用四个专家模型融合金融数据，提升股票预测与交易效果
TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
- 四类专用大模型分别分析新闻、行情、因子和基本面数据
- 综合评估显示在所有交易场景下表现优于现有方法
- 支持预测与排名两种模式，适合量化交易研究者使用
人工智能在金融领域的应用为量化交易开辟了新路径，尤其是大型语言模型（LLMs）的引入。然而，如何有效整合多源信息并融合结构化与非结构化数据仍是挑战。本文提出TradeExpert框架，采用混合专家（MoE）机制，使用四个专业化大模型分别处理新闻文章、市场数据、阿尔法因子和基本面数据。各专家模型的输出由一个通用专家大模型进一步融合，生成最终决策。通过特定提示，TradeExpert可切换至预测模式（用于股票走势预测）或排名模式（用于量化选股）。此外，我们还发布了大规模金融数据集，以全面评估其有效性。实验结果表明，TradeExpert在所有交易场景中均表现优异。
原文摘要 · Abstract (English)
The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios.
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