An AI development assistant for Openclaw Skills designed to generate runnable Python trading strategies for the Matic-MQuant platform.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mquant
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 mquant 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 mquant-coder?
The mquant-coder skill is a specialized technical assistant for quantitative traders using the Matic-MQuant platform. Its primary purpose is to transform trading logic into production-ready Python code while strictly adhering to official API specifications and internal templates. By integrating with Openclaw Skills, it ensures that generated strategies are not only syntactically correct but also directly executable within the target environment, prioritizing functional code over theoretical models.
This skill manages the entire strategy lifecycle, from initial environment setup to version control and error debugging. It utilizes a dual-layered documentation approach, combining static reference files with user-maintained custom documents to create a personalized development experience that evolves with the trader's expertise.
mquant-coder Use Cases
- Rapidly prototyping quantitative trading strategies such as Grid Trading or DualThrust.
- Automating the generation of Python strategy files directly into local Matic-MQuant installation directories.
- Maintaining a versioned repository of trading logic with a strict 5-version retention policy.
- Building a personalized knowledge base of API cookbooks and exchange-specific trading rules.
How mquant-coder Works
- Initializes the user environment by creating a dedicated custom_docs directory and personal note files for error tracking and trading philosophy.
- Configures the strategy saving path by validating user-provided directories within the Matic-MQuant ecosystem.
- Implements automated version control by naming files with a vN suffix and managing a rolling history of the most recent five versions.
- Configures strategy parameters through either direct user input or AI-driven inference based on natural language descriptions.
- Generates Python code that integrates official Matic APIs, standardized logging formats, and basic exception handling.
- Facilitates a debugging loop by analyzing runtime logs and updating the user's personal debugging guide to improve future code generation.
mquant-coder Setup
To begin using this skill within Openclaw Skills, follow these steps:
- Initialize the environment to create the necessary directory structure:
# This skill automatically creates custom_docs/ and personal MD files on first run
- Provide your Matic-MQuant strategy directory path when prompted (e.g.,
D:\Matic\M-quant\admin\). - Ensure your local environment has the necessary Matic-MQuant Python libraries available for strategy execution.
mquant-coder Data Schema & Taxonomy
The skill organizes strategy data and metadata using the following structure:
| File Type | Naming Convention | Description |
|---|---|---|
| Strategy Code | xxxxx_vN.py |
The runnable Python file (max 11 characters for name). |
| Metadata Log | xxxxx_vN.log |
Stores generation parameters and runtime metadata. |
| User Docs | custom_docs/*.user.md |
Persistent user-defined templates, API notes, and checklists. |
| Reference | reference/*.md |
Static API documentation and official MQuant structures. |
mquant-coder Advanced Features
- Automated Version Diffing: Compares logic changes between sequential versions of a strategy.
- AI Parameter Inference: Converts descriptions like 'Grid trading for Moutai between 1600-1800' into technical parameter sets.
- Self-Evolving Knowledge Base: Automatically suggests updates to your personal debugging guide based on resolved errors.
- Template Prioritization: Gives precedence to user-defined code snippets and templates over generic defaults to match individual coding styles.
SKILL.md
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