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Practical Application of Deep Reinforcement Learning to Optimal Trade Execution

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  • Woo Jae Byun

    (Qraft Technologies, Inc., 3040 Three IFC, 10 Gukjegeumyung-ro, Yeongdeungpo-gu, Seoul 07326, Republic of Korea
    These authors contributed equally to this work. Author ordering is randomly determined.)

  • Bumkyu Choi

    (Qraft Technologies, Inc., 3040 Three IFC, 10 Gukjegeumyung-ro, Yeongdeungpo-gu, Seoul 07326, Republic of Korea
    These authors contributed equally to this work. Author ordering is randomly determined.)

  • Seongmin Kim

    (Qraft Technologies, Inc., 3040 Three IFC, 10 Gukjegeumyung-ro, Yeongdeungpo-gu, Seoul 07326, Republic of Korea)

  • Joohyun Jo

    (Qraft Technologies, Inc., 3040 Three IFC, 10 Gukjegeumyung-ro, Yeongdeungpo-gu, Seoul 07326, Republic of Korea)

Abstract

Although deep reinforcement learning (DRL) has recently emerged as a promising technique for optimal trade execution, two problems still remain unsolved: (1) the lack of a generalized model for a large collection of stocks and execution time horizons; and (2) the inability to accurately train algorithms due to the discrepancy between the simulation environment and real market. In this article, we address the two issues by utilizing a widely used reinforcement learning (RL) algorithm called proximal policy optimization (PPO) with a long short-term memory (LSTM) network and by building our proprietary order execution simulation environment based on historical level 3 market data of the Korea Stock Exchange (KRX). This paper, to the best of our knowledge, is the first to achieve generalization across 50 stocks and across an execution time horizon ranging from 165 to 380 min along with dynamic target volume. The experimental results demonstrate that the proposed algorithm outperforms the popular benchmark, the volume-weighted average price (VWAP), highlighting the potential use of DRL for optimal trade execution in real-world financial markets. Furthermore, our algorithm is the first commercialized DRL-based optimal trade execution algorithm in the South Korea stock market.

Suggested Citation

  • Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
    • Handle: RePEc:gam:jfinte:v:2:y:2023:i:3:p:23-429:d:1182401
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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.
    2. Xiaodong Li & Pangjing Wu & Chenxin Zou & Qing Li, 2022. "Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization," Papers 2212.14670, arXiv.org.
    3. Dieter Hendricks & Diane Wilcox, 2014. "A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution," Papers 1403.2229, arXiv.org.
    4. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    5. Berkowitz, Stephen A & Logue, Dennis E & Noser, Eugene A, Jr, 1988. " The Total Cost of Transactions on the NYSE," Journal of Finance, American Finance Association, vol. 43(1), pages 97-112, March.
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