Parameters
        ----------
        fields : list of str
           'open', 'high', 'low', 'close', or 'volume'
        start_dt: Timestamp
           Beginning of the window range.
        end_dt: Timestamp
           End of the window range.
        sids : list of int
           The asset identifiers in the window.

        Returns
        -------
        list of np.ndarray
            A list with an entry per field of ndarrays with shape
            (minutes in range, sids) with a dtype of float64, containing the
            values for the respective field over start and end dt range.

def load_raw_arrays(self, columns, start_date, end_date, assets):
        """
        Parameters
        ----------
        fields : list of str
           'open', 'high', 'low', 'close', or 'volume'
        start_dt: Timestamp
           Beginning of the window range.
        end_dt: Timestamp
           End of the window range.
        sids : list of int
           The asset identifiers in the window.

        Returns
        -------
        list of np.ndarray
            A list with an entry per field of ndarrays with shape
            (minutes in range, sids) with a dtype of float64, containing the
            values for the respective field over start and end dt range.
        """
        rolls_by_asset = {}

        tc = self.trading_calendar
        start_session = tc.minute_to_session_label(start_date)
        end_session = tc.minute_to_session_label(end_date)

        for asset in assets:
            rf = self._roll_finders[asset.roll_style]
            rolls_by_asset[asset] = rf.get_rolls(
                asset.root_symbol,
                start_session,
                end_session, asset.offset)

        sessions = tc.sessions_in_range(start_date, end_date)

        minutes = tc.minutes_in_range(start_date, end_date)
        num_minutes = len(minutes)
        shape = num_minutes, len(assets)

        results = []

        # Get partitions
        partitions_by_asset = {}
        for asset in assets:
            partitions = []
            partitions_by_asset[asset] = partitions
            rolls = rolls_by_asset[asset]
            start = start_date
            for roll in rolls:
                sid, roll_date = roll
                start_loc = minutes.searchsorted(start)
                if roll_date is not None:
                    _, end = tc.open_and_close_for_session(
                        roll_date - sessions.freq)
                    end_loc = minutes.searchsorted(end)
                else:
                    end = end_date
                    end_loc = len(minutes) - 1
                partitions.append((sid, start, end, start_loc, end_loc))
                if roll[-1] is not None:
                    start, _ = tc.open_and_close_for_session(
                        tc.minute_to_session_label(minutes[end_loc + 1]))

        for column in columns:
            if column != 'volume':
                out = np.full(shape, np.nan)
            else:
                out = np.zeros(shape, dtype=np.uint32)
            for i, asset in enumerate(assets):
                partitions = partitions_by_asset[asset]
                for sid, start, end, start_loc, end_loc in partitions:
                    if column != 'sid':
                        result = self._bar_reader.load_raw_arrays(
                            [column], start, end, [sid])[0][:, 0]
                    else:
                        result = int(sid)
                    out[start_loc:end_loc + 1, i] = result
            results.append(out)
        return results