stock analyzer (tingo).com
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Stock Analyzer (Tingo)

Integrates with Tiingo API to perform technical analysis on stocks, providing key indicators for trading decisions.

Created byApr 22, 2025

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
  • relative-strength: Calculates a stock's relative strength compared to a benchmark
  • volume-profile: Analyzes volume distribution by price
  • detect-patterns: Identifies chart patterns in price data
  • position-size: Calculates optimal position size based on risk parameters
  • suggest-stops: Suggests stop loss levels based on technical analysis

Technical Analysis Capabilities

The server leverages several specialized analysis modules:
  • TechnicalAnalysis: Core technical indicators and trend analysis
  • RelativeStrength: Comparative performance analysis
  • VolumeProfile: Advanced volume analysis
  • PatternRecognition: Chart pattern detection
  • RiskAnalysis: Position sizing and risk management

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+

Environment Variables

Create a .env file:

Installing via Smithery

To install Trader for Claude Desktop automatically via Smithery:
This will:
  1. Install the MCP Trader server
  1. Configure it with your Tiingo API key
  1. 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

Docker Deployment

The project includes a Dockerfile for containerized deployment:
To run the container in HTTP server mode:

Configuration

Claude Desktop App

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development Configuration:

Development

Build and Run

HTTP Server Mode

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

Debugging

Use the MCP Inspector for debugging:

Example Usage

In Claude Desktop:
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:

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
  1. Create a feature branch (git checkout -b feature/amazing-feature)
  1. Commit your changes (git commit -m 'Add some amazing feature')
  1. Push to the branch (git push origin feature/amazing-feature)
  1. 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:

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
  • relative-strength: Calculates a stock's relative strength compared to a benchmark
  • volume-profile: Analyzes volume distribution by price
  • detect-patterns: Identifies chart patterns in price data
  • position-size: Calculates optimal position size based on risk parameters
  • suggest-stops: Suggests stop loss levels based on technical analysis

Technical Analysis Capabilities

The server leverages several specialized analysis modules:
  • TechnicalAnalysis: Core technical indicators and trend analysis
  • RelativeStrength: Comparative performance analysis
  • VolumeProfile: Advanced volume analysis
  • PatternRecognition: Chart pattern detection
  • RiskAnalysis: Position sizing and risk management

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+

Environment Variables

Create a .env file:

Installing via Smithery

To install Trader for Claude Desktop automatically via Smithery:
This will:
  1. Install the MCP Trader server
  1. Configure it with your Tiingo API key
  1. 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

Docker Deployment

The project includes a Dockerfile for containerized deployment:
To run the container in HTTP server mode:

Configuration

Claude Desktop App

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development Configuration:

Development

Build and Run

HTTP Server Mode

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

Debugging

Use the MCP Inspector for debugging:

Example Usage

In Claude Desktop:
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:

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
  1. Create a feature branch (git checkout -b feature/amazing-feature)
  1. Commit your changes (git commit -m 'Add some amazing feature')
  1. Push to the branch (git push origin feature/amazing-feature)
  1. 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: