# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement import talib.abstract as ta from pandas import DataFrame from typing import Dict, Any, Callable from functools import reduce import numpy from hyperopt import hp import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.interface import IHyperOpt class_name = 'DefaultHyperOpts' class DefaultHyperOpts(IHyperOpt): """ Default hyperopt provided by freqtrade bot. You can override it with your own hyperopt """ @staticmethod def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['adx'] = ta.ADX(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Bollinger bands 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'] # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy 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 @staticmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the buy strategy parameters to be used by hyperopt """ def populate_buy_trend(dataframe: DataFrame) -> DataFrame: """ Buy strategy Hyperopt will build and use """ conditions = [] # GUARDS AND TRENDS if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']: conditions.append(dataframe['ema50'] > dataframe['ema100']) if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: conditions.append(dataframe['macd'] < 0) if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']: conditions.append(dataframe['ema5'] > dataframe['ema10']) if 'mfi' in params and params['mfi']['enabled']: conditions.append(dataframe['mfi'] < params['mfi']['value']) if 'fastd' in params and params['fastd']['enabled']: conditions.append(dataframe['fastd'] < params['fastd']['value']) if 'adx' in params and params['adx']['enabled']: conditions.append(dataframe['adx'] > params['adx']['value']) if 'rsi' in params and params['rsi']['enabled']: conditions.append(dataframe['rsi'] < params['rsi']['value']) if 'over_sar' in params and params['over_sar']['enabled']: conditions.append(dataframe['close'] > dataframe['sar']) if 'green_candle' in params and params['green_candle']['enabled']: conditions.append(dataframe['close'] > dataframe['open']) if 'uptrend_sma' in params and params['uptrend_sma']['enabled']: prevsma = dataframe['sma'].shift(1) conditions.append(dataframe['sma'] > prevsma) # TRIGGERS triggers = { 'lower_bb': ( dataframe['close'] < dataframe['bb_lowerband'] ), 'lower_bb_tema': ( dataframe['tema'] < dataframe['bb_lowerband'] ), 'faststoch10': (qtpylib.crossed_above( dataframe['fastd'], 10.0 )), 'ao_cross_zero': (qtpylib.crossed_above( dataframe['ao'], 0.0 )), 'ema3_cross_ema10': (qtpylib.crossed_above( dataframe['ema3'], dataframe['ema10'] )), 'macd_cross_signal': (qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] )), 'sar_reversal': (qtpylib.crossed_above( dataframe['close'], dataframe['sar'] )), 'ht_sine': (qtpylib.crossed_above( dataframe['htleadsine'], dataframe['htsine'] )), 'heiken_reversal_bull': ( (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & (dataframe['ha_low'] == dataframe['ha_open']) ), 'di_cross': (qtpylib.crossed_above( dataframe['plus_di'], dataframe['minus_di'] )), } conditions.append(triggers.get(params['trigger']['type'])) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe return populate_buy_trend @staticmethod def indicator_space() -> Dict[str, Any]: """ Define your Hyperopt space for searching strategy parameters """ return { 'macd_below_zero': hp.choice('macd_below_zero', [ {'enabled': False}, {'enabled': True} ]), 'mfi': hp.choice('mfi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)} ]), 'fastd': hp.choice('fastd', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)} ]), 'adx': hp.choice('adx', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)} ]), 'rsi': hp.choice('rsi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)} ]), 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ {'enabled': False}, {'enabled': True} ]), 'over_sar': hp.choice('over_sar', [ {'enabled': False}, {'enabled': True} ]), 'green_candle': hp.choice('green_candle', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_sma': hp.choice('uptrend_sma', [ {'enabled': False}, {'enabled': True} ]), 'trigger': hp.choice('trigger', [ {'type': 'lower_bb'}, {'type': 'lower_bb_tema'}, {'type': 'faststoch10'}, {'type': 'ao_cross_zero'}, {'type': 'ema3_cross_ema10'}, {'type': 'macd_cross_signal'}, {'type': 'sar_reversal'}, {'type': 'ht_sine'}, {'type': 'heiken_reversal_bull'}, {'type': 'di_cross'}, ]), } @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: """ Generate the ROI table that will be used by Hyperopt """ roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table @staticmethod def stoploss_space() -> Dict[str, Any]: """ Stoploss Value to search """ return { 'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02), } @staticmethod def roi_space() -> Dict[str, Any]: """ Values to search for each ROI steps """ return { 'roi_t1': hp.quniform('roi_t1', 10, 120, 20), 'roi_t2': hp.quniform('roi_t2', 10, 60, 15), 'roi_t3': hp.quniform('roi_t3', 10, 40, 10), 'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01), 'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01), 'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01), }