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