freqtrade_origin/freqtrade/strategy/strategy_helper.py
2021-08-06 16:35:39 -06:00

86 lines
3.7 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
: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"]
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"] + 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
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
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)