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SMAOffsetV2

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neozhou2009/freqtrade-strategies/strategies/SMAOffsetV2/SMAOffsetV2_converted.py · first seen 2026-07-28 · repo updated 2026-04-20

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.2 has minimal roi custom stoploss process only new candles startup candle count: 200
Indicators EMA SMA talib
Methods custom_stoploss get_informative_indicators
15 related strategies (⧉ identical code, ≈ similar name)

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# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
# thanks tirail for original SMAOffset sharing
# added trend detection and stoploss

class SMAOffsetV2(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 1}
    stoploss = -0.2
    timeframe = '5m'
    informative_timeframe = '1h'
    use_exit_signal = True
    exit_profit_only = True
    process_only_new_candles = True
    use_custom_stoploss = True
    startup_candle_count = 200

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if current_time - timedelta(minutes=40) > trade.open_date_utc and current_profit < -0.1:
            return -0.01
        return -0.99

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    @staticmethod
    def get_informative_indicators(dataframe: DataFrame, metadata: dict):
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25)
        dataframe['go_long'] = (dataframe['ema_fast'] > dataframe['ema_slow']).astype('int') * 2
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.dp:
            return dataframe
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        informative = self.get_informative_indicators(informative.copy(), metadata)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
        # don't overwrite the base dataframe's HLCV information
        skip_columns = [s + '_' + self.informative_timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.rename(columns=lambda s: s.replace('_{}'.format(self.informative_timeframe), '') if not s in skip_columns else s, inplace=True)
        # ---------------------------------------------------------------------------------
        sma_offset = 1 - 0.04
        sma_offset_pos = 1 + 0.012
        base_nb_candles = 20
        dataframe['sma_30_offset'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset
        dataframe['sma_30_offset_pos'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset_pos
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['go_long'] > 0) & (dataframe['close'] < dataframe['sma_30_offset']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[((dataframe['go_long'] == 0) | (dataframe['close'] > dataframe['sma_30_offset_pos'])) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe
    plot_config = {'main_plot': {'sma_30_offset': {'color': 'orange'}, 'sma_30_offset_pos': {'color': 'orange'}, 'ema_fast': {'color': 'blue'}, 'ema_slow': {'color': 'green'}}}