This MCP trader server, developed by Seth Hobson, provides technical analysis capabilities for stock traders. It integrates with the Tiingo API to fetch financial data and leverages libraries like pandas and TA-Lib to perform comprehensive technical analysis on stocks. The server offers a single 'analyze-stock' tool that takes a stock symbol and returns key technical indicators including moving averages, momentum oscillators, and volume metrics. Built with Python and designed to run as a local MCP server, it's particularly useful for AI assistants or applications needing to generate detailed technical analysis reports or make data-driven trading recommendations based on current market data.
Performs technical analysis on a given stock symbol. Required argument: symbol (string, e.g. 'NVDA'). Returns comprehensive technical analysis including moving average trends, momentum indicators, volatility metrics, and volume analysis.
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 and performance comparison.
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, Value Area, and top volume price levels.
Identifies chart patterns in price data. Required argument: symbol (string, e.g. 'AMZN'). Returns detected chart patterns with confidence levels and price targets.
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.
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, percentage-based stops, and technical levels.
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