# --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List from functools import reduce from pandas import DataFrame, DatetimeIndex, merge # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa class YouPig(IStrategy): # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "0": 0.1 } # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.035 trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.003 trailing_only_offset_is_reached = True # Optimal ticker interval for the strategy ticker_interval = '5m' order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'market', 'stoploss': 'limit', 'stoploss_on_exchange': True, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # resample factor to establish our general trend. Basically don't buy if a trend is not given resample_factor = 12 resample_factor2 = 48 EMA_SHORT_TERM = 5 EMA_MEDIUM_TERM = 10 EMA_LONG_TERM = 20 EMA_XTRA_LONG_TERM = 200 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor) ################################################################################## # buy and sell indicators dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_SHORT_TERM ) dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_MEDIUM_TERM ) dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA( dataframe, timeperiod=self.EMA_LONG_TERM ) 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['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM) dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM) dataframe['cci'] = ta.CCI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7) dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 #################################################################################### dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) ################################################################################## # required for graphing bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( ( ( # (dataframe['average'].shift(5) > dataframe['average'].shift(4)) (dataframe['average'].shift(4) > dataframe['average'].shift(3)) &(dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < -100) & (dataframe['rsi'].shift(1) < 30) & (dataframe['mfi'].shift(1) > 20) ) | # simple v bottom shape (lopsided to the left to increase reactivity) # which has to be below a very slow average # this pattern only catches a few, but normally very good buy points ( (dataframe['close'] > dataframe['ema200']) &(dataframe['ema_{}'.format(self.EMA_SHORT_TERM, self.resample_factor2)] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM, self.resample_factor2)]) &(dataframe['ema_{}'.format(self.EMA_LONG_TERM, self.resample_factor2)] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM, self.resample_factor2)]) #&(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_LONG_TERM, self.resample_factor2)]) &(dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] < dataframe['ema_{}'.format(self.EMA_LONG_TERM)]) &(dataframe['ema_{}'.format(self.EMA_LONG_TERM)] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &(dataframe['close'].shift(0) > dataframe['open'].shift(0)) &(dataframe['close'].shift(1) < dataframe['open'].shift(1)) #&(dataframe['ema20'] > dataframe['ema5']*1.003) &(dataframe['ema20'] > dataframe['ema12']*1.0035) & (dataframe['low'].shift(1) < dataframe['bb_lowerband']) & (dataframe['low'] < dataframe['bb_lowerband']) & (dataframe['close'].shift(1) == dataframe['min']) ) ) # safeguard against down trending markets and a pump and dump & ( (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) & (dataframe['resample_sma'] < dataframe['close']) & (dataframe['resample_sma'].shift(1) < dataframe['resample_sma']) ) ) , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) & (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) & (dataframe['close'] >= dataframe['max']) & (dataframe['close'] >= dataframe['bb_upperband']) & (dataframe['mfi'] > 80) ) | # always sell on eight green candles # with a high rsi ( (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) & (dataframe['rsi'] > 70) ) , 'sell' ] = 1 return dataframe def resample(self, dataframe, interval, factor): # defines the reinforcement logic # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend df = dataframe.copy() df = df.set_index(DatetimeIndex(df['date'])) ohlc_dict = { 'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last' } df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict).dropna(how='any') df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close') df = df.drop(columns=['open', 'high', 'low', 'close']) df = df.resample(interval[:-1] + 'min') df = df.interpolate(method='time') df['date'] = df.index df.index = range(len(df)) dataframe = merge(dataframe, df, on='date', how='left') return dataframe