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): INTERFACE_VERSION = 3 '\n EMAPriceCrossoverWithThreshold\n author@: Paul Csapak\n github@: https://github.com/paulcpk/freqtrade-strategies-that-work\n\n How to use it?\n\n > freqtrade download-data --timeframes 1h --timerange=20180301-20200301\n > freqtrade backtesting --export trades -s EMAPriceCrossoverWithThreshold --timeframe 1h --timerange=20180301-20200301\n > freqtrade plot-dataframe -s EMAPriceCrossoverWithThreshold --indicators1 ema800 --timeframe 1h --timerange=20180301-20200301\n\n ' # 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Close price crossed above EMA # Ensure this candle had volume (important for backtesting) dataframe.loc[qtpylib.crossed_above(dataframe['close'], dataframe['ema800']) & (dataframe['volume'] > 0), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Close price crossed below EMA threshold dataframe.loc[qtpylib.crossed_below(dataframe['close'], dataframe['ema_threshold']), 'exit_long'] = 1 return dataframe