# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/MultiMA_TSL.py 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, BooleanParameter, CategoricalParameter, stoploss_from_open from pandas import DataFrame 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 ########################################################################################################### ## Github_DerSalvador_freqtrade_helm_chart__MultiMA_TSL__20260115_122204, 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 ## ## ## ########################################################################################################### # 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_TSL__20260115_122204(IStrategy): INTERFACE_VERSION = 3 entry_params = {'base_nb_candles_entry_ema': 50, 'low_offset_ema': 1.061, 'base_nb_candles_entry_zema': 30, 'low_offset_zema': 0.963, 'rsi_entry_zema': 50, 'base_nb_candles_entry_trima': 14, 'low_offset_trima': 0.963, 'rsi_entry_trima': 50, 'entry_roc_max': 45, 'entry_condition_trima_enable': True, 'entry_condition_zema_enable': True} exit_params = {'base_nb_candles_exit': 32, 'high_offset_ema': 1.002, 'base_nb_candles_exit_trima': 48, 'high_offset_trima': 1.085} # ROI table: minimal_roi = {'0': 100} stoploss = -0.15 # Multi Offset base_nb_candles_exit = IntParameter(5, 80, default=20, space='exit', optimize=False) base_nb_candles_exit_trima = IntParameter(5, 80, default=20, space='exit', optimize=False) high_offset_trima = DecimalParameter(0.99, 1.1, default=1.012, space='exit', optimize=False) base_nb_candles_entry_ema = IntParameter(5, 80, default=20, space='entry', optimize=False) low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='entry', optimize=False) high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, space='exit', optimize=False) rsi_entry_ema = IntParameter(30, 70, default=61, space='entry', optimize=False) base_nb_candles_entry_trima = IntParameter(5, 80, default=20, space='entry', optimize=False) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=False) rsi_entry_trima = IntParameter(30, 70, default=61, space='entry', optimize=False) base_nb_candles_entry_zema = IntParameter(5, 80, default=20, space='entry', optimize=False) low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='entry', optimize=False) rsi_entry_zema = IntParameter(30, 70, default=61, space='entry', optimize=False) entry_condition_enable_optimize = True 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) # Protection1 ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='entry', optimize=False) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, 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) entry_roc_max = DecimalParameter(20, 70, default=55, space='entry', optimize=False) entry_peak_max = DecimalParameter(1, 1.1, default=1.03, decimals=3, space='entry', optimize=False) entry_rsi_fast = IntParameter(0, 50, default=35, 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': 60, 'low_profit_min_req': 0.03, 'low_profit_stop_duration': 29, 'cooldown_lookback': 2, 'stoploss_lookback': 72, 'stoploss_stop_duration': 20} cooldown_lookback = IntParameter(2, 48, default=2, space='protection', optimize=False) low_profit_lookback = IntParameter(2, 60, default=20, space='protection', optimize=False) low_profit_stop_duration = IntParameter(12, 200, default=20, space='protection', optimize=False) low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space='protection', decimals=2, optimize=False) @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' # 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 = 200 #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 not self.config['runmode'].value in ('backtest', 'hyperopt'): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) >= 1: last_candle = dataframe.iloc[-1] if (last_candle['exit_copy'] == 1) & (last_candle['entry_copy'] == 0): sl_new = 0.001 return sl_new 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['roc_max'] = dataframe['close'].pct_change(48).rolling(12).max() * 100 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['zema_offset_entry'] = zema(dataframe, int(self.base_nb_candles_entry_zema.value)) * self.low_offset_zema.value dataframe['trima_offset_entry'] = ta.TRIMA(dataframe, int(self.base_nb_candles_entry_trima.value)) * self.low_offset_trima.value dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'entry_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 entry_offset_trima = self.entry_condition_trima_enable.value & (dataframe['close'] < dataframe['trima_offset_entry']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry_trima.value)) dataframe.loc[entry_offset_trima, 'enter_tag'] += 'trima ' conditions.append(entry_offset_trima) entry_offset_zema = self.entry_condition_zema_enable.value & (dataframe['close'] < dataframe['zema_offset_entry']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry_zema.value)) dataframe.loc[entry_offset_zema, 'enter_tag'] += 'zema ' conditions.append(entry_offset_zema) add_check = (dataframe['rsi_fast'] < self.entry_rsi_fast.value) & (dataframe['close'] < dataframe['ema_offset_entry']) & (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_copy'] = 0 dataframe['ema_offset_exit'] = ta.EMA(dataframe, int(self.base_nb_candles_exit.value)) * self.high_offset_ema.value dataframe['trima_offset_exit'] = ta.TRIMA(dataframe, int(self.base_nb_candles_exit_trima.value)) * self.high_offset_trima.value conditions = [] conditions.append((dataframe['close'] > dataframe['ema_offset_exit']) & (dataframe['volume'] > 0)) conditions.append((dataframe['close'] > dataframe['trima_offset_exit']) & (dataframe['volume'] > 0)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), ['exit_copy', 'exit_long']] = (1, 1) if not self.config['runmode'].value in ('backtest', 'hyperopt'): dataframe.loc[:, 'exit_long'] = 0 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