# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BBModCE.py import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta import pandas_ta as pta import pandas as pd from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series from datetime import datetime from freqtrade.strategy import DecimalParameter, IntParameter, informative, stoploss_from_open from functools import reduce def ewo(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif def williams_r(dataframe: DataFrame, period: int = 14) -> Series: highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() wr = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return wr * -100 class Github_remiotore_freqtrade__BBModCE__20260111_210550(IStrategy): minimal_roi = { "0": 100 } timeframe = '5m' process_only_new_candles = True startup_candle_count = 20 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_market_ratio': 0.99 } stoploss = -0.99 use_custom_stoploss = True leverage_optimize = False leverage_num = IntParameter(low=1, high=3, default=3, space='buy', optimize=leverage_optimize) is_optimize_ewo = True buy_rsi_fast = IntParameter(35, 50, default=45, space='buy', optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=35, space='buy', optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, space='buy', optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, space='buy', optimize=is_optimize_ewo) is_optimize_cofi = True buy_roc_1h = IntParameter(-25, 200, default=10, space='buy', optimize=is_optimize_cofi) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, space='buy', optimize=is_optimize_cofi) buy_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97, space='buy', optimize=is_optimize_cofi) buy_fastk = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi) buy_fastd = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi) buy_adx = IntParameter(0, 30, default=30, space='buy', optimize=is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, space='buy', optimize=is_optimize_cofi) buy_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5, space='buy', optimize=is_optimize_cofi) buy_cofi_r14 = DecimalParameter(-100, -44, default=-60, space='buy', optimize=is_optimize_cofi) trailing_optimize = True pHSL = DecimalParameter(-0.990, -0.040, default=-0.15, decimals=3, space='sell', optimize=False) pPF_1 = DecimalParameter(0.008, 0.100, default=0.03, decimals=3, space='sell', optimize=False) pSL_1 = DecimalParameter(0.01, 0.030, default=0.025, decimals=3, space='sell', optimize=trailing_optimize) pPF_2 = DecimalParameter(0.040, 0.200, default=0.080, decimals=3, space='sell', optimize=False) pSL_2 = DecimalParameter(0.050, 0.080, default=0.075, decimals=3, space='sell', optimize=trailing_optimize) 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 self.can_short: if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0: return 1 else: if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0: return 1 return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short) @informative('1h') def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['EWO'] = ewo(dataframe, 50, 200) dataframe['r_14'] = williams_r(dataframe, period=14) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' is_cofi = ( (dataframe['roc_1h'] < self.buy_roc_1h.value) & (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) & (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) & (dataframe['cti'] < self.buy_cofi_cti.value) & (dataframe['r_14'] < self.buy_cofi_r14.value) ) is_ewo = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) is_nfi_32 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) ) conditions.append(is_cofi) dataframe.loc[is_cofi, 'enter_tag'] += 'cofi ' conditions.append(is_ewo) dataframe.loc[is_ewo, 'enter_tag'] += 'ewo ' conditions.append(is_nfi_32) dataframe.loc[is_nfi_32, 'enter_tag'] += 'nfi_32 ' 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: dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out') 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