A professional tool for downloading A-share historical market data across multiple timeframes using natural language commands and the QMT quantitative platform.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stock-qdata
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install stock-qdata using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Stock-QData?
Stock-QData is a powerful utility within the Openclaw Skills ecosystem designed specifically for quantitative traders and financial analysts. It streamlines the process of fetching A-share historical K-line data from the QMT (Quantitative Management Tool) platform. By integrating natural language processing, it allows users to trigger complex data retrieval tasks through simple conversational prompts.
The skill provides a robust bridge between the QMT terminal and local storage, ensuring that high-quality financial data is organized and ready for backtesting or analysis. With its built-in web monitoring interface, developers can track progress in real-time, making it an essential component for any data-driven trading workflow using Openclaw Skills.
Stock-QData Use Cases
- Downloading historical daily, 30-minute, 5-minute, or 1-minute K-line data for the entire A-share market.
- Automating bulk data collection for quantitative strategy development and backtesting.
- Monitoring large-scale data download progress through a real-time web dashboard.
- Managing data integrity with automatic retries and error logging for failed downloads.
How Stock-QData Works
- The user initiates a task via a natural language instruction or specific CLI flags.
- Stock-QData connects to the running QMT quantitative trading platform client to access market data services.
- The skill parses the request to determine the data period, lookback days, and batch size.
- It fetches data in batches from the QMT server, providing real-time progress updates via Socket.IO if the web mode is enabled.
- Downloaded data is validated and saved as CSV files in a structured local directory.
- Smart error handling mechanisms automatically log failures and attempt to retry missing stock data.
Stock-QData Setup
To integrate this into your workflow with Openclaw Skills, follow these steps:
- Ensure the QMT quantitative trading platform is installed and logged in.
- Configure your Python environment (Python 3.7 to 3.12 supported).
- Install the required dependencies:
pip install -r requirements.txt
- Update
config.jsonwith your specific paths:
{
"base_path": "d:\\data\\",
"qmt_path": "D:\\QMT\\bin.x64"
}
- Start a download task using a natural language prompt:
python main.py "download daily data for the last 500 days using web mode"
Stock-QData Data Schema & Taxonomy
Stock-QData organizes data into a clean, hierarchical structure based on the timeframe requested:
| Directory Level | Description |
|---|---|
{base_path}/k_data/ |
The root directory for all market data. |
.../{period}/ |
Sub-directories categorized by timeframe (e.g., 1d, 30m, 5m). |
.../{symbol}.csv |
Individual CSV files for each stock (e.g., 000001.SZ.csv). |
Each CSV file contains standard OHLCV (Open, High, Low, Close, Volume) data suitable for direct import into pandas or other analytical tools.
Stock-QData Advanced Features
- Natural Language Interface: Supports intuitive prompts for task initiation without complex flag memorization.
- Real-time Web Dashboard: Monitor download speed, estimated completion time, and individual stock status via a Flask-based UI.
- Batch Processing Control: Manually adjust batch sizes and pause/resume tasks through the web interface to optimize system load.
- Automatic Service Discovery: Dynamically finds available ports (5018-5038) for the monitoring server to avoid conflicts.
- Smart Retry Logic: Automatically identifies and re-attempts data fetching for symbols that failed during the initial pass.
SKILL.md
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