# source: https://raw.githubusercontent.com/ssssi/freqtrade_strs/6cb07ebffc9da5c7c27910f366b1cc30b397ae75/binance/BBMod.py from typing import Dict, List import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas_ta as pta import pandas as pd from skopt.space import Dimension, Integer 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, CategoricalParameter from functools import reduce import warnings warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) # custom indicators # ################################################################################################## def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) 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 vwap_b(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df, window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std) df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std) return df['vwap_low'], df['vwap'], df['vwap_high'] def top_percent_change(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] # 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 def T3(dataframe, length=5): """ T3 Average by HPotter on Tradingview https://www.tradingview.com/script/qzoC9H1I-T3-Average/ """ df = dataframe.copy() df['xe1'] = ta.EMA(df['close'], timeperiod=length) df['xe2'] = ta.EMA(df['xe1'], timeperiod=length) df['xe3'] = ta.EMA(df['xe2'], timeperiod=length) df['xe4'] = ta.EMA(df['xe3'], timeperiod=length) df['xe5'] = ta.EMA(df['xe4'], timeperiod=length) df['xe6'] = ta.EMA(df['xe5'], timeperiod=length) b = 0.7 c1 = -b * b * b c2 = 3 * b * b + 3 * b * b * b c3 = -6 * b * b - 3 * b - 3 * b * b * b c4 = 1 + 3 * b + b * b * b + 3 * b * b df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3'] return df['T3Average'] def rmi(dataframe, *, length=20, mom=5): df = dataframe.copy() df["maxup"] = (df["close"] - df["close"].shift(mom)).clip(lower=0) df["maxdown"] = (df["close"].shift(mom) - df["close"]).clip(lower=0) df.fillna(0, inplace=True) df["emaInc"] = ta.EMA(df, price="maxup", timeperiod=length) df["emaDec"] = ta.EMA(df, price="maxdown", timeperiod=length) df["RMI"] = np.where(df["emaDec"] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"])) return df["RMI"] # ##################################################################################################### class github_ssssi_freqtrade_strs__BBMod__20220711_103451(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 = 200 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 # Buy params leverage_optimize = False leverage_num = IntParameter(low=1, high=3, default=3, space='buy', optimize=leverage_optimize) buy_con_op = True buy_is_bb_checked_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_sqzmom_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_ewo_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_r_deadfish_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_clucHA_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_cofi_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_gumbo_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_local_uptrend_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_local_uptrend2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_local_dip_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_ewo_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_nfi_32_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_nfix_39_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) buy_is_vwap_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=buy_con_op) is_optimize_dip = True buy_rmi = IntParameter(30, 50, default=35, space='buy', optimize=is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, space='buy', optimize=is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, space='buy', optimize=is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, space='buy', optimize=is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, space='buy', optimize=is_optimize_dip) is_optimize_break = True buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, space='buy', optimize=is_optimize_break) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, space='buy', optimize=is_optimize_break) break_closedelta = DecimalParameter(12.0, 18.0, default=15.0, space='buy', optimize=is_optimize_break) break_buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, space='buy', optimize=is_optimize_break) is_optimize_local_uptrend = True buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, space='buy', optimize=is_optimize_local_uptrend) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, space='buy', optimize=is_optimize_local_uptrend) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, space='buy', optimize=is_optimize_local_uptrend) is_optimize_local_uptrend2 = True buy_bb_factor2 = DecimalParameter(0.990, 0.999, default=0.995, space='buy', optimize=is_optimize_local_uptrend2) is_optimize_local_dip = True buy_ema_diff_local_dip = DecimalParameter(0.022, 0.027, default=0.025, space='buy', optimize=is_optimize_local_dip) buy_ema_high_local_dip = DecimalParameter(0.90, 1.2, default=0.942, space='buy', optimize=is_optimize_local_dip) buy_closedelta_local_dip = DecimalParameter(12.0, 18.0, default=15.0, space='buy', optimize=is_optimize_local_dip) buy_rsi_local_dip = IntParameter(15, 45, default=28, space='buy', optimize=is_optimize_local_dip) buy_crsi_local_dip = IntParameter(10, 18, default=10, space='buy', optimize=is_optimize_local_dip) 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_nfix_39 = True buy_nfix_39_ema = DecimalParameter(0.9, 1.2, default=0.97, space='buy', optimize=is_optimize_nfix_39) is_optimize_sqzmom_protection = True buy_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97, space='buy', optimize=is_optimize_sqzmom_protection) buy_sqzmom_ewo = DecimalParameter(-12, 12, default=0, space='buy', optimize=is_optimize_sqzmom_protection) buy_sqzmom_r14 = DecimalParameter(-100, -22, default=-50, space='buy', optimize=is_optimize_sqzmom_protection) is_optimize_ewo_2 = True buy_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, space='buy', optimize=is_optimize_ewo_2) buy_rsi_ewo_2 = IntParameter(15, 50, default=35, space='buy', optimize=is_optimize_ewo_2) buy_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970, space='buy', optimize=is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087, space='buy', optimize=is_optimize_ewo_2) buy_ewo_high_2 = DecimalParameter(2, 12, default=4.179, space='buy', optimize=is_optimize_ewo_2) is_optimize_r_deadfish = True buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087, space='buy', optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='buy', optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0, space='buy', optimize=is_optimize_r_deadfish) buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='buy', optimize=is_optimize_r_deadfish) buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, space='buy', optimize=is_optimize_r_deadfish) buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, space='buy', optimize=is_optimize_r_deadfish) is_optimize_cofi = True 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_clucha = True buy_clucha_bbdelta_close = DecimalParameter(0.01, 0.05, default=0.02206, space='buy', optimize=is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, space='buy', optimize=is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, space='buy', optimize=is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, space='buy', optimize=is_optimize_clucha) is_optimize_gumbo = True buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, space='buy', optimize=is_optimize_gumbo) buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, space='buy', optimize=is_optimize_gumbo) buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, space='buy', optimize=is_optimize_gumbo) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60, space='buy', optimize=is_optimize_gumbo) # custom stoploss trailing_optimize = True pHSL = DecimalParameter(-0.990, -0.040, default=-0.1, 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(0.02, 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(0.070, 0.080, default=0.075, decimals=3, space='sell', optimize=trailing_optimize) ############################################################################ class HyperOpt: @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: roi_table = {params['roi_t1']: 0} return roi_table @staticmethod def roi_space() -> List[Dimension]: roi_min_time = 10 roi_max_time = 360 roi_limits = { 'roi_t1_min': int(roi_min_time), 'roi_t1_max': int(roi_max_time) } return [ Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1') ] 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 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['ema_200'] = ta.EMA(dataframe, timeperiod=200) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_close'] = heikinashi['close'] dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # # 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['T3'] = T3(dataframe) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 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'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] # Other BB checks dataframe['bb_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) # BinH dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # SMA dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # CRSI (3, 2, 100) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC( dataframe['close'], 100)) / 3 # EMA dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Elliot dataframe['EWO'] = ewo(dataframe, 50, 200) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # BB 40 bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2) dataframe['bb_lowerband2_40'] = bollinger2_40['lower'] dataframe['bb_middleband2_40'] = bollinger2_40['mid'] dataframe['bb_upperband2_40'] = bollinger2_40['upper'] # ClucHA dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # vmap indicators vwap_low, vwap, vwap_high = vwap_b(dataframe, 20, 1) dataframe['vwap_low'] = vwap_low dataframe['tcp_percent_4'] = top_percent_change(dataframe, 4) for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = rmi(dataframe, length=val, mom=4) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] # True range dataframe['trange'] = ta.TRANGE(dataframe) # KC dataframe['range_ma_28'] = ta.SMA(dataframe['trange'], 28) dataframe['kc_upperband_28_1'] = dataframe['sma_28'] + dataframe['range_ma_28'] dataframe['kc_lowerband_28_1'] = dataframe['sma_28'] - dataframe['range_ma_28'] # Linreg dataframe['hh_20'] = ta.MAX(dataframe['high'], 20) dataframe['ll_20'] = ta.MIN(dataframe['low'], 20) dataframe['avg_hh_ll_20'] = (dataframe['hh_20'] + dataframe['ll_20']) / 2 dataframe['avg_close_20'] = ta.SMA(dataframe['close'], 20) dataframe['avg_val_20'] = (dataframe['avg_hh_ll_20'] + dataframe['avg_close_20']) / 2 dataframe['linreg_val_20'] = ta.LINEARREG(dataframe['close'] - dataframe['avg_val_20'], 20, 0) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) 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) # T3 Averag dataframe['T3'] = T3(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' is_bb_checked = ( self.buy_is_bb_checked_enable.value & (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) & (dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.break_closedelta.value / 1000) & # from BinH (dataframe['close'] < dataframe['bb_lowerband3'] * self.break_buy_bb_factor.value) ) is_sqzmom = ( self.buy_is_sqzmom_enable.value & (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) & (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1']) & (dataframe['linreg_val_20'].shift(2) > dataframe['linreg_val_20'].shift(1)) & (dataframe['linreg_val_20'].shift(1) < dataframe['linreg_val_20']) & (dataframe['linreg_val_20'] < 0) & (dataframe['close'] < dataframe['ema_13'] * self.buy_sqzmom_ema.value) & (dataframe['EWO'] < self.buy_sqzmom_ewo.value) & (dataframe['r_14'] < self.buy_sqzmom_r14.value) ) is_ewo_2 = ( self.buy_is_ewo_2_enable.value & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_ewo_2.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & (dataframe['EWO'] > self.buy_ewo_high_2.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & (dataframe['rsi'] < self.buy_rsi_ewo_2.value) ) is_r_deadfish = ( self.buy_is_r_deadfish_enable.value & (dataframe['ema_100'] < dataframe['ema_200'] * self.buy_r_deadfish_ema.value) & (dataframe['bb_width'] > self.buy_r_deadfish_bb_width.value) & (dataframe['close'] < dataframe['bb_middleband2'] * self.buy_r_deadfish_bb_factor.value) & (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.buy_r_deadfish_volume_factor.value) & (dataframe['cti'] < self.buy_r_deadfish_cti.value) & (dataframe['r_14'] < self.buy_r_deadfish_r14.value) ) is_clucha = ( self.buy_is_clucHA_enable.value & (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value) & ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift()) ) ) is_cofi = ( self.buy_is_cofi_enable.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_gumbo = ( self.buy_is_gumbo_enable.value & (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) & (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) & (dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) & (dataframe['cti'] < self.buy_gumbo_cti.value) & (dataframe['r_14'] < self.buy_gumbo_r14.value) ) is_local_uptrend = ( # from NFI next gen, credit goes to @iterativ self.buy_is_local_uptrend_enable.value & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000) ) is_local_uptrend2 = ( # use origin bb_rpb_tsl value self.buy_is_local_uptrend2_enable.value & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.026) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor2.value) & (dataframe['closedelta'] > dataframe['close'] * 17.922 / 1000) ) is_local_dip = ( self.buy_is_local_dip_enable.value & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff_local_dip.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ema_high_local_dip.value) & (dataframe['rsi'] < self.buy_rsi_local_dip.value) & (dataframe['crsi'] > self.buy_crsi_local_dip.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta_local_dip.value / 1000) ) is_ewo = ( # from SMA offset self.buy_is_ewo_enable.value & (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 = ( self.buy_is_nfi_32_enable.value & (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) ) is_nfix_39 = ( self.buy_is_nfix_39_enable.value & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['bb_lowerband2_40'].shift().gt(0)) & (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.01)) & (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)) & (dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['close'] > dataframe['ema_13'] * self.buy_nfix_39_ema.value) ) is_vwap = ( self.buy_is_vwap_enable.value & (dataframe['close'] < dataframe['vwap_low']) & (dataframe['tcp_percent_4'] > 0.04) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ) conditions.append(is_bb_checked) dataframe.loc[is_bb_checked, 'enter_tag'] += 'bb ' conditions.append(is_sqzmom) dataframe.loc[is_sqzmom, 'enter_tag'] += 'sqzmom ' conditions.append(is_ewo_2) dataframe.loc[is_ewo_2, 'enter_tag'] += 'ewo2 ' conditions.append(is_r_deadfish) dataframe.loc[is_r_deadfish, 'enter_tag'] += 'r_deadfish ' conditions.append(is_clucha) dataframe.loc[is_clucha, 'enter_tag'] += 'clucHA ' conditions.append(is_cofi) dataframe.loc[is_cofi, 'enter_tag'] += 'cofi ' conditions.append(is_gumbo) dataframe.loc[is_gumbo, 'enter_tag'] += 'gumbo ' conditions.append(is_local_uptrend) dataframe.loc[is_local_uptrend, 'enter_tag'] += 'local_uptrend ' conditions.append(is_local_dip) dataframe.loc[is_local_dip, 'enter_tag'] += 'local_dip ' 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 ' conditions.append(is_nfix_39) dataframe.loc[is_nfix_39, 'enter_tag'] += 'nfix_39 ' conditions.append(is_vwap) dataframe.loc[is_vwap, 'enter_tag'] += 'vwap ' conditions.append(is_local_uptrend2) dataframe.loc[is_local_uptrend2, 'enter_tag'] += 'local_uptrend2 ' 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