A professional-grade quantitative trading tool providing real-time market data and multi-dimensional scoring across global equity and commodity markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cn-stock-analyzer
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 cn-stock-analyzer 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 Trading Quant?
Trading Quant is a sophisticated analytical engine designed for the Openclaw Skills ecosystem. It aggregates real-time financial data from premium sources like Tencent, Sina, and East Money to provide a holistic view of the market. The skill specializes in technical, capital, and fundamental analysis, offering a unique scoring system that integrates LLM-driven sentiment and news evaluation.
By leveraging this skill, developers and traders can automate the monitoring of A-shares, US stocks, Hong Kong stocks, and precious metals. It is built to ensure data integrity by utilizing a fallback chain of data providers, ensuring that your trading agents always have access to the most reliable market snapshots.
Trading Quant Use Cases
- Monitoring real-time stock quotes and technical scores for A-shares, US, and HK markets.
- Analyzing market anomalies, including limit-up/limit-down stock pools and significant capital flow changes.
- Tracking Northbound capital movements to gauge foreign investment trends in the Chinese market.
- Conducting deep-dive technical scans using MACD, RSI, and KDJ indicators.
- Evaluating market sentiment and news impact through integrated LLM analysis.
How Trading Quant Works
- The skill receives a request for specific market data or a technical analysis of a stock code.
- It interfaces with the configured data source (e.g., Tencent Finance) to fetch real-time price, volume, and order book data.
- A multi-dimensional scoring algorithm processes the data, weighing technical (25%), capital (30%), fundamental (10%), news (20%), and sentiment (15%) factors.
- If primary data sources are unavailable, the system automatically triggers a fallback mechanism to alternative providers like Sina or East Money.
- The skill outputs a comprehensive report including a signal grade ranging from Strong Buy to Strong Sell based on the calculated score.
Trading Quant Setup
To integrate Trading Quant into your environment, ensure you have Python 3.12 installed and the necessary dependencies configured. Follow these steps:
# Navigate to your local Openclaw Skills repository
cd openclaw-skills-directory
# Verify system health and data source connectivity
python3.12 scripts/quant.py system_health
# (Optional) Warm up the local K-line cache for faster analysis
python3.12 scripts/quant.py warm_klines
Trading Quant Data Schema & Taxonomy
The skill organizes market information into structured objects for easy consumption by AI agents.
| Data Component | Source Hierarchy | Description |
|---|---|---|
| A-Shares | Tencent -> Sina -> East Money | Real-time price, volume, and capital flow |
| US Stocks | Tencent -> yfinance | Global equity data and technical indicators |
| Commodities | Sina Futures | Real-time precious metals and futures pricing |
| Scoring | Quantitative Engine | Technical, Fundamental, and Sentiment weights |
Trading Quant Advanced Features
- Multi-source fallback logic ensuring high availability of financial data within Openclaw Skills.
- LLM-powered news and sentiment analysis for qualitative market insights.
- Automated market anomaly detection for identifying unusual price movements and trading volumes.
- Integrated Northbound capital tracking for institutional flow analysis.
- Comprehensive signal grading system (STRONG_BUY to STRONG_SELL) for automated decision-making.
SKILL.md
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