January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How it works...
We started by downloading Google's stock prices from the given years and resampling them to weekly frequency by taking the last (adjusted) close price in every week. In Step 3, we applied the first difference in order to make the series stationary.
If we want to difference a given series more than once, we should use the np.diff function as it implements recursive differencing. Using the diff method of a DataFrame/Series with periods > 1 results in taking the difference between the current observations and the one from that many periods before.
Step 4 is very important because, because we determined the order of the ARIMA model based on these results. We tested for stationarity using a custom function called test_autocorrelation ...
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