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Universal Trading for Order Execution with Oracle Policy Distillation

Author

Listed:
  • Yuchen Fang
  • Kan Ren
  • Weiqing Liu
  • Dong Zhou
  • Weinan Zhang
  • Jiang Bian
  • Yong Yu
  • Tie-Yan Liu

Abstract

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.

Suggested Citation

  • Yuchen Fang & Kan Ren & Weiqing Liu & Dong Zhou & Weinan Zhang & Jiang Bian & Yong Yu & Tie-Yan Liu, 2021. "Universal Trading for Order Execution with Oracle Policy Distillation," Papers 2103.10860, arXiv.org.
    • Handle: RePEc:arx:papers:2103.10860
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    References listed on IDEAS

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    Cited by:

    1. Xiaodong Li & Pangjing Wu & Chenxin Zou & Qing Li, 2022. "Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization," Papers 2212.14670, arXiv.org.
    2. Zitao Song & Xuyang Jin & Chenliang Li, 2022. "Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading," Papers 2206.05910, arXiv.org.
    3. Xianfeng Jiao & Zizhong Li & Chang Xu & Yang Liu & Weiqing Liu & Jiang Bian, 2023. "Microstructure-Empowered Stock Factor Extraction and Utilization," Papers 2308.08135, arXiv.org.
    4. Shuo Sun & Molei Qin & Xinrun Wang & Bo An, 2023. "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets," Papers 2302.00586, arXiv.org, revised Mar 2023.
    5. Feiyang Pan & Tongzhe Zhang & Ling Luo & Jia He & Shuoling Liu, 2022. "Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution," Papers 2207.11152, arXiv.org.
    6. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.

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