p2-implement

This skill automates implementing quantitative trading strategies in Python by enforcing a strict 4-file structure: config, preprocessor, signal, and strategy. It translates concepts into production-ready code complete with comprehensive unit tests and quality checks.

The required file structure for production-ready financial algorithms involves four files: config.py, preprocessor.py, signal.py, and strategy.py. This modular approach separates configuration, data preprocessing, signal generation, and strategy execution.

This skill automates adding unit tests and linting to a quantitative finance codebase by enforcing comprehensive tests for configuration, preprocessing, signal generation, and strategy integration. It also runs linting, formatting, and type-checking to ensure code quality.

Yes, this strategy implementation tool works without external dependencies. It operates independently to generate the 4-file Python structure, register the new candidate strategy, and perform quality checks within your existing codebase.

The best way to register a candidate trading strategy for backtesting is using this automated skill. It standardizes the implementation process, ensures adherence to established coding standards, and registers the new strategy directly within the Python codebase.

Your quantitative development workflow fails to enforce coding standards without automated quality checks. This skill solves the problem by integrating linting, formatting, and type-checking to ensure financial algorithms meet production-ready standards.