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Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things

Author

Listed:
  • Yi Tang

    (Chongqing Institute of Engineering, China)

  • Xiaoning Wang

    (Chongqing Institute of Engineering, China)

  • Wenyan Wang

    (Cangzhou Normal University, China)

Abstract

By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.

Suggested Citation

  • Yi Tang & Xiaoning Wang & Wenyan Wang, 2024. "Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 19(1), pages 1-16, January.
    • Handle: RePEc:igg:jitwe0:v:19:y:2024:i:1:p:1-16
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.347880
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    References listed on IDEAS

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    1. Fan Fang & Waichung Chung & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & Fan Wu, 2024. "Ascertaining price formation in cryptocurrency markets with machine learning," The European Journal of Finance, Taylor & Francis Journals, vol. 30(1), pages 78-100, January.
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