A comprehensive financial data skill for AI agents to access real-time and historical futures, options, and market basis data via AKShare.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install futures-panda
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 futures-panda 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 futures-panda?
futures-panda is a specialized Openclaw Skills extension that leverages the AKShare library to provide deep access to Chinese and international financial markets. It enables AI agents to retrieve a wide array of data including stock futures, options, warehouse receipts, and market basis info. By bridging the gap between raw financial APIs and AI workflows, it allows for sophisticated market analysis and automated data retrieval within a standardized environment.
This skill is designed for developers and researchers who need high-fidelity financial data without building complex scrapers. It handles the nuances of different exchanges like SHFE, DCE, and CZCE, providing a unified interface for market intelligence and academic financial research.
futures-panda Use Cases
- Monitoring real-time price movements for specific futures contracts like RB2605.
- Conducting quantitative research using historical daily and minute-level market data.
- Analyzing market sentiment through seat positions and warehouse receipt data.
- Calculating trading costs by retrieving up-to-date handling fees and margin requirements.
- Managing automated trading calendars by checking exchange-specific trading rules.
How futures-panda Works
- The AI agent initializes the skill environment and ensures the AKShare and Pandas dependencies are active.
- When a data request is made, the skill maps the request to the appropriate AKShare interface (e.g., Sina or specific exchange APIs).
- Data is fetched from public financial sources in real-time or from historical archives based on the provided symbol and date parameters.
- The raw data is processed into structured Pandas DataFrames for easy manipulation or output.
- The formatted data is then returned to the user or passed to another agent in the workflow for further analysis.
futures-panda Setup
To get started with this skill, install the necessary Python dependencies and verify the installation:
# Install required dependencies
pip install akshare pandas
# Verify the installation and AKShare version
python -c "import akshare; print(akshare.__version__)"
futures-panda Data Schema & Taxonomy
The skill organizes data primarily through Pandas DataFrames. Key data structures include:
| Data Category | Primary Key | Description |
|---|---|---|
| Spot Market | symbol | Real-time price, volume, and turnover for specific contracts. |
| Historical Daily | date | Open, high, low, close, and volume for backtesting. |
| Contract Info | exchange | Specific details regarding contract specs for SHFE, DCE, CFFEX, etc. |
| Trading Fees | contract | Detailed margin rates and transaction fee percentages. |
Files generated via the scripts/ directory, such as futures_data.py, facilitate the export of this data into CSV or JSON formats for external tool consumption.
futures-panda Advanced Features
- Multi-Exchange Support: Unified access to SHFE, INE, DCE, CZCE, GFEX, and CFFEX through a single interface.
- Granular Timeframes: Access to high-frequency minute-level data (1, 5, 15, 30, and 60-minute intervals).
- Automated Scripting: Includes pre-built scripts like
futures_data.pyfor batch data collection and reporting. - Basis Analysis: Tools to compare spot and futures prices to identify market arbitrage opportunities.
- Continuous Contract Tracking: Integration with Sina’s continuous contract interface for long-term trend analysis.
SKILL.md
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