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OrdinaryLeastSquaredStatsModels
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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