# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BigZ03.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 class Github_remiotore_ccxt_freqtrade__BigZ03__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.028, # I feel lucky! "10": 0.018, "40": 0.005, "180": 0.018, # We're going up? } 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 = False startup_candle_count: int = 200 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": False, "buy_condition_11_enable": False, "buy_condition_12_enable": True, } 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_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, 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_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=False, 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_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, 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) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0): return 0.99 else: trade_time_50 = current_time - timedelta(minutes=50) if (trade_time_50 > trade.open_date_utc): try: number_of_candle_shift = int((trade_time_50 - trade.open_date_utc).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() if candle['rsi_1h'] < 35: return 0.99 if candle['close'] > candle['ema_200']: if current_rate * 1.025 < candle['open']: return 0.01 if current_rate * 1.015 < candle['open']: return 0.01 except IndexError as error: return 0.5 return 0.99 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.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) 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.EMA(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) 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: dataframe.loc[ ( 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) ) | ( self.buy_condition_11_enable.value & ((dataframe['high'] - dataframe['low']) < dataframe['open']/100) & (dataframe['open'] < dataframe['close']) & ((dataframe['high'].shift() - dataframe['low'].shift()) < dataframe['open'].shift()/100) & ((dataframe['high'].shift(2) - dataframe['low'].shift(2)) < dataframe['open'].shift(2)/100) & ((dataframe['high'].shift(3) - dataframe['low'].shift(3)) < dataframe['open'].shift(3)/100) & ((dataframe['high'].shift(4) - dataframe['low'].shift(4)) < dataframe['open'].shift(4)/100) & ((dataframe['high'].shift(5) - dataframe['low'].shift(5)) < dataframe['open'].shift(5)/100) & ((dataframe['high'].shift(6) - dataframe['low'].shift(6)) < dataframe['open'].shift(6)/100) & ((dataframe['high'].shift(7) - dataframe['low'].shift(7)) < dataframe['open'].shift(7)/100) & ((dataframe['high'].shift(8) - dataframe['low'].shift(8)) < dataframe['open'].shift(8)/100) & ((dataframe['high'].shift(9) - dataframe['low'].shift(9)) < dataframe['open'].shift(9)/100) & (dataframe['bb_middleband'] > dataframe['bb_middleband'].shift(9) * 1.005) & (dataframe['rsi'] < 68) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( self.buy_condition_0_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < 30) & (dataframe['close'] * 1.024 < dataframe['open'].shift(3)) & (dataframe['rsi_1h'] < 71) & (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 ) | ( 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'] < 69) & (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) ) | ( 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) ) | ( 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) ) | ( 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) ) | ( 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 ) | ( 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) ) | ( 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) ) | ( 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_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) ) | ( 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) ), '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