# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BinHV27_1.py from datetime import datetime from freqtrade.strategy import DecimalParameter, stoploss_from_open from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import numpy # noqa class Github_remiotore_freqtrade__BinHV27_1__20260111_210550(IStrategy): """ strategy sponsored by user BinH from slack """ minimal_roi = { "0": 1 } sell_params = { "pHSL": -0.25, "pPF_1": 0.023, "pPF_2": 0.042, "pSL_1": 0.018, "pSL_2": 0.041 } stoploss = -0.99 timeframe = '5m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False 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 } pHSL = DecimalParameter(-0.990, -0.040, default=-0.08, decimals=3, space='sell') 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') 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') def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 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[ ( ( ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & (dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) & dataframe['highsma'].gt(0) & dataframe['bigdown'] ) | ( ~dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & (dataframe['emarsi'].ge(75) | dataframe['close'].gt(dataframe['slowsma'])) & dataframe['bigdown'] ) | ( ~dataframe['preparechangetrendconfirm'] & dataframe['close'].gt(dataframe['highsma']) & dataframe['highsma'].gt(0) & dataframe['adx'].gt(30) & dataframe['emarsi'].ge(80) & dataframe['bigup'] ) | ( dataframe['preparechangetrendconfirm'] & ~dataframe['continueup'] & dataframe['slowingdown'] & dataframe['emarsi'].ge(75) & dataframe['slowsma'].gt(0) ) | ( dataframe['preparechangetrendconfirm'] & dataframe['minusdi'].lt(dataframe['plusdi']) & dataframe['close'].gt(dataframe['lowsma']) & dataframe['slowsma'].gt(0) ) ), 'sell'] = 1 return dataframe