# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BigZ04_TSL4_69.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, 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['close'] * 100 return emadif class Github_remiotore_freqtrade__BigZ04_TSL4_69__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 100.0 } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = False trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 use_custom_stoploss = True process_only_new_candles = True startup_candle_count: int = 400 order_types = { 'buy': 'market', 'sell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } buy_params = { "buy_condition_0_enable": True, "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": False, } sell_params = { "base_nb_candles_sell": 49, "high_offset": 1.006, "pHSL": -0.08, "pPF_1": 0.016, "pSL_1": 0.011, "pPF_2": 0.080, "pSL_2": 0.040, } buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.950, 1.050, default=0.989, decimals=3, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.700, 1.100, default=0.982, decimals=2, space='buy', optimize=False, load=True) buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True) buy_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=False, load=True) buy_volume_drop_2 = DecimalParameter(1, 10, default=3, space='buy', decimals=1, optimize=False, load=True) buy_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_0 = DecimalParameter(55.0, 85.0, default=71.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_1a = DecimalParameter(65.0, 78.0, default=69.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_0 = DecimalParameter(10.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True) buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True) buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True) buy_dip_0 = DecimalParameter(1.015, 1.040, default=1.024, space='buy', decimals=3, optimize=False, load=True) trade_time = IntParameter(25, 65, default=35, space='sell', optimize=False, load=True) rsi_1h_val = IntParameter(25, 45, default=32, space='sell', optimize=False, load=True) narrow_stop = DecimalParameter(1.005, 1.030, default=1.020, space='sell', decimals=3, optimize=False, load=True) wide_stop = DecimalParameter(1.010, 1.045, default=1.035, space='sell', decimals=3, optimize=False, load=True) base_nb_candles_sell = IntParameter(5, 80, default=49, space='sell', optimize=False, load=True) high_offset = DecimalParameter(0.99, 1.1, default=1.006, space='sell', optimize=False, load=True) pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=False, load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=False, load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=False, load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=False, load=True) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: return True def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): return False 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 return stoploss_from_open(sl_profit, current_profit) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) informative_1h['ema_50'] = ta.SMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb_lowerband'] = bollinger['lower'] informative_1h['bb_middleband'] = bollinger['mid'] informative_1h['bb_upperband'] = bollinger['upper'] return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean() dataframe['ema_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.buy_condition_12_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.993) & (dataframe['low'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['close'].shift() > dataframe['bb_lowerband']) & (dataframe['rsi_1h'] < 72.8) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_11_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift() > 0) & (dataframe['hist'].shift(2) > 0) & (dataframe['hist'].shift(3) > 0) & (dataframe['hist'].shift(5) > 0) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close']/200) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close']/100) & ((dataframe['bb_upperband'] - dataframe['bb_lowerband']) < (dataframe['close']*0.1)) & ((dataframe['open'].shift() - dataframe['close'].shift()) < (dataframe['close'] * 0.018)) & (dataframe['rsi'] > 51) & (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift() > dataframe['close'].shift()) & (dataframe['close'] > dataframe['bb_middleband']) & (dataframe['close'].shift() < dataframe['bb_middleband'].shift()) & (dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) conditions.append( ( self.buy_condition_0_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < self.buy_rsi_0.value) & ((dataframe['close'] * self.buy_dip_0.value < dataframe['open'].shift(3)) | (dataframe['close'] * self.buy_dip_0.value< dataframe['open'].shift(2)) | (dataframe['close'] * self.buy_dip_0.value < dataframe['open'].shift(1))) & (dataframe['rsi_1h'] < self.buy_rsi_1h_0.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) conditions.append( ( self.buy_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_1a.value) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.buy_rsi_3.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_3.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_4_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_5_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) conditions.append( ( self.buy_condition_6_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_2.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_7_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_8_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_10_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) & (dataframe['close_1h'] < dataframe['bb_lowerband_1h']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift(2) < 0) & (dataframe['rsi'] < 40.5) & (dataframe['hist'] > dataframe['close'] * 0.0012) & (dataframe['open'] < dataframe['close']) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, sell fast (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'sell' ] = 0 return dataframe