RQAlpha is a highly modular, event-driven algorithmic trading backtesting engine designed for the Chinese A-share and futures markets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rqalpha
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 rqalpha 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 RQAlpha Quantitative Backtesting Framework?
RQAlpha, originally developed by RiceQuant, is a comprehensive open-source solution for strategy development, backtesting, and paper trading. As a core component for financial engineers using Openclaw Skills, it provides a stable environment to simulate trading logic with high fidelity. The framework's modular architecture allows users to extend functionality through a sophisticated Mod system, making it suitable for both individual researchers and institutional-grade development.
The framework supports both A-share and futures markets, offering a complete lifecycle for quantitative research. By leveraging its event-driven design, developers can write strategies that respond to daily or minute-level market bars, handle portfolio management automatically, and generate detailed performance reports.
RQAlpha Quantitative Backtesting Framework Use Cases
- Developing and testing algorithmic trading strategies for the Shanghai and Shenzhen stock exchanges.
- Simulating commodity and index futures trading with realistic transaction costs and slippage.
- Analyzing historical performance metrics like Sharpe ratio, Alpha, and Maximum Drawdown to validate alpha generation.
- Implementing complex scheduling tasks such as monthly rebalancing or daily technical indicator checks.
- Integrating custom data sources or execution engines through the modular plugin architecture.
How RQAlpha Quantitative Backtesting Framework Works
- Initialize the strategy context and set parameters such as target tickers or global variables using the init function.
- Download historical data bundles containing daily or minute-level market bars via the command line interface.
- Define trading logic within event handlers like handle_bar, which triggers on every new data point, or through scheduled tasks.
- Execute the backtest via the rqalpha CLI or the Python API to simulate orders, commission costs, and portfolio changes.
- Analyze results using the built-in performance analysis module (sys_analyser) to generate technical indicators and equity curves.
RQAlpha Quantitative Backtesting Framework Setup
To begin using this tool within your Openclaw Skills environment, install the package via pip and download the initial data bundle:
# Install the core framework
pip install rqalpha
# Download the default A-share daily data bundle
rqalpha download-bundle
RQAlpha Quantitative Backtesting Framework Data Schema & Taxonomy
RQAlpha organizes data into a structured format accessible through the strategy context:
| Component | Description |
|---|---|
| Market Suffixes | Uses .XSHG (Shanghai), .XSHE (Shenzhen), and .CCFX (Futures) codes. |
| Portfolio | Tracks cash, total_value, market_value, and pnl. |
| Positions | A dictionary-like object storing quantity, sellable, and avg_price. |
| History Bars | Returns data as a pandas.DataFrame including open, close, high, low, and volume. |
| Mod Configuration | JSON-based configuration for enabling simulation, analysis, and risk modules. |
RQAlpha Quantitative Backtesting Framework Advanced Features
- Modular Architecture: Extend the core engine with custom Mods for data sourcing, matching engines, or risk management.
- Live Trading Integration: Connect to professional trading gateways like vn.py via the rqalpha-mod-vnpy plugin.
- Sophisticated Scheduling: Use the scheduler to run tasks at specific market times (e.g., 5 minutes after open).
- Multi-Asset Support: Simultaneously handle stock and futures accounts within a single backtesting session.
- Performance Metrics: Automated calculation of Sortino ratio, Tracking Error, and Information Ratio.
SKILL.md
Loading
Related Openclaw Skills
Open Exchange Rates
A robust integration for fetching real-time and historical currency exchange rates to power global financial applications.
agent-data-cli Source Authoring
A high-integrity development framework for designing and implementing stable data sources like RSS, APIs, and web scrapers for the agent-data-cli ecosystem.
Pet Promotion Note Generator (Redbook)
An automated content engine that generates persuasive Xiaohongshu pet product posts combined with consistent AI-generated lifestyle imagery.
Podcast Manager
A local-first podcast management system for finding, tracking, and summarizing RSS feeds with minimal token usage.
Phishing Reporter
An automated agent skill for reporting phishing, malware, and scam URLs to global and regional cyber security authorities.
EE Datasheet Master
A specialized technical analysis tool for extracting high-fidelity electrical specifications, pinouts, and register maps directly from electronic component datasheets.
Featured*
