A high-performance financial data skill for Openclaw agents to query A-share stock prices, intraday bars, and valuation factors.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-stock-data-skill
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 openclaw-stock-data-skill 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 Openclaw Stock Skill?
The Openclaw Stock Skill empowers AI agents to interact with professional-grade financial market data. By integrating with the diemeng.chat data service, agents can retrieve comprehensive stock information including daily K-lines, minute-by-minute historical data, and complex financial factors. This skill is a vital component for developers building trading assistants or market analysis tools within the Openclaw Skills ecosystem.
It provides a standardized, secure way to fetch A-share market data using an API key mechanism. Whether the agent needs to analyze historical price trends, compare industry valuations, or check trading calendars, this skill offers a robust set of HTTP-based tools designed for seamless invocation and high-accuracy financial reporting.
Openclaw Stock Skill Use Cases
- Retrieving historical daily K-line data for specific stock codes over defined time ranges.
- Analyzing intraday market movements using 1-minute to 60-minute frequency bars for technical analysis.
- Comparing industry valuation levels such as PE, PB, and dividend yields to identify market opportunities.
- Searching for stock basic information based on names, industries, or geographical regions.
- Monitoring real-time snapshots and verifying trading calendar dates to ensure execution during market hours.
How Openclaw Stock Skill Works
- The AI agent identifies a financial query from the user, such as a request for price history or valuation rankings.
- The skill retrieves the required STOCK_API_KEY from the process environment variables provided by the Openclaw Skills configuration.
- A structured HTTP POST or GET request is dispatched to the designated endpoint at data.diemeng.chat with the appropriate authentication headers.
- The API returns a JSON response containing the requested market or financial data with unified decimal precision.
- The agent parses the resulting data, applies necessary formatting or summarization, and presents actionable insights to the user.
Openclaw Stock Skill Setup
To enable this skill, you must provide a valid API key from the data provider. Configure your openclaw.json file to include the skill entry and environment variables:
# Set your environment variable in your shell or via the Openclaw UI
export STOCK_API_KEY="your_real_stock_api_key"
Update your configuration structure:
{
"skills": {
"entries": {
"openclaw-stock-skill": {
"enabled": true,
"apiKey": { "source": "env", "provider": "default", "id": "STOCK_API_KEY" },
"env": {
"STOCK_API_KEY": "YOUR_REAL_STOCK_API_KEY"
},
"config": {
"baseUrl": "https://data.diemeng.chat"
}
}
}
}
}
Openclaw Stock Skill Data Schema & Taxonomy
The skill organizes market data into several specific categories to ensure clarity and ease of analysis:
| Data Category | Key Fields | Description |
|---|---|---|
| Daily Bars | stock_code, trade_date, open, high, low, close, vol | Standard daily K-line metrics |
| Intraday Bars | trade_time, level (1min-60min), open, close | High-frequency historical data |
| Financial Factors | pe_ttm, pb, turnover_rate, total_mv, float_share | Valuation and liquidity metrics |
| Stock Valuation | pe_percentile, industry_pe_rank, roe, eps_growth | Comparative industry analysis |
| Trading Basic | date, is_open, stock_name, industry, area | Calendar and metadata taxonomy |
Openclaw Stock Skill Advanced Features
- Support for multi-stock code batch queries in a single request to optimize network overhead.
- Automated industry-wide valuation ranking and percentile calculations for sector analysis.
- Configurable base URL support allowing for private data proxy deployments.
- Integrated rate limiting and error handling (401, 403, 429) to ensure stable agent performance.
- Pagination support for handling large historical datasets without memory overflow in the agent context.
SKILL.md
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