# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/NASOSv4_customexit_2.py # --- Do not remove these libs --- from logging import FATAL 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 import technical.indicators as ftt buy_params = { "base_nb_candles_buy": 8, "ewo_high": 2.403, "ewo_high_2": -5.585, "ewo_low": -14.378, "lookback_candles": 3, "low_offset": 0.984, "low_offset_2": 0.942, "profit_threshold": 1.008, "rsi_buy": 72 } sell_params = { "base_nb_candles_sell": 16, "high_offset": 1.084, "high_offset_2": 1.401, # Relaxed custom stoploss parameters: # Increase the range and thresholds so the SL triggers less often. "pHSL": -0.20, # Was -0.15, now slightly more lenient "pPF_1": 0.02, # Was 0.016, trigger higher "pPF_2": 0.05, # Was 0.024, much higher second threshold "pSL_1": 0.016, # Was 0.014, slightly higher to reduce early triggers "pSL_2": 0.03 # Was 0.022, more relaxed trailing step } 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 return emadif class Github_remiotore_freqtrade__NASOSv4_customexit_2__20260111_210550(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 10 } # Stoploss: stoploss = -0.05 # Parameters base_nb_candles_buy = IntParameter(2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False) low_offset_2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) fast_ewo = 50 slow_ewo = 200 lookback_candles = IntParameter(1, 24, default=buy_params['lookback_candles'], space='buy', optimize=True) profit_threshold = DecimalParameter(1.0, 1.03, default=buy_params['profit_threshold'], space='buy', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=False) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False) rsi_buy = IntParameter(50, 100, default=buy_params['rsi_buy'], space='buy', optimize=False) # Relaxed custom stoploss parameters pHSL = DecimalParameter(-0.200, -0.040, default=sell_params['pHSL'], decimals=3, space='sell', optimize=False, load=True) pPF_1 = DecimalParameter(0.008, 0.040, default=sell_params['pPF_1'], decimals=3, space='sell', optimize=False, load=True) pSL_1 = DecimalParameter(0.008, 0.030, default=sell_params['pSL_1'], decimals=3, space='sell', optimize=False, load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=sell_params['pPF_2'], decimals=3, space='sell', optimize=False, load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=sell_params['pSL_2'], decimals=3, space='sell', optimize=False, load=True) # Trailing stop: # For live: trailing_stop = False, use_custom_stoploss = True # For backtest: trailing_stop = True, use_custom_stoploss = False trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.016 trailing_only_offset_is_reached = True exit_sell_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if len(dataframe) else None if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = last_candle if candle is not None: 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 informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) 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_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True ) dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] dont_buy_conditions.append( ( (dataframe['close_1h'].rolling(self.lookback_candles.value).max() < (dataframe['close'] * self.profit_threshold.value)) ) ) dataframe.loc[ ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewo1') dataframe.loc[ ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['rsi'] < 25) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewolow') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Reuse the entry logic for sell by inverting the price: rev_df = dataframe.copy() # Invert price rev_df['close'] = 1.0 / rev_df['close'] rev_df['open'] = 1.0 / rev_df['open'] rev_df['high'] = 1.0 / rev_df['high'] rev_df['low'] = 1.0 / rev_df['low'] # Recalculate the indicators on the inverted DF for val in self.base_nb_candles_buy.range: rev_df[f'ma_buy_{val}'] = ta.EMA(rev_df, timeperiod=val) for val in self.base_nb_candles_sell.range: rev_df[f'ma_sell_{val}'] = ta.EMA(rev_df, timeperiod=val) rev_df['hma_50'] = qtpylib.hull_moving_average(rev_df['close'], window=50) rev_df['ema_100'] = ta.EMA(rev_df, timeperiod=100) rev_df['sma_9'] = ta.SMA(rev_df, timeperiod=9) rev_df['EWO'] = EWO(rev_df, self.fast_ewo, self.slow_ewo) rev_df['rsi'] = ta.RSI(rev_df, timeperiod=14) rev_df['rsi_fast'] = ta.RSI(rev_df, timeperiod=4) rev_df['rsi_slow'] = ta.RSI(rev_df, timeperiod=20) # Now apply the original buy conditions on rev_df to get sell signals: rev_conditions = ( ((rev_df['rsi_fast'] < 35) & (rev_df['close'] < (rev_df[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (rev_df['EWO'] > self.ewo_high.value) & (rev_df['rsi'] < self.rsi_buy.value) & (rev_df['volume'] > 0) & (rev_df['close'] < (rev_df[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))) | ((rev_df['rsi_fast'] < 35) & (rev_df['close'] < (rev_df[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (rev_df['EWO'] > self.ewo_high_2.value) & (rev_df['rsi'] < self.rsi_buy.value) & (rev_df['volume'] > 0) & (rev_df['close'] < (rev_df[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (rev_df['rsi'] < 25)) | ((rev_df['rsi_fast'] < 35) & (rev_df['close'] < (rev_df[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (rev_df['EWO'] < self.ewo_low.value) & (rev_df['volume'] > 0) & (rev_df['close'] < (rev_df[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))) ) # Wherever rev_conditions are True (meaning inverted buy triggers), mark sell in original df dataframe.loc[rev_conditions, 'sell'] = 1 return dataframe