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ElliotV5HO

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remiotore/ccxt-freqtrade/strategies/ElliotV5HO_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.189 has minimal roi trailing custom stoploss process only new candles startup candle count: 79 hyperopt hyperopt params: 7
Indicators EMA RSI talib technical
Concepts trailing
Methods EWO custom_stoploss get_informative_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

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 ElliotV5HO(IStrategy):
    INTERFACE_VERSION = 3
    buy_params = {'base_nb_candles_buy': 17, 'ewo_high': 3.34, 'ewo_low': -17.457, 'low_offset': 0.978, 'rsi_buy': 60}
    sell_params = {'base_nb_candles_sell': 39, 'high_offset': 1.011}
    minimal_roi = {'0': 0.215, '40': 0.132, '87': 0.086, '201': 0.03}
    stoploss = -0.189
    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
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    timeframe = '5m'
    informative_timeframe = '1h'
    process_only_new_candles = True
    startup_candle_count = 79
    plot_config = {'main_plot': {f'ma_buy_{base_nb_candles_buy.value}': {'color': 'orange'}, f'ma_sell_{base_nb_candles_sell.value}': {'color': 'green'}}}
    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:
        if self.config['runmode'].value == 'hyperopt':
            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)
        else:
            dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value)
            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value)
        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:
        conditions = []
        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))
        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 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['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        candle = df.iloc[-1].squeeze()
        if current_profit < 0.001 and current_time - timedelta(minutes=140) > trade.open_date_utc:
            return -0.005
        return 1