Portfolio Optimization

Overview

This skill provides guidance for implementing high-performance portfolio optimization algorithms using Python C extensions. It covers the workflow for creating C extensions that interface with NumPy arrays, proper verification strategies, and common pitfalls to avoid when optimizing numerical computations.

When to Apply This Skill

Apply this skill when:

• Implementing portfolio risk calculations (variance, volatility, Sharpe ratio)
• Optimizing matrix-vector operations for large asset portfolios
• Creating C extensions for Python numerical code
• Performance requirements specify speedup ratios (e.g., >= 1.2x)
• Working with covariance matrices and portfolio weights

Recommended Workflow

Phase 1: Codebase Understanding