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MultiMA_TSL

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mrgolfprat/ft_sample/user_data/strategies/MultiMA_TSL_converted.py · ★16 · ⑂8 · first seen 2026-07-28 · repo updated 2021-11-17

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.15 has minimal roi trailing custom stoploss protections process only new candles startup candle count: 200 hyperopt hyperopt params: 26
Indicators EMA RSI talib technical
Concepts risk_management trailing
Methods EWO custom_stoploss get_ticker_indicator protections
15 related strategies (⧉ identical code, ≈ similar name)

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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open
from pandas import DataFrame
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime
from technical.indicators import zema
###########################################################################################################
##    MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister)             ##
##    Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset##
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##                                                                                                       ##
###########################################################################################################
# I hope you do enough testing before proceeding, either backtesting and/or dry run.
# Any profits and losses are all your responsibility

class MultiMA_TSL(IStrategy):
    INTERFACE_VERSION = 3
    buy_params = {'base_nb_candles_buy_ema': 50, 'low_offset_ema': 1.061, 'base_nb_candles_buy_zema': 30, 'low_offset_zema': 0.963, 'rsi_buy_zema': 50, 'base_nb_candles_buy_trima': 14, 'low_offset_trima': 0.963, 'rsi_buy_trima': 50, 'buy_roc_max': 45, 'buy_condition_trima_enable': True, 'buy_condition_zema_enable': True}
    sell_params = {'base_nb_candles_sell': 32, 'high_offset_ema': 1.002, 'base_nb_candles_sell_trima': 48, 'high_offset_trima': 1.085}
    # ROI table:
    minimal_roi = {'0': 100}
    stoploss = -0.15
    # Multi Offset
    base_nb_candles_sell = IntParameter(5, 80, default=20, space='sell', optimize=False)
    base_nb_candles_sell_trima = IntParameter(5, 80, default=20, space='sell', optimize=False)
    high_offset_trima = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False)
    base_nb_candles_buy_ema = IntParameter(5, 80, default=20, space='buy', optimize=False)
    low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=False)
    high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False)
    rsi_buy_ema = IntParameter(30, 70, default=61, space='buy', optimize=False)
    base_nb_candles_buy_trima = IntParameter(5, 80, default=20, space='buy', optimize=False)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=False)
    rsi_buy_trima = IntParameter(30, 70, default=61, space='buy', optimize=False)
    base_nb_candles_buy_zema = IntParameter(5, 80, default=20, space='buy', optimize=False)
    low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=False)
    rsi_buy_zema = IntParameter(30, 70, default=61, space='buy', optimize=False)
    buy_condition_enable_optimize = True
    buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_zema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    # Protection
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=False)
    fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False)
    slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False)
    buy_roc_max = DecimalParameter(20, 70, default=55, space='buy', optimize=False)
    buy_peak_max = DecimalParameter(1, 1.1, default=1.03, decimals=3, space='buy', optimize=False)
    buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False)
    # Trailing stoploss (not used)
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.018
    use_custom_stoploss = True
    # Protection hyperspace params:
    # value loaded from strategy
    # value loaded from strategy
    # value loaded from strategy
    protection_params = {'low_profit_lookback': 60, 'low_profit_min_req': 0.03, 'low_profit_stop_duration': 29, 'cooldown_lookback': 2, 'stoploss_lookback': 72, 'stoploss_stop_duration': 20}
    cooldown_lookback = IntParameter(2, 48, default=2, space='protection', optimize=False)
    low_profit_lookback = IntParameter(2, 60, default=20, space='protection', optimize=False)
    low_profit_stop_duration = IntParameter(12, 200, default=20, space='protection', optimize=False)
    low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space='protection', decimals=2, optimize=False)

    @property
    def protections(self):
        prot = []
        prot.append({'method': 'CooldownPeriod', 'stop_duration_candles': self.cooldown_lookback.value})
        prot.append({'method': 'LowProfitPairs', 'lookback_period_candles': self.low_profit_lookback.value, 'trade_limit': 1, 'stop_duration': int(self.low_profit_stop_duration.value), 'required_profit': self.low_profit_min_req.value})
        return prot
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200
    # credit to Perkmeister for this custom stoploss to help the strategy ride a green candle when the sell signal triggered

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        sl_new = 1
        if not self.config['runmode'].value in ('backtest', 'hyperopt'):
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if len(dataframe) >= 1:
                last_candle = dataframe.iloc[-1]
                if (last_candle['sell_copy'] == 1) & (last_candle['buy_copy'] == 0):
                    sl_new = 0.001
        return sl_new

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EWO
        dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['roc_max'] = dataframe['close'].pct_change(48).rolling(12).max() * 100
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) * self.low_offset_ema.value
        dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) * self.low_offset_zema.value
        dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * self.low_offset_trima.value
        dataframe.loc[:, 'enter_tag'] = ''
        dataframe.loc[:, 'buy_copy'] = 0
        dataframe.loc[:, 'enter_long'] = 0
        buy_offset_trima = self.buy_condition_trima_enable.value & (dataframe['close'] < dataframe['trima_offset_buy']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy_trima.value))
        dataframe.loc[buy_offset_trima, 'enter_tag'] += 'trima '
        conditions.append(buy_offset_trima)
        buy_offset_zema = self.buy_condition_zema_enable.value & (dataframe['close'] < dataframe['zema_offset_buy']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy_zema.value))
        dataframe.loc[buy_offset_zema, 'enter_tag'] += 'zema '
        conditions.append(buy_offset_zema)
        add_check = (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['volume'] > 0)
        if conditions:
            dataframe.loc[add_check & reduce(lambda x, y: x | y, conditions), ['buy_copy', 'enter_long']] = (1, 1)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'sell_copy'] = 0
        dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset_ema.value
        dataframe['trima_offset_sell'] = ta.TRIMA(dataframe, int(self.base_nb_candles_sell_trima.value)) * self.high_offset_trima.value
        conditions = []
        conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['volume'] > 0))
        conditions.append((dataframe['close'] > dataframe['trima_offset_sell']) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), ['sell_copy', 'exit_long']] = (1, 1)
        if not self.config['runmode'].value in ('backtest', 'hyperopt'):
            dataframe.loc[:, 'exit_long'] = 0
        return dataframe
# Elliot Wave Oscillator

def EWO(dataframe, sma1_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.EMA(df, timeperiod=sma1_length)
    sma2 = ta.EMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif