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 2 to 5, we downloaded Google's stock prices from 2010-2018, resampled the values to a monthly frequency, split the data into training (2010-2017) and test (2018) sets, and plotted the series.
In Step 6, we fitted three different SES models using the SimpleExpSmoothing class and its fit method. For fitting, we only used the training data. We could have manually selected the value of the smoothing parameter (smoothing_level), however, the best practice is to let statsmodels optimize it for the best fit. This optimization is done by minimizing the sum of squared residuals (errors). We created the forecasts using the forecast method, which requires the number of periods we want to forecast for (which is equal to the ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.