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EMAPriceCrossoverWithThreshold.py
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EMAPriceCrossoverWithThreshold.py
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from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class EMAPriceCrossoverWithThreshold(IStrategy):
"""
EMAPriceCrossoverWithThreshold
author@: Paul Csapak
github@: https://github.com/paulcpk/freqtrade-strategies-that-work
How to use it?
> freqtrade download-data --timeframes 1h --timerange=20180301-20200301
> freqtrade backtesting --export trades -s EMAPriceCrossoverWithThreshold --timeframe 1h --timerange=20180301-20200301
> freqtrade plot-dataframe -s EMAPriceCrossoverWithThreshold --indicators1 ema800 --timeframe 1h --timerange=20180301-20200301
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
# minimal_roi = {
# "40": 0.0,
# "30": 0.01,
# "20": 0.02,
# "0": 0.04
# }
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.15
# Optimal timeframe for the strategy
timeframe = '1h'
# trailing stoploss
trailing_stop = True
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
threshold_percentage = 1
dataframe['ema800'] = ta.EMA(dataframe, timeperiod=800)
dataframe['ema_threshold'] = dataframe['ema800'] * (100 - threshold_percentage) / 100
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# Close price crossed above EMA
(qtpylib.crossed_above(dataframe['close'], dataframe['ema800'])) &
# Ensure this candle had volume (important for backtesting)
(dataframe['volume'] > 0)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# Close price crossed below EMA threshold
(qtpylib.crossed_below(dataframe['close'], dataframe['ema_threshold']))
),
'sell'] = 1
return dataframe