Plot performance difference vs benchmark index

In the video, you learned how to calculate and plot the performance difference of a stock in percentage points relative to a benchmark index.

Let's compare the performance of Microsoft (MSFT) and Apple (AAPL) to the S&P 500 over the last 10 years.

This exercise is part of the course

Manipulating Time Series Data in Python

View Course

Exercise instructions

We have already imported pandas as pd and matplotlib.pyplot as plt.

Create the list tickers containing the two stock symbols.
Use pd.read_csv() to import 'msft_aapl.csv' and 'sp500.csv', creating a DatetimeIndex for each from the 'date' column using parse_dates and index_col, and assign the result to stocks and sp500, respectively.
Use pd.concat() to concatenate stocks and sp500 along axis=1, apply .dropna() to drop all missing values, and assign the result to data.
Normalize data by dividing by the first price, multiply by 100 and assign the output to normalized.
Select tickers from normalized, and subtract normalized['SP500'] with keyword axis=0 to align the indexes, then plot the result.

Hands-on interactive exercise

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

# Create tickers
tickers = ____

# Import stock data here
stocks = ____

# Import index here
sp500 = ____

# Concatenate stocks and index here
data = ____

# Normalize data
normalized = ____

# Subtract the normalized index from the normalized stock prices, and plot the result

Edit and Run Code