MCP Server for Financial Analysis
Introduction
This project is a Model Context Protocol (MCP) server designed to enable LLMs (such as Claude, Cursor, or GPT-based agents) to retrieve, analyze, and visualize stock prices and financial report data. It provides a set of robust tools for quantitative trading research, investment analysis, and financial education.
Usage Scenarios
- LLM-driven trading analysis: Let LLMs fetch and analyze stock data, financial statements, and technical indicators to generate trading insights.
- Financial metric calculation: Compute key ratios and metrics from income statements, balance sheets, and cash flow tables.
- Visualization: Generate price charts, financial metric trends, and highlight trading opportunities.
- Automated research: Integrate with LLMs to answer questions like "What is the ROE of AAPL?", "Show me the last 6 months of MSFT price data.", or "Plot buy/sell signals for TSLA."
Features
- Retrieve real-time and historical stock prices (single or multiple tickers)
- Extract and analyze financial statements (income, balance sheet, cash flow)
- Calculate key financial and technical metrics (PE, ROE, moving averages, etc.)
- Visualize price data, metrics, and trading signals
- LLM-friendly, JSON-serializable outputs
Installation
- Clone the repository
- Set up a Python virtual environment
- Install dependencies
How to Run
- Configure your LLM client (Claude, Cursor, etc.) to connect to the MCP server and call the available tools. Go to Claude settings
Select Developer --> Edit Config
Add new MCP server.
In the JSON File, add
'trader-mcp' should now be listed in your Claude tools.
- Start talking with your trading assistant!
Quries like:
Compare the stock prices of Nvidia and AMD in the past month.
How's Tesla stock like in the past 3 months?
Plot the trading opportunities of Microsoft in the past 3 months.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
Enables LLMs to retrieve, analyze, and visualize stock prices and financial report data for quantitative trading research and investment analysis. Provides real-time and historical stock data, financial statement analysis, key metric calculations, and trading signal visualization.
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