Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs. The study evaluates the effectiveness of various ensemble methods, comparing them with individual RL models across key financial metrics, including Cumulative Returns, Sharpe Ratios (SR), Calmar Ratios, and Maximum Drawdown (MDD). Our results demonstrate that ensemble methods consistently outperform base models in terms of risk-adjusted returns, providing better management of drawdowns and overall stability. However, we identify the sensitivity of ensemble performance to the choice of variance threshold τ, highlighting the importance of dynamic τ adjustment to achieve optimal performance. This study emphasizes the value of combining RL with classifiers for adaptive decision-making, with implications for financial trading, robotics, and other dynamic environments. Supplementary Material File (ensemble rl.pdf) - Download - 9.85 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 282views 117downloads Citations Download citation Zheli Xiong, Qichong Yang, Jianpeng Chen, et al. Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies. Authorea. 15 April 2025. DOI: https://doi.org/10.22541/au.174475089.97944012/v1 DOI: https://doi.org/10.22541/au.174475089.97944012/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu. Cited by - Global Cross-Market Trading Optimization Using Iterative Combined Algorithm: A Multi-Asset Approach with Stocks and Cryptocurrencies, Mathematics, 13, 8, (1317), (2025).https://doi.org/10.3390/math13081317 Loading...