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Practical Deep Reinforcement Learning Approach for Stock Trading

Author

Listed:
  • Xiao-Yang Liu
  • Zhuoran Xiong
  • Shan Zhong
  • Hongyang Yang
  • Anwar Walid

Abstract

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

Suggested Citation

  • Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
    • Handle: RePEc:arx:papers:1811.07522
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.
    3. Francesco Bertoluzzo & Marco Corazza, 2012. "Reinforcement Learning for automatic financial trading: Introduction and some applications," Working Papers 2012:33, Department of Economics, University of Venice "Ca' Foscari", revised 2012.
    4. Yong Zhang & Xingyu Yang, 2017. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 141-159, June.
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    Cited by:

    1. Uta Pigorsch & Sebastian Schafer, 2021. "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers 2112.04755, arXiv.org.
    2. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
    3. Supriya Bajpai, 2021. "Application of deep reinforcement learning for Indian stock trading automation," Papers 2106.16088, arXiv.org.
    4. 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.
    5. Huifang Huang & Ting Gao & Yi Gui & Jin Guo & Peng Zhang, 2022. "Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength," Papers 2205.15056, arXiv.org.
    6. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    7. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    8. Ali Hirsa & Joerg Osterrieder & Branka Hadji-Misheva & Jan-Alexander Posth, 2021. "Deep reinforcement learning on a multi-asset environment for trading," Papers 2106.08437, arXiv.org.
    9. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    10. Xinyi Li & Yinchuan Li & Xiao-Yang Liu & Christina Dan Wang, 2019. "Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction," Papers 1908.01112, arXiv.org.
    11. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
    12. Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021. "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers 2104.00620, arXiv.org.
    13. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    14. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
    15. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    16. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    17. Yizhuo Li & Peng Zhou & Fangyi Li & Xiao Yang, 2021. "An Improved Reinforcement Learning Model Based on Sentiment Analysis," Papers 2111.15354, arXiv.org.
    18. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    19. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.

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