# An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

arXiv: https://arxiv.org/abs/2309.00626 · HTML: https://mlsanity.com/paper/2309.00626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://mlsanity.com/paper/1612.01277/) (2016)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.02787/) (2018)
- [Deep Reinforcement Learning Approach for Trading Automation in The Stock Market](https://mlsanity.com/paper/2208.07165/) (2022)
- [Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.14194/) (2020)
- [Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach](https://mlsanity.com/paper/2111.03995/) (2021)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09403/) (2020)
- [Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset trading](https://mlsanity.com/paper/2203.04579/) (2022)

## 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.