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regulatory-knowledge

Models trading and regulatory constraints for equities, short-selling, sessions, and tax/costs across markets.

Updated May 5, 2026
Search Tags:#backtesting#regulatory#tax#short selling#trading execution#cross-market#market rules
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill regulatory-knowledge-wudye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regulatory-knowledge
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/regulatory-knowledge
Command: npx skills add https://github.com/wudye/traderAssistHK --skill regulatory-knowledge-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you prevent backtest distortion and real-trading compliance mistakes by documenting market-specific trading and tax/regulatory constraints across A-shares, Hong Kong stocks, US markets, and crypto.

Core Features & Use Cases

  • Cross-market trading rule modeling: Encodes key mechanics like limit up/down, T+N settlement differences, short-selling constraints, and trading-session auction/continuous phases to improve order execution assumptions.
  • Backtest impact guidance: Explains how each rule changes signal execution timing and feasibility (e.g., buy/sell blocking at limits, stop-loss effectiveness, PDT/LULD pauses).
  • Tax and cost awareness: Summarizes common tax implications and trading cost elements so strategy performance calculations reflect compliance and net returns.
  • Use Case: When building a multi-market strategy (e.g., A+HK paired trading with a short leg), use it to implement correct execution timing, borrowing/fees for shorts, and realistic friction that would otherwise inflate backtest results.

Quick Start

Ask the AI: “Using the regulatory-knowledge skill, generate a rule-constraint checklist for an A-share + Hong Kong paired strategy that includes T+N timing, limit rules, short-selling feasibility, and a backtest execution plan.”

Frequently Asked Questions about regulatory-knowledge

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

FAQPage Schema
How do I model cross-market trading rules for accurate backtesting?▼

To model cross-market trading rules, you must encode market-specific mechanics like limit up/down, T+N settlement, and short-selling constraints to prevent backtest distortion and reflect realistic execution feasibility.

Why does my backtest performance inflate when running a multi-market strategy?▼

Backtest performance inflates when you ignore regulatory constraints like trading-session phases, short-selling borrowing fees, and tax effects, causing unrealistic friction assumptions and execution timing errors.

What are the short-selling constraints and execution rules for A-shares and Hong Kong stocks?▼

Short-selling constraints vary by market, requiring explicit documentation of borrowing feasibility, fees, and execution timing rules across A-shares and Hong Kong stocks to ensure compliant and accurate backtesting.

How do I generate a regulatory constraint checklist for a paired trading strategy?▼

Generate a regulatory constraint checklist by applying structured rule coverage across your target markets, mapping T+N timing, limit rules, and short-selling feasibility to your specific strategy execution plan.

Does regulatory tax awareness affect net strategy returns in backtesting?▼

Regulatory tax awareness directly affects net strategy returns by summarizing tax implications and trading cost elements, ensuring performance calculations reflect compliance requirements and realistic net profitability.

What market-specific limitations should I check before implementing a crypto and equities backtest?▼

Before implementing a cross-market backtest, verify market-specific limitations including PDT/LULD pauses for equities, session auction mechanics, and settlement differences between crypto and traditional markets to avoid execution errors.

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