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Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

Author

Listed:
  • Weiguang Han
  • Boyi Zhang
  • Qianqian Xie
  • Min Peng
  • Yanzhao Lai
  • Jimin Huang

Abstract

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2301.10724
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
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    3. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    4. Nicolas Huck & Komivi Afawubo, 2015. "Pairs trading and selection methods: is cointegration superior?," Applied Economics, Taylor & Francis Journals, vol. 47(6), pages 599-613, February.
    5. Perlin, M., 2007. "M of a kind: A Multivariate Approach at Pairs Trading," MPRA Paper 8309, University Library of Munich, Germany.
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    7. Christopher Krauss, 2017. "Statistical Arbitrage Pairs Trading Strategies: Review And Outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 31(2), pages 513-545, April.
    8. 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.
    9. Nicolas Huck & Komivi Afawubo, 2015. "Pairs trading and selection methods: is cointegration superior?," Post-Print hal-01369852, HAL.
    10. Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    11. Ian Martin, 2021. "On the Autocorrelation of the Stock Market [X-CAPM: An Extrapolative Capital Asset Pricing Model]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 39-52.
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    Cited by:

    1. Weiguang Han & Jimin Huang & Qianqian Xie & Boyi Zhang & Yanzhao Lai & Min Peng, 2023. "Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning," Papers 2304.00364, arXiv.org.

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