regulatory-knowledge

Ensure compliant backtesting by referencing a cross-market regulatory database to adjust strategy logic for price limits and short-selling requirements in A-share and Hong Kong markets. This skill provides structured compliance matrices to validate your trading rules.

Short-selling rules and tax implications for US equities and cryptocurrency vary significantly. This skill delivers structured rule tables and cost calculations to help quant traders understand settlement periods, tax treatments, and compliance constraints across these specific markets.

Yes, you can calculate trading costs and tax impacts for multi-market strategies without external APIs. This skill requires only pandas and numpy for data handling, delivering structured regulatory information and cost calculations entirely offline.

Yes, the regulatory knowledge skill supports compliance checklists for quantitative trading strategy design. It offers practical examples showing how to adjust backtesting logic, calculate costs, and generate compliance check reports for A-share, Hong Kong, US equities, and cryptocurrency markets.

Market-specific regulatory constraints like price limits, settlement periods, and short-selling requirements often cause backtest distortions if ignored. This skill provides compliance matrices to identify these constraints and adjust backtesting logic to prevent illegal live-trading actions and unexpected tax liabilities.

Yes, you need pandas and numpy installed to use this skill, as it requires these libraries for data handling. The skill delivers structured regulatory knowledge, including rule tables and cost calculations, without needing any external API dependencies.