regulatory-knowledge

Name: regulatory-knowledge
Availability: InStock
Author: hxhyyy

Model market-specific trading rules, transaction costs, and tax implications for backtesting.

Updated Jul 8, 2026

Search Tags:#compliance #finance #risk-management #backtesting #trading #regulations

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill regulatory-knowledge-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: regulatory-knowledge
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/regulatory-knowledge
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill regulatory-knowledge-hxhyyy

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 detailed constraints for A-share, Hong Kong, US, and Crypto markets, including circuit breakers, T+N rules, and short-selling mechanics.
Compliance & Cost Modeling: Calculates realistic transaction costs (stamp duty, commissions, borrow fees) and tax implications for multi-market portfolios.
Use Case: Use this knowledge base to validate if a proposed cross-market pair trading strategy accounts for the T+1 A-share settlement and the specific short-selling costs of the underlying assets.

Quick Start

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

Frequently Asked Questions about regulatory-knowledge

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

FAQPage Schema

How do I model realistic transaction costs and tax implications for a multi-market portfolio backtest?▼

Model realistic transaction costs and tax implications by applying market-specific constraints like stamp duty, commissions, and borrow fees. This prevents backtest distortion and ensures quantitative strategies reflect real-world trading costs across A-share, Hong Kong, US, and crypto markets.

What are the trading constraints for cross-market pair trading involving A-share and US assets?▼

Cross-market trading constraints include T+1 settlement rules for A-shares and specific short-selling mechanics for US assets. Mapping these regulations ensures your pair trading strategy remains compliant and accurately models transaction costs across different global markets.

How does T+N settlement affect backtesting accuracy for A-share quantitative strategies?▼

T+N settlement affects backtesting accuracy by restricting how quickly assets can be traded after purchase. Modeling T+1 A-share rules prevents unrealistic same-day round-trip trades, eliminating backtest distortion and reflecting actual market liquidity constraints.

Can I use pandas and numpy to calculate compliance reports for global financial market regulations?▼

You can use pandas and numpy to process the regulatory knowledge base and calculate compliance reports. These dependencies support modeling market-specific rules, transaction costs, and circuit breakers for multi-market quantitative strategy validation.

When do I need to include circuit breakers and short-selling mechanics in my risk management model?▼

Include circuit breakers and short-selling mechanics in risk management when strategies span A-share, Hong Kong, US, or crypto markets. Modeling these constraints prevents regulatory violations and accurately simulates market halts and borrow fee impacts during backtesting.

Why does my backtest show unrealistic returns when trading across Hong Kong and US markets?▼

Backtests show unrealistic returns when they fail to model stamp duty, exchange-specific circuit breakers, and accurate borrow fees. Incorporating actual market regulations and transaction costs corrects this distortion and aligns simulated performance with real-world constraints.