MCP Server for Shioaji
A Model Context Protocol (MCP) server that provides AI assistants with access to Shioaji trading API for the Taiwanese financial market.
Overview
This server implements the MCP protocol to expose Shioaji API functionality as tools that can be used by AI assistants. It allows AI models to:
- Retrieve current stock prices
- Fetch historical data
- List available stocks
- And more…
Installation
Prerequisites
- Python 3.10 or higher
- uv (fast Python package manager)
Using uv
uv sync
Configuration
Before running the server, you need to configure your Shioaji API credentials. There are two ways to do this:
Environment Variables
Set the following environment variables:
export SHIOAJI_API_KEY="your_api_key"
export SHIOAJI_SECRET_KEY="your_secret_key"
Using .env File
Create a .env
file in the root directory with the following content:
SHIOAJI_API_KEY=your_api_key
SHIOAJI_SECRET_KEY=your_secret_key
Running the Server
Start the server with:
uv run mcp-server-shioaji
The server will start on http://0.0.0.0:8000
by default.
Available Tools
The server exposes the following tools via MCP:
get_stock_price
Get the current price of a stock by its symbol.
{
"tool": "get_stock_price",
"params": {
"symbols": "TW.2330,TW.2317"
}
}
Response will include price information for the requested stocks, including open, high, low, close prices, volume, and other trading data.
get_kbars
Fetch K-Bar (candlestick) data for a stock within a date range.
{
"tool": "get_kbars",
"params": {
"symbol": "TW.2330",
"start_date": "2023-12-01",
"end_date": "2023-12-15"
}
}
If start_date
is not provided, it defaults to today. If end_date
is not provided, it defaults to the same as start_date
.
scan_stocks
Scan stocks based on various ranking criteria.
{
"tool": "scan_stocks",
"params": {
"scanner_type": "VolumeRank",
"ascending": false,
"limit": 10
}
}
Supported scanner types:
VolumeRank
- Ranking by trading volumeAmountRank
- Ranking by trading amountTickCountRank
- Ranking by number of transactionsChangePercentRank
- Ranking by percentage changeChangePriceRank
- Ranking by price changeDayRangeRank
- Ranking by daily range
Default limit is 20, and results are sorted in descending order by default (set ascending
to true
for ascending order).
Development
Project Structure
mcp-server-shioaji/
├── src/
│ └── mcp_server_shioaji/
│ ├── __init__.py # Package entry point
│ └── server.py # MCP server implementation
├── pyproject.toml # Project metadata and dependencies
└── README.md # This file
Adding New Tools
To add new Shioaji functionality, modify server.py
and add new tool definitions using the @mcp.tool
decorator.
License
MIT
Acknowledgements
mcp-server-shioaji
Project Details
- Sinotrade/mcp-server-shioaji
- Last Updated: 4/5/2025
Recomended MCP Servers
github-enterprise-mcp
Local MCP server implementation for Starwind UI that you can use with Cursor, Windsurf, and other AI tools
MCP server for training Linear Regression Model.
An MCP server implementation that integrates with SearXNG, providing privacy-focused meta search capabilities.
A Model Control Protocol (MCP) server that allows Claude to communicate with locally running LLM models via LM...
Query OpenAI models directly from Claude using MCP protocol.
A simple MCP server for https://useautumn.com
An MCP server for nearby place searches with IP-based location detection.
MCP Server for AI Agent Marketplace Index from DeepNLP
Model Context Protocol (MCP) server for SonarQube
Unblock, scrape, and search tools for MCP clients