ba67537f1e96f20573a113821b7cf1ff3342bae8,finmarketpy/economics/seasonality.py,Seasonality,bus_day_of_month_seasonality,#Seasonality#,69

Before Change

        filter = Filter()

        if price_index:
            data_frame = data_frame.resample("B")           // resample into business days
            data_frame = calculations.calculate_returns(data_frame)

        data_frame.index = pandas.to_datetime(data_frame.index)
        data_frame = filter.filter_time_series_by_holidays(data_frame, cal)

        monthly_seasonality = calculations.average_by_month_day_by_bus_day(data_frame, cal)
        monthly_seasonality = monthly_seasonality.loc[month_list]

        if partition_by_month:
            monthly_seasonality = monthly_seasonality.unstack(level=0)

            if add_average:
               monthly_seasonality["Avg"] = monthly_seasonality.mean(axis=1)

        if cum is True:
            if partition_by_month:
                monthly_seasonality.loc[0] = numpy.zeros(len(monthly_seasonality.columns))
                // monthly_seasonality.index = monthly_seasonality.index + 1       // shifting index
                monthly_seasonality = monthly_seasonality.sort_index()

            monthly_seasonality = calculations.create_mult_index(monthly_seasonality)

        return monthly_seasonality

After Change

        filter = Filter()

        if price_index:
            data_frame = data_frame.resample("B").mean()           // resample into business days
            data_frame = calculations.calculate_returns(data_frame)

        data_frame.index = pandas.to_datetime(data_frame.index)
        data_frame = filter.filter_time_series_by_holidays(data_frame, cal)

        monthly_seasonality = calculations.average_by_month_day_by_bus_day(data_frame, cal)
        monthly_seasonality = monthly_seasonality.loc[month_list]

        if partition_by_month:
            monthly_seasonality = monthly_seasonality.unstack(level=0)

            if add_average:
               monthly_seasonality["Avg"] = monthly_seasonality.mean(axis=1)

        if cum is True:
            if partition_by_month:
                monthly_seasonality.loc[0] = numpy.zeros(len(monthly_seasonality.columns))
                // monthly_seasonality.index = monthly_seasonality.index + 1       // shifting index
                monthly_seasonality = monthly_seasonality.sort_index()

            monthly_seasonality = calculations.create_mult_index(monthly_seasonality)

        return monthly_seasonality

Instances