Advanced Stock Market Analysis Server

Advanced Stock Market Analysis Server

A powerful, extensible server for advanced stock market analysis and insights, built using the Model Context Protocol (MCP) and yfinance. This project provides a suite of tools for retrieving, analyzing, and comparing stock data, as well as sector and market sentiment analysis.

Features

• Get Stock Info: Retrieve basic information about any stock (name, price, market cap, P/E, etc.).
• Get Stock History: Fetch historical price and volume data for any stock, with robust error handling and retry logic.
• Analyze Stock Trend: Technical analysis using moving averages, RSI, MACD, Bollinger Bands, and volatility.
• Analyze Stock Risk: Quantitative risk metrics (volatility, Sharpe ratio, beta, VaR, drawdown, etc.).
• Compare Stocks: Compare multiple stocks on key metrics and performance.
• Sector Performance: Analyze the performance of major market sectors using sector ETFs.
• Market Sentiment: Assess overall market sentiment using major indices and the VIX.
• Get Stock News: Scrape and retrieve the latest news headlines and URLs for any stock from Finviz, with a recommended agent prompt for sentiment analysis and summary.

Technology Stack

• Python 3.8+
• yfinance for stock data
• fastapi (required by MCP server)
• requests and beautifulsoup4 for web scraping
• MCP (Model Context Protocol) for tool-based server architecture

Installation

1. Clone the repository:

   git clone https://github.com/edenmargolis/Advanced-Stock-Market-Analysis-MCP-Server
   cd Advanced-Stock-Market-Analysis-MCP-Server
   

2. Install dependencies:

   uv pip install -r requirements.txt
   

Usage

To start the server:

uv run mcp install main.py

You can then interact with the server using any MCP-compatible client or integration (such as Claude, Cursor, or other AI agents that support MCP tool calls).

Example Tool Calls

• Get stock info:

  { "ticker": "AAPL" }
  

• Get stock history:

  { "ticker": "MSFT", "period": "1y" }
  

• Analyze stock risk:

  { "ticker": "GOOGL" }
  

• Compare stocks:

  { "tickers": ["AAPL", "MSFT", "GOOGL"] }
  

• Get stock news:

  { "ticker": "TSLA", "count": 4 }
  

Project Structure

• main.py — Main server implementation and tool definitions
• requirements.txt — Python dependencies
• README.md — Project documentation

Notes

• All stock data is fetched live from Yahoo Finance via yfinance.
• News headlines and URLs are scraped from Finviz for the get_stock_news tool.
• Error handling and retry logic are built-in for robust operation.

Agent Prompt for News Sentiment Analysis

When using the get_stock_news tool, use the following prompt for best results with an LLM agent:

Get the latest news of {{ticker}} stock. For each article, read the full content, assess whether the sentiment is positive, negative, or neutral, and then write a summary of the overall sentiment and your conclusions about {{ticker}} based on these articles.

For questions or contributions, please open an issue or pull request on GitHub.