maverick-mcp

# MaverickMCP - Personal Stock Analysis MCP Server [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://github.com/jlowin/fastmcp) [](https://github.com/wshobson/maverick-mcp) [](https://github.com/wshobson/maverick-mcp/issues) [](https://github.com/wshobson/maverick-mcp/network/members) **MaverickMCP** is a personal-use FastMCP 2.0 server that provides professional-grade financial data analysis, technical indicators, and portfolio optimization tools directly to your Claude Desktop interface. Built for individual traders and investors, it offers comprehensive stock analysis capabilities without any authentication or billing complexity. The server comes pre-seeded with all 520 S&P 500 stocks and provides advanced screening recommendations across multiple strategies. It runs locally with HTTP/SSE/STDIO transport options for seamless integration with Claude Desktop and other MCP clients. ## Why MaverickMCP? MaverickMCP provides professional-grade financial analysis tools directly within your Claude Desktop interface. Perfect for individual traders and investors who want comprehensive stock analysis capabilities without the complexity of expensive platforms or commercial services. **Key Benefits:** - **No Setup Complexity**: Simple `make dev` command gets you running (or `uv sync` + `make dev`) - **Modern Python Tooling**: Built with `uv` for lightning-fast dependency management - **Claude Desktop Integration**: Native MCP support for seamless AI-powered analysis - **Comprehensive Analysis**: 29+ financial tools covering technical indicators, screening, and portfolio optimization - **Smart Caching**: Redis-powered performance with graceful fallbacks - **Fast Development**: Hot reload, smart error handling, and parallel processing - **Open Source**: MIT licensed, community-driven development - **Educational Focus**: Perfect for learning financial analysis and MCP development ## Features - **Pre-seeded Database**: 520 S&P 500 stocks with comprehensive screening recommendations - **Advanced Backtesting**: VectorBT-powered engine with 15+ built-in strategies and ML algorithms - **Fast Development**: Comprehensive Makefile, smart error handling, hot reload, and parallel processing - **Stock Data Access**: Historical and real-time stock data with intelligent caching - **Technical Analysis**: 20+ indicators including SMA, EMA, RSI, MACD, Bollinger Bands, and more - **Stock Screening**: Multiple strategies (Maverick Bullish/Bearish, Trending Breakouts) with parallel processing - **Portfolio Tools**: Correlation analysis, returns calculation, and optimization - **Market Data**: Sector performance, market movers, and earnings information - **Smart Caching**: Redis-powered performance with automatic fallback to in-memory storage - **Database Support**: SQLAlchemy integration with PostgreSQL/SQLite (defaults to SQLite) - **Multi-Transport Support**: HTTP, SSE, and STDIO transports for all MCP clients ## Quick Start ### Prerequisites - **Python 3.12+**: Core runtime environment - **[uv](https://docs.astral.sh/uv/)**: Modern Python package manager (recommended) - **TA-Lib**: Technical analysis library for advanced indicators - Redis (optional, for enhanced caching) - PostgreSQL or SQLite (optional, for data persistence) #### Installing TA-Lib TA-Lib is required for technical analysis calculations. **macOS and Linux (Homebrew):** ```bash brew install ta-lib ``` **Windows (Multiple Options):** **Option 1: Conda/Anaconda (Recommended - Easiest)** ```bash conda install -c conda-forge ta-lib ``` **Option 2: Pre-compiled Wheels** 1. Download the appropriate wheel for your Python version from: - [cgohlke/talib-build releases](https://github.com/cgohlke/talib-build/releases) - Choose the file matching your Python version (e.g., `TA_Lib-0.4.28-cp312-cp312-win_amd64.whl` for Python 3.12 64-bit) 2. Install using pip: ```bash pip install path/to/downloaded/TA_Lib-X.X.X-cpXXX-cpXXX-win_amd64.whl ``` **Option 3: Alternative Pre-compiled Package** ```bash pip install TA-Lib-Precompiled ``` **Option 4: Build from Source (Advanced)** If other methods fail, you can build from source: 1. Install Microsoft C++ Build Tools 2. Download and extract ta-lib C library to `C:\ta-lib` 3. Build using Visual Studio tools 4. Run `pip install ta-lib` **Verification:** Test your installation: ```bash python -c "import talib; print(talib.__version__)" ``` #### Installing uv (Recommended) ```bash # macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh