# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MultiMA_TSL_461_20250527_1157.py 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 dema, VIDYA class Github_remiotore_freqtrade__MultiMA_TSL_461_20250527_1157__20260111_210550(IStrategy): can_short = True INTERFACE_VERSION = 3 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_dema': 25, 'base_nb_candles_buy_dema2': 53, 'low_offset_dema': 0.958, 'low_offset_dema2': 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(-0.5, -0.04, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, 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_dema = True base_nb_candles_buy_dema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_dema) low_offset_dema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_dema) base_nb_candles_buy_dema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_dema) low_offset_dema2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_dema) 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_dema_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 # value loaded from strategy # value loaded from strategy # value loaded from strategy protection_params = {'low_profit_lookback': 48, 'low_profit_min_req': 0.04, 'low_profit_stop_duration': 14, 'cooldown_lookback': 2, 'stoploss_lookback': 72, 'stoploss_stop_duration': 20} 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_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 400 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) 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 enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.buy_tag is not None: enter_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] enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty' return f'New Sell Signal ({enter_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, exit_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_entry_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[:, 'enter_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'enter_long'] = 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, 'enter_tag'] += 'trima ' conditions.append(buy_offset_trima) if self.buy_condition_dema_enable.value: dataframe['dema_offset_buy'] = dema(dataframe, int(self.base_nb_candles_buy_dema.value)) * self.low_offset_dema.value dataframe['dema_offset_buy2'] = dema(dataframe, int(self.base_nb_candles_buy_dema2.value)) * self.low_offset_dema2.value buy_offset_dema = (dataframe['close'] < dataframe['dema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['dema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[buy_offset_dema, 'enter_tag'] += 'dema ' conditions.append(buy_offset_dema) 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, 'enter_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'] * 1.1) & (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', 'enter_long']] = (1, 1) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 return dataframe def blacklist(self): """ Lista de pares que devem ser ignorados pelo backtest/live trading. """ return [ "EOS/USDT:USDT", # Não disponível no Binance Futures "LUNA/USDT:USDT", # Exemplo: par descontinuado "TRX/USDT:USDT", # Pode adicionar mais se desejar ] class MultiMA_TSL3a(Github_remiotore_freqtrade__MultiMA_TSL_461_20250527_1157__20260111_210550): 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_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) 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 == '': enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.buy_tag is not None: enter_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] enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty' return f'{sell_tag} ({enter_tag})' return False def populate_entry_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[:, 'enter_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'enter_long'] = 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, 'enter_tag'] += 'trima ' conditions.append(buy_offset_trima) if self.buy_condition_dema_enable.value: dataframe['dema_offset_buy'] = dema(dataframe, int(self.base_nb_candles_buy_dema.value)) * self.low_offset_dema.value dataframe['dema_offset_buy2'] = dema(dataframe, int(self.base_nb_candles_buy_dema2.value)) * self.low_offset_dema2.value buy_offset_dema = (dataframe['close'] < dataframe['dema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['dema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[buy_offset_dema, 'enter_tag'] += 'dema ' conditions.append(buy_offset_dema) 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, 'enter_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'] * 1.1) & (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', 'enter_long']] = (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 = dema(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), 0.0) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.0) 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), 0.0) 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 0.0 pm = Series(pm_arr) pmx = np.where(pm_arr > 0.0, 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'] def populate_entry_trend_short(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) * (2 - self.low_offset_ema.value) dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) * (2 - self.low_offset_ema2.value) dataframe.loc[:, 'enter_short'] = 0 dataframe.loc[:, 'enter_tag'] = '' if self.buy_condition_trima_enable.value: dataframe['trima_offset_sell'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * (2 - self.low_offset_trima.value) dataframe['trima_offset_sell2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) * (2 - self.low_offset_trima2.value) sell_offset_trima = (dataframe['close'] > dataframe['trima_offset_sell']) | (dataframe['close'] > dataframe['trima_offset_sell2']) dataframe.loc[sell_offset_trima, 'enter_tag'] += 'trima ' conditions.append(sell_offset_trima) if self.buy_condition_dema_enable.value: dataframe['dema_offset_sell'] = dema(dataframe, int(self.base_nb_candles_buy_dema.value)) * (2 - self.low_offset_dema.value) dataframe['dema_offset_sell2'] = dema(dataframe, int(self.base_nb_candles_buy_dema2.value)) * (2 - self.low_offset_dema2.value) sell_offset_dema = (dataframe['close'] > dataframe['dema_offset_sell']) | (dataframe['close'] > dataframe['dema_offset_sell2']) dataframe.loc[sell_offset_dema, 'enter_tag'] += 'dema ' conditions.append(sell_offset_dema) if self.buy_condition_hma_enable.value: dataframe['hma_offset_sell'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma.value)) * (2 - self.low_offset_hma.value) dataframe['hma_offset_sell2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma2.value)) * (2 - self.low_offset_hma2.value) sell_offset_hma = (dataframe['close'] > dataframe['hma_offset_sell']) | (dataframe['close'] > dataframe['hma_offset_sell2']) dataframe.loc[sell_offset_hma, 'enter_tag'] += 'hma ' conditions.append(sell_offset_hma) add_check = ( (dataframe['live_data_ok']) & (dataframe['close'] > dataframe['Smooth_HA_H']) & (dataframe['rsi_fast'] > 70) & (dataframe['rsi_84'] > 50) & (dataframe['rsi_112'] > 50) & ((dataframe['ewo'] > self.ewo_high.value) | (dataframe['ewo'] < self.ewo_low.value)) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[add_check & reduce(lambda x, y: x | y, conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend_short(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_short'] = 0 dataframe.loc[dataframe['rsi'] < 50, 'exit_short'] = 1 return dataframe