import pandas as pd from freqtrade.exchange import timeframe_to_minutes def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame, timeframe: str, timeframe_inf: str, ffill: bool = True) -> 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 """ 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"] else: informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes_inf, 'm') # Rename columns to be unique informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns] # Combine the 2 dataframes # all indicators on the informative sample MUST be calculated before this point dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{timeframe_inf}', how='left') dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1) if ffill: dataframe = dataframe.ffill() return dataframe