Stock Market Analysis MCP Server

A comprehensive Model Context Protocol (MCP) server for advanced stock market analysis, trend identification, portfolio management, dividend tracking, sector analysis, and risk metrics.

📑 Table of Contents

• Features
• Installation
  • Prerequisites
  • Step-by-Step Setup
  • MCP Configuration
  • Troubleshooting
• Usage Examples
• Technical Indicators
• Architecture
• Data Storage & Privacy
• Best Practices
• Dependencies

🚀 Features

📈 Price & Data Tools (price_data.py)

• get_latest_price(ticker) - Get the most recent market price for any stock
• get_historical_data(ticker, period) - Retrieve historical OHLC data (Open, High, Low, Close, Volume)
• get_stock_info(ticker) - Comprehensive stock information including fundamentals, P/E ratio, market cap, dividend yield, etc.

💼 Portfolio Management (portfolio.py)

• add_holding(ticker, shares, purchase_price, purchase_date?) - Add stocks to your portfolio with automatic cost basis tracking
• remove_holding(ticker, shares, sale_price, sale_date?) - Sell stocks with automatic profit/loss calculation
• view_portfolio() - See all your holdings with real-time valuations and P&L
• view_transactions(limit?) - Review your transaction history

🔍 Technical Analysis (analysis.py)

• analyze_buy_opportunity(ticker) - Simple SMA crossover strategy (20/50 day)
• calculate_rsi(ticker, period?, timeframe?) - Relative Strength Index to identify overbought/oversold conditions
• calculate_macd(ticker, timeframe?) - Moving Average Convergence Divergence for trend momentum
• analyze_trends(ticker, timeframe?) - Multi-indicator comprehensive trend analysis
• compare_stocks(tickers[]) - Side-by-side comparison of multiple stocks

🔔 Alert System (alerts.py)

• set_price_alert(ticker, target_price, alert_type, alert_name?) - Set price alerts (above/below thresholds)
• set_rsi_alert(ticker, rsi_threshold, alert_type, alert_name?) - Set RSI alerts for overbought/oversold conditions
• check_alerts() - Check all active alerts and see which ones have been triggered
• list_alerts() - View all configured alerts
• clear_triggered_alerts() - Remove triggered alerts
• delete_all_alerts() - Delete all alerts

💰 Dividend Tracking (dividends.py)

• get_dividend_history(ticker, period?) - View dividend payment history with trends
• get_dividend_yield(ticker) - Get current dividend yield and related metrics
• calculate_portfolio_dividend_income() - Calculate expected annual dividend income from your portfolio
• find_high_dividend_stocks(min_yield?, sector?) - Discover high-yield dividend stocks

🏢 Sector Analysis (sector.py)

• analyze_sector(sector_name) - Comprehensive analysis of a specific market sector
• compare_sectors() - Compare performance across all major market sectors
• get_sector_leaders(sector_name, metric?) - Get top performing stocks in a sector
• analyze_portfolio_sector_allocation() - Analyze your portfolio's sector diversification

⚠️ Risk Metrics (risk.py)

• calculate_sharpe_ratio(ticker, risk_free_rate?, period?) - Measure risk-adjusted returns
• calculate_beta(ticker, benchmark?, period?) - Calculate stock volatility vs market
• calculate_portfolio_risk() - Comprehensive portfolio risk analysis with recommendations
• calculate_var(ticker, confidence_level?, period?, position_size?) - Value at Risk calculation
• calculate_drawdown(ticker, period?) - Maximum drawdown and peak-to-trough analysis

📦 Installation

Prerequisites

Before you begin, ensure you have:

• Python 3.8 or higher installed on your system
• Cursor IDE (or another MCP-compatible client)
• Git (optional, for cloning the repository)
• Internet connection for fetching stock market data

Step 1: Get the Code

Option A: Clone from GitHub

git clone https://github.com/CalvinLiuu/stock_mcp_server.git
cd stock_mcp_server

Option B: Download ZIP

• Download the repository as a ZIP file
• Extract it to your desired location
• Open a terminal and navigate to the extracted folder

Step 2: Set Up Virtual Environment

Create and activate a virtual environment (recommended):

On macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

On Windows:

python -m venv .venv
.venv\Scripts\activate

Step 3: Install Dependencies

With your virtual environment activated, install the required packages:

pip install -r requirements.txt

This will install:

• yfinance - For real-time stock market data
• pandas - For data analysis
• numpy - For numerical computations
• fastmcp - For MCP server functionality

Step 4: Configure MCP in Cursor

To use this server with Cursor IDE, you need to add it to your MCP configuration file.

