Plotting multi-period returns

The last time series method you have learned about in the video was .pct_change(). Let's use this function to calculate returns for various calendar day periods, and plot the result to compare the different patterns.

We'll be using Google stock prices from 2014-2016.

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, and matplotlib.pyplot as plt. We have also loaded 'GOOG' stock prices for the years 2014-2016, set the frequency to calendar daily, and assigned the result to google.

Create the columns 'daily_return', 'monthly_return', and 'annual_return' that contain the pct_change() of 'Close' for 1, 30 and 360 calendar days, respectively, and multiply each by 100.
Plot the result using subplots=True.

Hands-on interactive exercise

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

# Create daily_return
google['daily_return'] = ____

# Create monthly_return
google['monthly_return'] = ____

# Create annual_return
google['annual_return'] = ____

# Plot the result

Edit and Run Code