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An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

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  • Shuyang Wang
  • Diego Klabjan

Abstract

We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment of intraday cryptocurrency portfolio trading. We adopt a model selection method that evaluates on multiple validation periods, and propose a novel mixture distribution policy to effectively ensemble the selected models. We provide a distributional view of the out-of-sample performance on granular test periods to demonstrate the robustness of the strategies in evolving market conditions, and retrain the models periodically to address non-stationarity of financial data. Our proposed ensemble method improves the out-of-sample performance compared with the benchmarks of a deep reinforcement learning strategy and a passive investment strategy.

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  • Shuyang Wang & Diego Klabjan, 2023. "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers 2309.00626, arXiv.org.
    • Handle: RePEc:arx:papers:2309.00626
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    References listed on IDEAS

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    1. Taylan Kabbani & Ekrem Duman, 2022. "Deep Reinforcement Learning Approach for Trading Automation in The Stock Market," Papers 2208.07165, arXiv.org.
    2. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    3. Mao Guan & Xiao-Yang Liu, 2021. "Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach," Papers 2111.03995, arXiv.org, revised Dec 2021.
    4. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    5. Chien Yi Huang, 2018. "Financial Trading as a Game: A Deep Reinforcement Learning Approach," Papers 1807.02787, arXiv.org.
    6. Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
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