Compare index performance against benchmark II

The next step in analyzing the performance of your index is to compare it against a benchmark.

In the video, we have use the S&P 500 as benchmark. You can also use the Dow Jones Industrial Average, which contains the 30 largest stocks, and would also be a reasonable benchmark for the largest stocks from all sectors across the three exchanges.

This exercise is part of the course

Manipulating Time Series Data in Python

View Course

Exercise instructions

We have already imported numpy as np, pandas as pd, matplotlib.pyplot as plt for you. We have also loaded your Index and the Dow Jones Industrial Average (normalized) in a variable called data.

Inspect data and print the first five rows.
Define a function multi_period_return that takes a numpy array of period returns as input, and returns the total return for the period. Use the formula from the video - add 1 to the input, pass the result to np.prod(), subtract 1 and multiply by 100.
Create a .rolling() window of length '360D' from data, and apply multi_period_return. Assign to rolling_return_360.
Plot rolling_return_360 using the title 'Rolling 360D Return'.

Hands-on interactive exercise

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

# Inspect data
print(____)
print(____)

# Create multi_period_return function here
def multi_period_return(r):
    return (____) * 100

# Calculate rolling_return_360
rolling_return_360 = data.pct_change().____

# Plot rolling_return_360 here

Edit and Run Code