# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/NotAnotherSMAOffsetStrategy_uzi3.py # --- Do not remove these libs --- from typing import DefaultDict 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 import datetime from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter # @Rallipanos mod. Uzirox def zlema2(dataframe, fast): df = dataframe.copy() zema1 = ta.EMA(df['close'], fast) zema2 = ta.EMA(zema1, fast) d1 = zema1 - zema2 df['zlema2'] = zema1 + d1 return df['zlema2'] order_types = {'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False} 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__NotAnotherSMAOffsetStrategy_uzi3__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # ROI table: # "201": 0 minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016} # Stoploss: stoploss = -0.1 # Protection fast_ewo = 50 slow_ewo = 200 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.025 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.005 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' process_only_new_candles = True startup_candle_count = 400 slippage_protection = {'retries': 3, 'max_slippage': -0.02} entry_signals = {} def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if last_candle is not None: if exit_reason in ['exit_signal']: if last_candle['hma_50'] * 1.149 > last_candle['ema_100'] and last_candle['close'] < last_candle['ema_100'] * 0.951: # *1.2 return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = dataframe.iloc[-1].squeeze() slippage = rate / candle['close'] - 1 if slippage < self.slippage_protection['max_slippage']: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection['retries']: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if (dataframe['close'].iloc[-1] - dataframe['close'].iloc[-2]) / dataframe['close'].iloc[-2] * 100 < -2: # si scende pesante self.stoploss = -0.3 #stoploss # entry params self.trailing_stop_positive_offset = 0.03 self.base_nb_candles_entry = IntParameter(5, 80, default=14, space='entry', optimize=False) self.low_offset = DecimalParameter(0.9, 0.99, default=0.975, space='entry', optimize=False) self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.955, space='entry', optimize=False) self.ewo_low = DecimalParameter(-20.0, -8.0, default=-20.988, space='entry', optimize=False) self.ewo_high = DecimalParameter(2.0, 12.0, default=2.327, space='entry', optimize=False) self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.327, space='entry', optimize=False) self.rsi_entry = IntParameter(30, 70, default=69, space='entry', optimize=False) # exit params self.base_nb_candles_exit = IntParameter(5, 80, default=16, space='exit', optimize=True) self.high_offset = DecimalParameter(0.95, 1.1, default=0.991, space='exit', optimize=True) self.high_offset_2 = DecimalParameter(0.99, 1.5, default=0.997, space='exit', optimize=True) else: # normale - si sale # entry params self.base_nb_candles_entry = IntParameter(5, 80, default=14, space='entry', optimize=False) self.low_offset = DecimalParameter(0.9, 0.99, default=0.986, space='entry', optimize=False) self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.944, space='entry', optimize=False) self.ewo_low = DecimalParameter(-20.0, -8.0, default=-16.917, space='entry', optimize=False) self.ewo_high = DecimalParameter(2.0, 12.0, default=4.179, space='entry', optimize=False) self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.609, space='entry', optimize=False) self.rsi_entry = IntParameter(30, 70, default=58, space='entry', optimize=False) # exit params self.base_nb_candles_exit = IntParameter(5, 80, default=16, space='exit', optimize=True) self.high_offset = DecimalParameter(0.95, 1.1, default=1.054, space='exit', optimize=True) self.high_offset_2 = DecimalParameter(0.99, 1.5, default=1.018, space='exit', optimize=True) # 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) # *MAs dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_10'] = zlema2(dataframe, 10) 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) # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(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), ['enter_long', 'enter_tag']] = (1, 'ewo1') dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.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) & (dataframe['rsi'] < 25), ['enter_long', 'enter_tag']] = (1, 'ewo2') dataframe.loc[(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), ['enter_long', 'enter_tag']] = (1, 'ewolow') # entry in bull market dataframe.loc[(dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.031) & dataframe['closedelta'].gt(dataframe['close'] * 0.018) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.233) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) | (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 21) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'bb_bull') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe['sma_9']) & (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['sma_9'] > dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005) & (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_long'] = 1 return dataframe plot_config = {'main_plot': {'ema_100': {}, 'ema_10': {}, 'sma_9': {}}}