An AI-powered assistant for building, backtesting, and optimizing quantitative trading strategies using Python and factor analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-strategy
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 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 Assistant?
The Quant Strategy skill is a specialized tool for traders and developers designed to streamline the lifecycle of quantitative investment. By leveraging Openclaw Skills, this assistant helps users move from high-level investment hypotheses to concrete Python implementations. It focuses on factor-based stock selection, strategy coding using industry-standard frameworks, and rigorous backtesting analysis.
This skill is particularly valuable for bridging the gap between raw financial data and actionable trading logic. It provides the technical scaffolding needed to implement complex models such as value, growth, and momentum factors, while ensuring that the resulting code is compatible with popular backtesting engines like Backtrader and vn.py.
Quant Strategy Assistant Use Cases
- Designing multi-factor selection models including value, momentum, and quality factors.
- Developing automated trading strategies using Python frameworks like Backtrader or JoinQuant.
- Cleaning and preprocessing market and financial data for feature engineering.
- Evaluating strategy performance through Sharpe ratio, max drawdown, and win rate analysis.
- Optimizing position management and risk control rules for live-market readiness.
How Quant Strategy Assistant Works
- The user defines the core trading hypothesis or factor requirements.
- The assistant helps process raw market data, performing necessary cleaning and feature engineering.
- Python-based strategy code is generated, tailored for specific backtesting frameworks.
- Backtests are executed to simulate historical performance and calculate key risk-return metrics.
- The assistant provides optimization suggestions for parameters and risk management based on the results.
Quant Strategy Assistant Setup
To get started with this skill, ensure you have the Openclaw environment configured and the necessary Python libraries installed.
# Install the quant-strategy skill
openclaw install quant-strategy
# Install common quantitative analysis dependencies
pip install pandas numpy backtrader matplotlib
Quant Strategy Assistant Data Schema & Taxonomy
The skill organizes data into several logical layers to ensure robust analysis and strategy execution:
| Data Layer | Components |
|---|---|
| Market Data | OHLCV (Open, High, Low, Close, Volume) time-series data |
| Factor Library | Value, Growth, Momentum, Quality, and Volatility factors |
| Performance Metrics | Annualized Return, Max Drawdown, Sharpe Ratio, Volatility |
| Optimization Params | Grid search ranges, lookback windows, and risk thresholds |
Quant Strategy Assistant Advanced Features
- Multi-agent support for parallel factor research and strategy validation within Openclaw Skills.
- Automated parameter optimization and walk-forward analysis support.
- Deep integration with financial data APIs for seamless data acquisition.
- Custom hooks for designing sophisticated position management and exit logic.
SKILL.md
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