Python Financial Analysis: Returns, Correlation Matrix, & Performance Plots | Part 5 📈

Matt Macarty · Beginner ·📊 Data Analytics & Business Intelligence ·4y ago

About this lesson

@MattMacarty ## 🐍 Python Financial Analysis: Calculate Returns, Correlation Matrix, & Plot Performance with Pandas | Part 5 Welcome to Part 5 of the **Python Stock Analysis Course**! Now that we have aggregated all our historical closing prices (from Part 4), we can begin performing core financial analysis. This video teaches you how to build three fundamental functions using **Pandas** and **NumPy** to transform raw price data into actionable metrics. ### 🎯 Key Learning Outcomes: 1. **Calculate Returns:** Master the use of **Pandas** and **NumPy** to quickly calculate **instantaneous rate of returns** for all securities in your dataset. 2. **Correlation Matrix:** Create a function to generate a **Correlation Matrix** using the calculated returns, allowing you to instantly visualize the co-movement of all stocks. 3. **Performance Plotting:** Build a flexible function using **Matplotlib** to plot either the absolute prices or the **relative performance** (normalized against the starting price) of multiple securities on a single chart. ### ⏱️ Video Chapters (Jump Ahead!): 0:00 - Introduction & Review (Starting with Aggregated Closing Prices) 0:33 - Importing **NumPy** for Calculations 0:48 - **Function 1: Calculating Instantaneous Rate of Returns** 1:40 - Testing the Returns Function and Reviewing the Output 2:24 - **Function 2: Creating a Correlation Matrix with Pandas** 3:30 - Reviewing the Correlation Matrix Output (High correlation in Energy stocks) 3:48 - **Function 3: Plotting Security Performance with Matplotlib** 4:26 - Setting up Conditional Logic for CSV vs. Excel Input 4:54 - **Absolute vs. Relative Performance Plotting Logic** 5:05 - Using **Matplotlib** to Generate the Plot (Grid, Lines, & Adjustments) 6:13 - Testing the Plot Function with Absolute Prices (Hard to compare) 6:19 - Testing the Plot Function with **Relative Performance** (Easy to compare) 7:10 - Preview of Part 6: Calculating Daily Changes and Saving Data ### 🔗 Course Series & Re

Original Description

@MattMacarty ## 🐍 Python Financial Analysis: Calculate Returns, Correlation Matrix, & Plot Performance with Pandas | Part 5 Welcome to Part 5 of the **Python Stock Analysis Course**! Now that we have aggregated all our historical closing prices (from Part 4), we can begin performing core financial analysis. This video teaches you how to build three fundamental functions using **Pandas** and **NumPy** to transform raw price data into actionable metrics. ### 🎯 Key Learning Outcomes: 1. **Calculate Returns:** Master the use of **Pandas** and **NumPy** to quickly calculate **instantaneous rate of returns** for all securities in your dataset. 2. **Correlation Matrix:** Create a function to generate a **Correlation Matrix** using the calculated returns, allowing you to instantly visualize the co-movement of all stocks. 3. **Performance Plotting:** Build a flexible function using **Matplotlib** to plot either the absolute prices or the **relative performance** (normalized against the starting price) of multiple securities on a single chart. ### ⏱️ Video Chapters (Jump Ahead!): 0:00 - Introduction & Review (Starting with Aggregated Closing Prices) 0:33 - Importing **NumPy** for Calculations 0:48 - **Function 1: Calculating Instantaneous Rate of Returns** 1:40 - Testing the Returns Function and Reviewing the Output 2:24 - **Function 2: Creating a Correlation Matrix with Pandas** 3:30 - Reviewing the Correlation Matrix Output (High correlation in Energy stocks) 3:48 - **Function 3: Plotting Security Performance with Matplotlib** 4:26 - Setting up Conditional Logic for CSV vs. Excel Input 4:54 - **Absolute vs. Relative Performance Plotting Logic** 5:05 - Using **Matplotlib** to Generate the Plot (Grid, Lines, & Adjustments) 6:13 - Testing the Plot Function with Absolute Prices (Hard to compare) 6:19 - Testing the Plot Function with **Relative Performance** (Easy to compare) 7:10 - Preview of Part 6: Calculating Daily Changes and Saving Data ### 🔗 Course Series & Re

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Chapters (13)

Introduction & Review (Starting with Aggregated Closing Prices)

0:33 Importing **NumPy** for Calculations

0:48 **Function 1: Calculating Instantaneous Rate of Returns**

1:40 Testing the Returns Function and Reviewing the Output

2:24 **Function 2: Creating a Correlation Matrix with Pandas**

3:30 Reviewing the Correlation Matrix Output (High correlation in Energy stocks)

3:48 **Function 3: Plotting Security Performance with Matplotlib**

4:26 Setting up Conditional Logic for CSV vs. Excel Input

4:54 **Absolute vs. Relative Performance Plotting Logic**

5:05 Using **Matplotlib** to Generate the Plot (Grid, Lines, & Adjustments)

6:13 Testing the Plot Function with Absolute Prices (Hard to compare)

6:19 Testing the Plot Function with **Relative Performance** (Easy to compare)

7:10 Preview of Part 6: Calculating Daily Changes and Saving Data

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