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 = 2 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: protection_params = { "low_profit_lookback": 60, "low_profit_min_req": 0.03, "low_profit_stop_duration": 29, "cooldown_lookback": 2, # value loaded from strategy "stoploss_lookback": 72, # value loaded from strategy "stoploss_stop_duration": 20, # value loaded from strategy } 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_sell_signal = True sell_profit_only = False ignore_roi_if_buy_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_buy_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[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'buy'] = 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, 'buy_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, 'buy_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', 'buy'] ]=(1, 1) return dataframe def populate_sell_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', 'sell'] ]=(1, 1) if not self.config['runmode'].value in ('backtest', 'hyperopt'): dataframe.loc[:, 'sell'] = 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