January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How it works...
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 ...
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