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, append_timeframe: bool = True, date_column: str = 'date') -> 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. :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 append_timeframe: date_merge = f'date_merge_{timeframe_inf}' 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=date_merge, how='left') dataframe = dataframe.drop(date_merge, axis=1) if ffill: dataframe = dataframe.ffill() return dataframe def stoploss_from_open(open_relative_stop: float, current_profit: float) -> float: """ Given the current profit, and a desired stop loss value relative to the open price, return a stop loss value that is relative to the current price, and which can be returned from `custom_stoploss`. The requested stop can be positive for a stop above the open price, or negative for a stop below the open price. The return value is always >= 0. Returns 0 if the resulting stop price would be above the current price. :param open_relative_stop: Desired stop loss percentage relative to open price :param current_profit: The current profit percentage :return: Positive stop loss value relative to current price """ # formula is undefined for current_profit -1, return maximum value if current_profit == -1: return 1 stoploss = 1-((1+open_relative_stop)/(1+current_profit)) # negative stoploss values indicate the requested stop price is higher than the current price return max(stoploss, 0.0) def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float: """ Given current price and desired stop price, return a stop loss value that is relative to current price. :param stop_rate: Stop loss price. :param current_rate: Current asset price. :return: Positive stop loss value relative to current price """ return 1 - (stop_rate / current_rate)