# --- Do not remove these libs --- from typing import DefaultDict from freqtrade.strategy.interface import IStrategy from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter # @Rallipanos mod. Uzirox def zlema2(dataframe, fast): df = dataframe.copy() zema1=ta.EMA(df['close'], fast) zema2=ta.EMA(zema1, fast) d1=zema1-zema2 df['zlema2']=zema1+d1 return df['zlema2'] order_types = { 'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class NotAnotherSMAOffsetStrategy_uzi3(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.215, "40": 0.032, "87": 0.016, # "201": 0 } # Stoploss: stoploss = -0.1 # Protection fast_ewo = 50 slow_ewo = 200 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.025 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.005 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' process_only_new_candles = True startup_candle_count = 400 slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } buy_signals = {} def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2 return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = dataframe.iloc[-1].squeeze() slippage = (rate / candle['close']) - 1 if slippage < self.slippage_protection['max_slippage']: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection['retries']: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if(((dataframe['close'].iloc[-1] - dataframe['close'].iloc[-2])/dataframe['close'].iloc[-2])*100) < -2: # si scende pesante self.stoploss = -0.3 #stoploss # buy params self.trailing_stop_positive_offset = 0.03 self.base_nb_candles_buy = IntParameter(5, 80, default=14, space='buy', optimize=False) self.low_offset = DecimalParameter(0.9, 0.99, default=0.975, space='buy', optimize=False) self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.955, space='buy', optimize=False) self.ewo_low = DecimalParameter(-20.0, -8.0,default=-20.988, space='buy', optimize=False) self.ewo_high = DecimalParameter(2.0, 12.0, default=2.327, space='buy', optimize=False) self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.327, space='buy', optimize=False) self.rsi_buy = IntParameter(30, 70, default=69, space='buy', optimize=False) # sell params self.base_nb_candles_sell = IntParameter(5, 80, default=16, space='sell', optimize=True) self.high_offset = DecimalParameter(0.95, 1.1, default=0.991, space='sell', optimize=True) self.high_offset_2 = DecimalParameter(0.99, 1.5, default=0.997, space='sell', optimize=True) else: # normale - si sale # buy params self.base_nb_candles_buy = IntParameter(5, 80, default=14, space='buy', optimize=False) self.low_offset = DecimalParameter(0.9, 0.99, default=0.986, space='buy', optimize=False) self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.944, space='buy', optimize=False) self.ewo_low = DecimalParameter(-20.0, -8.0,default=-16.917, space='buy', optimize=False) self.ewo_high = DecimalParameter(2.0, 12.0, default=4.179, space='buy', optimize=False) self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.609, space='buy', optimize=False) self.rsi_buy = IntParameter(30, 70, default=58, space='buy', optimize=False) # sell params self.base_nb_candles_sell = IntParameter(5, 80, default=16, space='sell', optimize=True) self.high_offset = DecimalParameter(0.95, 1.1, default=1.054, space='sell', optimize=True) self.high_offset_2 = DecimalParameter(0.99, 1.5, default=1.018, space='sell', optimize=True) # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # *MAs dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod = 100) dataframe['ema_10'] = zlema2(dataframe, 10) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod = 9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi_fast'] <35)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewo1') dataframe.loc[ ( (dataframe['rsi_fast'] <35)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi']<25) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (dataframe['rsi_fast'] < 35)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ), ['buy', 'buy_tag']] = (1, 'ewolow') # buy in bull market dataframe.loc[ ( (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['close'] * 0.031)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.018)) & (dataframe['tail'].lt(dataframe['bbdelta'] * 0.233)) & (dataframe['close'].lt(dataframe['lower'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['volume'] > 0) ) | ( (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) & (dataframe['volume'] > 0) ), ['buy', 'buy_tag']] = (1, 'bb_bull') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['rsi']>50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) | ( (dataframe['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1)*0.005)) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) & (dataframe['rsi_fast']>dataframe['rsi_slow']) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe plot_config = { 'main_plot':{ 'ema_100':{}, 'ema_10':{}, 'sma_9':{} } }