Advanced Stock Market Analysis Server MCP Server

Description

# 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](https://github.com/ranaroussi/yfinance) for stock data - [fastapi](https://fastapi.tiangolo.com/) (required by MCP server) - [requests](https://docs.python-requests.org/) and [beautifulsoup4](https://www.crummy.com/software/BeautifulSoup/) for web scraping - **MCP (Model Context Protocol)** for tool-based server architecture ## Installation 1. **Clone the repository:** ```bash git clone https://github.com/edenmargolis/Advanced-Stock-Market-Analysis-MCP-Server cd Advanced-Stock-Market-Analysis-MCP-Server ``` 2. **Install dependencies:** ```bash uv pip install -r requirements.txt ``` ## Usage To start the server: ```bash 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:** ```json { "ticker": "AAPL" } ``` - **Get stock history:** ```json { "ticker": "MSFT", "period": "1y" } ``` - **Analyze stock risk:** ```json { "ticker": "GOOGL" } ``` - **Compare stocks:** ```json { "tickers": ["AAPL", "MSFT", "GOOGL"] } ``` - **Get stock news:** ```json { "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.*

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