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Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

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  • Taewook Kim
  • Ha Young Kim

Abstract

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

Suggested Citation

  • Taewook Kim & Ha Young Kim, 2019. "Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    • Handle: RePEc:hin:complx:3582516
    DOI: 10.1155/2019/3582516
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    References listed on IDEAS

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    1. Tourin, Agnès & Yan, Raphael, 2013. "Dynamic pairs trading using the stochastic control approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 1972-1981.
    2. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
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    5. Danni Chen & Jing Cui & Yan Gao & Leilei Wu, 2017. "Pairs trading in Chinese commodity futures markets: an adaptive cointegration approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(5), pages 1237-1264, December.
    6. Zhengqin Zeng & Chi-Guhn Lee, 2014. "Pairs trading: optimal thresholds and profitability," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 1881-1893, November.
    7. Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2016. "The profitability of pairs trading strategies: distance, cointegration and copula methods," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1541-1558, October.
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    Cited by:

    1. Yen-Wu Ti & Tian-Shyr Dai & Kuan-Lun Wang & Hao-Han Chang & You-Jia Sun, 2024. "Improving Cointegration-Based Pairs Trading Strategy with Asymptotic Analyses and Convergence Rate Filters," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2717-2745, November.
    2. Han, Chulwoo & He, Zhaodong & Toh, Alenson Jun Wei, 2023. "Pairs trading via unsupervised learning," European Journal of Operational Research, Elsevier, vol. 307(2), pages 929-947.
    3. Guo, Minjia & Liu, Jianhe & Luo, Ziping & Han, Xiao, 2024. "Deep reinforcement learning for pairs trading: Evidence from China black series futures," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 981-993.
    4. Weiqing Wang & Shuhao Liang & Liukai Wang & Yu Xiong, 2025. "Mixed frequency data and portfolio selection: A novel approach integrating DEA with mixed frequency data sources," Annals of Operations Research, Springer, vol. 347(3), pages 1533-1565, April.
    5. Weiguang Han & Boyi Zhang & Qianqian Xie & Min Peng & Yanzhao Lai & Jimin Huang, 2023. "Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning," Papers 2301.10724, arXiv.org, revised Feb 2023.

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