Fundamentals and Strategies in Portfolio Management: A Python Approach

Module Organization

This module develops the investment decisions behind portfolio construction: how to measure performance and risk, choose asset weights, manage downside exposure, and invest against future liabilities. Work through the four lessons in order; each builds on the financial concepts introduced earlier.

Core Topics:

1. Understanding Returns and Assessing Risks: distinguish compounded growth from average returns, measure volatility and drawdowns, and interpret Value at Risk and expected shortfall.
2. Essentials of Portfolio Optimization: combine expected returns and covariance, construct efficient portfolios, and examine how constraints and cash financing change the allocation.
3. Strategies Beyond Diversification: investigate changing correlations, dynamic risk allocation, and simulated investment paths.
4. Fundamentals of Asset-Liability Management: value future obligations, analyze interest-rate exposure, and compare portfolios designed around funding goals.

Working Through the Examples

Start with each financial explanation and worked calculation, then run the Python example to check the result. Use the suggested experiments to see which conclusions change when the assumptions change. The goal is to understand the investment problem and interpret the evidence.

Examples run in your browser. Within a lesson, run dependent cells in order: they share Python variables, and later examples often reuse earlier data and estimates. Reloading the page clears that Python session, although saved code edits remain. Use reset editor to return an edited cell to the lesson's current example.

Historical estimates describe the supplied data sample; simulated outcomes describe the stated model. Neither is a promise of future performance. Keep the return horizon, units, investment constraints, and financing assumptions consistent when comparing results.