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ElliotV8HO

🏆 League #73 / 1937
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davidzr/freqtrade-strategies/strategies/ElliotV8HO/ElliotV8HO_converted.py · ★563 · ⑂259 · first seen 2026-07-28 · repo updated 2024-02-10

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.189 has minimal roi trailing process only new candles startup candle count: 400 hyperopt hyperopt params: 8
Indicators EMA HMA RSI SMA talib
Concepts 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 functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import DecimalParameter, IntParameter

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 ElliotV8HO(IStrategy):
    INTERFACE_VERSION = 3
    # Sell hyperspace params: v1
    # sell_params = {
    #     "base_nb_candles_sell": 24,
    #     "high_offset": 0.991,
    #     "high_offset_2": 0.997
    # }
    # Sell hyperspace params: v5
    # sell_params = {
    #     "base_nb_candles_sell": 30,
    #     "high_offset": 0.973,
    #     "high_offset_2": 1.121,
    # }
    # Sell hyperspace params: v6
    sell_params = {'base_nb_candles_sell': 24, 'high_offset': 1.011, 'high_offset_2': 0.997}
    # Buy hyperspace params: v1
    buy_params = {'base_nb_candles_buy': 19, 'ewo_high': 5.417, 'ewo_low': -17.251, 'low_offset': 0.983, 'rsi_buy': 61}
    # ROI table:
    minimal_roi = {'0': 0.08, '40': 0.032, '87': 0.016}
    # Stoploss:
    stoploss = -0.189
    # SMAOffset
    base_nb_candles_buy = IntParameter(15, 60, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(15, 60, 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(0.9, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(0.99, 1.2, default=sell_params['high_offset_2'], space='sell', optimize=True)
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -15.0, default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(1.0, 8.0, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(25, 75, default=buy_params['rsi_buy'], space='buy', optimize=True)
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False
    # Optional order time in force.
    # 'sell': 'ioc'
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    # Optimal timeframe for the strategy
    timeframe = '5m'
    informative_timeframe = '1h'
    process_only_new_candles = True
    startup_candle_count = 400
    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:
        if self.config['runmode'].value == 'hyperopt':
            # 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)
        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['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((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['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['close'] > 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['close'] < 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