Python Quantitative Finance & Stock Analysis Course (Pandas, NumPy, SciPy) 🐍 - Python Matplotlib: Visualize Returns, Volatility Scatter, & Relative Performance | Part 12 📈

@MattMacarty ## 🐍 Python Matplotlib: Visualize Returns, Volatility Scatter, & Relative Performance | Part 12 Welcome to **Part 12** of the Python Stock Analysis Course! Continuing our work on the custom `Stock` class, this video focuses on adding more complex visualizations using **Matplotlib**. We implement two crucial plotting methods: one for **risk visualization** and one for **price performance**. You will learn how to create a scatter plot to visualize **volatility outliers** and a clean line plot to show the **relative (normalized) performance** of the stock over the entire data range. ### 🎯 Key Learning Outcomes: 1. **Volatility Scatter Plot:** Create a scatter plot to visualize **Daily Returns** (Y-axis) against **Movement Magnitude** (X-axis, measured in standard deviations). This helps identify and visualize volatility outliers and risk. 2. **Relative Performance Plot:** Implement a line plot that visualizes the stock's **Normalized Performance** (percentage gain/loss since the start of the data). 3. **Data Normalization:** Learn the Pandas/NumPy calculation for normalizing the close price: `(Close / First Close) - 1`. 4. **Matplotlib Formatting:** Use the **`PercentFormatter`** from `matplotlib.ticker` to correctly display the Y-axis of the performance plot as clean percentages. ### ⏱️ Video Chapters (Jump Ahead!): 0:00 - Introduction & Review (Continuing with the `Stock` Class) 0:36 - Defining the **`plot_volatility`** Method 1:08 - Creating the **Returns vs. Magnitude Scatter Plot** 1:35 - Adding Reference Lines for Interpretation 1:58 - Testing and Analyzing the Volatility Scatter Plot 2:46 - Defining the **`plot_performance`** Method 3:17 - Calculating **Relative Performance** (Normalization) 4:00 - Formatting the Y-Axis as a **Percentage** 4:18 - Testing and Reviewing the Final Performance Plot 4:55 - Preview of Part 13: More Data Transformations ### 🔗 Course Series & Resources: * **Part 11 (Returns & Volatility Calculation):** [https://youtu.be/XFuOftGQZY4](https://youtu.be/XFuOftGQZY4) * **Get the Code:** https://github.com/mjmacarty/python-stock-analysis ***Disclaimer: This video is for educational purposes only. The information provided should not be construed as investment advice. *** \#PythonMatplotlib \#VolatilityScatterPlot \#RelativePerformance \#