# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/startegy%20test/1/*BinHV27F/BinHV27F.py from datetime import datetime from functools import reduce from freqtrade.strategy import IntParameter, CategoricalParameter from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import numpy # noqa class Github_hamidreza07_freqai_strategy__BinHV27F__20231226_132626(IStrategy): """ strategy sponsored by user BinH from slack """ minimal_roi = { "0": 100 } buy_params = { 'buy_adx1': 25, 'buy_emarsi1': 20, 'buy_adx2': 30, 'buy_emarsi2': 20, 'buy_adx3': 35, 'buy_emarsi3': 20, 'buy_adx4': 30, 'buy_emarsi4': 25 } sell_params = { # sell params 'emarsi1': 75, 'adx2': 30, 'emarsi2': 80, 'emarsi3': 75 } stoploss = -0.25 timeframe = '5m' process_only_new_candles = True startup_candle_count = 240 order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': "market", 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # buy params buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=True) buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=True) buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=True) buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=True) buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=True) buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=True) buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=True) buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=True) # buy_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True) # buy_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True) # buy_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True) # buy_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True) # # sell_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True) # sell_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True) # sell_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True) # sell_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True) # sell_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True) leverage_optimize = False leverage_num = IntParameter(low=1, high=3, default=3, space='buy', optimize=leverage_optimize) # sell params adx2 = IntParameter(low=10, high=100, default=30, space='sell', optimize=True) emarsi1 = IntParameter(low=10, high=100, default=75, space='sell', optimize=True) emarsi2 = IntParameter(low=20, high=100, default=80, space='sell', optimize=True) emarsi3 = IntParameter(low=20, high=100, default=75, space='sell', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5)) rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'}) dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5)) dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe)) dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe)) minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'}) dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25)) dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe)) plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'}) dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5)) dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60)) dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120)) dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120)) dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240)) dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ( (dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300) dataframe['bigdown'] = ~dataframe['bigup'] dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma'] dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift()) dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt( dataframe['trend'].shift(2)) dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe[ 'slowsma'].shift().gt(dataframe['slowsma'].shift(2)) dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift() dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift()) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' buy_1 = ( # self.buy_1_enable.value & dataframe['slowsma'].gt(0) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx1.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi1.value) ) buy_2 = ( # self.buy_2_enable.value & dataframe['slowsma'].gt(0) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx2.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi2.value) ) buy_3 = ( # self.buy_3_enable.value & dataframe['slowsma'].gt(0) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx3.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi3.value) ) buy_4 = ( # self.buy_4_enable.value & dataframe['slowsma'].gt(0) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx4.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi4.value) ) conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' conditions.append(buy_2) dataframe.loc[buy_2, 'enter_tag'] += 'buy_2' conditions.append(buy_3) dataframe.loc[buy_3, 'enter_tag'] += 'buy_3' conditions.append(buy_4) dataframe.loc[buy_4, 'enter_tag'] += 'buy_4' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'exit_tag'] = '' s1 = ( # self.sell_1_enable.value & ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & (dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) & dataframe['highsma'].gt(0) & dataframe['bigdown'] ) s2 = ( # self.sell_2_enable.value & ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & (dataframe['emarsi'].ge(self.emarsi1.value) | dataframe['close'].gt(dataframe['slowsma'])) & dataframe['bigdown'] ) s3 = ( # self.sell_3_enable.value & ~dataframe['preparechangetrendconfirm'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & dataframe['adx'].gt(self.adx2.value) & dataframe['emarsi'].ge(self.emarsi2.value) & dataframe['bigup'] ) s4 = ( # self.sell_4_enable.value & dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['slowingdown'] & dataframe['emarsi'].ge(self.emarsi3.value) & dataframe['slowsma'].gt(0) ) s5 = ( # self.sell_5_enable.value & dataframe['preparechangetrendconfirm'] & dataframe['minusdi'].lt(dataframe['plusdi']) & dataframe['close'].gt(dataframe['lowsma']) & dataframe['slowsma'].gt(0) ) conditions.append(s1) dataframe.loc[s1, 'exit_tag'] += 's1 ' conditions.append(s2) dataframe.loc[s2, 'exit_tag'] += 's2 ' conditions.append(s3) dataframe.loc[s3, 'exit_tag'] += 's3 ' conditions.append(s4) dataframe.loc[s4, 'exit_tag'] += 's4 ' conditions.append(s5) dataframe.loc[s5, 'exit_tag'] += 's5 ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return self.leverage_num.value