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ElliotV7_392

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remiotore/ccxt-freqtrade/strategies/ElliotV7_392_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.32 has minimal roi trailing custom stoploss process only new candles startup candle count: 39 hyperopt hyperopt params: 8
Indicators Bollinger_Bands EMA HMA MACD RSI SMA talib technical
Concepts trailing
Methods EWO custom_stoploss informative_1h_indicators
15 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
buy_params = {'base_nb_candles_buy': 14, 'ewo_high': 2.327, 'ewo_low': -19.988, 'low_offset': 0.975, 'rsi_buy': 69}
sell_params = {'base_nb_candles_sell': 24, 'high_offset': 0.991, 'high_offset_2': 0.997}

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

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    return dataframe

class ElliotV7_392(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016, '201': 0}
    stoploss = -0.32
    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(0.95, 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
    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 = False
    order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'}
    timeframe = '5m'
    inf_1h = '1h'
    process_only_new_candles = True
    startup_candle_count = 39
    plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}}
    use_custom_stoploss = False

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if current_profit < -0.1 and current_time - timedelta(minutes=720) > trade.open_date_utc:
            return -0.01
        return -0.99

    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:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        informative_1h['ema_fast'] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h['ema_slow'] = ta.EMA(informative_1h, timeperiod=25)
        informative_1h['uptrend'] = (informative_1h['ema_fast'] > informative_1h['ema_slow']).astype('int')
        informative_1h['rsi_100'] = ta.RSI(informative_1h, timeperiod=100)
        return informative_1h

    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)
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_lowerband'] = bollinger['lower']
        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['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['rsi_100'] = ta.RSI(dataframe, timeperiod=100)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['uptrend_1h'] > 0) & (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))
        conditions.append((dataframe['uptrend_1h'] > 0) & (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))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['sma_9'] > dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['sma_9'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.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_long'] = 1
        return dataframe