💬 Forum

SMAOffset_4

♡

remiotore/ccxt-freqtrade/strategies/SMAOffset_4_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.5 has minimal roi trailing custom stoploss process only new candles startup candle count: 30 hyperopt hyperopt params: 6
Indicators EMA SMA talib technical
Concepts trailing
Methods custom_stoploss
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

🔒 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
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
ma_types = {'SMA': ta.SMA, 'EMA': ta.EMA}

class SMAOffset_4(IStrategy):
    INTERFACE_VERSION = 3
    buy_params = {'base_nb_candles_buy': 30, 'buy_trigger': 'SMA', 'low_offset': 0.958}
    sell_params = {'base_nb_candles_sell': 30, 'high_offset': 1.012, 'sell_trigger': 'EMA'}
    stoploss = -0.5
    minimal_roi = {'0': 1}
    base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy')
    base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell')
    low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy')
    high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell')
    buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy')
    sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell')
    trailing_stop = False
    trailing_stop_positive = 0.0001
    trailing_stop_positive_offset = 0
    trailing_only_offset_is_reached = False
    timeframe = '5m'
    use_exit_signal = True
    exit_profit_only = False
    process_only_new_candles = True
    startup_candle_count = 30
    plot_config = {'main_plot': {'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}}}
    use_custom_stoploss = False

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        return 1

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
            dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
        dataframe.loc[(dataframe['close'] < dataframe['ma_offset_buy']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
        dataframe.loc[(dataframe['close'] > dataframe['ma_offset_sell']) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe