✨ AI Search 🔒
💬 Forum

RalliV1_disable56

♡

DerSalvador/freqtrade-helm-chart/chart/deployed_strategies/binance-futures-k8s-namespace/RalliV1_disable56.py · first seen 2026-07-16 · repo updated 2026-04-16

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

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

Download Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
# --- 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:
entry_params = {'base_nb_candles_entry': 14, 'ewo_high': 2.327, 'ewo_high_2': -2.327, 'ewo_low': -20.988, 'low_offset': 0.975, 'low_offset_2': 0.955, 'rsi_entry': 60, 'rsi_entry_2': 45}
# Sell hyperspace params:
exit_params = {'base_nb_candles_exit': 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_disable56(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_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True)
    base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True)
    low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True)
    low_offset_2 = DecimalParameter(0.9, 0.99, default=entry_params['low_offset_2'], space='entry', optimize=True)
    high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True)
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True)
    ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=entry_params['ewo_high_2'], space='entry', optimize=True)
    rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True)
    rsi_entry_2 = IntParameter(30, 70, default=entry_params['rsi_entry_2'], space='entry', optimize=True)
    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.005
    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_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}}

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        if last_candle is not None:
            if exit_reason in ['exit_signal']:
                if last_candle['rsi'] < 45 and last_candle['hma_50'] > last_candle['ema_100']:  #*1.2
                    return False
        return True
    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_entry values
        for val in self.base_nb_candles_entry.range:
            dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_exit values
        for val in self.base_nb_candles_exit.range:
            dataframe[f'ma_exit_{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 = []
        conditions.append((dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry_2.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value))
        conditions.append((dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_entry_2.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['rsi'] < 25))
        conditions.append((dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] < dataframe['ema_100']) & (dataframe['sma_9'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value))
        conditions.append((dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] > dataframe['ema_100']) & (dataframe['rsi_fast'] < 35) & (dataframe['rsi_fast'] > 4) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value))
        # conditions.append(
        #     (   
        #         (dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] > dataframe['ema_100'])&
        #         (dataframe['rsi_fast'] <35)&
        #         (dataframe['rsi_fast'] >4)&
        #         (dataframe['close'] < (dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value)) &
        #         (dataframe['EWO'] > self.ewo_high_2.value) &
        #         (dataframe['rsi'] < self.rsi_entry.value) &
        #         (dataframe['volume'] > 0)&
        #         (dataframe['close'] < (dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value))&
        #         (dataframe['rsi']<25)
        #     )
        # )        
        # conditions.append(
        #     (   (dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] > dataframe['ema_100'])&
        #         (dataframe['rsi_fast'] < 35)&
        #         (dataframe['rsi_fast'] >4)&
        #         (dataframe['close'] < (dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value)) &
        #         (dataframe['EWO'] < self.ewo_low.value) &
        #         (dataframe['volume'] > 0)&
        #         (dataframe['close'] < (dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value)) 
        #     )
        # )
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['hma_50'] > dataframe['ema_100']) & (dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['close'] < dataframe['ema_100']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.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'] = 1
        return dataframe