# Source: generated via dynamic_strategy_generator from freqtrade.strategy import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class ACO_100_2(IStrategy): timeframe = '1h' # Standard ROI and Stoploss minimal_roi = {"0": 0.1, "60": 0.05, "120": 0.0} stoploss = -0.05 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['obv'] = ta.OBV(dataframe) dataframe['obv_sma'] = ta.SMA(dataframe, timeperiod=10, price='obv') dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['natr'] = ta.NATR(dataframe, timeperiod=14) dataframe['willr'] = ta.WILLR(dataframe, timeperiod=7) dataframe['bop'] = ta.BOP(dataframe) res = ta.AROON(dataframe, timeperiod=14) dataframe['aroondown'] = res.iloc[:, 0] dataframe['aroonup'] = res.iloc[:, 1] dataframe['t3'] = ta.T3(dataframe, timeperiod=5, vfactor=0.7) bbands = ta.BBANDS(dataframe, timeperiod=14, nbdevup=2.0, nbdevdn=2.0) dataframe['upperband'] = bbands['upperband'] dataframe['middleband'] = bbands['middleband'] dataframe['lowerband'] = bbands['lowerband'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['adx'] > 25) ) & ( qtpylib.crossed_above(dataframe['obv'], dataframe['obv_sma']) ) & ( (dataframe['natr'] > 2.0) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['willr'] > -25) ) & ( qtpylib.crossed_below(dataframe['bop'], 0) ) & ( qtpylib.crossed_below(dataframe['aroonup'], dataframe['aroondown']) ) & ( qtpylib.crossed_below(dataframe['close'], dataframe['t3']) ) & ( (dataframe['close'] > dataframe['upperband'] * 0.98) ), 'exit_long'] = 1 return dataframe