让大模型精准获取真实金融数据，避免幻觉。
QuantMCP: Grounding Large Language Models in Verifiable Financial Reality
- 通过标准化工具调用协议连接金融数据API
- 支持自然语言查询实时财务数据并生成可靠分析
- 适合金融分析师与需要可信数据的AI应用
大型语言模型在金融分析中潜力巨大，但常因数据幻觉和缺乏实时可验证信息而受限。本文提出QuantMCP框架，利用模型上下文协议（MCP）实现对Python可访问金融数据API（如Wind、yfinance）的安全标准调用。用户可通过自然语言精准获取最新财务数据，突破大模型事实记忆缺陷。更重要的是，在获得经验证的结构化数据后，大模型可开展复杂数据分析与洞察生成，显著提升金融决策支持能力。QuantMCP为对话式AI与复杂金融数据世界之间构建了稳健、可扩展且安全的桥梁，旨在增强大模型在金融领域的可靠性与分析深度。
原文摘要 · Abstract (English)
Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often hampered by issues of data hallucination and lack of access to real-time, verifiable financial information. This paper introduces QuantMCP, a novel framework designed to rigorously ground LLMs in financial reality. By leveraging the Model Context Protocol (MCP) for standardized and secure tool invocation, QuantMCP enables LLMs to accurately interface with a diverse array of Python-accessible financial data APIs (e.g., Wind, yfinance). Users can interact via natural language to precisely retrieve up-to-date financial data, thereby overcoming LLM's inherent limitations in factual data recall. More critically, once furnished with this verified, structured data, the LLM's analytical capabilities are unlocked, empowering it to perform sophisticated data interpretation, generate insights, and ultimately support more informed financial decision-making processes. QuantMCP provides a robust, extensible, and secure bridge between conversational AI and the complex world of financial data, aiming to enhance both the reliability and the analytical depth of LLM applications in finance.
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