Building a Python Framework for Stress Testing Financial Portfolios

I remember when I first started managing a portfolio. It was exhilarating, watching the numbers climb. Then the market took a downturn. I hadn’t truly grasped the potential for loss. That’s when I realized the critical importance of stress testing. Stress testing isn’t about predicting the future; it’s about understanding how your portfolio might perform under extreme, unlikely but possible, events. This tutorial will guide you through building a robust Python framework for stress testing financial portfolios, providing a hands-on approach to managing risk. We’ll go beyond basic theory and dive into practical implementation, using Python’s powerful libraries to build a framework you can adapt and expand. This framework will enable you to simulate various stress scenarios, analyze their impact on your portfolio and ultimately, make more informed investment decisions.

Photo by Austin Distel on Unsplash

Table of Contents

• Defining Stress Scenarios: Exploring stress testing methodologies (historical, hypothetical, Monte Carlo) and implementing scenario generation functions.
• Portfolio Construction and Data Handling: Building classes for diverse asset types and integrating market data sources.
• Risk Factors and Sensitivity Analysis: Implementing risk metric calculations (VaR, Expected Shortfall)…