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.

Cet exercice fait partie du cours

Manipulating Time Series Data in Python

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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'.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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

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