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Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs

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  • Koya Ishikawa
  • Kazuhide Nakata

Abstract

In recent years, a wide range of investment models have been created using artificial intelligence. Automatic trading by artificial intelligence can expand the range of trading methods, such as by conferring the ability to operate 24 hours a day and the ability to trade with high frequency. Automatic trading can also be expected to trade with more information than is available to humans if it can sufficiently consider past data. In this paper, we propose an investment agent based on a deep reinforcement learning model, which is an artificial intelligence model. The model considers the transaction costs involved in actual trading and creates a framework for trading over a long period of time so that it can make a large profit on a single trade. In doing so, it can maximize the profit while keeping transaction costs low. In addition, in consideration of actual operations, we use online learning so that the system can continue to learn by constantly updating the latest online data instead of learning with static data. This makes it possible to trade in non-stationary financial markets by always incorporating current market trend information.

Suggested Citation

  • Koya Ishikawa & Kazuhide Nakata, 2021. "Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs," Papers 2106.03035, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2106.03035
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    File URL: http://arxiv.org/pdf/2106.03035
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    References listed on IDEAS

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    1. Rosdyana Mangir Irawan Kusuma & Trang-Thi Ho & Wei-Chun Kao & Yu-Yen Ou & Kai-Lung Hua, 2019. "Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market," Papers 1903.12258, arXiv.org.
    2. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    3. 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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    Cited by:

    1. Zahra Pourahmadi & Dariush Fareed & Hamid Reza Mirzaei, 2024. "A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis," Annals of Data Science, Springer, vol. 11(5), pages 1653-1674, October.
    2. Ge, Hengshun & Yang, Haijun & Doukas, John A., 2024. "The optimal strategies of competitive high-frequency traders and effects on market liquidity," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 653-679.

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