miniqmt-skills

Name: miniqmt-skills
Availability: InStock
Author: NNQuant

Access XtQuant data, trading, and backtesting APIs from Python.

39|13|Updated Jan 30, 2026

Search Tags:#api #data #backtesting #trading #xtquant #xtdata #xttrader

Ready to run

npx skills add https://github.com/nnquant/miniqmt-skills --skill miniqmt-skills

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: miniqmt-skills
Source: https://github.com/nnquant/miniqmt-skills/tree/main
Command: npx skills add https://github.com/nnquant/miniqmt-skills --skill miniqmt-skills

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

XtQuant is a Python strategy runtime framework based on MiniQMT that provides the necessary market data, trading, and backtesting APIs, enabling developers to build and test quant strategies end-to-end.

Core Features & Use Cases

Xtdata module for historical and real-time market data, including K-lines and tick data, financials, and sector information.
Xttrader module for order placement, cancellation, account querying, and receiving trade notifications.
Backtesting and strategy development workflows, as well as VIP data server integration and data subscription.

Quick Start

Run a simple XtQuant-enabled script to connect to MiniQMT, subscribe to data, and execute a test trade.

Frequently Asked Questions about miniqmt-skills

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I get historical K-line and tick market data using XtQuant?▼

To get historical K-line and tick market data using XtQuant, you utilize the xtdata module. It provides the necessary APIs to subscribe to and retrieve real-time and historical market data, including financials and sector information, directly from the MiniQMT data server.

Can I automate order placement and account querying with XtQuant?▼

Yes, you can automate order placement and account querying with XtQuant by using the xttrader module. It handles order placement, cancellations, and receives trade notifications, enabling fully automated trading workflows within your Python strategy.

Does XtQuant support backtesting for quant trading strategies?▼

XtQuant does support backtesting for quant trading strategies. It provides a strategy runtime framework based on MiniQMT, allowing developers to build, test, and refine automated trading strategies end-to-end before live execution.

What is required to connect a Python script to MiniQMT for data subscription?▼

To connect a Python script to MiniQMT for data subscription, you need the XtQuant framework configured with a root SKILL.md containing YAML frontmatter. This setup enables access to the xtdata and xttrader APIs for executing automated workflows.

Are there limitations when using XtQuant APIs for VIP data server integration?▼

XtQuant APIs facilitate VIP data server integration and data subscription, but they require a MiniQMT environment to function. The framework is specifically designed for quant trading development, meaning it is not suitable for general-purpose data extraction outside this context.

Why use XtQuant instead of other Python frameworks for quant trading?▼

You use XtQuant for quant trading because it directly bridges Python scripts to MiniQMT's data, trading, and backtesting APIs. Unlike generic frameworks, it provides specialized xtdata and xttrader modules for processing market data and executing trades natively.