portfolio-optimization
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
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