A comprehensive development toolkit for building, optimizing, and deploying JoinQuant quantitative trading strategies within the Cursor IDE.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install joinquant-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 joinquant-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 JoinQuant Strategy for Cursor?
The JoinQuant Strategy skill is a specialized resource designed to streamline the creation of algorithmic trading models on the JoinQuant platform using the Cursor AI editor. It bridges the gap between complex financial APIs and developer productivity by providing a library of standardized templates, reusable code blocks, and instant documentation. By utilizing Openclaw Skills like this, quantitative developers can significantly reduce the time spent on boilerplate setup and focus on refining their alpha-generating logic.
This skill organizes critical trading components—such as data acquisition, order execution, and risk management—into a modular structure that the AI agent can easily reference. Whether you are building simple moving average crossovers or complex ETF rotation models, this collection of Openclaw Skills ensures your code adheres to JoinQuant best practices and standard technical specifications.
JoinQuant Strategy for Cursor Use Cases
- Rapidly scaffolding new trading strategies using basic or dual-strategy Python templates.
- Implementing robust risk control modules including stop-loss, take-profit, and position management logic.
- Automating the fetching of historical and financial data using optimized code snippets.
- Calculating technical indicators and performing technical analysis within the JoinQuant environment.
- Referencing core API functions and order handling methods directly during the AI coding process.
How JoinQuant Strategy for Cursor Works
- Initialize your project by selecting a strategy template such as basic_template.py or etf_rotation_template.py to establish the strategy lifecycle.
- Utilize the /snippet command to inject pre-written blocks for data fetching or technical analysis.
- Consult the api_reference directory to understand the parameters and return values of JoinQuant core functions.
- Integrate risk control logic from the snippets directory to ensure the strategy handles market volatility safely.
- Review the provided momentum ETF examples to learn how to structure complex, multi-asset trading logic efficiently using Openclaw Skills.
JoinQuant Strategy for Cursor Setup
To get started with this skill in your environment, follow these steps:
# Ensure you have the JoinQuant SDK installed if testing locally
pip install jqdatasdk
# Add the joinquant-strategy directory to your Cursor project structure
# Ensure the .cursorrules or agent configuration points to the Openclaw Skills path
Once added, you can prompt your AI agent to use the templates in the templates/ folder as a starting point for any new .py strategy file.
JoinQuant Strategy for Cursor Data Schema & Taxonomy
The skill organizes data and reference materials into a structured hierarchy for easy AI navigation:
| Directory | Content Type | Description |
|---|---|---|
templates/ |
.py Files |
Standardized strategy skeletons (Basic, Dual, ETF Rotation). |
snippets/ |
.md Files |
Reusable code for data, orders, risk, and technical indicators. |
api_reference/ |
.md Files |
Detailed documentation for core functions and data APIs. |
examples/ |
.py Files |
Fully implemented strategies like Momentum ETF for reference. |
JoinQuant Strategy for Cursor Advanced Features
- Support for complex ETF rotation frameworks with pre-configured momentum logic.
- Modular risk control snippets that can be injected into any strategy to manage drawdowns.
- Comprehensive API mapping for core, data, and order functions to minimize syntax errors.
- Best-practice implementation for dual-strategy frameworks, allowing for multi-logic execution within a single JoinQuant instance.
- Deep integration with Openclaw Skills to provide a seamless automated trading development experience.
SKILL.md
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