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Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets

Author

Listed:
  • Peer Nagy
  • Jan-Peter Calliess
  • Stefan Zohren

Abstract

We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment and utilise it to simulate a realistic trading environment for NASDAQ equities based on historic order book messages. To train a trading agent that learns to maximise its trading return in this environment, we use Deep Duelling Double Q-learning with the APEX (asynchronous prioritised experience replay) architecture. The agent observes the current limit order book state, its recent history, and a short-term directional forecast. To investigate the performance of RL for adaptive trading independently from a concrete forecasting algorithm, we study the performance of our approach utilising synthetic alpha signals obtained by perturbing forward-looking returns with varying levels of noise. Here, we find that the RL agent learns an effective trading strategy for inventory management and order placing that outperforms a heuristic benchmark trading strategy having access to the same signal.

Suggested Citation

  • Peer Nagy & Jan-Peter Calliess & Stefan Zohren, 2023. "Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets," Papers 2301.08688, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2301.08688
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    References listed on IDEAS

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    1. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Applied Mathematical Finance, Taylor & Francis Journals, vol. 28(1), pages 79-95, January.
    2. Antonio Briola & Jeremy Turiel & Riccardo Marcaccioli & Alvaro Cauderan & Tomaso Aste, 2021. "Deep Reinforcement Learning for Active High Frequency Trading," Papers 2101.07107, arXiv.org, revised Aug 2023.
    3. Michael Karpe & Jin Fang & Zhongyao Ma & Chen Wang, 2020. "Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation," Papers 2006.05574, arXiv.org, revised Sep 2020.
    4. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Papers 2102.08811, arXiv.org, revised Jul 2021.
    5. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
    6. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    Full references (including those not matched with items on IDEAS)

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