# source: https://raw.githubusercontent.com/ssssi/freqtrade_strs/41edddd429c5b68870448b5d8d7e64c1d394f24c/gateio/BinHV/BinHV27V1.py from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, stoploss_from_open, CategoricalParameter from freqtrade.strategy.interface import IStrategy from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy # noqa class github_ssssi_freqtrade_strs__BinHV27V1__20220328_084848(IStrategy): """ strategy sponsored by user BinH from slack """ minimal_roi = { "0": 1 } sell_params = { # custom stoploss params, come from BB_RPB_TSL "pHSL": -0.25, "pPF_1": 0.023, "pPF_2": 0.042, "pSL_1": 0.018, "pSL_2": 0.041, "sell_1": True, "sell_2": True, "sell_3": True, "sell_4": True, "sell_5": True, } stoploss = -0.25 timeframe = '5m' # Make sure these match or are not overridden in config use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Custom stoploss use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 240 order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'limit', 'forcebuy': "limit", 'forcesell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # hard stoploss profit pHSL = DecimalParameter(-0.990, -0.040, default=-0.08, decimals=3, space='sell') # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.050, default=0.016, decimals=3, space='sell') pSL_1 = DecimalParameter(0.008, 0.050, default=0.011, decimals=3, space='sell') # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell') pSL_2 = DecimalParameter(0.040, 0.100, default=0.040, decimals=3, space='sell') sell_1 = CategoricalParameter([True, False], default=True, space="sell") sell_2 = CategoricalParameter([True, False], default=True, space="sell") sell_3 = CategoricalParameter([True, False], default=True, space="sell") sell_4 = CategoricalParameter([True, False], default=True, space="sell") sell_5 = CategoricalParameter([True, False], default=True, space="sell") def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # Only for hyperopt invalid return if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ 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(25) & dataframe['bigdown'] & dataframe['emarsi'].le(20) ) | ( ~dataframe['preparechangetrend'] & dataframe['continueup'] & dataframe['adx'].gt(30) & dataframe['bigdown'] & dataframe['emarsi'].le(20) ) | ( ~dataframe['continueup'] & dataframe['adx'].gt(35) & dataframe['bigup'] & dataframe['emarsi'].le(20) ) | ( dataframe['continueup'] & dataframe['adx'].gt(30) & dataframe['bigup'] & dataframe['emarsi'].le(25) ) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(), 'sell'] = 0 return dataframe def custom_sell(self, pair: str, trade: Trade, current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if current_profit >= 0.023: return None if self.sell_1.value: if ( (~last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['close'] > last_candle['lowsma'] or last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['bigdown']) ): return "sell_1" if self.sell_2.value: if ( (~last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['emarsi'] > 75 or last_candle['close'] > last_candle['slowsma']) and (last_candle['bigdown']) ): return "sell_2" if self.sell_3.value: if ( (~last_candle['preparechangetrendconfirm']) and (last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['adx'] > 30) and (last_candle['emarsi'] >= 80) and (last_candle['bigup']) ): return "sell_3" if self.sell_4.value: if ( (last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['slowingdown']) and (last_candle['emarsi'] >= 75) and (last_candle['slowsma'] > 0) ): return "sell_4" if self.sell_5.value: if ( (last_candle['preparechangetrendconfirm']) and (last_candle['minusdi'] < last_candle['plusdi']) and (last_candle['close'] > last_candle['lowsma']) and (last_candle['slowsma'] > 0) ): return "sell_5"