# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/BinClucMadDevelop.py # directory_url: https://github.com/DerSalvador/freqtrade-helm-chart/blob/main/chart/deployed_strategies/binance-michael-k8s-namespace/ # User: DerSalvador # Repository: freqtrade-helm-chart # --------------------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 import logging # ------------------------------------------------------------------------------------------------- # --- logger for parameter merging output, only remove if you remove it further down too! --------- logger = logging.getLogger(__name__) # ------------------------------------------------------------------------------------------------- # 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__BinClucMadDevelop__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # minimal_roi = {"0": 0.10, "10": 0.028, "40": 0.015, "180": 0.018 } # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018 } minimal_roi = {'0': 0.2, '38': 0.074, '78': 0.025, '194': 0} stoploss = -0.99 timeframe = '5m' informative_timeframe = '1h' # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.001 ignore_roi_if_entry_signal = True # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.05 # Custom stoploss use_custom_stoploss = False # 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 ############# # Enable/Disable conditions # avg 0.47 dd 27% # no trade # avg 2.32 # avg 1.12 dd 6% # avg 0.74 # avg 0.41 dd 37% # avg 1.37 # avg 0.41 # avg 1.29 entry_params = {'entry_minimum_conditions': 1, 'v6_entry_condition_0_enable': False, 'v6_entry_condition_1_enable': True, 'v6_entry_condition_2_enable': True, 'v6_entry_condition_3_enable': True, 'v8_entry_condition_0_enable': True, 'v8_entry_condition_1_enable': False, 'v8_entry_condition_2_enable': True, 'v8_entry_condition_3_enable': False, 'v8_entry_condition_4_enable': True, 'v9_entry_condition_0_enable': False, 'v9_entry_condition_1_enable': True, 'v9_entry_condition_2_enable': True, 'v9_entry_condition_3_enable': True, 'v9_entry_condition_4_enable': False, 'v9_entry_condition_5_enable': True, 'v9_entry_condition_6_enable': True, 'v9_entry_condition_7_enable': True, 'v9_entry_condition_8_enable': False, 'v9_entry_condition_9_enable': False, 'v9_entry_condition_10_enable': False} ############# # Enable/Disable conditions exit_params = {'v9_exit_condition_0_enable': False, 'v8_exit_condition_0_enable': True, 'v8_exit_condition_1_enable': True} # if you want to see which entry conditions were met # or if there is an trade exit override due to high RSI set to True # logger will output the entry and trade exit conditions cust_log_verbose = False ############################################################################ # Buy CombinedBinHClucAndMADV6 v6_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v6_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v6_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v6_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) # Buy CombinedBinHClucV8 v8_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v8_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v8_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v8_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v8_entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True) v8_exit_condition_0_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True) v8_exit_condition_1_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True) v8_exit_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='exit', decimals=2, optimize=False, load=True) 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=False, load=True) entry_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='entry', optimize=False, load=True) entry_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space='entry', optimize=False, load=True) entry_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space='entry', optimize=False, load=True) entry_bb20_volume = IntParameter(18, 36, default=29, space='entry', optimize=False, load=True) entry_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space='entry', decimals=2, optimize=False, load=True) entry_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space='entry', decimals=2, optimize=False, load=True) entry_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space='entry', decimals=2, optimize=False, load=True) entry_rsi = DecimalParameter(30.0, 40.0, default=38.5, space='entry', decimals=2, optimize=False, load=True) entry_mfi = DecimalParameter(36.0, 65.0, default=36.0, space='entry', decimals=2, optimize=False, 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) exit_custom_roi_profit_1 = DecimalParameter(0.01, 0.03, default=0.02, space='exit', decimals=2, optimize=True, load=True) exit_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='exit', decimals=2, optimize=True, load=True) exit_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='exit', decimals=2, optimize=True, load=True) exit_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='exit', decimals=2, optimize=True, load=True) exit_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='exit', decimals=2, optimize=True, 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=True, 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=True, load=True) exit_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='exit', decimals=3, optimize=True, load=True) exit_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='exit', decimals=2, optimize=True, load=True) exit_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='exit', decimals=3, optimize=True, load=True) exit_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=True, load=True) exit_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='exit', decimals=2, optimize=True, load=True) exit_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='exit', decimals=3, optimize=True, load=True) exit_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='exit', decimals=2, optimize=True, load=True) # Buy CombinedBinHClucAndMADV9 