📊 FinanceMCP-Alpha

A powerful Model Context Protocol (MCP) server for calculating WorldQuant 101 Alpha factors using real-time Chinese stock market data from Tushare. Built with TypeScript and designed for quantitative trading analysis and research.

✨ Features

• 🎯 7 Alpha Factors: Alpha3, Alpha13, Alpha15, Alpha16, Alpha44, Alpha50, Alpha55
• 📈 Real-time Data: Integrates with Tushare API for A-share market data
• 🔒 Secure: Token-based authentication via HTTP headers
• 🚀 Streamable HTTP: Modern MCP protocol support
• 📊 Comprehensive Analysis: Detailed factor statistics and trading signals
• 🎨 Beautiful Reports: Markdown-formatted analysis with visual indicators

🏗️ Architecture

src/
├── index.ts              # MCP server entry point
├── tools/
│   └── calculate_alpha.ts # Alpha calculation tool
├── alphas/
│   └── index.ts          # Alpha factor implementations
└── utils/
    ├── tushare.ts        # Tushare API client
    └── operators.ts      # Mathematical operators

📦 Installation

Option 1: Use as npm package

npm install finance-mcp-alpha

Option 2: Clone and build locally

git clone https://github.com/guangxiangdebizi/FinanceMCP-Alpha.git
cd FinanceMCP-Alpha
npm install
npm run build

🚀 Quick Start

1. Start the Server

npm start

The server will start on http://localhost:3000 by default.

🚀 FinanceMCP-Alpha Server Started
=====================================
Transport: Streamable HTTP
MCP Endpoint: http://localhost:3000/mcp
Health Check: http://localhost:3000/health
=====================================

2. Configure MCP Client

Add to your MCP client configuration (e.g., mcp.json or Claude Desktop config):

{
  "mcpServers": {
    "finance-alpha": {
      "type": "streamableHttp",
      "url": "http://localhost:3000/mcp",
      "headers": {
        "X-Tushare-Token": "YOUR_TUSHARE_TOKEN_HERE"
      },
      "timeout": 600
    }
  }
}

⚠️ Important: Get your free Tushare token at https://tushare.pro/register

3. Use the Tool

Once connected, you can use the calculate_alpha tool:

Calculate Alpha factors for stock 000001.SZ from 20240101 to 20241011 
with factors Alpha3, Alpha13, Alpha50

📖 Alpha Factors

This package implements the following WorldQuant 101 Alpha factors:

Alpha#3

Formula: (-1 * correlation(rank(open), rank(volume), 10))
Use Case: Measures negative correlation between opening price ranks and volume ranks. High values suggest contrarian price-volume behavior.

Alpha#13

Formula: (-1 * rank(covariance(rank(close), rank(volume), 5)))
Use Case: Captures ranked covariance between closing prices and volume. Identifies price-volume anomalies.

Alpha#15

Formula: (-1 * sum(rank(correlation(rank(high), rank(volume), 3)), 3))
Use Case: Sum of ranked correlations between high prices and volume. Detects short-term momentum shifts.

Alpha#16

Formula: (-1 * rank(covariance(rank(high), rank(volume), 5)))
Use Case: Similar to Alpha13 but focuses on intraday volatility patterns using high prices.

Alpha#44

Formula: (-1 * correlation(high, rank(volume), 5))
Use Case: Negative correlation between high prices and volume ranks. Identifies volume divergence from price peaks.

Alpha#50

Formula: (-1 * ts_max(rank(correlation(rank(volume), rank(vwap), 5)), 5))
Use Case: Maximum ranked correlation between volume and VWAP. Measures volume-price efficiency.

Alpha#55

Formula: (-1 * correlation(rank((close - ts_min(low, 12)) / (ts_max(high, 12) - ts_min(low, 12))), rank(volume), 6))
Use Case: Corr

... View full README on GitHub