# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-futures-k8s-namespace/NotAnotherSMAOffsetStrategyX1.py # --- Do not remove these libs --- # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt import pandas_ta as pta # @Rallipanos 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['low'] * 100 emadif = (ema1 - ema2) / df['close'] * 100 return emadif class Github_DerSalvador_freqtrade_helm_chart__NotAnotherSMAOffsetStrategyX1__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 14, 'ewo_high': 2.327, 'ewo_high_2': -2.327, 'ewo_low': -19.988, 'low_offset': 0.975, 'low_offset_2': 0.955, 'rsi_entry': 69} # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 24, 'high_offset': 0.991, 'high_offset_2': 0.997, 'pHSL': -0.99, 'pPF_1': 0.022, 'pSL_1': 0.021, 'pPF_2': 0.08, 'pSL_2': 0.04} # ROI table: minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016, '201': 0} # Stoploss: stoploss = -0.35 # SMAOffset base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(5, 80, 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) low_offset_2 = DecimalParameter(0.9, 0.99, default=entry_params['low_offset_2'], space='entry', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=entry_params['ewo_high_2'], space='entry', optimize=True) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) # trailing stoploss hyperopt parameters # hard stoploss profit pHSL = DecimalParameter(-0.2, -0.04, default=-0.08, decimals=3, space='exit', optimize=False, load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', optimize=True, load=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', optimize=True, load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', optimize=True, load=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', optimize=True, load=True) # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 1, # "stop_duration_candles": 6, # "only_per_pair": True # }, # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 2, # "stop_duration_candles": 6, # "only_per_pair": False # }, protections = [{'method': 'LowProfitPairs', 'lookback_period_candles': 60, 'trade_limit': 1, 'stop_duration': 60, 'required_profit': -0.05}, {'method': 'CooldownPeriod', 'stop_duration_candles': 2}] # Trailing stop: trailing_stop = False trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # Custom stoploss use_custom_stoploss = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 400 plot_config = {'main_plot': {'ma_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}} 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 return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 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) # dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['hma_50'] = pta.hma(dataframe['close'], 50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) 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 custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit 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 # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. 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 return stoploss_from_open(sl_profit, current_profit) 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), ['entry', 'entry_tag']] = (1, 'ewo1') "\n dataframe.loc[\n (\n (dataframe['rsi_fast'] <35)&\n (dataframe['close'] < (dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value)) &\n (dataframe['EWO'] > self.ewo_high_2.value) &\n (dataframe['rsi'] < self.rsi_entry.value) &\n (dataframe['volume'] > 0)&\n (dataframe['close'] < (dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value))&\n (dataframe['rsi']<25)\n ),\n ['entry', 'entry_tag']] = (1, 'ewo2')\n " 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), ['entry', 'entry_tag']] = (1, 'ewolow') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] #(dataframe['close']>dataframe['sma_9'])& 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