysis of Stock Trading Algorithms Based on Reinforcement Learning Methods
摘要
Abstract
[Objective]Stock trading represents a critical topic within the field of finance and has gar-nered significant attention for the integration of reinforcement learning models into trading al-gorithms.[Coverage]This paper collects literature related to Reinforcement Learning algo-rithms 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 analyz-es state-of-the-art stock reinforcement learning algorithms.By comparing the performance of three representative algorithms,this study conducts an empirical evaluation from three dimen-sions:return stability,risk control,and computational efficiency.Based on the experimental re-sults,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 re-search needs to advance both efficient algorithm design and hardware adaptation optimization to address current limitations.关键词
强化学习/股票交易算法/高性能计算Key words
reinforcement learning/stock trading/high-performance computing引用本文复制引用
廖禹铭,卢宇彤..基于强化学习方法的股票交易算法分析[J].数据与计算发展前沿,2026,8(1):35-44,10.基金项目
广东省基础与应用基础研究重大项目(2019B030302002) （2019B030302002）
