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ElliotV4

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5drei1/freqtrade_pub_strats/ElliotV4_converted.py · ★56 · ⑂37 · first seen 2026-07-28 · repo updated 2026-02-17

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.325 has minimal roi trailing process only new candles startup candle count: 39 hyperopt hyperopt params: 7
Indicators ADX Aroon Bollinger_Bands CCI EMA Heikin_Ashi Keltner MACD MFI ROC RSI SAR Stoch_RSI Stochastic TEMA UO talib technical
Concepts mean_reversion trailing
Methods EWO get_informative_indicators
15 related strategies (⧉ identical code, ≈ similar name)

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# --- 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, 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 hyperspace params:
buy_params = {'base_nb_candles_buy': 14, 'ewo_high': 2.34, 'ewo_low': -17.457, 'low_offset': 0.983, 'rsi_buy': 61}
# Sell hyperspace params:
sell_params = {'base_nb_candles_sell': 24, 'high_offset': 0.992}

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:
    # Momentum Indicators
    # ------------------------------------
    # ADX
    dataframe['adx'] = ta.ADX(dataframe)
    # Plus Directional Indicator / Movement
    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
    # Minus Directional Indicator / Movement
    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
    # Aroon, Aroon Oscillator
    aroon = ta.AROON(dataframe)
    dataframe['aroonup'] = aroon['aroonup']
    dataframe['aroondown'] = aroon['aroondown']
    dataframe['aroonosc'] = ta.AROONOSC(dataframe)
    # Awesome Oscillator
    dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
    # Keltner Channel
    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']
    # Ultimate Oscillator
    dataframe['uo'] = ta.ULTOSC(dataframe)
    # Commodity Channel Index: values [Oversold:-100, Overbought:100]
    dataframe['cci'] = ta.CCI(dataframe)
    # RSI
    dataframe['rsi'] = ta.RSI(dataframe)
    # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
    rsi = 0.1 * (dataframe['rsi'] - 50)
    dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
    # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
    dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
    # # Stochastic Slow
    stoch = ta.STOCH(dataframe)
    dataframe['slowd'] = stoch['slowd']
    dataframe['slowk'] = stoch['slowk']
    # Stochastic Fast
    stoch_fast = ta.STOCHF(dataframe)
    dataframe['fastd'] = stoch_fast['fastd']
    dataframe['fastk'] = stoch_fast['fastk']
    # # Stochastic RSI
    # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
    # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
    stoch_rsi = ta.STOCHRSI(dataframe)
    dataframe['fastd_rsi'] = stoch_rsi['fastd']
    dataframe['fastk_rsi'] = stoch_rsi['fastk']
    # MACD
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']
    # MFI
    dataframe['mfi'] = ta.MFI(dataframe)
    # # ROC
    dataframe['roc'] = ta.ROC(dataframe)
    # Overlap Studies
    # ------------------------------------
    # Bollinger Bands
    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']
    # Parabolic SAR
    dataframe['sar'] = ta.SAR(dataframe)
    # TEMA - Triple Exponential Moving Average
    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
    # Cycle Indicator
    # ------------------------------------
    # Hilbert Transform Indicator - SineWave
    hilbert = ta.HT_SINE(dataframe)
    dataframe['htsine'] = hilbert['sine']
    dataframe['htleadsine'] = hilbert['leadsine']
    # Pattern Recognition - Bullish candlestick patterns
    # ------------------------------------
    # Hammer: values [0, 100]
    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
    # Inverted Hammer: values [0, 100]
    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
    # Dragonfly Doji: values [0, 100]
    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
    # Piercing Line: values [0, 100]
    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe)  # values [0, 100]
    # Morningstar: values [0, 100]
    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe)  # values [0, 100]
    # Three White Soldiers: values [0, 100]
    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)  # values [0, 100]
    # Pattern Recognition - Bearish candlestick patterns
    # ------------------------------------
    # Hanging Man: values [0, 100]
    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
    # Shooting Star: values [0, 100]
    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
    # Gravestone Doji: values [0, 100]
    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
    # Dark Cloud Cover: values [0, 100]
    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
    # Evening Doji Star: values [0, 100]
    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
    # Evening Star: values [0, 100]
    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
    # Pattern Recognition - Bullish/Bearish candlestick patterns
    # ------------------------------------
    # Three Line Strike: values [0, -100, 100]
    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
    # Spinning Top: values [0, -100, 100]
    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe)  # values [0, -100, 100]
    # Engulfing: values [0, -100, 100]
    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)  # values [0, -100, 100]
    # Harami: values [0, -100, 100]
    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe)  # values [0, -100, 100]
    # Three Outside Up/Down: values [0, -100, 100]
    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)  # values [0, -100, 100]
    # Three Inside Up/Down: values [0, -100, 100]
    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)  # values [0, -100, 100]
    # # Chart type
    # # ------------------------------------
    # # Heikin Ashi Strategy
    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 ElliotV4(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016, '201': 0}
    # Stoploss:
    stoploss = -0.325
    # SMAOffset
    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)
    # 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)
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.049
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True
    ## Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'}
    # Optimal timeframe for the strategy
    timeframe = '5m'
    informative_timeframe = '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 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:
        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        # RSI
        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