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Exercise

Calculating portfolio returns

In order to build and backtest a portfolio, you have to be comfortable working with the returns of multiple assets in a single object.

In this exercise, you will be using a pandas DataFrame object, already stored as the variable StockReturns, to hold the returns of multiple assets and to calculate the returns of a model portfolio.

The model portfolio is constructed with pre-defined weights for some of the largest companies in the world just before January 2017:

Company Name Ticker Portfolio Weight
Apple AAPL 12%
Microsoft MSFT 15%
Exxon Mobil XOM 8%
Johnson & Johnson JNJ 5%
JP Morgan JPM 9%
Amazon AMZN 10%
General Electric GE 11%
Facebook FB 14%
AT&T T 16%

Note that the portfolio weights should sum to 100% in most cases

Instructions
100 XP
  • Finish defining the numpy array of model portfolio_weights with the values according to the table above.
  • Use the .mul() method to multiply the portfolio_weights across the rows of StockReturns to get weighted stock returns.
  • Then use the .sum() method across the rows on the WeightedReturns object to calculate the portfolio returns.
  • Finally, review the plot of cumulative returns over time.
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