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  3. Manipulating Time Series Data in Python

Exercise

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.

Instructions

100 XP

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.