Annual return correlations among several stocks

You have seen in the video how to calculate correlations, and visualize the result.

In this exercise, we have provided you with the historical stock prices for Apple (AAPL), Amazon (AMZN), IBM (IBM), WalMart (WMT), and Exxon Mobile (XOM) for the last 4,000 trading days from July 2001 until the end of May 2017.

You'll calculate the year-end returns, the pairwise correlations among all stocks, and visualize the result as an annotated heatmap.

This exercise is part of the course

Manipulating Time Series Data in Python

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Exercise instructions

We have already imported pandas as pd, seaborn as sns, and matplotlib.pyplot as plt. We have loaded the daily close price for the five stocks in a variable called data.

Inspect using .info().
Apply .resample() with year-end frequency (alias: 'A') to data and select the .last() price from each subperiod; assign this to annual_prices.
Calculate annual_returns by applying .pct_change() to annual_prices.
Calculate correlations by applying .corr() to annual_returns and print the result.
Visualize correlations as an annotated sns.heatmap().

Hands-on interactive exercise

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

# Inspect data here
print(____)

# Calculate year-end prices here
annual_prices = ____

# Calculate annual returns here
annual_returns = ____

# Calculate and print the correlation matrix here
correlations = ____
print(____)

# Visualize the correlations as heatmap here

Edit and Run Code