Portfolio Management and Optimization with Python

Managing an investment portfolio effectively requires more than just picking winning stocks; it demands rigorous risk management and mathematical optimization. Python has become the industry-standard tool for automating these complex financial calculations with ease. In this text-based course, you will learn how to transition from manual spreadsheets to programmatic portfolio management. You will understand how to write clean Python code to analyze asset returns, evaluate portfolio risk, run historical backtests, and implement advanced optimization strategies. What you'll learn: - Understand foundational portfolio theory, risk-adjusted metrics, and asset return calculations. - Analyze portfolio performance using modern pandas workflows and vectorized operations. - Implement historical backtesting to evaluate how your asset allocation strategies would have performed over time. - Apply risk management techniques, including Value at Risk (VaR) and drawdown analysis. - Optimize portfolios using classic Markowitz Mean-Variance optimization and modern hierarchical methods like Hierarchical Risk Parity (HRP). - Write clean, maintainable financial code using Python type hints and structured data containers. The course begins with foundational financial definitions and Python setup, gradually progressing from basic return calculations to advanced mathematical optimization models. You will work through detailed written explanations and step-by-step code snippets designed for clear comprehension. This course is designed for beginners to quantitative finance and Python programmers looking to apply their skills to investment management. No prior experience in portfolio theory is required, though a basic familiarity with Python syntax is helpful. Start building and optimizing your own data-driven portfolios today.