MCP Trader Server: Real-Time Analytics & Strategy Optimization

MCP Trader Server

A Model Context Protocol (MCP) server for stock traders.

Features

Tools

The server provides the following tools for stock analysis and trading:

• analyze-stock : Performs technical analysis on a given stock symbol

  • Required argument: symbol (string, e.g. "NVDA")
  • Returns comprehensive technical analysis including:
    • Moving average trends (20, 50, 200 SMA)
    • Momentum indicators (RSI, MACD)
    • Volatility metrics (ATR, ADRP)
    • Volume analysis

• relative-strength : Calculates a stock's relative strength compared to a benchmark

  • Required argument: symbol (string, e.g. "AAPL")
  • Optional argument: benchmark (string, default: "SPY")
  • Returns relative strength metrics across multiple timeframes (21, 63, 126, 252 days)
  • Includes performance comparison between the stock and benchmark

• volume-profile : Analyzes volume distribution by price

  • Required argument: symbol (string, e.g. "MSFT")
  • Optional argument: lookback_days (integer, default: 60)
  • Returns volume profile analysis including:
    • Point of Control (POC) - price level with highest volume
    • Value Area (70% of volume range)
    • Top volume price levels

• detect-patterns : Identifies chart patterns in price data

  • Required argument: symbol (string, e.g. "AMZN")
  • Returns detected chart patterns with confidence levels and price targets

• position-size : Calculates optimal position size based on risk parameters

  • Required arguments:
    • symbol (string, e.g. "TSLA")
    • stop_price (number)
    • risk_amount (number)
    • account_size (number)
  • Optional argument: price (number, default: current price)
  • Returns recommended position size, dollar risk, and potential profit targets

• suggest-stops : Suggests stop loss levels based on technical analysis

  • Required argument: symbol (string, e.g. "META")
  • Returns multiple stop loss suggestions based on:
    • ATR-based stops (1x, 2x, 3x ATR)
    • Percentage-based stops (2%, 5%, 8%)
    • Technical levels (moving averages, recent swing lows)

Technical Analysis Capabilities

The server leverages several specialized analysis modules:

• TechnicalAnalysis : Core technical indicators and trend analysis

  • Moving averages (SMA 20, 50, 200)
  • Momentum indicators (RSI, MACD)
  • Volatility metrics (ATR, Average Daily Range Percentage)
  • Volume analysis (20-day average volume)

• RelativeStrength : Comparative performance analysis

  • Multi-timeframe relative strength scoring (21, 63, 126, 252 days)
  • Performance comparison against benchmark indices
  • Outperformance/underperformance classification

• VolumeProfile : Advanced volume analysis

  • Price level volume distribution
  • Point of Control (POC) identification
  • Value Area calculation (70% of volume)

• PatternRecognition : Chart pattern detection

  • Support/resistance levels
  • Common chart patterns (head and shoulders, double tops/bottoms, etc.)
  • Confidence scoring for detected patterns

• RiskAnalysis : Position sizing and risk management

  • Risk-based position sizing
  • Multiple stop loss strategies
  • R-multiple profit target calculation

Data Sources

The server uses the Tiingo API for market data:

• Historical daily OHLCV data
• Adjusted prices for accurate backtesting
• Up to 1 year of historical data by default

Setup

Prerequisites

• Python 3.11+
• uv
• ta-lib
• Tiingo API Key

Environment Variables

Create a .env file:

TIINGO_API_KEY=your_api_key_here

Installing via Smithery

To install Trader for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-trader --client claude

This will:

1. Install the MCP Trader server
2. Configure it with your Tiingo API key
3. Set up the Claude Desktop integration

Smithery Configuration

The server includes a smithery.yaml configuration file that defines:

• Required configuration parameters (Tiingo API key)
• Command function to start the MCP server
• Integration with Claude Desktop

You can customize the Smithery configuration by editing the smithery.yaml file.

Installation

uv venv --python 3.11
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv sync

Docker Deployment

The project includes a Dockerfile for containerized deployment:

# Build the Docker image
docker build -t mcp-trader .

# Run the container with your API key
docker run -e TIINGO_API_KEY=your_api_key_here -p 8000:8000 mcp-trader

To run the container in HTTP server mode:

docker run -e TIINGO_API_KEY=your_api_key_here -p 8000:8000 mcp-trader uv run mcp-trader --http

Configuration

Claude Desktop App

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development Configuration:

{
  "mcpServers": {
    "stock-analyzer": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/mcp-trader",
        "run",
        "mcp-trader"
      ]
      "env": {
        "TIINGO_API_KEY": "your_api_key_here"
      }
    }
  }
}

Development

Build and Run

uv build
uv run mcp-trader

HTTP Server Mode

The server can also run as a standalone HTTP server for testing or integration with other applications:

uv run mcp-trader --http

This starts an HTTP server on http://localhost:8000 with the following endpoints:

• GET /list-tools : Returns a list of available tools and their schemas
• POST /call-tool : Executes a tool with the provided arguments

  • Request body format:

          {
    "name": "analyze-stock",
    "arguments": {
      "symbol": "AAPL"
    }
    

    }

  • Returns an array of content items (text, images, etc.)

Debugging

Use the MCP Inspector for debugging:

npx @modelcontextprotocol/inspector uv --directory /path/to/mcp-trader run mcp-trader

Example Usage

In Claude Desktop:

Analyze the technical setup for NVDA

The server will return a technical analysis summary including trend status, momentum indicators, and key metrics.

Dependencies

See pyproject.toml for full dependency list:

- aiohttp >=3.11.11
- mcp >=1.2.0
- numpy ==1.26.4
- pandas >=2.2.3
- pandas-ta >=0.3.14b0
- python-dotenv >=1.0.1
- setuptools >=75.8.0
- ta-lib >=0.6.0

Contributing

Contributions to MCP Trader are welcome! Here are some ways you can contribute:

• Add new tools : Implement additional technical analysis tools or trading strategies
• Improve existing tools : Enhance the accuracy or performance of current tools
• Add data sources : Integrate additional market data providers
• Documentation : Improve the documentation or add examples
• Bug fixes : Fix issues or improve error handling

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Future Plans

The MCP Trader project has several planned enhancements:

• Portfolio Analysis : Tools for analyzing and optimizing portfolios
• Backtesting : Capabilities to test trading strategies on historical data
• Sentiment Analysis : Integration with news and social media sentiment data
• Options Analysis : Tools for analyzing options strategies and pricing
• Real-time Data : Support for real-time market data feeds
• Custom Strategies : Framework for implementing and testing custom trading strategies
• Alerts : Notification system for price and technical indicator alerts

Further Reading

Learn more about this project through these detailed blog posts:

• Building a Stock Analysis Server with MCP, Part 1 - Initial setup, architecture, and core technical analysis features
• Building a Stock Analysis Server with MCP, Part 2 - Relative Strength, Volume, Pattern Recognition, Risk analysis