stable/freqtrade/strategy/strategy_helper.py
2020-09-04 20:09:02 +02:00

48 lines
2.0 KiB
Python

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
"""
# Rename columns to be unique
minutes_inf = timeframe_to_minutes(timeframe_inf)
if timeframe == timeframe_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')
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