def highs(self, assets, dt):
        """
        The high field's aggregation returns the largest high seen between
        the market open and the current dt.
        If there has been no data on or before the `dt` the high is `nan`.

        Returns
        -------
        np.array with dtype=float64, in order of assets parameter.
        """
        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')

        highs = []
        session_label = self._trading_calendar.minute_to_session_label(dt)

        for asset in assets:
            if not asset.is_alive_for_session(session_label):
                highs.append(np.NaN)
                continue

            if prev_dt is None:
                val = self._minute_reader.get_value(asset, dt, 'high')
                entries[asset] = (dt_value, val)
                highs.append(val)
                continue
            else:
                try:
                    last_visited_dt, last_max = entries[asset]
                    if last_visited_dt == dt_value:
                        highs.append(last_max)
                        continue
                    elif last_visited_dt == prev_dt:
                        curr_val = self._minute_reader.get_value(
                            asset, dt, 'high')
                        if pd.isnull(curr_val):
                            val = last_max
                        elif pd.isnull(last_max):
                            val = curr_val
                        else:
                            val = max(last_max, curr_val)
                        entries[asset] = (dt_value, val)
                        highs.append(val)
                        continue
                    else:
                        after_last = pd.Timestamp(
                            last_visited_dt + self._one_min, tz='UTC')
                        window = self._minute_reader.load_raw_arrays(
                            ['high'],
                            after_last,
                            dt,
                            [asset],
                        )[0].T
                        val = np.nanmax(np.append(window, last_max))
                        entries[asset] = (dt_value, val)
                        highs.append(val)
                        continue
                except KeyError:
                    window = self._minute_reader.load_raw_arrays(
                        ['high'],
                        market_open,
                        dt,
                        [asset],
                    )[0].T
                    val = np.nanmax(window)
                    entries[asset] = (dt_value, val)
                    highs.append(val)
                    continue
        return np.array(highs)
The high field's aggregation returns the largest high seen between
        the market open and the current dt.
        If there has been no data on or before the `dt` the high is `nan`.

        Returns
        -------
        np.array with dtype=float64, in order of assets parameter.