regulatory-knowledge

Name: regulatory-knowledge
Availability: InStock
Author: 20YN04

Map global market regulations and trading constraints for algorithmic backtesting.

Updated Jun 30, 2026

Search Tags:#compliance #finance #backtesting #trading #regulations #quantitative

Ready to run

npx skills add https://github.com/20YN04/vibe-trading-macos --skill regulatory-knowledge-20yn04

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: regulatory-knowledge
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/regulatory-knowledge
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill regulatory-knowledge-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This Skill addresses the critical gap between theoretical trading strategies and real-world market constraints, preventing backtest distortion and regulatory violations.

Core Features & Use Cases

Cross-Market Rule Mapping: Provides standardized constraints for A-share, Hong Kong, US, and Crypto markets, including T+N rules, circuit breakers, and short-selling mechanics.
Compliance & Risk Modeling: Helps integrate transaction costs, tax implications, and liquidity constraints into quantitative strategy development.
Use Case: Use this knowledge base to validate if a cross-market pair trading strategy correctly accounts for A-share T+1 settlement and the specific融券 (securities lending) costs associated with shorting.

Quick Start

Use the regulatory-knowledge skill to generate a compliance check report for a proposed trading strategy involving A-share and US-market assets.

Frequently Asked Questions about regulatory-knowledge

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

FAQPage Schema

How do I integrate A-share T+1 settlement rules and short-selling constraints into my backtesting models?▼

To integrate A-share T+1 settlement and short-selling constraints into backtesting, map standardized regulatory rules for transaction costs, tax liabilities, and liquidity limits directly into your quantitative trading models to prevent backtest distortion.

What is the best way to ensure cross-market trading compliance for US, Hong Kong, and Crypto strategies?▼

The best way to ensure cross-market trading compliance is to apply a comprehensive knowledge base of global financial market regulations, mapping circuit breakers, short-selling mechanics, and specific settlement rules for US, Hong Kong, and Crypto markets.

How does regulatory risk modeling work for quantitative strategy development?▼

Regulatory risk modeling for quantitative strategy development works by mapping global financial market constraints and integrating tax implications, transaction costs, and liquidity parameters into algorithmic trading models to validate strategy compliance.

Can I use pandas and numpy to validate cross-market pair trading strategies against regulatory constraints?▼

Yes, you can use pandas and numpy to validate cross-market pair trading strategies by applying standardized regulatory constraints, such as A-share T+1 rules and specific securities lending costs, to calculate realistic transaction costs and compliance risks.

Why does my theoretical trading strategy fail in real-world market conditions?▼

Your theoretical trading strategy fails in real-world conditions because of a critical gap between theoretical models and actual market constraints, requiring the integration of regulatory risk parameters, tax liabilities, and specific transaction costs to correct backtest distortion.

When do I need to include tax liabilities and circuit breaker rules in my algorithmic trading models?▼

You need to include tax liabilities and circuit breaker rules in your algorithmic trading models whenever developing cross-market quantitative strategies, ensuring accurate backtesting and strict adherence to global financial market regulations across A-share, Hong Kong, US, and Crypto markets.