A comprehensive suite of verified quantitative trading strategy frameworks designed for stock market analysis, backtesting, and signal generation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-strategy-bundle-chen
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 quant-strategy-bundle-chen 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 Quant Strategy Bundle?
The Quant Strategy Bundle is a specialized collection of verified A-stock quantitative trading strategy frameworks. It empowers developers and traders to move beyond manual analysis by providing a robust foundation for momentum, reversal, and trend-following strategies. As part of the wider ecosystem of Openclaw Skills, this tool simplifies the complex process of strategy development and backtesting.
Whether you are a quantitative trading beginner or an experienced developer, this bundle provides the essential logic needed to interface with market data and generate actionable trading signals. By leveraging industry-standard libraries, it ensures that your financial models are both performant and scalable within your automated trading pipelines.
Quant Strategy Bundle Use Cases
- Rapidly prototyping quantitative trading models for the A-stock market.
- Conducting historical backtests to validate the profitability of momentum and trend-following approaches.
- Automating signal generation to identify entry and exit points in range-bound or trending markets.
- Learning the architectural foundations of professional-grade quantitative strategy frameworks.
How Quant Strategy Bundle Works
- Initialization: Select and initialize one of the verified strategy classes (Momentum, Reversal, or Trend) based on current market conditions.
- Data Integration: Connect to financial data APIs (like Tushare) using the provided configuration schema to pull stock pool and factor data.
- Signal Generation: Process market factors through the strategy logic to identify specific buy/sell signals.
- Backtesting: Execute the backtest engine against historical price data to evaluate performance metrics and strategy viability.
Quant Strategy Bundle Setup
To begin using this skill, ensure you have the necessary Python environment and dependencies installed:
pip install pandas numpy xgboost tushare
After installation, configure your credentials and stock pool parameters in the config.json file:
{
"tushare_token": "YOUR_TOKEN",
"stock_pool": ["000001.SZ", "600000.SH"],
"factor_parameters": {},
"trading_parameters": {}
}
Quant Strategy Bundle Data Schema & Taxonomy
The skill organizes its operations through a structured data workflow involving configuration files and strategy outputs:
| Component | Format | Description |
|---|---|---|
config.json |
JSON | Contains Tushare API tokens, stock pool definitions, and factor weights. |
signals |
DataFrame | The output of the generation phase, mapping timestamps to buy/sell actions. |
backtest_results |
Object/Log | Statistical summary of strategy performance including returns and drawdowns. |
strategy_logic |
Python | Modular classes for Momentum, Reversal, and Trend frameworks. |
Quant Strategy Bundle Advanced Features
- Multi-Strategy Support: Seamlessly switch between different market regimes (Bull, Range-bound, Trending) within a single implementation.
- Factor-Based Customization: Fine-tune factor parameters in the configuration to adapt to changing market microstructures.
- Openclaw Skills Integration: Easily wrap these strategies into larger AI agent workflows for automated daily market reports and execution.
- XGBoost Compatibility: Leverages machine learning libraries for advanced factor weighting and predictive modeling.
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