Locate Your MCP Configuration

The MCP configuration file is typically located at:

• macOS/Linux: ~/.cursor/mcp.json
• Windows: %USERPROFILE%\.cursor\mcp.json

Add the Server Configuration

Open the mcp.json file and add the following configuration (adjust the path to match your installation):

Option A: Using Python Module (Recommended)

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/absolute/path/to/stock_mcp_server/.venv/bin/python",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "/absolute/path/to/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Option B: Using fastmcp CLI

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/absolute/path/to/stock_mcp_server/.venv/bin/fastmcp",
      "args": [
        "dev",
        "/absolute/path/to/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Important:

• Replace /absolute/path/to/stock_mcp_server/ with the actual full path to where you installed the server
• On Windows, use backslashes and the appropriate paths (e.g., C:\\Users\\YourName\\...)
• Make sure to use the Python interpreter from your virtual environment (.venv/bin/python)

Example for macOS:

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "/Users/johndoe/Documents/stock_mcp_server/.venv/bin/python",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "/Users/johndoe/Documents/stock_mcp_server/stock.server.py"
      ],
      "env": {}
    }
  }
}

Example for Windows:

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "C:\\Users\\JohnDoe\\Documents\\stock_mcp_server\\.venv\\Scripts\\python.exe",
      "args": [
        "-m",
        "mcp.server.fastmcp",
        "run",
        "C:\\Users\\JohnDoe\\Documents\\stock_mcp_server\\stock.server.py"
      ],
      "env": {}
    }
  }
}

Step 5: Restart Cursor

After updating your mcp.json configuration:

1. Save the file
2. Completely restart Cursor (close and reopen)
3. The MCP server should automatically start when Cursor launches

Step 6: Verify Installation

You can verify the installation by checking the MCP logs in Cursor or by trying to use one of the tools:

In Cursor's AI chat, try:

Can you get the latest price for AAPL using the stock analyzer?

You should see the server responding with real-time stock data.

Or check available tools:

What tools are available in the stock-analyzer MCP server?

🧪 Testing the Server Manually (Optional)

To test the server outside of Cursor:

# Activate your virtual environment first
source .venv/bin/activate  # macOS/Linux
# or
.venv\Scripts\activate  # Windows

# Run the server in development mode
fastmcp dev stock.server.py

You should see output like:

Registering price data tools...
Registering portfolio management tools...
Registering technical analysis tools...
Registering alert system tools...
Registering dividend tracking tools...
Registering sector analysis tools...
Registering risk analysis tools...
✅ All tools registered successfully!
📊 Stock Market Analyzer v0.3.0 is ready!

🔧 Troubleshooting

Server not starting?

• Verify the paths in your mcp.json are absolute paths (not relative)
• Ensure you're using the Python from your virtual environment (.venv/bin/python)
• Check that all dependencies are installed: pip list should show yfinance, pandas, numpy, fastmcp

"Module not found" errors?

• Make sure your virtual environment is activated
• Reinstall dependencies: pip install -r requirements.txt

Can't fetch stock data?

• Check your internet connection
• Some stocks may have delayed data or require different ticker symbols
• Try a common stock like "AAPL" or "MSFT" first

MCP server not appearing in Cursor?

• Ensure the mcp.json file is valid JSON (no trailing commas, proper syntax)
• Check Cursor's MCP logs for error messages
• Try restarting Cursor completely

Permission errors on macOS/Linux?

• Make sure the Python executable is executable: chmod +x .venv/bin/python

📱 Quick Start After Installation

Once installed and configured, you can immediately start using the tools through Cursor's AI chat:

# Check a stock price
"Get the latest price for Tesla"

# Add to your portfolio
"Add 10 shares of AAPL at $150 to my portfolio"

# View your portfolio
"Show me my current portfolio"

# Set an alert
"Alert me when TSLA goes below $250"

# Analyze a stock
"Analyze trends for NVDA"

🎯 Usage Examples

Portfolio Management

# Add stocks to your portfolio
add_holding("AAPL", 10, 150.00)
add_holding("MSFT", 5, 380.00)
add_holding("NVDA", 8, 450.00)

