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DoubleEMACrossoverWithTrend.py
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DoubleEMACrossoverWithTrend.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 DoubleEMACrossoverWithTrend(IStrategy):
"""
DoubleEMACrossoverWithTrend
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 DoubleEMACrossoverWithTrend --timeframe 1h --timerange=20180301-20200301
> freqtrade plot-dataframe -s DoubleEMACrossoverWithTrend --indicators1 ema200 --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 = -0.2
# Optimal timeframe for the strategy
timeframe = '1h'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.03
trailing_stop_positive_offset = 0.04
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# fast ema crosses above slow ema
(qtpylib.crossed_above(dataframe['ema9'], dataframe['ema21'])) &
(dataframe['low'] > dataframe['ema200']) & # Candle low is above EMA
# 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[
(
# fast ema crosses below slow ema
(qtpylib.crossed_below(dataframe['ema9'], dataframe['ema21'])) |
(dataframe['low'] < dataframe['ema200']) # OR price is below trend ema
),
'sell'] = 1
return dataframe