January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How to do it...
Execute the following steps to forecast Google's stock prices using ARIMA models.
- Download additional test data and resample it weekly frequency:
df = yf.download('GOOG', start='2019-01-01', end='2019-03-31', adjusted=True, progress=False)test = df.resample('W') \ .last() \ .rename(columns={'Adj Close': 'adj_close'}) \ .adj_close
- Obtain the forecasts from the first model and store them in a DataFrame:
n_forecasts = len(test)arima_pred = arima.forecast(n_forecasts)arima_pred = [pd.DataFrame(arima_pred[0], columns=['prediction']), pd.DataFrame(arima_pred[2], columns=['ci_lower', 'ci_upper'])]arima_pred = pd.concat(arima_pred, axis=1).set_index(test.index)
- Obtain the forecasts from the second model and store them in a ...
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