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) from pandas import DataFrame from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta class MultiMA_TSL_76(IStrategy): INTERFACE_VERSION = 2 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, } minimal_roi = { "0": 100 } stoploss = -0.15 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=False) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) rsi_buy_trima = IntParameter(30, 70, default=61, space='buy', optimize=False) 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_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 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 } ] timeframe = '5m' process_only_new_candles = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count: int = 200 def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0.3): return 0.05 elif (current_profit > 0.2): return 0.04 elif (current_profit > 0.1): return 0.03 elif (current_profit > 0.06): return 0.02 elif (current_profit > 0.03): return 0.01 elif (current_profit > 0.016): return 0.005 return 0.99 def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): if(current_time < trade.open_date_utc + timedelta(minutes=5)) : return None dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return None last_candle = dataframe.iloc[-1].squeeze() if(current_profit <= 0.016): if(last_candle['close'] > last_candle['ema_offset_sell']): return "Sell Signal" return None def get_ticker_indicator(self): return int(self.timeframe[:-1]) 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) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1].squeeze() if (rate > (last_candle['close'] * 1)) : return False return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.config['runmode'].value == 'hyperopt': 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['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) *self.high_offset_ema.value dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.config['runmode'].value == 'hyperopt': 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['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) *self.high_offset_ema.value dataframe.loc[:, 'buy_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, 'buy_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, 'buy_tag'] += 'trima ' conditions.append(buy_offset_trima) add_check = ( (dataframe['rsi_fast'] < 30) & (dataframe['close'] < dataframe['ema_offset_sell']) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[:, 'buy'] = reduce(lambda x, y: (x | y) & add_check, conditions) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value == 'hyperopt': dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) *self.high_offset_ema.value dataframe.loc[:,'sell'] = 0 return dataframe 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