OrdinaryLeastSquaredStatsModels

OrdinaryLeastSquaredStatsModels.py

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