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RalliV1

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freqle/uploads/user-90abf0da8f3178f3/RalliV1_converted.py · first seen 2026-07-28

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.3 has minimal roi trailing custom stoploss protections process only new candles startup candle count: 200 hyperopt hyperopt params: 11
Indicators EMA HMA RSI SMA talib technical
Concepts risk_management trailing
Methods EWO confirm_trade_entry custom_exit custom_stoploss
12 related strategies (⧉ identical code, ≈ similar name)

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# --- Do not remove these libs ---
# --- 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
# @Rallipanos
# Buy hyperspace params:
buy_params = {'base_nb_candles_buy': 14, 'ewo_high': 2.327, 'ewo_high_2': -2.327, 'ewo_low': -20.988, 'low_offset': 0.975, 'low_offset_2': 0.955, 'rsi_buy': 60, 'rsi_buy_2': 45}
# Sell hyperspace params:
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['low'] * 100
    return emadif

class RalliV1(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {'0': 0.04, '40': 0.032, '87': 0.018, '201': 0}
    # Stoploss:
    stoploss = -0.3
    # 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)
    low_offset_2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_2'], 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)
    # 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)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
    rsi_buy_2 = IntParameter(30, 70, default=buy_params['rsi_buy_2'], space='buy', optimize=True)
    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03
    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 = False
    ## Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'
    process_only_new_candles = True
    startup_candle_count = 200
    plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}}
    protections = [{'method': 'CooldownPeriod', 'stop_duration_candles': 2}]

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        if rate > last_candle['close']:
            return False
        return True

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        enter_tag = 'empty'
        if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
            enter_tag = trade.buy_tag
        else:
            trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            buy_signal = dataframe.loc[dataframe['date'] < trade_open_date]
            if not buy_signal.empty:
                buy_signal_candle = buy_signal.iloc[-1]
                enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty'
        buy_tags = buy_tag.split()
        current_profit = trade.calc_profit_ratio(current_rate)
        if current_profit <= -0.3:
            return 'stoploss ( ' + enter_tag + ')'
        trade_time_40 = trade.open_date_utc + timedelta(minutes=40)
        trade_time_87 = trade.open_date_utc + timedelta(minutes=87)
        trade_time_201 = trade.open_date_utc + timedelta(minutes=201)
        if current_time < trade_time_40:
            if current_profit >= 0.04:
                return 'roi 0 ( ' + enter_tag + ')'
        elif current_time >= trade_time_40 and current_time < trade_time_87:
            if current_profit >= 0.032:
                return 'roi 40 ( ' + enter_tag + ')'
        elif current_time >= trade_time_87 and current_time < trade_time_201:
            if current_profit >= 0.018:
                return 'roi 87 ( ' + enter_tag + ')'
        elif current_time >= trade_time_201:
            if current_profit >= 0:
                return 'roi 201 ( ' + enter_tag + ')'
        last_candle = dataframe.iloc[-1]
        sell_1 = (last_candle['hma_50'] > last_candle['ema_100']) & (last_candle['close'] > last_candle['sma_9']) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (last_candle['rsi_fast'] > last_candle['rsi_slow'])
        sell_2 = (last_candle['close'] < last_candle['ema_100']) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (last_candle['rsi_fast'] > last_candle['rsi_slow'])
        sell_3 = last_candle['rsi'] > 45
        sell_4 = last_candle['hma_50'] < last_candle['ema_100']
        if sell_1 and sell_3:
            return 'sell 1-3 ( ' + enter_tag + ')'
        elif sell_1 and sell_4:
            return 'sell 1-4 ( ' + enter_tag + ')'
        elif sell_2 and sell_3:
            return 'sell 2-3 ( ' + enter_tag + ')'
        elif sell_2 and sell_4:
            return 'sell 2-4 ( ' + enter_tag + ')'
        return None
    use_custom_stoploss = True

    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

    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)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['hma_9'] = qtpylib.hull_moving_average(dataframe['close'], window=9)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['ema_9'] = ta.EMA(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 = []
        dataframe.loc[:, 'enter_tag'] = ''
        buy_1 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (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_2.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)
        dataframe.loc[buy_1, 'enter_tag'] += '1 '
        conditions.append(buy_1)
        buy_2 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy_2.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['rsi'] < 25)
        dataframe.loc[buy_2, 'enter_tag'] += '2 '
        conditions.append(buy_2)
        buy_3 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (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)
        dataframe.loc[buy_3, 'enter_tag'] += '3 '
        conditions.append(buy_3)
        buy_4 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (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)
        dataframe.loc[buy_4, 'enter_tag'] += '4 '
        conditions.append(buy_4)
        buy_5 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.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) & (dataframe['rsi'] < 25)
        dataframe.loc[buy_5, 'enter_tag'] += '5 '
        conditions.append(buy_5)
        buy_6 = (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (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)
        dataframe.loc[buy_6, 'enter_tag'] += '6 '
        conditions.append(buy_6)
        if conditions:
            dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe