mlsanityData to 7 Oct 2026

An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

arXiv 2309.00626 ↗First posted 27 Jul 2023q-fin.TR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 27 Jul 2023.

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17 of 36 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  5. 2019Deep Reinforcement Learning for Tradingcited by 73
  6. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
  7. 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
  8. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  9. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 31
  10. 2020An Application of Deep Reinforcement Learning to Algorithmic Tradingcited by 30
  11. 2018Financial Trading as a Game: A Deep Reinforcement Learning Approachcited by 22
  12. 2022Deep Reinforcement Learning Approach for Trading Automation in The Stock Marketcited by 9
5 more
  1. 2020Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learningcited by 7
  2. 2021Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approachcited by 5
  3. 2022Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfittingcited by 5
  4. 2020Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learningcited by 4
  5. 2022Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset tradingcited by 2

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