Python Class: Calculate Log Returns, Rolling Volatility, & Plot Distribution | Part 11 📊
@MattMacarty
🐍 Python Class: Calculate Log Returns, Rolling Volatility, & Plot Distribution | Part 11
Welcome to *Part 11* of the Python Stock Analysis Course!
Continuing our journey into *Individual Security Analysis**, this video focuses on adding sophisticated **data transformation* and *visualization* methods to our custom `Stock` class. We move beyond raw price data to calculate core financial metrics.
You will learn how to implement a dedicated method to calculate various measures of daily change, volatility, and magnitude, and then visualize the distribution of returns using **Matplotlib**.
🎯 Key Learning Outcomes:
1. *Log Returns Calculation:* Implement the calculation of *Instantaneous Rate of Return (Log Returns)* using NumPy, which is standard for volatility modeling.
2. *Rolling Volatility:* Calculate *21-day Rolling Volatility* (daily standard deviation of returns), a key measure of risk, and store it as a new column