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A parallel-network continuous quantitative trading model with GARCH and PPO

Author

Listed:
  • Zhishun Wang
  • Wei Lu
  • Kaixin Zhang
  • Tianhao Li
  • Zixi Zhao

Abstract

It is a difficult task for both professional investors and individual traders continuously making profit in stock market. With the development of computer science and deep reinforcement learning, Buy\&Hold (B\&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment. Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models.

Suggested Citation

  • Zhishun Wang & Wei Lu & Kaixin Zhang & Tianhao Li & Zixi Zhao, 2021. "A parallel-network continuous quantitative trading model with GARCH and PPO," Papers 2105.03625, arXiv.org, revised May 2021.
    • Handle: RePEc:arx:papers:2105.03625
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    File URL: http://arxiv.org/pdf/2105.03625
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    References listed on IDEAS

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    1. Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    3. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
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    Cited by:

    1. Yuling Huang & Xiaoping Lu & Chujin Zhou & Yunlin Song, 2023. "DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy," Mathematics, MDPI, vol. 11(17), pages 1-27, August.

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