Structural break-aware pairs trading strategy using deep reinforcement learning

Jing You Lu, Hsu Chao Lai, Wen Yueh Shih, Yi-Feng Chen, Shen Hang Huang, Hao-Han Chang, Jun Zhe Wang, Jiun-Long Huang, Tian-Shyr Dai

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy (SAPT), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.
    Original languageAmerican English
    Number of pages40
    JournalJournal of Supercomputing
    DOIs
    StatePublished - 17 Aug 2021

    Keywords

    • Pairs trading strategy
    • Structural break detection
    • deep reinforcement learning
    • Continuous wavelet CNN

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