TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
This work addresses the problem of integrating structured and unstructured data for financial traders, though it appears incremental as it builds on existing MoE and LLM methods.
The paper tackles the challenge of synthesizing diverse financial data for quantitative trading by introducing TradeExpert, a framework using a mixture of expert LLMs, which achieves superior performance across all trading scenarios.
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.