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