TradingMoE：在动态市场中路由合适的专家

中文摘要 AI 辅助

大型语言模型（LLM）在金融分析与交易领域展现出强大潜力，但直接开展交易仍具挑战，因为所需的预测能力会随资产、决策领域及市场条件的不同而变化。现有基于LLM的交易系统要么协调人工定义的外部专家，要么采用传统的内部混合专家（Mixture-of-Experts，MoE）路由机制，这类机制未直接评估单个专家对交易决策的贡献，且当市场条件变化导致不活跃专家变得更适用时，这些路由机制无法收到直接信号。研究发现，原生路由机制的评分无法准确反映单个专家对交易决策的提升程度，常导致更优的备选专家未被选中；还进一步发现，针对不同token的专家效用呈现出紧凑的低维结构。基于这些发现，提出TradingMoE，这是一种面向交易的稀疏MoE，通过轻量级残差专家增强冻结的密集LLM。引入Query-Key路由机制，将当前市场背景下每个token所需的专业知识表示为低维查询，并与可学习的专家键进行匹配；还提出稀疏专家选择更新机制，在训练期间采样少量不活跃专家，评估其是否应替换当前Top-k路由中最弱的专家。该机制使路由机制能随市场条件变化更新专家选择，同时保持稀疏计算。针对股票和加密货币市场的22个基准的实验表明，TradingMoE的累计收益较表现最佳的基准分别提升30.89%和30.7%；滚动纸面交易实验进一步证明，其优势在仅向前部署的场景下依然存在。

英文摘要

Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.

发表机构

• University of Science and Technology of China（中国科学技术大学）
• The Chinese University of Hong Kong（香港中文大学）
• The University of Tokyo（东京大学）
• Singapore Management University（新加坡管理大学）

机构由 AI 辅助整理，请以论文原文为准。