# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/el_hz.py # --- 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, timedGithub_remiotore_freqtrade__el_hz__20260111_210550ta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt 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_remiotore_freqtrade__el_hz__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short = True # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 12, "ewo_high": 4.428, "ewo_low": -12.383, "low_offset": 0.915, "rsi_buy": 44, } # SGithub_remiotore_freqtrade__el_hz__20260111_210550l hyperspace params: sGithub_remiotore_freqtrade__el_hz__20260111_210550l_params = { "base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l": 72, "high_offset": 1.008, } # ROI table (агрессивная настройка): minimal_roi = { "0": 0.15, # 15% для быстрого выхода "10": 0.08, # 8% через 10 свечей "30": 0.04, # 4% через 30 свечей "120": 0 # Безубыток через 120 свечей } # Stoploss: stoploss = -0.242 # Trailing stop (более агрессивный): trailing_stop = True trailing_stop_positive = 0.15 trailing_stop_positive_offset = 0.20 trailing_only_offset_is_reached = True # Max Open Trades: max_open_trades = 12 # Увеличено для диверсификации # SMAOffset base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l = IntParameter(5, 80, default=sGithub_remiotore_freqtrade__el_hz__20260111_210550l_params['base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l'], space='sGithub_remiotore_freqtrade__el_hz__20260111_210550l', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=sGithub_remiotore_freqtrade__el_hz__20260111_210550l_params['high_offset'], space='sGithub_remiotore_freqtrade__el_hz__20260111_210550l', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Profit Protection profit_threshold_1 = DecimalParameter(0.03, 0.08, default=0.05, space='sGithub_remiotore_freqtrade__el_hz__20260111_210550l', optimize=True) profit_threshold_2 = DecimalParameter(0.08, 0.15, default=0.10, space='sGithub_remiotore_freqtrade__el_hz__20260111_210550l', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # SGithub_remiotore_freqtrade__el_hz__20260111_210550l signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 95 plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sGithub_remiotore_freqtrade__el_hz__20260111_210550l': {'color': 'orange'}}} # Активируем кастомные функции защиты use_custom_stoploss = True use_custom_stake_amount = True def informative_pairs(sGithub_remiotore_freqtrade__el_hz__20260111_210550f): pairs = sGithub_remiotore_freqtrade__el_hz__20260111_210550f.dp.current_whitGithub_remiotore_freqtrade__el_hz__20260111_210550ist() informative_pairs = [(pair, sGithub_remiotore_freqtrade__el_hz__20260111_210550f.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, metadata: dict): dataframe = sGithub_remiotore_freqtrade__el_hz__20260111_210550f.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=sGithub_remiotore_freqtrade__el_hz__20260111_210550f.informative_timeframe) return dataframe def populate_indicators(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, dataframe: DataFrame, metadata: dict) -> DataFrame: # Рассчитываем все индикаторы dataframe['adx'] = ta.ADX(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) aroon = ta.AROON(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] dataframe['aroonosc'] = ta.AROONOSC(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) kGithub_remiotore_freqtrade__el_hz__20260111_210550tner = qtpylib.kGithub_remiotore_freqtrade__el_hz__20260111_210550tner_channGithub_remiotore_freqtrade__el_hz__20260111_210550(dataframe) dataframe['kc_upperband'] = kGithub_remiotore_freqtrade__el_hz__20260111_210550tner['upper'] dataframe['kc_lowerband'] = kGithub_remiotore_freqtrade__el_hz__20260111_210550tner['lower'] dataframe['kc_middleband'] = kGithub_remiotore_freqtrade__el_hz__20260111_210550tner['mid'] dataframe['kc_percent'] = (dataframe['close'] - dataframe['kc_lowerband']) / (dataframe['kc_upperband'] - dataframe['kc_lowerband']) dataframe['kc_width'] = (dataframe['kc_upperband'] - dataframe['kc_lowerband']) / dataframe['kc_middleband'] dataframe['uo'] = ta.ULTOSC(dataframe) dataframe['cci'] = ta.CCI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['roc'] = ta.ROC(dataframe) 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['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] dataframe['sar'] = ta.SAR(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Добавляем ATR для волатильности dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_pct'] = dataframe['atr'] / dataframe['close'] # Средний объем dataframe['volume_ma'] = ta.SMA(dataframe['volume'], timeperiod=20) # EMA для стратегии for