# View your portfolio with current valuations
view_portfolio()

# Sell some shares
remove_holding("AAPL", 5, 175.00)

# View transaction history
view_transactions(limit=20)

Alert System

# Set a price alert
set_price_alert("TSLA", 250.00, "below")

# Set an RSI alert for oversold conditions
set_rsi_alert("NVDA", 30, "below")

# Check if any alerts triggered
check_alerts()

# View all configured alerts
list_alerts()

Dividend Analysis

# View dividend history
get_dividend_history("JNJ", period="5y")

# Check dividend yield
get_dividend_yield("KO")

# Calculate portfolio dividend income
calculate_portfolio_dividend_income()

# Find high-yield stocks in utilities sector
find_high_dividend_stocks(min_yield=4.0, sector="Utilities")

Sector Analysis

# Analyze technology sector
analyze_sector("Technology")

# Compare all sectors
compare_sectors()

# Get sector leaders by performance
get_sector_leaders("Healthcare", metric="return")

# Check portfolio sector diversification
analyze_portfolio_sector_allocation()

Risk Analysis

# Calculate Sharpe ratio
calculate_sharpe_ratio("AAPL", risk_free_rate=0.04, period="1y")

# Calculate beta vs S&P 500
calculate_beta("TSLA", benchmark="SPY")

# Comprehensive portfolio risk analysis
calculate_portfolio_risk()

# Calculate Value at Risk
calculate_var("NVDA", confidence_level=0.95, position_size=10000)

# Analyze maximum drawdown
calculate_drawdown("MSFT", period="5y")

Technical Analysis

# Get detailed stock information
get_stock_info("GOOGL")

# Check RSI for overbought/oversold signals
calculate_rsi("TSLA", period=14, timeframe="3mo")

# Analyze MACD for momentum
calculate_macd("MSFT", timeframe="6mo")

# Comprehensive trend analysis
analyze_trends("NVDA", timeframe="1y")

# Compare multiple stocks
compare_stocks(["AAPL", "MSFT", "GOOGL"])

📊 Technical Indicators Explained

RSI (Relative Strength Index)

• Above 70: Overbought - potential sell signal
• Below 30: Oversold - potential buy opportunity
• 30-70: Neutral range

MACD (Moving Average Convergence Divergence)

• Bullish Crossover: MACD line crosses above signal line
• Bearish Crossover: MACD line crosses below signal line
• Used to identify momentum and trend changes

SMA Crossover Strategy

• Buy Signal: Short-term SMA (20-day) crosses above long-term SMA (50-day)
• Sell Signal: Short-term SMA crosses below long-term SMA

Sharpe Ratio

• > 2.0: Excellent risk-adjusted returns
• 1.0-2.0: Good
• 0-1.0: Fair
• < 0: Poor (returns below risk-free rate)

Beta

• β > 1: More volatile than market
• β = 1: Moves with market
• 0 < β < 1: Less volatile than market
• β < 0: Moves opposite to market

🏗️ Architecture

stock_mcp_server/
├── stock.server.py         # Main server entry point
├── utils.py                # Shared utilities (load/save data)
├── price_data.py           # Price and stock information
├── portfolio.py            # Portfolio management
├── analysis.py             # Technical analysis tools
├── alerts.py               # Alert system
├── dividends.py            # Dividend tracking
├── sector.py               # Sector analysis
├── risk.py                 # Risk metrics
├── portfolio.json          # Portfolio data (auto-generated)
├── alerts.json             # Alert data (auto-generated)
├── requirements.txt        # Python dependencies
└── README.md               # Documentation

Modular Design

Each module is self-contained and can be updated independently:

• utils.py: Shared functions for data persistence
• price_data.py: Basic stock data retrieval
• portfolio.py: Holdings and transaction tracking
• analysis.py: Technical indicators and trend analysis
• alerts.py: Price and RSI alert system
• dividends.py: Dividend history and income tracking
• sector.py: Sector-wide analysis and comparison
• risk.py: Risk metrics and portfolio risk management

📁 Data Storage & Privacy

🔒 Local Storage Only

Your data stays completely private and local:

• ✅ All portfolio data is stored on your computer only
• ✅ No cloud storage or external servers
• ✅ No authentication or account required
• ✅ Full control over your data files
• ✅ Can backup/edit JSON files directly

What gets stored locally:

• Your stock holdings and transactions → portfolio.json
• Your price and RSI alerts → alerts.json

What goes to the internet:

• Only market data requests (stock prices, fundamentals, etc.) via Yahoo Finance API
• Your personal portfolio data is NEVER transmitted anywhere

Portfolio Data (portfolio.json)

Location: Same directory as stock.server.py

Automatically created and saved with:

• Current holdings with average cost basis
• Complete transaction history (buy/sell)
• Profit/loss calculations
• Last update dates

Example structure:

{
  "holdings": {
    "AAPL": {
      "shares": 10,
      "avg_price": 150.00,
      "last_updated": "2025-01-15"
    }
  },
  "transactions": [
    {
      "type": "BUY",
      "ticker": "AAPL",
      "shares": 10,
      "price": 150.00,
      "date": "2025-01-15",
      "total": 1500.00
    }
  ]
}

Alert Data (alerts.json)

Location: Same directory as stock.server.py

Automatically created and saved with:

• Active price alerts (trigger above/below thresholds)
• Active RSI alerts (overbought/oversold conditions)
• Alert status and trigger history

Example structure:

{
  "price_alerts": [
    {
      "id": "alert_123",
      "ticker": "TSLA",
      "target_price": 250.00,
      "alert_type": "below",
      "status": "active"
    }
  ],
  "rsi_alerts": []
}

Backup Your Data

Since all data is stored locally in JSON files, you can easily:

• Backup: Copy portfolio.json and alerts.json to another location
• Restore: Replace the files with your backup
• Edit: Manually edit the JSON files if needed (be careful with formatting)
• Version Control: Add to Git (but remember to add to .gitignore if sharing publicly)

🆕 What's New in v0.3.0

New Features

1. 🔔 Alert System - Set price and RSI alerts, check status automatically
2. 💰 Dividend Tracking - Track dividend history, yields, and portfolio income
3. 🏢 Sector Analysis - Analyze entire sectors, compare performance, check diversification
4. ⚠️ Risk Metrics - Sharpe ratio, beta, VaR, drawdown, comprehensive portfolio risk

Enhanced Features

• Modular architecture for better maintainability
• Separated concerns into dedicated modules
• Improved error handling across all tools
• Better data persistence with separate files for alerts

Previous Features (v0.2.0)

• Portfolio management with P&L tracking
• Technical indicators (RSI, MACD, SMA)
• Comprehensive trend analysis
• Stock comparison tools

Original Features (v0.1.0)

• Basic price data retrieval
• Historical data access
• Simple SMA crossover analysis

🔧 Configuration

Risk-Free Rate

Default: 4% (0.04) - Can be adjusted in Sharpe ratio calculations

Available Sectors

• Technology
• Healthcare
• Financial Services
• Energy
• Consumer Cyclical
• Consumer Defensive
• Utilities
• Industrials
• Real Estate
• Materials
• Communication Services

Alert Types

• Price Alerts: above or below target price
• RSI Alerts: above or below RSI threshold

📚 Dependencies

• yfinance (>=0.2.40): Real-time and historical market data
• pandas (>=2.0.0): Data manipulation and analysis
• numpy (>=1.24.0): Numerical computations for risk metrics
• fastmcp (>=0.1.0): MCP server framework

🎓 Best Practices

Portfolio Management

• Diversify across 10-20 different stocks
• Keep largest position below 20% of portfolio
• Regular rebalancing (quarterly or annually)
• Track cost basis for tax purposes

Risk Management

• Target Sharpe ratio > 1.0 for good risk-adjusted returns
• Keep portfolio beta between 0.8-1.2 for moderate risk
• Monitor maximum drawdown - be prepared for historical volatility
• Use VaR to understand daily loss potential

Sector Allocation

• Diversify across at least 5 different sectors
• Avoid concentration > 35% in any single sector
• Consider sector rotation based on economic cycles
• Balance growth and defensive sectors

Dividend Investing

• Look for payout ratios < 80% for sustainability
• Prefer dividend growth over just high yield
• Reinvest dividends for compound growth
• Track ex-dividend dates for planning

⚠️ Disclaimer

This tool is for educational and informational purposes only. It is NOT financial advice.

• Market data may be delayed
• Past performance doesn't guarantee future results
• Always do your own research before investing
• Consider consulting a financial advisor
• Be aware of tax implications

🚀 Future Enhancements

Potential additions:

• Options analysis and Greeks calculation
• Fibonacci retracement levels
• Support/resistance identification
• News sentiment analysis
• Backtesting capabilities
• Tax lot tracking for capital gains
• Portfolio rebalancing suggestions
• Correlation analysis between holdings
• Monte Carlo simulations
• Real-time streaming quotes

📄 License

MIT License - Feel free to use and modify as needed.

🤝 Contributing

Contributions are welcome! Please ensure:

• Code follows the modular architecture
• New features are in appropriate modules
• Documentation is updated
• Error handling is comprehensive

📞 Support

For issues or questions:

1. Check the documentation above
2. Review the module-specific code
3. Ensure all dependencies are installed
4. Verify API data is accessible

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Version: 0.3.0
Last Updated: 2025
Author: Stock Market Analysis MCP Server