An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

• URL: http://arxiv.org/abs/2309.00626v1
• Date: Thu, 27 Jul 2023 04:00:09 GMT
• Title: An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
• Authors: Shuyang Wang and Diego Klabjan
• Abstract summary: We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms. 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.
• Score: 16.78239969166596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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