让交易模型学会识别风险，提升在动荡市场中的决策能力。
Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
- 融合多种不确定性估计方法，动态感知市场风险
- 在5大美股指数上实现更高收益与更好风控表现
- 适合关注量化交易鲁棒性的研究者和从业者
强化学习（RL）在金融交易中展现出强大潜力，使智能体通过直接市场交互学习最优策略。然而，金融市场高度不确定，价格波动受随机波动率、模型局限性和市场结构突变影响。传统RL模型在动态环境中适应性差，难以应对突发市场冲击，导致次优决策。为此，我们提出一种具备不确定性感知的强化学习框架，集成分布式、认知性和随机性不确定性估计。方法结合SHAP加权重构不确定性、蒙特卡洛丢弃法及基于LSTM的技术指标共识机制，显著提升不确定性建模能力。在五个主要美国股票指数上的实验表明，引入不确定性估计的RL代理在收益与风险控制方面均显著优于传统模型。本研究推进了基于强化学习的金融交易中不确定性估计的发展，未来可扩展至其他资产类别及更灵活的RL架构。
原文摘要 · Abstract (English)
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.
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