An Ensemble Method of Deep Reinforcement Learning for Automated
  Cryptocurrency Trading
Shuyang Wang Diego Klabjan
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.