mcp-trader
If you are the rightful owner of mcp-trader and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcpreview.com.
A Model Context Protocol (MCP) server designed for stock traders, offering a suite of tools for technical analysis and trading.
The MCP Trader Server is a comprehensive platform for stock traders, providing a range of tools for technical analysis and trading strategies. It leverages advanced modules for technical analysis, relative strength comparison, volume profiling, pattern recognition, and risk management. The server integrates with the Tiingo API to access historical market data, ensuring accurate and up-to-date analysis. Users can perform detailed stock analysis, calculate relative strength, analyze volume distribution, detect chart patterns, and determine optimal position sizes. The server is designed to be flexible and can be deployed via Docker or integrated with platforms like Claude Desktop using Smithery. It supports both command-line and HTTP server modes, making it versatile for different use cases. The server is built on Python and requires specific dependencies, including aiohttp, numpy, pandas, and ta-lib, among others.
Features
- Technical Analysis Capabilities: Provides core technical indicators and trend analysis, including moving averages, momentum indicators, and volatility metrics.
- Relative Strength Analysis: Offers comparative performance analysis across multiple timeframes against benchmark indices.
- Volume Profile Analysis: Delivers advanced volume analysis, identifying key price levels and value areas.
- Pattern Recognition: Detects common chart patterns and provides confidence scoring for identified patterns.
- Risk Management Tools: Includes position sizing and multiple stop loss strategies for effective risk management.
Tools
analyze-stock
Technical analysis of a given stock symbol
relative-strength
Calculate the relative strength of a stock relative to a benchmark
volume-profile
Analyze the price trading volume distribution
detect-patterns
Identify chart patterns in price data
position-size
Calculate the optimal position size based on risk parameters
suggest-stops
Stop loss level based on technical analysis