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Adversarial Attacks on Deep Algorithmic Trading Policies

Author

Listed:
  • Yaser Faghan
  • Nancirose Piazza
  • Vahid Behzadan
  • Ali Fathi

Abstract

Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attacks. It follows that algorithmic trading DRL agents may also be compromised by such adversarial techniques, leading to policy manipulation. In this paper, we develop a threat model for deep trading policies, and propose two attack techniques for manipulating the performance of such policies at test-time. Furthermore, we demonstrate the effectiveness of the proposed attacks against benchmark and real-world DQN trading agents.

Suggested Citation

  • Yaser Faghan & Nancirose Piazza & Vahid Behzadan & Ali Fathi, 2020. "Adversarial Attacks on Deep Algorithmic Trading Policies," Papers 2010.11388, arXiv.org.
    • Handle: RePEc:arx:papers:2010.11388
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    File URL: http://arxiv.org/pdf/2010.11388
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.

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