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MultiMA_TSL

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webclinic017/test-1/MultiMA_TSL_converted.py · ★3 · ⑂6 · first seen 2026-07-28 · repo updated 2021-09-05

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.15 has minimal roi trailing custom stoploss protections process only new candles startup candle count: 300 hyperopt hyperopt params: 17
Indicators EMA RSI talib
Concepts risk_management trailing
Methods EWO confirm_trade_entry confirm_trade_exit custom_exit custom_stoploss get_ticker_indicator
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, CategoricalParameter, stoploss_from_open
from pandas import DataFrame
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from freqtrade.exchange import timeframe_to_prev_date
###########################################################################################################
##    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': 6, 'low_offset_ema': 0.985, 'rsi_buy_ema': 61, 'base_nb_candles_buy_trima': 6, 'low_offset_trima': 0.981, 'rsi_buy_trima': 59}
    sell_params = {'base_nb_candles_sell': 30, 'high_offset_ema': 1.004}
    # ROI table:
    minimal_roi = {'0': 100}
    stoploss = -0.15
    # Multi Offset
    base_nb_candles_sell = IntParameter(5, 80, default=20, load=True, space='sell', optimize=False)
    base_nb_candles_buy_ema = IntParameter(5, 80, default=20, load=True, space='buy', optimize=False)
    low_offset_ema = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, 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, load=True, space='buy', optimize=True)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    rsi_buy_trima = IntParameter(30, 70, default=61, space='buy', optimize=True)
    # Protection
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, load=True, space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, load=True, space='buy', optimize=False)
    fast_ewo = IntParameter(10, 50, default=50, load=True, space='buy', optimize=False)
    slow_ewo = IntParameter(100, 200, default=200, load=True, space='buy', optimize=False)
    # Trailing stoploss (not used)
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.018
    use_custom_stoploss = True
    protections = [{'method': 'LowProfitPairs', 'lookback_period_candles': 20, 'trade_limit': 1, 'stop_duration': 20, 'required_profit': -0.05}, {'method': 'CooldownPeriod', 'stop_duration_candles': 2}]
    # 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 = 300
    # trailing stoploss hyperopt parameters
    # hard stoploss profit
    pHSL = DecimalParameter(-0.2, -0.04, default=-0.15, decimals=3, space='sell', optimize=False, load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.018, decimals=3, space='sell', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.013, decimals=3, space='sell', optimize=False, load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='sell', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='sell', optimize=False, load=True)
    # Custom Trailing Stoploss by Perkmeister

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value 
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        return stoploss_from_open(sl_profit, current_profit)

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

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        enter_tag = 'empty'
        if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
            enter_tag = trade.buy_tag
        else:
            trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            buy_signal = dataframe.loc[dataframe['date'] < trade_open_date]
            if not buy_signal.empty:
                buy_signal_candle = buy_signal.iloc[-1]
                enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty'
        buy_tags = buy_tag.split()
        last_candle = dataframe.iloc[-1].squeeze()
        if last_candle['close'] > last_candle['ema_offset_sell']:
            return 'sell signal (' + enter_tag + ')'
        return None

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        if rate > last_candle['close']:
            return False
        return True

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        current_profit = trade.calc_profit_ratio(rate)
        if sell_reason.startswith('sell signal ') and current_profit > self.pPF_1.value:
            # Reject sell signal when trailing stoplosses
            return False
        return True

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    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)
        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['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * self.low_offset_trima.value
        dataframe.loc[:, 'enter_tag'] = ''
        buy_offset_ema = (dataframe['close'] < dataframe['ema_offset_buy']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy_ema.value))
        dataframe.loc[buy_offset_ema, 'enter_tag'] += 'ema '
        conditions.append(buy_offset_ema)
        buy_offset_trima = (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)
        add_check = dataframe['volume'] > 0
        if conditions:
            dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: (x | y) & add_check, conditions)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset_ema.value
        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