import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter from pandas import DataFrame from functools import reduce from freqtrade.persistence import Trade ########################################################################################################### ## NostalgiaForInfinityV3 by iterativ ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 80 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). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### class NostalgiaForInfinityV3(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 10, } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' custom_info = {} # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = True # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.15 # 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 = 400 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, } sell_params = { ############# # Enable/Disable conditions "sell_condition_1_enable": True, "sell_condition_2_enable": True, "sell_condition_3_enable": True, "sell_condition_4_enable": True, "sell_condition_5_enable": True, "sell_condition_6_enable": True, "sell_condition_7_enable": True, "sell_condition_8_enable": True, } ############################################################################ # Buy buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.03, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_1 = DecimalParameter(0.001, 0.2, default=0.12, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.05, 0.4, default=0.3, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True) buy_volume_1 = DecimalParameter(1.0, 30.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_min_inc_1 = DecimalParameter(0.005, 0.05, default=0.029, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_min_1 = DecimalParameter(40.0, 70.0, default=45.25, space='buy', decimals=2, optimize=False, load=True) buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=85.06, space='buy', decimals=2, optimize=False, load=True) buy_rsi_1 = DecimalParameter(30.0, 40.0, default=36.64, space='buy', decimals=2, optimize=False, load=True) buy_mfi_1 = DecimalParameter(36.0, 65.0, default=45.25, space='buy', decimals=2, optimize=False, load=True) buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.96, space='buy', decimals=2, optimize=False, load=True) buy_ema_relative_2 = DecimalParameter(0.005, 0.08, default=0.006, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_min_2 = DecimalParameter(40.0, 70.0, default=63.91, space='buy', decimals=2, optimize=False, load=True) buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=89.94, space='buy', decimals=2, optimize=False, load=True) buy_rsi_1h_diff_2 = DecimalParameter(35.0, 55.0, default=38.69, space='buy', decimals=2, optimize=False, load=True) buy_mfi_2 = DecimalParameter(36.0, 65.0, default=53.89, space='buy', decimals=2, optimize=False, load=True) buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True) buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True) buy_bb40_tail_bbdelta = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True) buy_bb20_close_bblowerband = DecimalParameter(0.7, 1.1, default=0.98, space='buy', optimize=False, load=True) buy_bb20_volume = IntParameter(18, 35, default=24, space='buy', optimize=False, load=True) buy_volume_5 = DecimalParameter(1.0, 10.0, default=4.12, space='buy', decimals=2, optimize=False, load=True) buy_ema_open_mult_5 = DecimalParameter(0.01, 0.04, default=0.019, space='buy', decimals=3, optimize=False, load=True) buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.48, space='buy', decimals=2, optimize=False, load=True) buy_ema_open_mult_6 = DecimalParameter(0.025, 0.05, default=0.033, space='buy', decimals=3, optimize=False, load=True) buy_volume_7 = DecimalParameter(1.0, 10.0, default=7.04, space='buy', decimals=2, optimize=False, load=True) buy_ema_open_mult_7 = DecimalParameter(0.015, 0.03, default=0.02, space='buy', decimals=3, optimize=False, load=True) buy_rsi_7 = DecimalParameter(24.0, 50.0, default=41.09, space='buy', decimals=2, optimize=False, load=True) buy_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_9 = DecimalParameter(1.0, 30.0, default=17.0, space='buy', decimals=1, optimize=False, load=True) buy_bb_offset_9 = DecimalParameter(0.97, 1.05, default=0.98, space='buy', decimals=3, optimize=False, load=True) buy_volume_10 = DecimalParameter(1.0, 26.0, default=9.6, space='buy', decimals=1, optimize=False, load=True) buy_bb_offset_10 = DecimalParameter(0.97, 1.05, default=0.994, space='buy', decimals=3, optimize=False, load=True) buy_rsi_1h_10 = DecimalParameter(15.0, 40.0, default=30.2, space='buy', decimals=1, optimize=False, load=True) # Sell sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.382, space='sell', optimize=False, load=True) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=87.708, space='sell', decimals=1, optimize=False, load=True) sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_1 = DecimalParameter(0.01, 0.20, default=0.01, space='sell', decimals=2, optimize=False, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.65, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_2 = DecimalParameter(0.01, 0.20, default=0.05, space='sell', decimals=2, optimize=False, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.37, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_3 = DecimalParameter(0.15, 0.30, default=0.25, space='sell', decimals=2, optimize=False, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=51.87, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_4 = DecimalParameter(0.3, 0.7, default=0.45, space='sell', decimals=2, optimize=False, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.35, space='sell', decimals=2, optimize=False, load=True) sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.025, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_profit_3 = DecimalParameter(0.05, 0.3, default=0.07, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.166, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.38, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.154, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.035, 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=0.045, space='sell', decimals=3, optimize=False, load=True) ############################################################################ def custom_sell(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_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value): return 'target_profit_4' elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return 'target_profit_3' elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return 'target_profit_2' elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return 'target_profit_1' elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['close'] < last_candle['ema_200']): return 'target_profit_u_1' elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['sma_200_dec']): return 'target_profit_u_2' 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 'target_profit_t_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 'target_profit_t_2' return None def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. 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_15'] = ta.EMA(informative_1h, timeperiod=15) 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_50'] = ta.SMA(informative_1h, timeperiod=50) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # BB 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: # BB 40 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() # BB 20 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_30'] = 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_100'] = ta.EMA(dataframe, timeperiod=100) 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) # Alligator dataframe['lips'] = ta.SMA(dataframe, timeperiod=5) dataframe['smma_lips'] = dataframe['lips'].rolling(3).mean() dataframe['teeth'] = ta.SMA(dataframe, timeperiod=8) dataframe['smma_teeth'] = dataframe['teeth'].rolling(5).mean() dataframe['jaw'] = ta.SMA(dataframe, timeperiod=13) dataframe['smma_jaw'] = dataframe['jaw'].rolling(8).mean() # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) # If don't exceed the dip limits dataframe['safe_dips'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_0.value) & (((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)) 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.buy_condition_1_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_1.value > dataframe['volume']) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_2_enable.value & (dataframe['close'] < dataframe['sma_5']) & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_100']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) & (((dataframe['close'] - dataframe['ema_200']) / dataframe['ema_200']) < self.buy_ema_relative_2.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_dips']) & 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) ) ) conditions.append( ( self.buy_condition_4_enable.value & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_100']) & (dataframe['ema_15_1h'] > dataframe['ema_50_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['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume.value)) ) ) conditions.append( ( self.buy_condition_5_enable.value & #(dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_6_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_6.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_7_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_7.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi'] < self.buy_rsi_7.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_8_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['smma_lips']) & (dataframe['smma_lips'] > dataframe['smma_teeth']) & (dataframe['smma_teeth'] > dataframe['smma_jaw']) & (dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) & (dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) & (dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) & (dataframe['rsi'] < self.buy_rsi_8.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_10_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips']) & (dataframe['volume_mean_4'] * self.buy_volume_10.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.sell_condition_1_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_1.value) & (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(3) > dataframe['bb_upperband'].shift(3)) & (dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) & (dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_2_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_2.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_3_enable.value & (dataframe['rsi'] > self.sell_rsi_main_3.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_4_enable.value & (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_5_enable.value & (dataframe['close'] < dataframe['ema_200']) & (((dataframe['ema_200'] - dataframe['close']) / dataframe['close']) < self.sell_ema_relative_5.value) & (dataframe['rsi'] > dataframe['rsi_1h'] + self.sell_rsi_diff_5.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_6_enable.value & (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > self.sell_rsi_under_6.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_7_enable.value & (dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) & (qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26'])) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_8_enable.value & (dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe