An intelligent, multi-market stock selection and backtesting utility featuring SQLite caching and multi-factor quantitative strategies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stockaskill
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 stockaskill 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 stockaskill?
The stockaskill is a professional multi-market quantitative analysis engine developed specifically for Openclaw Skills. Engineered to automate intelligent stock selection, portfolio optimization, and strategy backtesting, this tool spans major global equity markets, including Chinese A-shares, Hong Kong (HK) markets, US stock exchanges, and exchange-traded funds (ETFs/LOFs). By leveraging the public AKShare API alongside a fast, local SQLite database, it grants developers and quantitative analysts access to high-performance equity scanning tools with zero proprietary API key requirements.
Integrating this asset into your suite of Openclaw Skills enables autonomous AI agents to perform advanced market diagnostic services on demand. It evaluates financial assets against 6 integrated strategies and 7 factor dimensions (including quality, momentum, low-volatility, and valuation) to output actionable market signals, explicit risk warnings, and asset allocation blueprints. With local cache storage and resilient fallback protocols, stockaskill is a foundational asset for automated retail trading and market research.
stockaskill Use Cases
- Multi-market equity scanning: Run extensive scans across Chinese A-shares, Hong Kong markets, US tickers, and index funds using multi-factor ranking pipelines.
- Automated stock diagnosis: Analyze individual assets like 600519 or AAPL to generate a comprehensive BUY/SELL/HOLD decision sheet equipped with stop-loss and take-profit targets.
- Strategy backtesting: Evaluate long-term algorithmic model performance using historical data pools (requiring 1500+ trading days) directly from your command line using Openclaw Skills.
- Portfolio construction: Leverage math-driven allocation methods such as equal weighting, risk parity, or minimum variance strategies to secure balanced weights.
- Data caching pipelines: Maintain a local, high-speed SQLite copy of financial datasets to bypass daily API limit restrictions during active development.
How stockaskill Works
- Triggering Intent: A user prompts the AI agent using specific stock codes, Chinese company names, or intent-based keywords like scan, portfolio, or backtest within the Openclaw Skills context.
- Database Lookup: The tool queries a local SQLite database to retrieve cached financial profiles, metrics, and historical daily bar charts.
- API Fetching (Fallback): If the local cache has expired or is cold, the skill calls the AKShare API to update its fundamental and K-line database files.
- Factor Scoring Engine: The system runs the equity through its parallel processing module, scoring it against 7 factors (e.g., F-Score, Alpha Momentum, low volatility).
- Report & Output Generation: Detailed markdown reports or structured JSON payloads are exported to local directories according to user requirements.
stockaskill Setup
Ensure you have Python 3.10+ installed and active internet access. No API key is needed for this Openclaw Skills package.
First, install all necessary analytical libraries via your package manager:
pip install akshare efinance baostock
Next, warm up your local database by performing a full pool fetch from the root of your project directory:
python scripts/run.py fetch pool
Run your first evaluation test with a quick single stock analysis:
python scripts/run.py analyze 600519 --market A
stockaskill Data Schema & Taxonomy
The stockaskill maintains its local data structures and results under standard formats. The following table highlights the files generated and structural layouts:
| File Path | Output Format | Purpose / Schema |
|---|---|---|
sqlite.db |
SQLite Database | Holds local copies of K-line historical bars, stock profile databases, and ETF assets |
./reports/ |
Directory | Destination for all auto-generated JSON and Markdown reports |
./reports/*.json |
JSON Object | Structured analytical data, exposing final_decision.signal, adjusted_score, and risk vectors |
./reports/*.md |
Markdown Report | Human-readable analysis featuring core factors, technical parameters, and execution bounds |
stockaskill Advanced Features
- Thread-Parallel Alpha Scoring: Leverages a highly-efficient thread pool of 8 active worker threads to process quantitative alpha momentum scans across hundreds of assets simultaneously.
- Core-Satellite Portfolio Optimization: Supports advanced risk parity, equal-weight, and enhanced minimum-variance formulas for asset allocation calculations.
- Resilient Fallback Protocols: Ensures continuous workflow operation within Openclaw Skills by utilizing cached SQLite data if daily third-party API limits (500 queries/day) are encountered.
- Sector-Specific Scanning: Provides programmatic Python access points to run scanning operations filtered precisely by target economic sectors and industries.
SKILL.md
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