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Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks

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  • David W. Lu

Abstract

With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.

Suggested Citation

  • David W. Lu, 2017. "Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks," Papers 1707.07338, arXiv.org.
    • Handle: RePEc:arx:papers:1707.07338
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    File URL: http://arxiv.org/pdf/1707.07338
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    References listed on IDEAS

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    1. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Cited by:

    1. Ali Al-Ameer & Khaled Alshehri, 2021. "Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach," Papers 2109.14438, arXiv.org.
    2. Hans Buhler & Lukas Gonon & Josef Teichmann & Ben Wood, 2018. "Deep Hedging," Papers 1802.03042, arXiv.org.
    3. Alexandre Carbonneau & Fr'ed'eric Godin, 2020. "Equal Risk Pricing of Derivatives with Deep Hedging," Papers 2002.08492, arXiv.org, revised Jun 2020.
    4. Kinyua, Johnson D. & Mutigwe, Charles & Cushing, Daniel J. & Poggi, Michael, 2021. "An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    5. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    6. Alexandre Carbonneau & Frédéric Godin, 2023. "Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures," Risks, MDPI, vol. 11(8), pages 1-27, August.
    7. Luca De Gennaro Aquino & Carole Bernard, 2019. "Bounds on Multi-asset Derivatives via Neural Networks," Papers 1911.05523, arXiv.org, revised Nov 2020.
    8. Svitlana Vyetrenko & David Byrd & Nick Petosa & Mahmoud Mahfouz & Danial Dervovic & Manuela Veloso & Tucker Hybinette Balch, 2019. "Get Real: Realism Metrics for Robust Limit Order Book Market Simulations," Papers 1912.04941, arXiv.org.
    9. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    10. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.

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