Parameters
        ----------
        columns : list of str
           'open', 'high', 'low', 'close', or 'volume'
        start_date: Timestamp
           Beginning of the window range.
        end_date: Timestamp
           End of the window range.
        assets : 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
        ----------
        columns : list of str
           'open', 'high', 'low', 'close', or 'volume'
        start_date: Timestamp
           Beginning of the window range.
        end_date: Timestamp
           End of the window range.
        assets : 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.
        """
        self._validate_timestamp(start_date)
        self._validate_timestamp(end_date)

        start = start_date.asm8
        end = end_date.asm8
        date_slice = self._compute_date_range_slice(start, end)
        n_dates = date_slice.stop - date_slice.start

        # Create a buffer into which we'll read data from the h5 file.
        # Allocate an extra row of space that will always contain null values.
        # We'll use that space to provide "data" for entries in ``assets`` that
        # are unknown to us.
        full_buf = np.zeros((len(self.sids) + 1, n_dates), dtype=np.uint32)
        # We'll only read values into this portion of the read buf.
        mutable_buf = full_buf[:-1]

        # Indexer that converts an array aligned to self.sids (which is what we
        # pull from the h5 file) into an array aligned to ``assets``.
        #
        # Unknown assets will have an index of -1, which means they'll always
        # pull from the last row of the read buffer. We allocated an extra
        # empty row above so that these lookups will cause us to fill our
        # output buffer with "null" values.
        sid_selector = self._make_sid_selector(assets)

        out = []
        for column in columns:
            # Zero the buffer to prepare to receive new data.
            mutable_buf.fill(0)

            dataset = self._country_group[DATA][column]

            # Fill the mutable portion of our buffer with data from the file.
            dataset.read_direct(
                mutable_buf,
                np.s_[:, date_slice],
            )

            # Select data from the **full buffer**. Unknown assets will pull
            # from the last row, which is always empty.
            out.append(self._postprocessors[column](full_buf[sid_selector].T))

        return out