A comprehensive financial data tool for querying real-time A-share market trends, financial statements, and technical indicators.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install a-stock-data
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 a-stock-data 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 A-share Stock Data Analysis Skill?
This skill provides an extensive suite of tools for A-share market analysis based on the AkShare open-source library. It enables AI agents to retrieve real-time quotes from major Chinese exchanges, including Shanghai, Shenzhen, and Beijing, while also offering deep dives into historical price movements and corporate financial reports. By integrating these Openclaw Skills, developers can build sophisticated financial assistants capable of monitoring market sentiment and performing deep-dive stock research with high-frequency data updates.
A-share Stock Data Analysis Skill Use Cases
- Retrieving real-time stock prices and daily limit status for SH/SZ/BJ exchanges.
- Analyzing historical K-line data with adjustable price settings (QFQ/HFQ) for technical backtesting.
- Performing technical analysis using indicators like MACD, RSI, and Bollinger Bands to identify market trends.
- Filtering stocks across the entire market based on specific multi-factor selection strategies.
- Accessing detailed corporate financial statements including balance sheets, profit sheets, and cash flow reports.
How A-share Stock Data Analysis Skill Works
- The AI agent identifies financial queries related to stock codes, company names, or specific technical patterns.
- It triggers the AkShare interface to fetch raw market or financial data from various financial providers.
- For technical queries, the skill utilizes TA-Lib to compute indicators such as Moving Averages, RSI, or KDJ on the retrieved data frames.
- Data is organized into structured formats, applying logic for price adjustments (forward/backward) and stock code padding.
- The agent synthesizes the findings into readable reports, tables, or actionable trading signals for the user.
A-share Stock Data Analysis Skill Setup
To enable these Openclaw Skills, you need to install the core data and analysis libraries in your environment:
pip install akshare
pip install ta-lib
Ensure your Python environment is configured to handle Pandas DataFrames, as all financial data is returned in this structure for optimal manipulation.
A-share Stock Data Analysis Skill Data Schema & Taxonomy
The skill returns structured data primarily as Pandas DataFrames. The schema varies by query type:
Real-time Market Data
| Field | Description |
|---|---|
| Code | Stock identifier (e.g., 600519) |
| Name | Company name |
| Latest Price | Current trading price |
| Change % | Percentage fluctuation |
| Turnover | Trading volume and total turnover |
Historical K-line Data
| Field | Description |
|---|---|
| Date | Transaction date |
| Open/Close | Opening and closing prices |
| High/Low | Price range during the period |
| Volume | Number of shares traded |
A-share Stock Data Analysis Skill Advanced Features
- Multi-factor stock screening allowing users to combine indicators like MA Golden Cross with RSI and volume ratios.
- Dedicated support for GEM (Chinext) and STAR Market (Science and Technology Innovation Board) specific queries.
- Capital Flow monitoring, including Dragon and Tiger list (Longhu Bang) statistics and sector-specific trends.
- Comprehensive financial indicator analysis covering dynamic PE ratios, total market capitalization, and circulating market value.
- Flexible data adjustment options for historical research, supporting qfq (forward), hfq (backward), and original price formats.
SKILL.md
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