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!

Cet exercice fait partie du cours

Manipulating Time Series Data in Python

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'.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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

Modifier et exécuter le code