OrdinaryLeastSquaredStatsModels
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| def run_ordinary_least_squares(ols_dates, ols_data, statsmodels_settings): | |
| """ | |
| This method receives the dates and prices of a Quandl data-set as well as settings for the StatsModels package, | |
| it then calculates the regression lines and / or the confidence lines are returns the objects | |
| """ | |
| intercept = np.column_stack((ols_dates, ols_dates ** statsmodels_settings.exponent)) | |
| constant = sm.add_constant(intercept) | |
| statsmodel_regression = sm.OLS(ols_prices, constant).fit() | |
| print(statsmodel_regression.summary()) | |
| if statsmodels_settings.confidence: | |
| prstd, lower, upper = wls_prediction_std(statsmodel_regression) | |
| return statsmodel_regression, lower, upper | |
| else: | |
| return statsmodel_regression |
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