Beginner
Practice Drills: Building a Covariance Matrix and Running PCA on a Stock Portfolio
A practice-drill course for engineers, coders, and systematic traders who want to build real quant intuition. You will take a small portfolio of Indian stocks from raw daily returns to a full covariance matrix, then run principal component analysis on it to uncover the hidden risk factors driving the portfolio, all worked by hand first and then automated in Python.
Covariance and CorrelationPortfolio RiskPrincipal Component AnalysisPython for Quant Finance
MODULES
3
DURATION
~2 hrs
TRACK
Quantitative Finance
What You'll Master
Why portfolio risk cannot be measured by averaging individual stock volatilities
How to compute a covariance matrix from real NSE stock return data, by hand and in Python
What eigenvectors and eigenvalues of a covariance matrix actually represent
How to run PCA on a stock portfolio and interpret the principal components as risk factors
How to spot hidden concentration risk in a portfolio that looks diversified by name but isn't
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates