Visualize your index constituent correlations

To better understand the characteristics of your index constituents, you can calculate the return correlations.

Use the daily stock prices or your index companies, and show a heatmap of the daily return correlations!

This exercise is part of the course

Manipulating Time Series Data in Python

Exercise instructions

We have already imported pandas as pd, matplotlib.pyplot as plt, and seaborn as sns. We have also loaded the historical price series of your index constituents into the variable stock_prices.

Inspect stock_prices using .info().
Calculate the daily returns for stock_prices and assign the result to returns.
Calculate the pairwise correlations for returns, assign them to correlations and print the result.
Plot a seaborn annotated heatmap of the daily return correlations with the title 'Daily Return Correlations'.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Inspect stock_prices here
print(____)

# Calculate the daily returns
returns = ____

# Calculate and print the pairwise correlations
correlations = ____
print(____)

# Plot a heatmap of daily return correlations

Edit and Run Code