Python Quantitative Finance & Stock Analysis Course (Pandas, NumPy, SciPy) 🐍 - 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