v9_entry_condition_0_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_1_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_2_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_3_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_4_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_5_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_6_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_7_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_8_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_9_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) v9_entry_condition_10_enable = CategoricalParameter([True, False], default=False, space='entry', optimize=False, load=True) # Sell v9_exit_condition_0_enable = CategoricalParameter([True, False], default=False, space='exit', optimize=False, load=True) entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, 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=4, 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_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) # minimum conditions to match in entry entry_minimum_conditions = IntParameter(1, 2, default=1, space='entry', optimize=False, load=True) # Sell Hyperopt params exit_roi_profit_1 = DecimalParameter(0.08, 0.16, default=0.1, space='exit', decimals=2, optimize=False, load=True) exit_roi_rsi_1 = DecimalParameter(30.0, 38.0, default=34, space='exit', decimals=2, optimize=False, load=True) exit_roi_profit_2 = DecimalParameter(0.02, 0.05, default=0.05, space='exit', decimals=2, optimize=False, load=True) exit_roi_rsi_2 = DecimalParameter(34.0, 44.0, default=38, space='exit', decimals=2, optimize=False, load=True) exit_roi_profit_3 = DecimalParameter(0.0, 0.0, default=0.03, space='exit', decimals=2, optimize=False, load=True) exit_roi_rsi_3 = DecimalParameter(48.0, 56.0, default=50, space='exit', 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 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Prevent exit, if there is more potential, in order to maximize profit if last_candle is not None: current_profit = trade.calc_profit_ratio(rate) if exit_reason == 'roi': if current_profit > self.exit_roi_profit_1.value: if last_candle['rsi'] > self.exit_roi_rsi_1.value: return False elif current_profit > self.exit_roi_profit_2.value: if last_candle['rsi'] > self.exit_roi_rsi_2.value: return False elif current_profit > self.exit_roi_profit_3.value: if last_candle['rsi'] > self.exit_roi_rsi_3.value: return False return True 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=240) # trade_time_240 = trade.open_date_utc + timedelta(minutes=240) # 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() if candle['sma_200_dec'] & candle['sma_200_dec_1h']: return 0.01 # 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 custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): # return False 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.informative_timeframe) 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.informative_timeframe) # 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 informative_1h['ssl-dir'] = np.where(ssl_up_1h > ssl_down_1h, 'up', 'down') return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 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() # strategy ClucMay72018 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=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.EMA(dataframe, timeperiod=5) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # MFI dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, 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 = [] # reset additional dataframe rows dataframe.loc[:, 'v9_entry_condition_1_enable'] = False dataframe.loc[:, 'v9_entry_condition_2_enable'] = False dataframe.loc[:, 'v9_entry_condition_3_enable'] = False dataframe.loc[:, 'v9_entry_condition_4_enable'] = False dataframe.loc[:, 'v9_entry_condition_5_enable'] = False dataframe.loc[:, 'v9_entry_condition_6_enable'] = False dataframe.loc[:, 'v9_entry_condition_7_enable'] = False dataframe.loc[:, 'v9_entry_condition_8_enable'] = False dataframe.loc[:, 'v9_entry_condition_9_enable'] = False dataframe.loc[:, 'v9_entry_condition_10_enable'] = False dataframe.loc[:, 'v6_entry_condition_0_enable'] = False dataframe.loc[:, 'v6_entry_condition_1_enable'] = False dataframe.loc[:, 'v6_entry_condition_2_enable'] = False dataframe.loc[:, 'v6_entry_condition_3_enable'] = False dataframe.loc[:, 'v8_entry_condition_0_enable'] = False dataframe.loc[:, 'v8_entry_condition_1_enable'] = False dataframe.loc[:, 'v8_entry_condition_2_enable'] = False dataframe.loc[:, 'v8_entry_condition_3_enable'] = False dataframe.loc[:, 'v8_entry_condition_4_enable'] = False dataframe.loc[:, 'conditions_count'] = 0 dataframe.loc[(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()) & (self.v8_entry_condition_0_enable.value == True), 'v8_entry_condition_0_enable'] = 1 dataframe.loc[(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_50']) & (dataframe['close'] < self.entry_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * self.entry_bb20_volume.value) & (self.v8_entry_condition_1_enable.value == True), 'v8_entry_condition_1_enable'] = 1 dataframe.loc[(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) & (self.v8_entry_condition_2_enable.value == True), 'v8_entry_condition_2_enable'] = 1 dataframe.loc[(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) & (self.v8_entry_condition_3_enable.value == True), 'v8_entry_condition_3_enable'] = 1 dataframe.loc[(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']) & (self.v8_entry_condition_4_enable.value == True), 'v8_entry_condition_4_enable'] = 1 # start from here dataframe.loc[(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['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (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)) & (self.v9_entry_condition_1_enable.value == True), 'v9_entry_condition_1_enable'] = 1 dataframe.loc[(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(30) * self.entry_volume_pump_1.value) & (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)) & (self.v9_entry_condition_2_enable.value == True), 'v9_entry_condition_2_enable'] = 1 dataframe.loc[(dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.entry_rsi_3.