Calculate mean returns
In this exercise, you're going to calculate performance for a four stock portfolio over the period January 2015 through March 2019. The portfolio consists of Proctor & Gamble, Microsoft, JP Morgan and General Electric stocks. You'll discover that multiplying the mean return of each stock with its portfolio weight, is a very quick and straightforward way to calculate portfolio performance over a given period of time.
The four columns in the DataFrame data
contain the prices of these four stocks mentioned above. Have a look at data
by inspecting it in the console.
This exercise is part of the course
Introduction to Portfolio Analysis in Python
Exercise instructions
- Calculate the percentage returns of the stocks in the DataFrame
data
by comparing today's price with yesterday's price. - Calculate the mean returns of each stock in the new
returns
DataFrame. - Assign the weights of the stocks to the
weights
array. The weights are 0.5, 0.2, 0.2 and 0.1. - Multiply the percentage returns with the weights, and take the total sum, to calculate the total portfolio performance and print the results.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate percentage returns
returns = data.____()
# Calculate individual mean returns
meanDailyReturns = ____.____()
# Define weights for the portfolio
weights = np.array([____, ____, ____, ____])
# Calculate expected portfolio performance
portReturn = np.____(____*____)
# Print the portfolio return
print(____)