A professional-grade quantitative investment assistant for A-share and global markets, enabling factor mining, backtesting, and real-time risk management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 Quant: AI Quantitative Investment Assistant?
Quant is a sophisticated AI-powered skill designed to streamline the quantitative trading lifecycle. As a featured entry in the Openclaw Skills ecosystem, it provides developers and investors with a modular framework to handle everything from macro data acquisition to complex strategy execution. By integrating with high-fidelity data sources like Tushare, AkShare, and yfinance, Quant ensures that users have the technical infrastructure needed to build alpha-seeking strategies.
Built with security and transparency in mind, all data processing occurs locally. This skill serves as a bridge between raw market noise and structured financial insights, allowing users to focus on strategy development rather than data plumbing. Whether you are managing a personal portfolio or developing enterprise-grade trading bots, this tool provides the necessary hooks and modules to scale your investment operations.
Quant: AI Quantitative Investment Assistant Use Cases
- Automating the retrieval of stock, index, and macro-economic data.
- Calculating over 50 traditional and alternative factors including momentum, growth, and sentiment.
- Performing rigorous strategy backtesting using multiple engines like Backtrader and VectorBT.
- Monitoring real-time portfolio risk metrics such as Sharpe ratio and Maximum Drawdown.
- Generating automated trade signals delivered via system notifications or voice alerts.
How Quant: AI Quantitative Investment Assistant Works
- The user initializes the environment by running the setup command to configure data provider credentials.
- Data modules fetch requested historical or real-time market information from global providers.
- Factor engines process the raw data to calculate valuation, flow, and technical indicators.
- The backtesting module simulates strategy performance, accounting for slippage and portfolio constraints.
- Risk management logic applies models like Black-Litterman to optimize positions and monitor drawdowns.
- Trade signals are generated and pushed to the user through local system interfaces.
Quant: AI Quantitative Investment Assistant Setup
To get started with this addition to your Openclaw Skills, use the following commands:
# Install required dependencies
quant install
# Configure API tokens (e.g., Tushare)
quant setup
After setup, verify your installation by fetching data for a sample ticker:
quant data "600519.SH" 2024-01-01 2024-12-31
Quant: AI Quantitative Investment Assistant Data Schema & Taxonomy
Quant maintains a structured directory and configuration schema to ensure data integrity:
| File/Folder | Purpose |
|---|---|
lib/ |
Contains core modules for data, factors, backtest, and risk. |
config.yaml |
Stores API keys, data source preferences, and default symbols. |
examples/ |
Includes strategy templates to help users build their first algorithms. |
SKILL.md |
Documentation and command reference for the agent. |
Quant: AI Quantitative Investment Assistant Advanced Features
- Support for Black-Litterman position optimization for advanced portfolio construction.
- Dual-engine backtesting support for both event-driven (Backtrader) and vector-based (VectorBT) analysis.
- Custom signal notification hooks including Windows clipboard integration and popup alerts.
- Open-source codebase allowing for full security auditing and local-first data processing.
SKILL.md
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