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

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.

Take Hint (-30 XP)

Exercise

Calculating portfolio returns

Instructions