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

Zheli Xiong

TL;DR

The paper addresses risk management in trading by fusing ensemble reinforcement learning with classifier-based decisions. It introduces a variance-aware ensemble framework that aggregates confidence across multiple RL agents and classifiers, applying adaptive decision rules governed by a variance threshold $\tau$. Empirical results in a FinRL setting show that ensemble methods consistently improve risk-adjusted performance (higher Cumulative Returns and Sharpe/Calmar metrics) and reduce drawdowns, though their effectiveness is highly sensitive to $\tau$ and benefits from dynamic adjustment. The approach offers a robust, adaptive strategy with potential applications beyond finance to robotics and other dynamic decision-making domains.

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.

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

TL;DR

The paper addresses risk management in trading by fusing ensemble reinforcement learning with classifier-based decisions. It introduces a variance-aware ensemble framework that aggregates confidence across multiple RL agents and classifiers, applying adaptive decision rules governed by a variance threshold . Empirical results in a FinRL setting show that ensemble methods consistently improve risk-adjusted performance (higher Cumulative Returns and Sharpe/Calmar metrics) and reduce drawdowns, though their effectiveness is highly sensitive to and benefits from dynamic adjustment. The approach offers a robust, adaptive strategy with potential applications beyond finance to robotics and other dynamic decision-making domains.

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.

Paper Structure

This paper contains 19 sections, 9 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. abstract
  2. introduction
  3. related work
  4. Safe RL
  5. Ensemble RL
  6. Preliminary
  7. Method
  8. Experimental Ananlyses
  9. Metrics
  10. Cumulative Returns
  11. Max Drawdown
  12. Sharpe Ratio
  13. Calmar Ratio
  14. Experiment Settings
  15. Experiment Results
  16. ...and 4 more sections

Figures (5)

  • Figure 1: portfolio strategy process
  • Figure 2: decision block at each step
  • Figure 3: Performance Metrics of Models in Classifier Group 1 Across the Entire Year of 2020
  • Figure 4: Comparative Study on Risk-Return Trade-offs Across Classifier Groups
  • Figure 5: For different Variance threshold $\tau$ , using an ensemble of classifier group 1, compare the results of different base models, Model1 and Model2. Each result represents the average value over 30 backtesting iterations.