77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
import pandas as pd
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from freqtrade.exchange import timeframe_to_minutes
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def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
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timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
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"""
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Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
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Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
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1h candle that starts at 15:00 will result in all candles to know the close at 16:00
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which they should not know.
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Moves the date of the informative pair by 1 time interval forward.
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This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
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last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
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Assuming inf_tf = '1d' - then the resulting columns will be:
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date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
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:param dataframe: Original dataframe
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:param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
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:param timeframe: Timeframe of the original pair sample.
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:param timeframe_inf: Timeframe of the informative pair sample.
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:param ffill: Forwardfill missing values - optional but usually required
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:return: Merged dataframe
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:raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
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"""
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minutes_inf = timeframe_to_minutes(timeframe_inf)
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minutes = timeframe_to_minutes(timeframe)
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if minutes == minutes_inf:
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# No need to forwardshift if the timeframes are identical
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informative['date_merge'] = informative["date"]
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elif minutes < minutes_inf:
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# Subtract "small" timeframe so merging is not delayed by 1 small candle
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# Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
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informative['date_merge'] = (
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informative["date"] + pd.to_timedelta(minutes_inf, 'm') - pd.to_timedelta(minutes, 'm')
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)
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else:
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raise ValueError("Tried to merge a faster timeframe to a slower timeframe."
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"This would create new rows, and can throw off your regular indicators.")
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# Rename columns to be unique
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informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
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# Combine the 2 dataframes
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# all indicators on the informative sample MUST be calculated before this point
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dataframe = pd.merge(dataframe, informative, left_on='date',
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right_on=f'date_merge_{timeframe_inf}', how='left')
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dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
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if ffill:
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dataframe = dataframe.ffill()
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return dataframe
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def stoploss_from_open(open_relative_stop: float, current_profit: float) -> float:
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"""
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Given the current profit, and a desired stop loss value relative to the open price,
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return a stop loss value that is relative to the current price, and which can be
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returned from `custom_stoploss`.
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:param open_relative_stop: Desired stop loss value relative to open price
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:param current_profit: The current profit percentage
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:return: Stop loss value relative to current price
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"""
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if current_profit == -1:
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return 1
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return 1-((1+open_relative_stop)/(1+current_profit))
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