def METHOD_NAME(dataframe: pd.DataFrame, informative: pd.DataFrame,
                           timeframe: str, timeframe_inf: str, ffill: bool = True,
                           append_timeframe: bool = True,
                           date_column: str = 'date',
                           suffix: Optional[str] = None) -> pd.DataFrame:
    """
    Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
    Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
    1h candle that starts at 15:00 will result in all candles to know the close at 16:00
    which they should not know.
    Moves the date of the informative pair by 1 time interval forward.
    This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
    last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
    Assuming inf_tf = '1d' - then the resulting columns will be:
    date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
    :param dataframe: Original dataframe
    :param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
    :param timeframe: Timeframe of the original pair sample.
    :param timeframe_inf: Timeframe of the informative pair sample.
    :param ffill: Forwardfill missing values - optional but usually required
    :param append_timeframe: Rename columns by appending timeframe.
    :param date_column: A custom date column name.
    :param suffix: A string suffix to add at the end of the informative columns. If specified,
                   append_timeframe must be false.
    :return: Merged dataframe
    :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
    """
    minutes_inf = timeframe_to_minutes(timeframe_inf)
    minutes = timeframe_to_minutes(timeframe)
    if minutes == minutes_inf:
        # No need to forwardshift if the timeframes are identical
        informative['date_merge'] = informative[date_column]
    elif minutes < minutes_inf:
        # Subtract "small" timeframe so merging is not delayed by 1 small candle
        # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
        informative['date_merge'] = (
            informative[date_column] + pd.to_timedelta(minutes_inf, 'm') -
            pd.to_timedelta(minutes, 'm')
        )
    else:
        raise ValueError("Tried to merge a faster timeframe to a slower timeframe."
                         "This would create new rows, and can throw off your regular indicators.")
    # Rename columns to be unique
    date_merge = 'date_merge'
    if suffix and append_timeframe:
        raise ValueError("You can not specify `append_timeframe` as True and a `suffix`.")
    elif append_timeframe:
        date_merge = f'date_merge_{timeframe_inf}'
        informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
    elif suffix:
        date_merge = f'date_merge_{suffix}'
        informative.columns = [f"{col}_{suffix}" for col in informative.columns]
    # Combine the 2 dataframes
    # all indicators on the informative sample MUST be calculated before this point
    if ffill:
        # https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
        # merge_ordered - ffill method is 2.5x faster than seperate ffill()
        dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date',
                                     right_on=date_merge, how='left')
    else:
        dataframe = pd.merge(dataframe, informative, left_on='date',
                             right_on=date_merge, how='left')
    dataframe = dataframe.drop(date_merge, axis=1)
    # if ffill:
    #     dataframe = dataframe.ffill()
    return dataframe