Portfolio Construction and Risk Analysis with Python

Modern investment management relies heavily on computational power to analyze risk, return, and asset allocation. Transitioning from theoretical finance to practical, code-driven execution is essential for anyone looking to manage portfolios effectively today. This course bridges the gap between financial theory and software implementation. You will read clear explanations of quantitative finance concepts, analyze structured code snippets, and learn how to construct and optimize diverse investment portfolios using Python. By the end of this course, you will be able to write clean, reproducible Python code to evaluate risk-return profiles, implement modern portfolio theory, and run robust asset allocation strategies. What you'll learn: - Understand foundational portfolio metrics, including expected returns, volatility, and covariance. - Calculate advanced risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) using historical and parametric methods. - Implement Mean-Variance Optimization and robust allocation models using modern Python libraries. - Apply clean Python programming practices, including type hints and robust data handling with Pandas and NumPy, to financial datasets. - Analyze portfolio performance using drawdown metrics, Sharpe ratios, and tracking errors. - Design systematic rebalancing strategies and backtest them using structured code. The course begins with fundamental concepts of risk and return before moving step-by-step through optimization models, modern allocation techniques, and backtesting frameworks. You will progress through written explanations and practical code implementations designed to build your quantitative intuition. This course is designed for finance professionals, analysts, and aspiring quantitative investors who want to apply Python to portfolio management. While some basic familiarity with Python is helpful, the course starts with foundational definitions and guides you through the code step-by-step. Start building and optimizing your own investment portfolios with code today.