# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/CombinedBinHAndClucV8Hyper.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy import DecimalParameter, IntParameter from freqtrade.strategy.interface import IStrategy from freqtrade.persistence import Trade from pandas import DataFrame from datetime import datetime, timedelta from functools import reduce ########################################################################################################### ## CombinedBinHAndClucV8 by iterativ ## ## ## ## Freqtrade https://github.com/freqtrade/freqtrade ## ## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ## ## V8 by iterativ. ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 60 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m) & exit_profit_only (must be true). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### # SSL Channels def SSLChannels(dataframe, length=7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return (df['sslDown'], df['sslUp']) class Github_DerSalvador_freqtrade_helm_chart__CombinedBinHAndClucV8Hyper__20260115_122204(IStrategy): INTERFACE_VERSION = 3 minimal_roi = {'0': 10} stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' # informative tf # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_entry_signal = True # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 # 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': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False} # Buy Hyperopt params entry_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space='entry', decimals=3, optimize=False, load=True) entry_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='entry', decimals=2, optimize=False, load=True) entry_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space='entry', decimals=2, optimize=False, load=True) entry_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space='entry', decimals=2, optimize=False, load=True) entry_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space='entry', optimize=True, load=True) entry_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='entry', optimize=True, load=True) entry_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space='entry', optimize=True, load=True) entry_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space='entry', optimize=True, load=True) entry_bb20_volume = IntParameter(18, 36, default=29, space='entry', optimize=True, load=True) entry_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space='entry', decimals=2, optimize=True, load=True) entry_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space='entry', decimals=2, optimize=True, load=True) entry_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space='entry', decimals=2, optimize=True, load=True) entry_rsi = DecimalParameter(30.0, 40.0, default=38.5, space='entry', decimals=2, optimize=True, load=True) entry_mfi = DecimalParameter(36.0, 65.0, default=36.0, space='entry', decimals=2, optimize=True, load=True) entry_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space='entry', decimals=2, optimize=False, load=True) entry_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space='entry', decimals=3, optimize=False, load=True) # Sell Hyperopt params exit_custom_roi_profit_1 = DecimalParameter(0.01, 0.03, default=0.01, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space='exit', decimals=2, optimize=False, load=True) exit_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=2, optimize=False, load=True) exit_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='exit', decimals=3, optimize=False, load=True) exit_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='exit', decimals=2, optimize=False, load=True) exit_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True) exit_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True) exit_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='exit', decimals=2, optimize=False, load=True) exit_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='exit', decimals=3, optimize=False, load=True) exit_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='exit', decimals=2, optimize=False, load=True) exit_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='exit', decimals=2, optimize=True, load=True) # Buy hyperspace params: entry_params = {'entry_bb20_close_bblowerband': 0.991, 'entry_bb20_volume': 34, 'entry_bb40_bbdelta_close': 0.029, 'entry_bb40_closedelta_close': 0.012, 'entry_bb40_tail_bbdelta': 0.212, 'entry_mfi': 57.29, 'entry_min_inc': 0.05, 'entry_rsi': 34.94, 'entry_rsi_1h': 42.66, 'entry_rsi_diff': 45.82} # Sell hyperspace params: exit_params = {'exit_rsi_main': 76.4} # ROI table: minimal_roi = {'0': 0.107, '15': 0.047, '75': 0.013, '106': 0} # Stoploss: stoploss = -0.274 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.314 trailing_stop_positive_offset = 0.411 trailing_only_offset_is_reached = 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) & (current_time - timedelta(minutes=280) > trade.open_date_utc): return 0.01 elif current_profit < self.exit_custom_stoploss_1.value: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if last_candle is not None: if last_candle['sma_200_dec'] & last_candle['sma_200_dec_1h']: return 0.01 return 0.99 def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if last_candle is not None: if (current_profit > self.exit_custom_roi_profit_4.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_4.value): return 'roi_target_4' elif (current_profit > self.exit_custom_roi_profit_3.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_3.value): return 'roi_target_3' elif (current_profit > self.exit_custom_roi_profit_2.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_2.value): return 'roi_target_2' elif (current_profit > self.exit_custom_roi_profit_1.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_1.value): return 'roi_target_1' elif (current_profit > 0) & (current_profit < self.exit_custom_roi_profit_5.value) & last_candle['sma_200_dec']: return 'roi_target_5' elif (current_profit > self.exit_trail_profit_min_1.value) & (current_profit < self.exit_trail_profit_max_1.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.exit_trail_down_1.value): return 'trail_target_1' elif (current_profit > self.exit_trail_profit_min_2.value) & (current_profit < self.exit_trail_profit_max_2.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.exit_trail_down_2.value): return 'trail_target_2' return None def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_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_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() 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['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() # EMA dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # MFI dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) # 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((dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.entry_bb40_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.entry_bb40_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.entry_bb40_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0)) conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < self.entry_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * self.entry_bb20_volume.value)) conditions.append((dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.entry_rsi_diff.value) & (dataframe['volume'] > 0)) conditions.append((dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(16)) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & ((dataframe['open'].rolling(24).min() - dataframe['close']) / dataframe['close'] > self.entry_min_inc.value) & (dataframe['rsi_1h'] > self.entry_rsi_1h.value) & (dataframe['rsi'] < self.entry_rsi.value) & (dataframe['mfi'] < self.entry_mfi.value) & (dataframe['volume'] > 0)) conditions.append((dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & (dataframe['volume'].rolling(4).mean() * self.entry_volume_1.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (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: conditions = [] conditions.append((dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0)) conditions.append((dataframe['rsi'] > self.exit_rsi_main.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe