# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/BigZ04_TSL3.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 ########################################################################################################### ## Github_DerSalvador_freqtrade_helm_chart__BigZ04_TSL3__20260416_224245 by Perkmeister, based on BigZ04 by ilya ## ## ## ## https://github.com/i1ya/freqtrade-strategies ## ## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ## ## ## ## This is a modified version of BigZ04 that uses custom_stoploss() to implement a hard stoploss ## ## of 8%, and to replace the roi table with a trailing stoploss to extract more profit when prices ## ## start to rise above a profit threshold. It's quite simple and crude and is a 'first stab' at the ## ## hard stoploss problem, use live at your own risk ;). The exit signals from SMAOffsetProtectOptV1 ## ## have been added but are currently disabled as had no benefit. ## ## ## ########################################################################################################### ## The main point of this strat is: ## ## - make drawdown as low as possible ## ## - entry at dip ## ## - soft check if market if rising ## ## - hard check is market if fallen ## ## - 11 entry signals ## ## - hard stoploss function preventing from big fall ## ## - trailing stoploss while in profit ## ## - no exit signal. Uses custom stoploss ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ## ## ## ## As a pairlist it is recommended to use a static pairlst such as iterativ's orginal: ## ## https://discord.com/channels/700048804539400213/702584639063064586/838038600368783411 ## ## ## ## Ensure that you don't override any variables in your config.json. Especially ## ## the timeframe (must be 5m). ## ## ## ## ## ########################################################################################################### ## 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_DerSalvador_freqtrade_helm_chart__BigZ04_TSL3__20260416_224245(IStrategy): INTERFACE_VERSION = 3 minimal_roi = {'0': 100.0} stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' # Sell 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} ############# # Enable/Disable conditions entry_params = {'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} # V1 original # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 49, 'high_offset': 1.006, 'pHSL': -0.08, 'pPF_1': 0.016, 'pSL_1': 0.011, 'pPF_2': 0.08, 'pSL_2': 0.04} ############################################################################ # Buy 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) # hyperopt parameters for custom_stoploss() trade_time = IntParameter(25, 65, default=35, space='exit', optimize=False, load=True) rsi_1h_val = IntParameter(25, 45, default=32, space='exit', optimize=False, load=True) narrow_stop = DecimalParameter(1.005, 1.03, default=1.02, space='exit', decimals=3, optimize=False, load=True) wide_stop = DecimalParameter(1.01, 1.045, default=1.035, space='exit', decimals=3, optimize=False, load=True) # hyperopt parameters for SMAOffsetProtectOptV1 exit signal base_nb_candles_exit = IntParameter(5, 80, default=49, space='exit', optimize=False, load=True) high_offset = DecimalParameter(0.99, 1.1, default=1.006, space='exit', optimize=False, load=True) # trailing stoploss hyperopt parameters # hard stoploss profit pHSL = DecimalParameter(-0.2, -0.04, default=-0.08, decimals=3, space='exit', optimize=False, load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', optimize=False, load=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', optimize=False, load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', optimize=False, load=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', 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 # new custom stoploss, both hard and trailing functions. Trailing stoploss first rises at a slower # rate than the current rate until a profit threshold is reached, after which it rises at a constant # percentage as per a normal trailing stoploss. This allows more margin for pull-backs during a rise. 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 current_profit > PF_1: return stoploss_from_open(sl_profit, current_profit) else: return stoploss_from_open(HSL, current_profit) return stoploss_from_open(HSL, 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.' # 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(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() # 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) # ------ ATR stuff dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) 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)) # Make sure Volume is not 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)) 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)) # Make sure Volume is not 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)) 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' ] = 0 """ conditions = [] conditions.append((dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 0 return dataframe