💬 Forum

el_extrema

♡

remiotore/ccxt-freqtrade/strategies/el_extrema.py · ★3 · ⑂2 · first seen 2026-07-16 · repo updated 2026-01-11

Basics mode: futures timeframe: 5m interface version: 3
Settings stoploss: -0.05 has minimal roi protections process only new candles hyperopt hyperopt params: 10
Indicators EMA RSI SMA scipy talib
Concepts risk_management
Methods get_informative_indicators leverage plot_config protections
10 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

🔒 Log in to copy 🔒 Log in to 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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
import numpy
import warnings
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, errors
from datetime import datetime
import numpy
from scipy.signal import argrelextrema
from freqtrade.strategy import (
    IStrategy, 
    DecimalParameter, 
    IntParameter, 
    CategoricalParameter, 
    BooleanParameter
)

warnings.simplefilter(action="ignore", category=errors.PerformanceWarning)


class el_extrema(IStrategy):
    INTERFACE_VERSION = 3

    can_short = True

    entry_params = {
        'base_nb_candles_entry': 12,
        'ewo_high': 4.428,
        'ewo_low': -12.383,
        'low_offset': 0.915,
        'rsi_entry': 44,
    }

    exit_params = {
        'base_nb_candles_exit': 72,
        'high_offset': 1.008,
    }

    minimal_roi = {
        '0': 0.5,
        '60': 0.45,
        '120': 0.4,
        '240': 0.3,
        '360': 0.25,
        '720': 0.2,
        '1440': 0.15,
        '2880': 0.1,
        '3600': 0.05,
        '7200': 0.02,
    }

    stoploss = -0.05
    max_open_trades = 9
    timeframe = '5m'
    informative_timeframe = '1h'
    process_only_new_candles = True
    use_custom_stoploss = False
    
    trailing_stop = False
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True
    
    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)
    high_offset = DecimalParameter(0.99, 1.1, default=exit_params['high_offset'], space='exit', optimize=True)
    
    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)
    rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True)

    cooldown_lookback = IntParameter(2, 48, default=1, space='protection', optimize=True)
    stop_duration = IntParameter(12, 200, default=4, space='protection', optimize=True)
    use_stop_protection = BooleanParameter(default=True, space='protection', optimize=True)

    @property
    def protections(self):
        prot = []
        prot.append(
            {
                'method': 'CooldownPeriod', 
                'stop_duration_candles': self.cooldown_lookback.value
            }
        )
        if self.use_stop_protection.value:
            prot.append(
                {
                    'method': 'StoplossGuard',
                    'lookback_period_candles': 24 * 3,
                    'trade_limit': 2,
                    'stop_duration_candles': self.stop_duration.value,
                    'only_per_pair': False,
                }
            )
        return prot
    
    @property
    def plot_config(self):
        plot_config = {}

        plot_config['main_plot'] = {
            '%-obv' : {},
            '&-s_close' : {}
        }
        plot_config['subplots'] = {
            'RSI': {
                'rsi': {}
            },
            'SMA' : {
                'sma5' : {},
                'sma35' : {},
            },
            'EWO' : {
                'EWO' : {}
            }
        }

        return plot_config
    
    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:
        for val in self.base_nb_candles_entry.range:
            dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)

        for val in self.base_nb_candles_exit.range:
            dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val)
    
        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma35'] = ta.SMA(dataframe, timeperiod=35)
        dataframe['EWO'] = (dataframe['sma5']  - dataframe['sma35']) / dataframe['close'] * 100
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['&s-extrema'] = 0
        dataframe.loc[argrelextrema(dataframe['close'].values, numpy.less, order=5)[0], '&s-extrema'] = -1
        dataframe.loc[argrelextrema(dataframe['close'].values, numpy.greater, order=5)[0], '&s-extrema'] = 1

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        entry_conditions = [
            (
                (dataframe['&s-extrema'] < 0) &
                (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['&s-extrema'] < 0) &
                (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)
            )
        ]

        if entry_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, entry_conditions), 'enter_long'] = 1

        exit_conditions = [
            (
                (dataframe['&s-extrema'] > 0) &
                (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & 
                (dataframe['volume'] > 0)
            )
        ]

        if exit_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, exit_conditions), 'enter_short'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        exit_long_conditions = [
            (
                (dataframe['&s-extrema'] > 0) &
                (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & 
                (dataframe['volume'] > 0)
            )
        ]

        if exit_long_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), 'exit_long'] = 1

        exit_short_conditions = [
            (
                (dataframe['&s-extrema'] < 0) &
                (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_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & 
                (dataframe['EWO'] < self.ewo_low.value) & 
                (dataframe['volume'] > 0)
            )
        ]
        if exit_short_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, exit_short_conditions), 'exit_short'] = 1

        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag:str, side: str, **kwargs) -> float:
        return 10.0