An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
This work addresses the problem of robust trading in stochastic cryptocurrency markets for investors, but it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackled improving generalization in automated cryptocurrency trading by proposing an ensemble method for deep reinforcement learning strategies, resulting in enhanced out-of-sample performance compared to benchmarks.
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.