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el_3

🏆 League #98 / 1928
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remiotore/ccxt-freqtrade/strategies/el_3.py · ★3 · ⑂2 · first seen 2026-07-16 · repo updated 2026-01-11

Basics mode: futures timeframe: 5m interface version: 3
Settings stoploss: -0.242 has minimal roi trailing process only new candles startup candle count: 2000 hyperopt hyperopt params: 7
Indicators ADX Aroon Bollinger_Bands CCI EMA Heikin_Ashi Keltner MACD MFI ROC RSI SAR SMA Stoch_RSI Stochastic TEMA UO talib technical
Concepts trailing
Methods EWO get_informative_indicators leverage
6 related strategies (⧉ identical code, ≈ similar name)

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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 el_3(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