A comprehensive A-share quantitative analysis toolkit for data retrieval, technical indicator calculation, and strategy backtesting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install china-stock-quant
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 china-stock-quant 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 China Stock Quant?
The China Stock Quant skill is a specialized toolkit designed for developers and traders focusing on the Chinese stock market. Built on top of the robust akshare library, it provides seamless access to A-share and ETF market data without requiring expensive API tokens. This skill empowers users to transition from raw market data to actionable insights by automating the calculation of essential technical indicators and performing rigorous risk assessments.
By integrating with Openclaw Skills, this tool simplifies the complexity of quantitative finance. It handles everything from daily K-line fetching to intraday ETF analysis, making it an essential asset for anyone looking to build automated trading bots or conduct deep financial research on the Shanghai and Shenzhen exchanges.
China Stock Quant Use Cases
- Analyzing A-share market trends and individual stock performance.
- Implementing and backtesting ETF day-trading strategies such as grid trading.
- Calculating technical indicators like MACD, KDJ, RSI, and Bollinger Bands for automated signals.
- Performing risk assessments using metrics like Sharpe ratio and maximum drawdown.
- Developing trend-following or mean-reversion strategies for Chinese equity markets.
How China Stock Quant Works
- Data Acquisition: The skill utilizes scripts to fetch real-time and historical data for stocks and ETFs via akshare.
- Indicator Processing: Raw price and volume data are passed through technical analysis functions to generate signals.
- Strategy Execution: Users apply predefined or custom strategies (e.g., Grid, MA Crossover) to the processed data.
- Backtesting: The engine simulates trades based on historical data to evaluate potential profitability.
- Risk Analysis: The system outputs a comprehensive risk profile, including volatility and Calmar ratios, to validate the strategy's safety.
China Stock Quant Setup
To get started with this skill, ensure you have the necessary Python environment. Install the required dependencies using the following command:
pip install akshare pandas numpy matplotlib
You can then begin fetching data by importing the provided utility scripts within your Openclaw Skills environment.
China Stock Quant Data Schema & Taxonomy
The skill organizes data into structured DataFrames and maintains a clear repository of scripts and references:
| File/Directory | Purpose |
|---|---|
scripts/fetch_data.py |
Handles all API interactions and data cleaning. |
scripts/technical_indicators.py |
Contains logic for MACD, KDJ, RSI, and Bollinger calculations. |
scripts/backtest.py |
The core engine for strategy simulation and risk metrics. |
references/strategies.md |
Documentation for built-in trading logic. |
references/api-reference.md |
Quick look-up for akshare interface parameters. |
China Stock Quant Advanced Features
- Multi-strategy support including Grid Trading, MA Crossover, and Bollinger Regression.
- Integrated risk management parameters such as maximum position limits and automated stop-loss/take-profit thresholds.
- Intraday ETF analysis specifically optimized for "T+0" trading simulations.
- High-performance signal detection across large datasets using optimized pandas operations.
SKILL.md
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