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 ############################################################################### # this is the final adjustment version for all clucbinmad snip. # here i am goin to ad (v5&v6) & v8 &v9 lets see what we can achive # remember its production version no dev. should be lightweight # ################################################################################ # 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 BinClucMadV1(IStrategy): INTERFACE_VERSION = 3 # minimal_roi = { # "0": 0.028, # I feel lucky! # "10": 0.018, # "40": 0.005, # } # I feel lucky! # We're going up? minimal_roi = {'0': 0.038, '10': 0.028, '40': 0.015, '180': 0.018} stoploss = -0.99 # effectively disabled. timeframe = '5m' informative_timeframe = '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 # 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 ############# # Enable/Disable conditions buy_params = {'v6_buy_condition_0_enable': True, 'v6_buy_condition_1_enable': True, 'v6_buy_condition_2_enable': True, 'v6_buy_condition_3_enable': True, 'v8_buy_condition_0_enable': True, 'v8_buy_condition_1_enable': True, 'v8_buy_condition_2_enable': True, 'v8_buy_condition_3_enable': True, 'v8_buy_condition_4_enable': True, 'v9_buy_condition_0_enable': False, 'v9_buy_condition_1_enable': False, 'v9_buy_condition_2_enable': False, 'v9_buy_condition_3_enable': False, 'v9_buy_condition_4_enable': False, 'v9_buy_condition_5_enable': False, 'v9_buy_condition_6_enable': False, 'v9_buy_condition_7_enable': False, 'v9_buy_condition_8_enable': False, 'v9_buy_condition_9_enable': False, 'v9_buy_condition_10_enable': False} ############# # Enable/Disable conditions sell_params = {'v9_sell_condition_0_enable': False, 'v8_sell_condition_0_enable': True, 'v8_sell_condition_1_enable': False} ############################################################################ # Buy CombinedBinHClucAndMADV6 v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) # Buy CombinedBinHClucV8 v8_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v8_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v8_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v8_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v8_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='sell', decimals=2, optimize=False, load=True) buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space='buy', decimals=2, optimize=False, load=True) buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space='buy', optimize=False, load=True) buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='buy', optimize=False, load=True) buy_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space='buy', optimize=False, load=True) buy_bb20_volume = IntParameter(18, 36, default=29, space='buy', optimize=False, load=True) buy_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space='buy', decimals=2, optimize=False, load=True) buy_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space='buy', decimals=2, optimize=False, load=True) buy_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space='buy', decimals=2, optimize=False, load=True) buy_rsi = DecimalParameter(30.0, 40.0, default=38.5, space='buy', decimals=2, optimize=False, load=True) buy_mfi = DecimalParameter(36.0, 65.0, default=36.0, space='buy', decimals=2, optimize=False, load=True) buy_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True) sell_custom_roi_profit_1 = DecimalParameter(0.01, 0.03, default=0.01, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=2, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True) sell_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='sell', decimals=2, optimize=False, load=True) # Buy CombinedBinHClucAndMADV9 v9_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) v9_buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) # Sell v9_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=False, load=True) buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True) buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True) buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True) buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True) buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True) def custom_stoplossv8(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_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_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.sell_custom_stoploss_1.value: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-1].squeeze() if candle is not None: # 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 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.sell_custom_roi_profit_4.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_4.value): return 'roi_target_4' elif (current_profit > self.sell_custom_roi_profit_3.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_3.value): return 'roi_target_3' elif (current_profit > self.sell_custom_roi_profit_2.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_2.value): return 'roi_target_2' elif (current_profit > self.sell_custom_roi_profit_1.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_1.value): return 'roi_target_1' elif (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & last_candle['sma_200_dec']: return 'roi_target_5' elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_trail_down_1.value): return 'trail_target_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_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 = [] # START V6 if self.v6_buy_condition_0_enable.value: # strategy ClucMay72018 # Guard is on, candle should dig not so hard (0,99) # Try to exclude pumping # (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. conditions.append((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_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0)) if self.v6_buy_condition_1_enable.value: # strategy ClucMay72018 # Guard is off, candle should dig hard (0,975) # Don't buy if someone drop the market. # Buy only at dip # Try to exclude pumping # Make sure Volume is not 0 conditions.append((dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['rsi_1h'] < 15) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0)) if self.v6_buy_condition_2_enable.value: # strategy MACD Low buy # Don't buy if someone drop the market. # Try to exclude pumping # Make sure Volume is not 0 conditions.append((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['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0)) if self.v6_buy_condition_3_enable.value: # strategy MACD Low buy # Don't buy if someone drop the market. # Make sure Volume is not 0 conditions.append((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']) & (dataframe['volume'] > 0)) # END V6 if self.v8_buy_condition_0_enable.value: 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.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0)) if self.v8_buy_condition_1_enable.value: 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.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * self.buy_bb20_volume.value) & (dataframe['volume'] > 0)) if self.v8_buy_condition_2_enable.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.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_diff.value) & (dataframe['volume'] > 0)) if self.v8_buy_condition_3_enable.value: 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.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & ((dataframe['open'].rolling(24).min() - dataframe['close']) / dataframe['close'] > self.buy_min_inc.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h.value) & (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['mfi'] < self.buy_mfi.value) & (dataframe['volume'] > 0)) if self.v8_buy_condition_4_enable.value: 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.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_1.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] > 0)) # START VERSION9 if self.v9_buy_condition_1_enable.value: conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0)) if self.v9_buy_condition_2_enable.value: conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0)) if self.v9_buy_condition_3_enable.value: conditions.append((dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.buy_rsi_3.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_4_enable.value: conditions.append((dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_5_enable.value: # Make sure Volume is not 0 conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_6_enable.value: conditions.append((dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_macd_2.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_7_enable.value: conditions.append((dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_8_enable.value: conditions.append((dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_9_enable.value: conditions.append((dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.buy_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.buy_volume_pump_1.value) & (dataframe['volume'] > 0)) if self.v9_buy_condition_10_enable.value: conditions.append((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) & (dataframe['volume'] > 0)) # END V9 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 = [] if self.v9_sell_condition_0_enable.value: # Don't be gready, sell fast # Make sure Volume is not 0 conditions.append((dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0)) if self.v8_sell_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_sell_condition_1_enable.value: conditions.append(qtpylib.crossed_above(dataframe['rsi'], self.v8_sell_rsi_main.value) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe