import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, timeframe_to_minutes, stoploss_from_open) from pandas import DataFrame, Series from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta from freqtrade.exchange import timeframe_to_prev_date from technical.indicators import zema, VIDYA class MultiMA_TSL_461(IStrategy): INTERFACE_VERSION = 2 DATESTAMP = 0 SELLMA = 1 SELL_TRIGGER = 2 buy_params = { "base_nb_candles_buy_trima": 15, "base_nb_candles_buy_trima2": 38, "low_offset_trima": 0.959, "low_offset_trima2": 0.949, "base_nb_candles_buy_ema": 9, "base_nb_candles_buy_ema2": 75, "low_offset_ema": 1.067, "low_offset_ema2": 0.973, "base_nb_candles_buy_zema": 25, "base_nb_candles_buy_zema2": 53, "low_offset_zema": 0.958, "low_offset_zema2": 0.961, "base_nb_candles_buy_hma": 70, "base_nb_candles_buy_hma2": 12, "low_offset_hma": 0.948, "low_offset_hma2": 0.941, "ewo_high": 2.615, "ewo_high2": 2.188, "ewo_low": -19.632, "ewo_low2": -19.955, "rsi_buy": 60, "rsi_buy2": 45, } sell_params = { "base_nb_candles_ema_sell": 5, "high_offset_sell_ema": 0.994, "pHSL": -0.32, "pPF_1": 0.02, "pPF_2": 0.047, "pSL_1": 0.02, "pSL_2": 0.046, } minimal_roi = { "0": 100 } stoploss = -0.15 pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) optimize_sell_ema = True base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=True) high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=True) base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=True) optimize_buy_ema = True base_nb_candles_buy_ema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) base_nb_candles_buy_ema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) optimize_buy_trima = True base_nb_candles_buy_trima = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) base_nb_candles_buy_trima2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) optimize_buy_zema = True base_nb_candles_buy_zema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) base_nb_candles_buy_zema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) optimize_buy_hma = True base_nb_candles_buy_hma = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma) base_nb_candles_buy_hma2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma) buy_condition_enable_optimize = True buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_zema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) ewo_check_optimize = True ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) rsi_buy_optimize = True rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=True) fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=True) slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=True) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.018 use_custom_stoploss = True protection_params = { "low_profit_lookback": 48, "low_profit_min_req": 0.04, "low_profit_stop_duration": 14, "cooldown_lookback": 2, # value loaded from strategy "stoploss_lookback": 72, # value loaded from strategy "stoploss_stop_duration": 20, # value loaded from strategy } cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=True) low_profit_optimize = True low_profit_lookback = IntParameter(2, 60, default=20, space="protection", optimize=low_profit_optimize) low_profit_stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=low_profit_optimize) low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space="protection", decimals=2, optimize=low_profit_optimize) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.low_profit_lookback.value, "trade_limit": 1, "stop_duration": int(self.low_profit_stop_duration.value), "required_profit": self.low_profit_min_req.value }) return prot timeframe = '5m' custom_info = {} process_only_new_candles = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count: int = 400 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) if (len(dataframe) < 1): return False last_candle = dataframe.iloc[-1] if (self.custom_info[pair][self.DATESTAMP] != last_candle['date']): sell_ema = self.custom_info[pair][self.SELLMA] if (sell_ema == 0): sell_ema = last_candle['ema_sell'] emaLength = 32 alpha = 2 / (1 + emaLength) sell_ema = (alpha * last_candle['close']) + ((1 - alpha) * sell_ema) self.custom_info[pair][self.SELLMA] = sell_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] if ((last_candle['close'] > (sell_ema * self.high_offset_sell_ema.value)) & (last_candle['buy_copy'] == 0)): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty' return f'New Sell Signal ({buy_tag})' return False def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if (len(dataframe) < 1): return False last_candle = dataframe.iloc[-1].squeeze() if ((rate > last_candle['close'])): return False self.custom_info[pair][self.DATESTAMP] = last_candle['date'] self.custom_info[pair][self.SELLMA] = last_candle['ema_sell'] return True def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: self.custom_info[pair][self.SELL_TRIGGER] = 0 return True def get_ticker_indicator(self): return int(self.timeframe[:-1]) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe = HA(dataframe, 4) if self.config['runmode'].value in ('live', 'dry_run'): dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) else: dataframe['live_data_ok'] = True if not metadata["pair"] in self.custom_info: self.custom_info[metadata["pair"]] = ['', 0, 0] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) * self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) * self.low_offset_ema2.value dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe.loc[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'buy'] = 0 if (self.buy_condition_trima_enable.value): dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * self.low_offset_trima.value dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) * self.low_offset_trima2.value buy_offset_trima = ( ( (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_trima, 'buy_tag'] += 'trima ' conditions.append(buy_offset_trima) if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) * self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) * self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'buy_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int( self.base_nb_candles_buy_hma.value)) * self.low_offset_hma.value dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int( self.base_nb_candles_buy_hma2.value)) * self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'buy_tag'] += 'hma ' conditions.append(buy_offset_hma) add_check = ( (dataframe['live_data_ok']) & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < (dataframe['ema_sell'] * self.high_offset_sell_ema.value)) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10)) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), ['buy_copy', 'buy'] ] = (1, 1) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe class MultiMA_TSL3a(MultiMA_TSL_461): informative_timeframe = '1h' timeframe_15m = '15m' min_rsi_sell = 50 min_rsi_sell_15m = 70 max_change_pump = 35 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] informative_pairs.extend([(pair, self.timeframe_15m) for pair in pairs]) return informative_pairs def get_informative_15m_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.timeframe_15m) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_15m = self.get_informative_15m_indicators(metadata) dataframe = merge_informative_pair(dataframe, informative_15m, self.timeframe, self.timeframe_15m, ffill=True) drop_columns = [(s + "_" + self.timeframe_15m) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe = HA(dataframe, 4) dataframe['pump'] = pump_warning(dataframe, perc=int(self.max_change_pump)) # 25% di pump if self.config['runmode'].value in ('live', 'dry_run'): dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) else: dataframe['live_data_ok'] = True if not metadata["pair"] in self.custom_info: self.custom_info[metadata["pair"]] = ['', 0, 0] return dataframe 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) if (len(dataframe) < 1): return False last_candle = dataframe.iloc[-1] if (self.custom_info[pair][self.DATESTAMP] != last_candle['date']): sell_ema = self.custom_info[pair][self.SELLMA] if (sell_ema == 0): sell_ema = last_candle['ema_sell'] emaLength = 32 alpha = 2 / (1 + emaLength) sell_ema = (alpha * last_candle['close']) + ((1 - alpha) * sell_ema) self.custom_info[pair][self.SELLMA] = sell_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] sell_tag = '' if ( (last_candle['close'] > (sell_ema * self.high_offset_sell_ema.value)) & (last_candle['buy_copy'] == 0) & (last_candle['rsi'] > self.min_rsi_sell) ): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False sell_tag = 'Decaying EMA' if ( (last_candle['rsi_fast_15m'] > self.min_rsi_sell_15m) & (last_candle['buy_copy'] == 0) & (last_candle['rsi'] > self.min_rsi_sell) ): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False sell_tag = 'RSI 15m Overbought' if not (sell_tag == ''): buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty' return f'{sell_tag} ({buy_tag})' return False def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) * self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) * self.low_offset_ema2.value dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe.loc[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'buy'] = 0 if (self.buy_condition_trima_enable.value): dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * self.low_offset_trima.value dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) * self.low_offset_trima2.value buy_offset_trima = ( ( (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_trima, 'buy_tag'] += 'trima ' conditions.append(buy_offset_trima) if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) * self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) * self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'buy_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int( self.base_nb_candles_buy_hma.value)) * self.low_offset_hma.value dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int( self.base_nb_candles_buy_hma2.value)) * self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'buy_tag'] += 'hma ' conditions.append(buy_offset_hma) add_check = ( (dataframe['live_data_ok']) & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < (dataframe['ema_sell'] * self.high_offset_sell_ema.value)) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10)) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['pump'].rolling(20).max() < 1) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), ['buy_copy', 'buy'] ] = (1, 1) return dataframe def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier / 10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier / 10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx def HA(dataframe, smoothing=None): df = dataframe.copy() df['HA_Close'] = (df['open'] + df['high'] + df['low'] + df['close']) / 4 df.reset_index(inplace=True) ha_open = [(df['open'][0] + df['close'][0]) / 2] [ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df) - 1)] df['HA_Open'] = ha_open df.set_index('index', inplace=True) df['HA_High'] = df[['HA_Open', 'HA_Close', 'high']].max(axis=1) df['HA_Low'] = df[['HA_Open', 'HA_Close', 'low']].min(axis=1) if smoothing is not None: sml = abs(int(smoothing)) if sml > 0: df['Smooth_HA_O'] = ta.EMA(df['HA_Open'], sml) df['Smooth_HA_C'] = ta.EMA(df['HA_Close'], sml) df['Smooth_HA_H'] = ta.EMA(df['HA_High'], sml) df['Smooth_HA_L'] = ta.EMA(df['HA_Low'], sml) return df def pump_warning(dataframe, perc=15): df = dataframe.copy() df["change"] = df["high"] - df["low"] df["test1"] = (df["close"] > df["open"]) df["test2"] = ((df["change"] / df["low"]) > (perc / 100)) df["result"] = (df["test1"] & df["test2"]).astype('int') return df['result']