Financial Data MCP Server

A comprehensive Model Context Protocol (MCP) server for financial data analysis, portfolio management, and automated trading recommendations.

Features

• 📊 Real-time Stock Data - Uses free yfinance (Yahoo Finance) API - no API keys required

• 💼 Portfolio Management - Track multiple portfolios with automated analysis

• 📈 Technical Analysis - EMA-based trend detection and MACD charts

• 🎯 Trading Signals - Automated buy/sell recommendations with confidence levels

• 📧 Email Reports - Automated batch analysis reports with chart attachments

• 📉 Performance Tracking - Daily monitoring of recommendation performance

• 🤖 MCP Integration - Full integration with Claude Code and other MCP clients

Related MCP server: Stock Market Analysis MCP Server

Architecture

Core Components

1. financial_mcp_server.py - Main MCP server

   • Provides MCP tools and resources for financial analysis

   • Integrates with Claude Code

   • Real-time stock data via yfinance

2. batch_fin_mcp_server.py - Batch analysis engine

   • Analyzes all portfolios at once

   • Generates comprehensive reports and charts

   • Implements 4 trading scenarios based on EMA analysis

3. email_report_script.py - Email automation

   • Sends analysis results via email

   • Attaches charts and detailed reports

   • Saves buy recommendations for tracking

4. daily_tracking_script.py - Performance tracking

   • Monitors buy recommendation performance

   • Creates tracking charts

   • Generates daily performance reports

Installation

1. Clone the repository

git clone https://github.com/j1c4b/finance_mcp_server.git
cd finance_mcp_server

2. Create and activate virtual environment

python3 -m venv mcp_fin_server_venv
source mcp_fin_server_venv/bin/activate  # On Windows: mcp_fin_server_venv\Scripts\activate

3. Install dependencies

pip install -r clean_requirements.txt

Configuration

Portfolio Setup

Edit portfolio.json to add your portfolios:

{
  "tech_stocks": {
    "portfolio": "Technology Giants",
    "stock_list": ["AAPL", "GOOGL", "MSFT", "AMZN", "META"]
  },
  "dividend_portfolio": {
    "portfolio": "Dividend Champions",
    "stock_list": ["JNJ", "PG", "KO", "PEP", "MMM"]
  }
}

Email Configuration (Optional)

For email reports, create email_config.json:

{
  "smtp_server": "smtp.gmail.com",
  "smtp_port": 587,
  "sender_email": "your_email@gmail.com",
  "sender_password": "your_app_password",
  "recipient_emails": ["recipient@example.com"],
  "subject_prefix": "📊 Financial Analysis Report",
  "max_attachment_size_mb": 25
}

Usage

Running the MCP Server

source mcp_fin_server_venv/bin/activate
python3 financial_mcp_server.py

Batch Analysis

Analyze all portfolios and generate reports:

python3 batch_fin_mcp_server.py

Results are saved to batch_financial_charts/

Send Email Reports

python3 email_report_script.py

Track Recommendations

python3 daily_tracking_script.py

Results are saved to tracking_charts/

MCP Tools

The server provides these tools for Claude Code integration:

• load_portfolio - Load portfolio data from portfolio.json

• analyze_portfolio - Detailed analysis of specific portfolio

• portfolio_performance - Performance metrics over time

• get_stock_info - Comprehensive stock information

• get_earnings_calendar - Upcoming earnings announcements

• get_analyst_changes - Recent analyst upgrades/downgrades

• generate_macd_chart - MACD technical analysis charts

• get_market_overview - Major market indices status

Trading Scenarios

The batch analyzer identifies 4 key trading scenarios:

• Scenario A: Price >10% above 50 EMA → SELL signal

• Scenario B: Price above 50 EMA, touched recently → BUY signal

• Scenario C: Price >5% below 50 EMA, decreasing 3+ days, above 200 EMA → BUY signal

• Scenario D: Price below 50 EMA, touched 200 EMA recently → BUY signal

Technical Analysis

• Trend Detection: Golden Cross / Death Cross analysis

• EMAs: 50-day and 200-day exponential moving averages

• MACD: Moving Average Convergence Divergence charts

• Volume Analysis: Trading volume patterns

• Confidence Scores: Each recommendation includes confidence level

Project Structure

finance_mcp_server/
├── financial_mcp_server.py      # Main MCP server
├── batch_fin_mcp_server.py      # Batch analysis engine
├── email_report_script.py       # Email automation
├── daily_tracking_script.py     # Performance tracking
├── portfolio.json               # Portfolio configuration
├── requirements.txt             # Python dependencies
├── clean_requirements.txt       # Cleaned dependencies
├── CLAUDE.md                    # AI assistant guidance
├── mcp-http-bridge/             # HTTP bridge for MCP
├── batch_financial_charts/      # Generated analysis charts
└── tracking_charts/             # Performance tracking charts

Requirements

• Python 3.8+

• yfinance (free Yahoo Finance API)

• pandas, numpy, matplotlib

• MCP SDK (mcp>=1.0.0)

Disclaimer

⚠️ This software is for informational purposes only. It does not constitute financial advice. Always do your own research before making investment decisions.

License

MIT License - See LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub.