# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/ElliotV8HO.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import DecimalParameter, IntParameter def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class Github_DerSalvador_freqtrade_helm_chart__ElliotV8HO__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # Sell hyperspace params: v1 # exit_params = { # "base_nb_candles_exit": 24, # "high_offset": 0.991, # "high_offset_2": 0.997 # } # Sell hyperspace params: v5 # exit_params = { # "base_nb_candles_exit": 30, # "high_offset": 0.973, # "high_offset_2": 1.121, # } # Sell hyperspace params: v6 exit_params = {'base_nb_candles_exit': 24, 'high_offset': 1.011, 'high_offset_2': 0.997} # Buy hyperspace params: v1 entry_params = {'base_nb_candles_entry': 19, 'ewo_high': 5.417, 'ewo_low': -17.251, 'low_offset': 0.983, 'rsi_entry': 61} # ROI table: minimal_roi = {'0': 0.08, '40': 0.032, '87': 0.016} # Stoploss: stoploss = -0.189 # SMAOffset base_nb_candles_entry = IntParameter(15, 60, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(15, 60, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True) high_offset = DecimalParameter(0.9, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.2, default=exit_params['high_offset_2'], space='exit', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -15.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(1.0, 8.0, default=entry_params['ewo_high'], space='entry', optimize=True) rsi_entry = IntParameter(25, 75, default=entry_params['rsi_entry'], space='entry', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optional order time in force. # 'exit': 'ioc' order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 400 plot_config = {'main_plot': {'ma_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}} use_custom_stoploss = False def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value == 'hyperopt': # Calculate all ma_entry values for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) else: dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_entry.value) dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_exit.value) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) conditions.append((dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow'])) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit'] = 1 return dataframe