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Deep Deterministic Portfolio Optimization

Author

Listed:
  • Ayman Chaouki
  • Stephen Hardiman
  • Christian Schmidt
  • Emmanuel S'eri'e
  • Joachim de Lataillade
Abstract
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.

Suggested Citation

  • Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
    • Handle: RePEc:arx:papers:2003.06497
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    References listed on IDEAS

    as
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    Cited by:

    1. Joachim de Lataillade & Ayman Chaouki, 2020. "Equations and Shape of the Optimal Band Strategy," Papers 2003.04646, arXiv.org, revised Mar 2020.
    2. Thibault Jaisson, 2021. "Deep differentiable reinforcement learning and optimal trading," Papers 2112.02944, arXiv.org, revised Apr 2022.
    3. Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
    4. Brini, Alessio & Tantari, Daniele, 2023. "Deep reinforcement trading with predictable returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    5. Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021. "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers 2104.00620, arXiv.org.
    6. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.

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