✨ AI Search 🔒
💬 Forum

SMAOffsetProtectOptV1_336

♡

remiotore/ccxt-freqtrade/strategies/SMAOffsetProtectOptV1_336_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.15 has minimal roi trailing protections process only new candles startup candle count: 30 hyperopt hyperopt params: 7
Indicators EMA RSI talib technical
Concepts risk_management trailing
Methods EWO get_informative_indicators
15 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
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

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 SMAOffsetProtectOptV1_336(IStrategy):
    INTERFACE_VERSION = 3
    buy_params = {'base_nb_candles_buy': 13, 'ewo_high': 5.835, 'ewo_low': -19.909, 'low_offset': 0.978, 'rsi_buy': 55}
    sell_params = {'base_nb_candles_sell': 18, 'high_offset': 1.012}
    minimal_roi = {'0': 100.0}
    stoploss = -0.15
    protections = [{'method': 'LowProfitPairs', 'lookback_period_candles': 60, 'trade_limit': 1, 'stop_duration': 60, 'required_profit': -0.05}, {'method': 'CooldownPeriod', 'stop_duration_candles': 2}]
    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)
    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 = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True
    timeframe = '5m'
    informative_timeframe = '1h'
    process_only_new_candles = True
    startup_candle_count = 30
    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':
            for val in self.base_nb_candles_buy.range:
                dataframe[f'ma_{val}'] = ta.EMA(dataframe, timeperiod=val)
        else:
            dataframe[f'ma_{self.base_nb_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value)
            dataframe[f'ma_{self.base_nb_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        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_{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_{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_{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