@classmethod
    def make_minute_bar_data(cls):
        asset_minutes = cls.env.minutes_for_days_in_range(
            cls.data_start,
            cls.END_DATE,
        )
        minutes_count = len(asset_minutes)
        minutes_arr = np.arange(minutes_count) + 1
        split_data = pd.DataFrame(
            {
                'open': minutes_arr + 1,
                'high': minutes_arr + 2,
                'low': minutes_arr - 1,
                'close': minutes_arr,
                'volume': 100 * minutes_arr,
            },
            index=asset_minutes,
        )
        split_data.iloc[780:] = 2.0 / split_data.iloc[780:]
        return merge(
            {
                sid: create_minute_df_for_asset(
                    cls.env,
                    cls.data_start,
                    cls.sim_params.period_end,
                )
                for sid in (1, 8554)
            },
            {
                2: create_minute_df_for_asset(
                    cls.env,
                    cls.data_start,
                    cls.sim_params.period_end,
                    50,
                ),
                cls.SPLIT_ASSET_SID: split_data,
            },
        )
dataset/ETHPy150Open quantopian/zipline/tests/test_algorithm.py/TestBeforeTradingStart.make_minute_bar_data/swapped operands of `/`