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (self.v9_entry_condition_3_enable.value == True), 'v9_entry_condition_3_enable'] = 1 dataframe.loc[(dataframe['rsi_1h'] < self.entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (self.v9_entry_condition_4_enable.value == True), 'v9_entry_condition_4_enable'] = 1 dataframe.loc[(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(30) * self.entry_volume_pump_1.value) & (self.v9_entry_condition_5_enable.value == True), 'v9_entry_condition_5_enable'] = 1 dataframe.loc[(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'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (self.v9_entry_condition_6_enable.value == True), 'v9_entry_condition_6_enable'] = 1 dataframe.loc[(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(30) * self.entry_volume_pump_1.value) & (self.v9_entry_condition_7_enable.value == True), 'v9_entry_condition_7_enable'] = 1 dataframe.loc[(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_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (self.v9_entry_condition_8_enable.value == True), 'v9_entry_condition_8_enable'] = 1 dataframe.loc[(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(30) * self.entry_volume_pump_1.value) & (self.v9_entry_condition_9_enable.value == True), 'v9_entry_condition_9_enable'] = 1 dataframe.loc[(dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) & (self.v9_entry_condition_10_enable.value == True), 'v9_entry_condition_10_enable'] = 1 dataframe.loc[(dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) & ((dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 21) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (self.v6_entry_condition_0_enable.value == True), 'v6_entry_condition_0_enable'] = 1 # Don't entry if someone drop the market. dataframe.loc[(dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & ((dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 20) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (dataframe['rsi_1h'] < 15) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (self.v6_entry_condition_1_enable.value == True), 'v6_entry_condition_1_enable'] = 1 dataframe.loc[(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'] * 0.02) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & ((dataframe['volume'] < dataframe['volume'].shift() * 4) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (dataframe['close'] < dataframe['bb_lowerband']) & (self.v6_entry_condition_2_enable.value == True), 'v6_entry_condition_2_enable'] = 1 dataframe.loc[(dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.03) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['close'] < dataframe['bb_lowerband']) & (self.v6_entry_condition_3_enable.value == True), 'v6_entry_condition_3_enable'] = 1 # count the amount of conditions met dataframe.loc[:, 'conditions_count'] = dataframe['v9_entry_condition_1_enable'].astype(int) + dataframe['v9_entry_condition_2_enable'].astype(int) + dataframe['v9_entry_condition_3_enable'].astype(int) + dataframe['v9_entry_condition_4_enable'].astype(int) + dataframe['v9_entry_condition_5_enable'].astype(int) + dataframe['v9_entry_condition_6_enable'].astype(int) + dataframe['v9_entry_condition_7_enable'].astype(int) + dataframe['v9_entry_condition_8_enable'].astype(int) + dataframe['v9_entry_condition_9_enable'].astype(int) + dataframe['v9_entry_condition_10_enable'].astype(int) + dataframe['v6_entry_condition_0_enable'].astype(int) + dataframe['v6_entry_condition_1_enable'].astype(int) + dataframe['v6_entry_condition_2_enable'].astype(int) + dataframe['v6_entry_condition_3_enable'].astype(int) + dataframe['v8_entry_condition_0_enable'].astype(int) + dataframe['v8_entry_condition_1_enable'].astype(int) + dataframe['v8_entry_condition_2_enable'].astype(int) + dataframe['v8_entry_condition_3_enable'].astype(int) + dataframe['v8_entry_condition_4_enable'].astype(int) # append the minimum amount of conditions to be met conditions.append(dataframe['conditions_count'] >= self.entry_minimum_conditions.value) conditions.append(dataframe['volume'].gt(0)) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1 # verbose logging enable only for verbose information or troubleshooting if self.cust_log_verbose == True: for index, row in dataframe.iterrows(): if row['entry'] == 1: # entry_cond_details = f"count={int(row['conditions_count'])}/bin={int(row['entry_cond_bin'])}/cluc={int(row['entry_cond_cluc'])}/v9_={int(row['entry_cond_long'])}" entry_cond_details = f"count={int(row['conditions_count'])}/v9_1={int(row['v9_entry_condition_1_enable'])}/v9_2={int(row['v9_entry_condition_2_enable'])}/v9_3={int(row['v9_entry_condition_3_enable'])}/v9_4={int(row['v9_entry_condition_4_enable'])}/v9_5={int(row['v9_entry_condition_5_enable'])}/v9_6={int(row['v9_entry_condition_6_enable'])}/v9_7={int(row['v9_entry_condition_7_enable'])}/v9_8={int(row['v9_entry_condition_8_enable'])}/v9_9={int(row['v9_entry_condition_9_enable'])}/v9_10={int(row['v9_entry_condition_10_enable'])}/v6_0={int(row['v6_entry_condition_0_enable'])}/v6_1={int(row['v6_entry_condition_1_enable'])}/v6_2={int(row['v6_entry_condition_2_enable'])}/v6_3={int(row['v6_entry_condition_3_enable'])}/v8_0={int(row['v8_entry_condition_0_enable'])}/v8_1={int(row['v8_entry_condition_1_enable'])}/v8_2={int(row['v8_entry_condition_2_enable'])}/v8_3={int(row['v8_entry_condition_3_enable'])}/v8_4={int(row['v8_entry_condition_4_enable'])}" logger.info(f"{metadata['pair']} - candle: {row['date']} - entry condition - details: {entry_cond_details}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.v9_exit_condition_0_enable.value: # Don't be gready, exit fast # Make sure Volume is not 0 conditions.append((dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0)) if self.v8_exit_condition_0_enable.value: 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)) if self.v8_exit_condition_1_enable.value: conditions.append((dataframe['rsi'] > self.v8_exit_rsi_main.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit'] = 1 return dataframe # --- custom indicators --------------------------------------------------------------------------- def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ 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'])