An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

Shuyang Wang and Diego Klabjan

Papers from arXiv.org

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.

Date: 2023-07
New Economics Papers: this item is included in nep-big and nep-cmp
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