Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (7): 276-285.DOI: 10.3778/j.issn.1002-8331.2109-0507

• Engineering and Applications • Previous Articles     Next Articles

Research on Financial Trading Algorithm Based on Deep Reinforcement Learning

XU Jie, ZHU Yukun, XING Chunxiao   

  1. 1.PBC School of Finance, Tsinghua University, Beijing 100084, China
    2.Beijing National Research Center for Information Science and Technology(BNRist), Tsinghua University, Beijing 100084, China
  • Online:2022-04-01 Published:2022-04-01



  1. 1.清华大学 五道口金融学院,北京 100084 
    2.清华大学 北京信息科学与技术国家研究中心,北京 100084

Abstract: Trading strategy plays a very important role in automated stock trading. How to select trading strategy in the complex and dynamic financial market is an important research direction of modern finance program trading. Reinforcement learning algorithm is to find the optimal dynamic trading strategy via interaction with the actual environment and maximize the benefits. This paper proposes a mixed CNN with LSTM end-to-end deep reinforcement learning automated trading algorithm, CNN module perceives stock dynamic market environments and dynamically extracts features, LSTM module learns dynamic time sequence rules, the eventually profits are accumulated through the deep reinforcement learning which interacts with the unknown environment, and trading strategies are made finally. Experiments on real stock data show that this method is significantly better than the benchmark method, with better scalability and robustness.

Key words: trading strategy, reinforcement learning, deep learning, quantitative finance

摘要: 交易策略在金融资产交易中具有十分重要的作用,如何在复杂动态金融市场中自动化选择交易策略是现代金融重要研究方向。强化学习算法通过与实际环境交互作用,寻找最优动态交易策略,最大化获取收益。提出了一个融合了CNN与LSTM的端到端深度强化学习自动化交易算法,CNN模块感知股票动态市场条件以及抽取动态特征,LSTM模块循环学习动态时间序列规律,最后通过强化学习方法累积最终收益并做出交易策略。在真实股票数据上的实验结果表明,该方法显著优于基准方法,可扩展性更强,鲁棒性更好。

关键词: 交易策略, 强化学习, 深度学习, 量化金融