investment-data
获取高质量 A 股投资数据，基于 investment_data 项目。支持日终价格、涨跌停数据、指数数据等。每日更新，多数据源交叉验证。触发词：股票数据、A股数据、金融数据、量化数据、历史行情。
Why use this skill?
Access, query, and analyze high-quality A-share market data with the OpenClaw investment-data skill. Perfect for quantitative research and backtesting.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/stanleychanh/investment-dataWhat This Skill Does
The investment-data skill serves as a robust gateway for accessing high-quality, verified A-share financial market data. Built upon the comprehensive investment_data project, this tool automates the retrieval, storage, and querying of complex financial information, including daily closing prices, limit-up/limit-down status, index weightings, and historical market data. It acts as an integration layer between your OpenClaw agent and large-scale financial datasets, ensuring that your quantitative analysis and investment decision-making processes are fueled by accurate, cross-validated data points. Whether you are conducting research on individual stocks or performing bulk analysis on entire sectors, this skill simplifies the data pipeline through both a user-friendly Python API and a powerful command-line interface.
Installation
To integrate this skill into your environment, run the following command in your OpenClaw-enabled terminal:
clawhub install openclaw/skills/skills/stanleychanh/investment-data
After installation, ensure you have configured your environment variables, specifically INVESTMENT_DATA_DIR for storage paths, and an optional TUSHARE_TOKEN if you require real-time updates beyond the daily batch syncs.
Use Cases
This skill is ideal for financial analysts and developers working with Qlib or other quantitative platforms. Key use cases include: 1) Performing historical backtesting of trading strategies using accurate, split-adjusted data. 2) Generating custom CSV/Excel reports for portfolio performance reviews. 3) Automating daily market monitoring through cron jobs that pull the latest updates every morning. 4) Analyzing index component changes and sector-wide movements to stay ahead of market shifts.
Example Prompts
- "获取 000001.SZ 从 2024 年 1 月 1 日到 2024 年 12 月 31 日的历史日线数据，并导出为 CSV 格式。"
- "查询沪深 300 指数（000300.SH）在 2024 年 12 月 1 日的成分股权重分布。"
- "帮我检查一下有哪些 A 股股票在近期发生了退市，并列出相关数据。"
Tips & Limitations
- Storage: Ensure you have at least 5GB of free disk space, as historical financial datasets grow rapidly.
- Data Latency: Note that data is updated daily (T+1), so it is optimized for analysis rather than high-frequency trading.
- Dependencies: The skill relies on external access to GitHub and DoltHub. Ensure your firewall or network environment allows these connections.
- Maintenance: Regularly use the update scripts provided to keep your local cache synchronized with the upstream repository for improved accuracy and completeness.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-stanleychanh-investment-data": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-write, file-read, external-api, code-execution