mcp trader

Local 2025-09-01 00:12:49 0

The MCP Trader Server conducts comprehensive technical analysis on stocks, offering insights into trends, momentum indicators, volatility metrics, and volume analysis to support stock trading decisions.

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:

Install the MCP Trader server
Configure it with your Tiingo API key
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: .venvScriptsactivate
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.

NVDA Technical Analysis

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

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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