import numpy import warnings import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter ) warnings.filterwarnings('ignore') class elAI(IStrategy): INTERFACE_VERSION = 3 can_short = True entry_params = { 'base_nb_candles_entry': 12, 'ewo_high': 4.428, 'ewo_low': -12.383, 'low_offset': 0.915, 'rsi_entry': 44, } exit_params = { 'base_nb_candles_exit': 72, 'high_offset': 1.008, } minimal_roi = { '0': 0.5, '60': 0.45, '120': 0.4, '240': 0.3, '360': 0.25, '720': 0.2, '1440': 0.15, '2880': 0.1, '3600': 0.05, '7200': 0.02, } stoploss = -0.05 max_open_trades = 9 base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True plot_config = {'main_plot': {'ma_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}} use_custom_stoploss = False cooldown_lookback = IntParameter(2, 48, default=1, space='protection', optimize=True) stop_duration = IntParameter(12, 200, default=4, space='protection', optimize=True) use_stop_protection = BooleanParameter(default=True, space='protection', optimize=True) @property def protections(self): prot = [] prot.append( { 'method': 'CooldownPeriod', 'stop_duration_candles': self.cooldown_lookback.value } ) if self.use_stop_protection.value: prot.append( { 'method': 'StoplossGuard', 'lookback_period_candles': 24 * 3, 'trade_limit': 2, 'stop_duration_candles': self.stop_duration.value, 'only_per_pair': False, } ) return prot def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-rsi-period'] = ta.RSI(dataframe, timeperiod=period) dataframe['%-mfi-period'] = ta.MFI(dataframe, timeperiod=period) dataframe['%-adx-period'] = ta.ADX(dataframe, timeperiod=period) dataframe['%-sma-period'] = ta.SMA(dataframe, timeperiod=period) dataframe['%-ema-period'] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2) dataframe['bb_lowerband-period'] = bollinger['lower'] dataframe['bb_middleband-period'] = bollinger['mid'] dataframe['bb_upperband-period'] = bollinger['upper'] dataframe['%-bb_width-period'] = (dataframe['bb_upperband-period'] - dataframe['bb_lowerband-period']) / dataframe['bb_middleband-period'] dataframe['%-close-bb_lower-period'] = dataframe['close'] / dataframe['bb_lowerband-period'] dataframe['%-roc-period'] = ta.ROC(dataframe, timeperiod=period) dataframe['%-relative_volume-period'] = (dataframe['volume'] / dataframe['volume'].rolling(period).mean()) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-pct-change'] = dataframe['close'].pct_change() dataframe['%-raw_volume'] = dataframe['volume'] dataframe['%-raw_price'] = dataframe['close'] dataframe['%-obv'] = ta.OBV(dataframe) return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-day_of_week'] = dataframe['date'].dt.dayofweek dataframe['%-hour_of_day'] = dataframe['date'].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: dataframe['&-s_close'] = (dataframe['close'].shift(-self.freqai_info['feature_parameters']['label_period_candles']).rolling(self.freqai_info['feature_parameters']['label_period_candles']).mean()/ dataframe['close']- 1) return dataframe def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag, side: str, **kwargs, ) -> bool: df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = df.iloc[-1].squeeze() if side == 'long': if rate > (last_candle['close'] * (1 + 0.0025)): return False else: if rate < (last_candle['close'] * (1 - 0.0025)): return False return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma35'] = ta.SMA(dataframe, timeperiod=35) dataframe['EWO'] = (dataframe['sma5'] - dataframe['sma35']) / dataframe['close'] * 100 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) aroon = ta.AROON(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] dataframe['aroonosc'] = ta.AROONOSC(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) keltner = qtpylib.keltner_channel(dataframe) dataframe['kc_upperband'] = keltner['upper'] dataframe['kc_lowerband'] = keltner['lower'] dataframe['kc_middleband'] = keltner['mid'] dataframe['kc_percent'] = (dataframe['close'] - dataframe['kc_lowerband']) / (dataframe['kc_upperband'] - dataframe['kc_lowerband']) dataframe['kc_width'] = (dataframe['kc_upperband'] - dataframe['kc_lowerband']) / dataframe['kc_middleband'] dataframe['uo'] = ta.ULTOSC(dataframe) dataframe['cci'] = ta.CCI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['roc'] = ta.ROC(dataframe) 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['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] dataframe['sar'] = ta.SAR(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: entry_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] > 0.01) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) ), ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] > 0.01) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) ] if entry_conditions: dataframe.loc[reduce(lambda x, y: x | y, entry_conditions), 'enter_long'] = 1 exit_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < -0.01) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) ) ] if exit_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) ) ] if exit_long_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), 'exit_long'] = 1 exit_short_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) ), ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) ] if exit_short_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_short_conditions), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag:str, side: str, **kwargs) -> float: return 10.0