In Steps 1 to 4, we followed the already established approach—imported the libraries, set up the parameters, downloaded stock prices of four US tech companies (Apple, IBM, Microsoft, and Twitter) over the years 2017-2018, and calculated simple returns, using the adjusted close prices.
In Step 5, we created a list of weights, each one equal to 1 / n_assets, where n_assets is the number of assets we want to have in our portfolio. Next, we calculated the portfolio returns as a matrix multiplication (also known as the dot product—np.dot) of the portfolio weights and a transposed matrix of asset returns. To transpose the matrix, we used the T method of a pandas DataFrame. Then, we stored the portfolio returns as a pandas Series ...
