# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/el_3.py 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 def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.SMA(df, timeperiod=ema_length) ema2 = ta.SMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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) keltner = qtpylib.keltner_channel(dataframe) dataframe['kc_upperband'] = keltner['upper'] dataframe['kc_lowerband'] = keltner['lower'] dataframe['kc_middleband'] = keltner['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'] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe class Github_remiotore_ccxt_freqtrade__el_3__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short = True buy_params = { "base_nb_candles_buy": 12, "ewo_high": 4.428, "ewo_low": -12.383, "low_offset": 0.915, "rsi_buy": 44, } sell_params = { "base_nb_candles_sell": 72, "high_offset": 1.008, } minimal_roi = { "0": 0.219, "24": 0.087, "67": 0.024, "164": 0 } stoploss = -0.242 trailing_stop = True trailing_stop_positive = 0.103 trailing_stop_positive_offset = 0.2 trailing_only_offset_is_reached = True max_open_trades = 9 base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(5, 80, 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=True) high_offset = DecimalParameter(0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) 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) trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 2000 plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'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: 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['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: buy_conditions = [] buy_conditions.append((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)) buy_conditions.append((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)) if buy_conditions: dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'enter_long'] = 1 sell_conditions = [] sell_conditions.append((dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) if sell_conditions: dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [] exit_long_conditions.append((dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0)) 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_{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)) exit_short_conditions.append((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)) if exit_short_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_short_conditions), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag:str, side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return 10.0