An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading

ArXiv ID: 2309.00626 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Deep Reinforcement Learning, Ensemble Method, Cryptocurrency Trading, Non-Stationarity, Portfolio Trading, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like MDPs, policy ensembling, and distributional views of performance, but also includes significant empirical elements like a 4-year backtest, portfolio trading, and performance metrics.

  flowchart TD
    A["Research Goal: Improve DRL Strategy Generalization for Crypto Trading"] --> B["Data: Intraday Cryptocurrency Prices & Volumes"]
    B --> C["Methodology: Ensemble DRL with Model Selection"]
    C --> D{"Key Component: Mixture Distribution Policy"}
    D --> E["Retraining: Address Data Non-Stationarity"]
    E --> F["Outcome: Out-of-Sample Evaluation"]
    F --> G["Result: Superior Performance vs. DRL & Passive Benchmarks"]