val in sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l.range: dataframe[f'ma_sGithub_remiotore_freqtrade__el_hz__20260111_210550l_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['EWO'] = EWO(dataframe, sGithub_remiotore_freqtrade__el_hz__20260111_210550f.fast_ewo, sGithub_remiotore_freqtrade__el_hz__20260111_210550f.slow_ewo) return dataframe def populate_entry_trend(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, dataframe: DataFrame, metadata: dict) -> DataFrame: # Фильтры для повышения качества входов volume_filter = dataframe['volume'] > dataframe['volume_ma'] * 1.2 volatility_filter = dataframe['atr_pct'] < 0.08 trend_filter = dataframe['adx'] > 25 # Условия для лонгов buy_conditions = [] buy_conditions.append( (dataframe['close'] < dataframe[f'ma_buy_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_buy.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.low_offset.value) & (dataframe['EWO'] > sGithub_remiotore_freqtrade__el_hz__20260111_210550f.ewo_high.value) & (dataframe['rsi'] < sGithub_remiotore_freqtrade__el_hz__20260111_210550f.rsi_buy.value) & volume_filter & trend_filter ) buy_conditions.append( (dataframe['close'] < dataframe[f'ma_buy_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_buy.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.low_offset.value) & (dataframe['EWO'] < sGithub_remiotore_freqtrade__el_hz__20260111_210550f.ewo_low.value) & volume_filter & volatility_filter ) if buy_conditions: dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'enter_long'] = 1 # Условия для шортов sGithub_remiotore_freqtrade__el_hz__20260111_210550l_conditions = [] sGithub_remiotore_freqtrade__el_hz__20260111_210550l_conditions.append( (dataframe['close'] > dataframe[f'ma_sGithub_remiotore_freqtrade__el_hz__20260111_210550l_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.high_offset.value) & volume_filter & trend_filter ) if sGithub_remiotore_freqtrade__el_hz__20260111_210550l_conditions: dataframe.loc[reduce(lambda x, y: x | y, sGithub_remiotore_freqtrade__el_hz__20260111_210550l_conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, dataframe: DataFrame, metadata: dict) -> DataFrame: # Частичный выход для фиксации прибыли dataframe.loc[ (dataframe['close'] > dataframe['open'] * 1.05) & (qtpylib.crossed_above(dataframe['rsi'], 70)), 'exit_long' ] = 0.5 # Закрываем 50% позиции # Основные условия выхода exit_long_conditions = [] exit_long_conditions.append( (dataframe['close'] > dataframe[f'ma_sGithub_remiotore_freqtrade__el_hz__20260111_210550l_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_sGithub_remiotore_freqtrade__el_hz__20260111_210550l.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.high_offset.value) ) if exit_long_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), 'exit_long'] = 1 exit_short_conditions = [] exit_short_conditions.append( (dataframe['close'] < dataframe[f'ma_buy_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_buy.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.low_offset.value) & (dataframe['EWO'] > sGithub_remiotore_freqtrade__el_hz__20260111_210550f.ewo_high.value) & (dataframe['rsi'] < sGithub_remiotore_freqtrade__el_hz__20260111_210550f.rsi_buy.value) ) exit_short_conditions.append( (dataframe['close'] < dataframe[f'ma_buy_{sGithub_remiotore_freqtrade__el_hz__20260111_210550f.base_nb_candles_buy.value}'] * sGithub_remiotore_freqtrade__el_hz__20260111_210550f.low_offset.value) & (dataframe['EWO'] < sGithub_remiotore_freqtrade__el_hz__20260111_210550f.ewo_low.value) ) if exit_short_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_short_conditions), 'exit_short'] = 1 return dataframe # Адаптивный стоп-лосс с защитой прибыли def custom_stoploss(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Жесткий стоп при убытке >12% if current_profit < -0.12: return -1 # Защита прибыли if current_profit > sGithub_remiotore_freqtrade__el_hz__20260111_210550f.profit_threshold_2.value: return -0.02 # Очень тугой стоп при +10% Github_remiotore_freqtrade__el_hz__20260111_210550if current_profit > sGithub_remiotore_freqtrade__el_hz__20260111_210550f.profit_threshold_1.value: return -0.05 # Защищаем 5% прибыли # Базовый стоп return -0.242 # Управление размером позиции (риск 3% на сделку) def custom_stake_amount(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, leverage: float, entry_tag: str, side: str, **kwargs) -> float: total_balance = sGithub_remiotore_freqtrade__el_hz__20260111_210550f.wallets.get_total('USDT') return min(proposed_stake, total_balance * 0.50) def leverage(sGithub_remiotore_freqtrade__el_hz__20260111_210550f, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: return 10.0