# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/MultiMA_TSL3.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 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 ########################################################################################################### ## MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister) ## ## Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ## Thanks to ## ## - Perkmeister, for their snippets for the exit signals and decaying EMA exit ## ## - ChangeToTower, for the PMax idea ## ## - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ## ## - froggleston, for the Heikinashi check snippet from Cryptofrog ## ## - Uzirox, for their pump detection code ## ## ## ## ## ########################################################################################################### # I hope you do enough testing before proceeding, either backtesting and/or dry run. # Any profits and losses are all your responsibility class Github_DerSalvador_freqtrade_helm_chart__MultiMA_TSL3__20260115_122204(IStrategy): INTERFACE_VERSION = 3 DATESTAMP = 0 SELLMA = 1 SELL_TRIGGER = 2 entry_params = {'base_nb_candles_entry_trima': 15, 'base_nb_candles_entry_trima2': 38, 'low_offset_trima': 0.959, 'low_offset_trima2': 0.949, 'base_nb_candles_entry_ema': 9, 'base_nb_candles_entry_ema2': 75, 'low_offset_ema': 1.067, 'low_offset_ema2': 0.973, 'base_nb_candles_entry_zema': 25, 'base_nb_candles_entry_zema2': 53, 'low_offset_zema': 0.958, 'low_offset_zema2': 0.961, 'base_nb_candles_entry_hma': 70, 'base_nb_candles_entry_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_entry': 60, 'rsi_entry2': 45} exit_params = {'base_nb_candles_ema_exit': 5, 'high_offset_exit_ema': 0.994} # ROI table: minimal_roi = {'0': 100} stoploss = -0.15 optimize_exit_ema = False base_nb_candles_ema_exit = IntParameter(5, 80, default=20, space='exit', optimize=False) high_offset_exit_ema = DecimalParameter(0.99, 1.1, default=1.012, space='exit', optimize=False) base_nb_candles_ema_exit2 = IntParameter(5, 80, default=20, space='exit', optimize=False) # Multi Offset optimize_entry_ema = False base_nb_candles_entry_ema = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='entry', optimize=optimize_entry_ema) base_nb_candles_entry_ema2 = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_ema) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.958, space='entry', optimize=optimize_entry_ema) optimize_entry_trima = False base_nb_candles_entry_trima = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_trima) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_trima) base_nb_candles_entry_trima2 = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_trima) low_offset_trima2 = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_trima) optimize_entry_zema = False base_nb_candles_entry_zema = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_zema) low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_zema) base_nb_candles_entry_zema2 = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_zema) low_offset_zema2 = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_zema) optimize_entry_hma = False base_nb_candles_entry_hma = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_hma) base_nb_candles_entry_hma2 = IntParameter(5, 80, default=20, space='entry', optimize=optimize_entry_hma) low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=optimize_entry_hma) entry_condition_enable_optimize = False entry_condition_trima_enable = BooleanParameter(default=True, space='entry', optimize=entry_condition_enable_optimize) entry_condition_zema_enable = BooleanParameter(default=True, space='entry', optimize=entry_condition_enable_optimize) entry_condition_hma_enable = BooleanParameter(default=True, space='entry', optimize=entry_condition_enable_optimize) # Protection ewo_check_optimize = False ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='entry', optimize=ewo_check_optimize) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='entry', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, space='entry', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, space='entry', optimize=ewo_check_optimize) rsi_entry_optimize = False rsi_entry = IntParameter(30, 70, default=50, space='entry', optimize=rsi_entry_optimize) rsi_entry2 = IntParameter(30, 70, default=50, space='entry', optimize=rsi_entry_optimize) entry_rsi_fast = IntParameter(0, 50, default=35, space='entry', optimize=False) fast_ewo = IntParameter(10, 50, default=50, space='entry', optimize=False) slow_ewo = IntParameter(100, 200, default=200, space='entry', optimize=False) # Trailing stoploss (not used) 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 hyperspace params: # 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=False) low_profit_optimize = False 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 # Optimal timeframe for the strategy. timeframe = '5m' # storage dict for custom info custom_info = {} # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals 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']: # new candle, update EMA and check exit # smoothing coefficients exit_ema = self.custom_info[pair][self.SELLMA] if exit_ema == 0: exit_ema = last_candle['ema_exit'] emaLength = 32 alpha = 2 / (1 + emaLength) # update exit_ema exit_ema = alpha * last_candle['close'] + (1 - alpha) * exit_ema self.custom_info[pair][self.SELLMA] = exit_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] if (last_candle['close'] > exit_ema * self.high_offset_exit_ema.value) & (last_candle['entry_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.entry_tag is not None: enter_tag = trade.entry_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) entry_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not entry_signal.empty: entry_signal_candle = entry_signal.iloc[-1] enter_tag = entry_signal_candle['enter_tag'] if entry_signal_candle['enter_tag'] != '' else 'empty' return f'New Sell Signal ({enter_tag})' return False #credit to Perkmeister for this custom stoploss to help the strategy ride a green candle when the exit signal triggered def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if self.custom_info[pair][self.SELL_TRIGGER] == 1: if self.config['runmode'].value in ('live', 'dry_run'): sl_new = 0.001 if current_profit > 0.2: sl_new = 0.05 elif current_profit > 0.1: sl_new = 0.03 elif current_profit > 0.06: sl_new = 0.02 elif current_profit > 0.03: sl_new = 0.01 return sl_new 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_exit'] 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: # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI 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) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi['volume'] = dataframe['volume'] # Profit Maximizer - PMAX 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'): # Exchange downtime protection dataframe['live_data_ok'] = dataframe['volume'].rolling(window=72, min_periods=72).min() > 0 else: dataframe['live_data_ok'] = True # Check if the entry already exists if not metadata['pair'] in self.custom_info: # Create empty entry for this pair {datestamp, exitma, exit_trigger} self.custom_info[metadata['pair']] = ['', 0, 0] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_entry'] = ta.EMA(dataframe, int(self.base_nb_candles_entry_ema.value)) * self.low_offset_ema.value dataframe['ema_offset_entry2'] = ta.EMA(dataframe, int(self.base_nb_candles_entry_ema2.value)) * self.low_offset_ema2.value dataframe['ema_exit'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_exit.value)) dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'entry_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 if self.entry_condition_trima_enable.value: dataframe['trima_offset_entry'] = ta.TRIMA(dataframe, int(self.base_nb_candles_entry_trima.value)) * self.low_offset_trima.value dataframe['trima_offset_entry2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_entry_trima2.value)) * self.low_offset_trima2.value entry_offset_trima = (dataframe['close'] < dataframe['trima_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['trima_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[entry_offset_trima, 'enter_tag'] += 'trima ' conditions.append(entry_offset_trima) if self.entry_condition_zema_enable.value: dataframe['zema_offset_entry'] = zema(dataframe, int(self.base_nb_candles_entry_zema.value)) * self.low_offset_zema.value dataframe['zema_offset_entry2'] = zema(dataframe, int(self.base_nb_candles_entry_zema2.value)) * self.low_offset_zema2.value entry_offset_zema = (dataframe['close'] < dataframe['zema_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['zema_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[entry_offset_zema, 'enter_tag'] += 'zema ' conditions.append(entry_offset_zema) if self.entry_condition_hma_enable.value: dataframe['hma_offset_entry'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_entry_hma.value)) * self.low_offset_hma.value dataframe['hma_offset_entry2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_entry_hma2.value)) * self.low_offset_hma2.value entry_offset_hma = ((dataframe['close'] < dataframe['hma_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) | (dataframe['close'] < dataframe['hma_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30)) & (dataframe['rsi_fast'] < 30) dataframe.loc[entry_offset_hma, 'enter_tag'] += 'hma ' conditions.append(entry_offset_hma) add_check = dataframe['live_data_ok'] & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < dataframe['ema_exit'] * self.high_offset_exit_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.entry_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & ((dataframe['close'] < dataframe['ema_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value)) | (dataframe['close'] < dataframe['ema_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low2.value) | (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_entry2.value))) & (dataframe['volume'] > 0) if conditions: dataframe.loc[add_check & reduce(lambda x, y: x | y, conditions), ['entry_copy', 'enter_long']] = (1, 1) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 return dataframe class Github_DerSalvador_freqtrade_helm_chart__MultiMA_TSL3__20260115_122204a(Github_DerSalvador_freqtrade_helm_chart__MultiMA_TSL3__20260115_122204): informative_timeframe = '1h' timeframe_15m = '15m' min_rsi_exit = 50 min_rsi_exit_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) # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI 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) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi['volume'] = dataframe['volume'] # Profit Maximizer - PMAX 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) # pump detector dataframe['pump'] = pump_warning(dataframe, perc=int(self.max_change_pump)) #25% di pump if self.config['runmode'].value in ('live', 'dry_run'): # Exchange downtime protection dataframe['live_data_ok'] = dataframe['volume'].rolling(window=72, min_periods=72).min() > 0 else: dataframe['live_data_ok'] = True # Check if the entry already exists if not metadata['pair'] in self.custom_info: # Create empty entry for this pair {datestamp, exitma, exit_trigger} 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']: # new candle, update EMA and check exit # smoothing coefficients exit_ema = self.custom_info[pair][self.SELLMA] if exit_ema == 0: exit_ema = last_candle['ema_exit'] emaLength = 32 alpha = 2 / (1 + emaLength) # update exit_ema exit_ema = alpha * last_candle['close'] + (1 - alpha) * exit_ema self.custom_info[pair][self.SELLMA] = exit_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] exit_tag = '' if (last_candle['close'] > exit_ema * self.high_offset_exit_ema.value) & (last_candle['entry_copy'] == 0) & (last_candle['rsi'] > self.min_rsi_exit): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False exit_tag = 'Decaying EMA' if (last_candle['rsi_fast_15m'] > self.min_rsi_exit_15m) & (last_candle['entry_copy'] == 0) & (last_candle['rsi'] > self.min_rsi_exit): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False exit_tag = 'RSI 15m Overbought' if not exit_tag == '': enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.entry_tag is not None: enter_tag = trade.entry_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) entry_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not entry_signal.empty: entry_signal_candle = entry_signal.iloc[-1] enter_tag = entry_signal_candle['enter_tag'] if entry_signal_candle['enter_tag'] != '' else 'empty' return f'{exit_tag} ({enter_tag})' return False def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_entry'] = ta.EMA(dataframe, int(self.base_nb_candles_entry_ema.value)) * self.low_offset_ema.value dataframe['ema_offset_entry2'] = ta.EMA(dataframe, int(self.base_nb_candles_entry_ema2.value)) * self.low_offset_ema2.value dataframe['ema_exit'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_exit.value)) dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'entry_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 if self.entry_condition_trima_enable.value: dataframe['trima_offset_entry'] = ta.TRIMA(dataframe, int(self.base_nb_candles_entry_trima.value)) * self.low_offset_trima.value dataframe['trima_offset_entry2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_entry_trima2.value)) * self.low_offset_trima2.value entry_offset_trima = (dataframe['close'] < dataframe['trima_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['trima_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[entry_offset_trima, 'enter_tag'] += 'trima ' conditions.append(entry_offset_trima) if self.entry_condition_zema_enable.value: dataframe['zema_offset_entry'] = zema(dataframe, int(self.base_nb_candles_entry_zema.value)) * self.low_offset_zema.value dataframe['zema_offset_entry2'] = zema(dataframe, int(self.base_nb_candles_entry_zema2.value)) * self.low_offset_zema2.value entry_offset_zema = (dataframe['close'] < dataframe['zema_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['zema_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) dataframe.loc[entry_offset_zema, 'enter_tag'] += 'zema ' conditions.append(entry_offset_zema) if self.entry_condition_hma_enable.value: dataframe['hma_offset_entry'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_entry_hma.value)) * self.low_offset_hma.value dataframe['hma_offset_entry2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_entry_hma2.value)) * self.low_offset_hma2.value entry_offset_hma = ((dataframe['close'] < dataframe['hma_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) | (dataframe['close'] < dataframe['hma_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30)) & (dataframe['rsi_fast'] < 30) dataframe.loc[entry_offset_hma, 'enter_tag'] += 'hma ' conditions.append(entry_offset_hma) add_check = dataframe['live_data_ok'] & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < dataframe['ema_exit'] * self.high_offset_exit_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.entry_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['pump'].rolling(20).max() < 1) & ((dataframe['close'] < dataframe['ema_offset_entry']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value)) | (dataframe['close'] < dataframe['ema_offset_entry2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low2.value) | (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_entry2.value))) & (dataframe['volume'] > 0) if conditions: dataframe.loc[add_check & reduce(lambda x, y: x | y, conditions), ['entry_copy', 'enter_long']] = (1, 1) return dataframe # Elliot Wave Oscillator 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 # PMAX 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}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema 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), 0.0) 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) # Mark the trend direction up/down pmx = np.where(pm_arr > 0.0, np.where(mavalue < pm_arr, 'down', 'up'), np.NaN) return (pm, pmx) # smoothed Heiken Ashi 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']