# source: https://raw.githubusercontent.com/yydhYYDH/QuantStrategies/7f05ae2160042efdb277a972cd7245ec28c4740d/user_data/strategies/archive/2025-2026/BigZ04.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 from functools import reduce ########################################################################################################### ## Github_yydhYYDH_QuantStrategies__BigZ04__20260516_150654 by ilya ## ## ## ## https://github.com/i1ya/freqtrade-strategies ## ## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ## ## ## ## ########################################################################################################### ## The main point of this strat is: ## ## - make drawdown as low as possible ## ## - entry at dip ## ## - exit quick as fast as you can (release money for the next entry) ## ## - soft check if market if rising ## ## - hard check is market if fallen ## ## - 11 entry signals ## ## - stoploss function preventing from big fall ## ## - no exit signal. Whether ROI or stoploss =) ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ## ## ## ## As a pairlist you can use VolumePairlist. ## ## ## ## Ensure that you don't override any variables in your config.json. Especially ## ## the timeframe (must be 5m). ## ## ## ## exit_profit_only: ## ## True - risk more (gives you higher profit and higher Drawdown) ## ## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ## ## ## ########################################################################################################### ## DONATIONS 2 @iterativ (author of the original strategy) ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### class Github_yydhYYDH_QuantStrategies__BigZ04__20260516_150654(IStrategy): INTERFACE_VERSION = 3 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' # exit signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_entry_signal = False # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } entry_params = { ############# # Enable/Disable conditions "entry_condition_0_enable": True, "entry_condition_1_enable": True, "entry_condition_2_enable": True, "entry_condition_3_enable": True, "entry_condition_4_enable": True, "entry_condition_5_enable": True, "entry_condition_6_enable": True, "entry_condition_7_enable": True, "entry_condition_8_enable": True, "entry_condition_9_enable": True, "entry_condition_10_enable": True, "entry_condition_11_enable": True, "entry_condition_12_enable": True, "entry_condition_13_enable": False, } ############################################################################ # entry entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_5_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_6_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_7_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_8_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_9_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_10_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_11_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_12_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_condition_13_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='entry', optimize=False, load=True) entry_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='entry', optimize=False, load=True) entry_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='entry', decimals=1, optimize=False, load=True) entry_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='entry', decimals=1, optimize=False, load=True) entry_volume_drop_2 = DecimalParameter(1, 10, default=3, space='entry', decimals=1, optimize=False, load=True) entry_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='entry', decimals=1, optimize=False, load=True) entry_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='entry', decimals=1, optimize=False, load=True) entry_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='entry', decimals=2, optimize=False, load=True) entry_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='entry', decimals=2, optimize=False, load=True) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool: return True def custom_exit(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: # Manage losing trades and open room for better ones. if (current_profit > 0): return 0.99 else: trade_time_50 = trade.open_date_utc + timedelta(minutes=50) # Trade open more then 60 minutes. For this strategy it's means -> loss # Let's try to minimize the loss if (current_time > trade_time_50): try: number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() # We are at bottom. Wait... if candle['rsi_1h'] < 30: return 0.99 # Are we still sinking? 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: # Whoops, set stoploss at 10% return 0.1 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." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), 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() # EMA dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) # MACD dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) # SMA dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.entry_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.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_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.entry_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.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) conditions.append( ( self.entry_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_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.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.entry_rsi_3.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_3.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_4_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_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.entry_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.entry_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) conditions.append( ( self.entry_condition_6_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.entry_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.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_7_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.entry_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_8_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_3.value) & (dataframe['rsi'] < self.entry_rsi_1.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_9_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_4.value) & (dataframe['rsi'] < self.entry_rsi_2.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.entry_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.entry_condition_10_enable.value & (dataframe['rsi_1h'] < self.entry_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), 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, exit fast (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'exit_long' ] = 0 return dataframe