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

ArXiv ID: 2502.17518 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Ensemble Reinforcement Learning, A2C/PPO/SAC, Classifier integration, Risk-adjusted returns, Trading strategies, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like Q-ensembles, CVaR, and variance-based filtering with theoretical bounds, indicating high mathematical complexity. It also demonstrates strong empirical rigor by evaluating multiple RL algorithms (A2C, PPO, SAC) with classifiers across standard financial metrics (Sharpe, Calmar, MDD), though it lacks explicit mention of backtested datasets or code implementation.

  flowchart TD
    A["Research Goal:<br>Enhance Risk-Return Trade-offs<br>in Trading via Ensemble RL"] --> B["Data: Equities Market Data"]
    B --> C{"Methodology: Ensemble Integration"}
    C --> D["Base RL Models<br>A2C, PPO, SAC"]
    C --> E["Classifier Models<br>SVM, Decision Trees, Logistic Regression"]
    D & E --> F["Computational Process:<br>Ensemble Integration with<br>Dynamic Variance Threshold τ"]
    F --> G["Key Findings/Outcomes"]
    G --> H["Outperforms Base Models<br>Risk-Adjusted Returns & Stability"]
    G --> I["Identified Sensitivity to τ<br>Dynamic Adjustment Needed"]