Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
2026-07-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explore how reinforcement learning (RL), a method where computer agents learn by trying things out, can be improved for trading stocks. They note that markets are unpredictable and traditional RL models often struggle when sudden changes occur. To fix this, they created an RL framework that carefully estimates different types of uncertainty using advanced techniques like SHAP values, MC Dropout, and LSTMs. Their experiments show that this uncertainty-aware approach helps trading agents make better decisions and manage risk more effectively. They suggest their method could be tested on other investments and RL styles in future work.
Reinforcement LearningFinancial TradingUncertainty EstimationSHAP ValuesMC DropoutLSTMStochastic VolatilityEpistemic UncertaintyAleatoric UncertaintyRisk Management
Authors
Lin Li, Li Rong Wang, Hsuan Fu, Xiuyi Fan
Abstract
Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction. However, financial markets are highly uncertain, with price fluctuations driven by stochastic volatility, model limitations, and regime shifts. Traditional RL models struggle in dynamic environments, often failing to adapt to sudden market disruptions, leading to suboptimal trading decisions. To address this challenge, we propose an uncertainty-aware RL framework that integrates distributional, epistemic, and aleatoric uncertainty estimations. Our approach enhances uncertainty estimation using SHAP-weighted reconstruction uncertainty, MC Dropout, and an LSTM-based technical indicator consensus mechanism. Experimental results on five major U.S. stock indices demonstrate that RL agents equipped with uncertainty estimation significantly outperform traditional models in return and risk management. This study advances uncertainty estimation in RL-based financial trading, with future research extending its application to other asset classes and alternative RL architectures for greater adaptability.