import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta import pandas_ta as pta 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 # custom indicators # ################################################################################################## 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 # Williams %R 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 BBModCESpot(IStrategy): minimal_roi = { "0": 100 } # Optimal timeframe for the strategy timeframe = '5m' # Run "populate_indicators()" only for new candle. 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 } # Disabled stoploss = -0.99 # Custom stoploss use_custom_stoploss = True 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) is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32) # custom stoploss trailing_optimize = True pSL_1 = DecimalParameter(0.010, 0.020, default=0.016, decimals=3, space='sell', optimize=trailing_optimize) pSL_2 = DecimalParameter(0.030, 0.050, default=0.04, decimals=3, space='sell', optimize=trailing_optimize) sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True) def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = -0.1 PF_1 = 0.02 SL_1 = self.pSL_1.value PF_2 = 0.05 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 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) # # Bollinger bands 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) # EMA 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) # Elliot dataframe['EWO'] = ewo(dataframe, 50, 200) dataframe['r_14'] = williams_r(dataframe, period=14) # Cofi 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'] < self.buy_rsi_fast_32.value) & (dataframe['rsi'] > self.buy_rsi_32.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & (dataframe['cti'] < self.buy_cti_32.value) ) 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: conditions = [] dataframe.loc[:, 'exit_tag'] = '' fastk_cross = ( (qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value)) ) conditions.append(fastk_cross) dataframe.loc[fastk_cross, 'exit_tag'] += 'fastk_cross ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe