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, fields, start_dt, end_dt, sids):
        """
        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.
        """
        start_idx = self._find_position_of_minute(start_dt)
        end_idx = self._find_position_of_minute(end_dt)

        num_minutes = (end_idx - start_idx + 1)

        results = []

        indices_to_exclude = self._exclusion_indices_for_range(
            start_idx, end_idx)
        if indices_to_exclude is not None:
            for excl_start, excl_stop in indices_to_exclude:
                length = excl_stop - excl_start + 1
                num_minutes -= length

        shape = num_minutes, len(sids)

        for field in fields:
            if field != 'volume':
                out = np.full(shape, np.nan)
            else:
                out = np.zeros(shape, dtype=np.uint32)

            for i, sid in enumerate(sids):
                carray = self._open_minute_file(field, sid)
                values = carray[start_idx:end_idx + 1]
                if indices_to_exclude is not None:
                    for excl_start, excl_stop in indices_to_exclude[::-1]:
                        excl_slice = np.s_[
                            excl_start - start_idx:excl_stop - start_idx + 1]
                        values = np.delete(values, excl_slice)

                where = values != 0
                # first slice down to len(where) because we might not have
                # written data for all the minutes requested
                if field != 'volume':
                    out[:len(where), i][where] = (
                        values[where] * self._ohlc_ratio_inverse_for_sid(sid))
                else:
                    out[:len(where), i][where] = values[where]

            results.append(out)
        return results