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A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks

Author

Listed:
  • Jie Zou
  • Jiashu Lou
  • Baohua Wang
  • Sixue Liu

Abstract

More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings. In this paper, to capture the hidden information, we propose a DRL based stock trading system using cascaded LSTM, which first uses LSTM to extract the time-series features from stock daily data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Experiments in DJI in the US market and SSE50 in the Chinese stock market show that our model outperforms previous baseline models in terms of cumulative returns and Sharp ratio, and this advantage is more significant in the Chinese stock market, a merging market. It indicates that our proposed method is a promising way to build a automated stock trading system.

Suggested Citation

  • Jie Zou & Jiashu Lou & Baohua Wang & Sixue Liu, 2022. "A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks," Papers 2212.02721, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2212.02721
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    References listed on IDEAS

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    1. 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.
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    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Chien Yi Huang, 2018. "Financial Trading as a Game: A Deep Reinforcement Learning Approach," Papers 1807.02787, arXiv.org.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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