Fix some tests and rebase issues
This commit is contained in:
Matthias
parent
8044846d37
commit
7b62e71f23
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@ -20,6 +20,7 @@ from pandas import DataFrame
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from freqtrade import misc, constants, OperationalException
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from freqtrade.exchange import Exchange
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from freqtrade.arguments import TimeRange
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from freqtrade.optimize.default_hyperopt import DefaultHyperOpts # noqa: F401
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logger = logging.getLogger(__name__)
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@ -2,11 +2,11 @@
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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 typing import Dict, Any, Callable, List
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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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from skopt.space import Categorical, Dimension, Integer, Real
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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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@ -21,193 +21,52 @@ class DefaultHyperOpts(IHyperOpt):
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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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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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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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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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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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def buy_strategy_generator(self, 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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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> 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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if 'mfi-enabled' in params and params['mfi-enabled']:
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conditions.append(dataframe['mfi'] < params['mfi-value'])
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if 'fastd-enabled' in params and params['fastd-enabled']:
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conditions.append(dataframe['fastd'] < params['fastd-value'])
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if 'adx-enabled' in params and params['adx-enabled']:
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conditions.append(dataframe['adx'] > params['adx-value'])
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if 'rsi-enabled' in params and params['rsi-enabled']:
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conditions.append(dataframe['rsi'] < params['rsi-value'])
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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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if params['trigger'] == 'bb_lower':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(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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))
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if params['trigger'] == 'sar_reversal':
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conditions.append(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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))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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@ -218,64 +77,21 @@ class DefaultHyperOpts(IHyperOpt):
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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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def indicator_space() -> List[Dimension]:
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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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return [
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Integer(10, 25, name='mfi-value'),
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Integer(15, 45, name='fastd-value'),
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Integer(20, 50, name='adx-value'),
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Integer(20, 40, name='rsi-value'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='fastd-enabled'),
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Categorical([True, False], name='adx-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
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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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@ -291,24 +107,24 @@ class DefaultHyperOpts(IHyperOpt):
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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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def stoploss_space() -> List[Dimension]:
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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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return [
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Real(-0.5, -0.02, name='stoploss'),
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]
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@staticmethod
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def roi_space() -> Dict[str, Any]:
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def roi_space() -> List[Dimension]:
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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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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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Real(0.01, 0.04, name='roi_p1'),
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Real(0.01, 0.07, name='roi_p2'),
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Real(0.01, 0.20, name='roi_p3'),
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]
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@ -105,7 +105,7 @@ class Hyperopt(Backtesting):
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best_result['params']
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)
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if 'roi_t1' in best_result['params']:
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logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
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logger.info('ROI table:\n%s', self.custom_hyperopt.generate_roi_table(best_result['params']))
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def log_results(self, results) -> None:
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"""
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@ -147,19 +147,20 @@ class Hyperopt(Backtesting):
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"""
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spaces: List[Dimension] = []
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if self.has_space('buy'):
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spaces = {**spaces, **self.custom_hyperopt.indicator_space()}
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spaces += self.custom_hyperopt.indicator_space()
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if self.has_space('roi'):
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spaces = {**spaces, **self.custom_hyperopt.roi_space()}
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spaces += self.custom_hyperopt.roi_space()
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if self.has_space('stoploss'):
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spaces = {**spaces, **self.custom_hyperopt.stoploss_space()}
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spaces += self.custom_hyperopt.stoploss_space()
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return spaces
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def generate_optimizer(self, params: Dict) -> Dict:
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def generate_optimizer(self, _params: Dict) -> Dict:
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params = self.get_args(_params)
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if self.has_space('roi'):
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self.analyze.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
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self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
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if self.has_space('buy'):
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self.populate_buy_trend = self.custom_hyperopt.buy_strategy_generator(params)
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self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
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if self.has_space('stoploss'):
|
||||
self.strategy.stoploss = params['stoploss']
|
||||
|
|
|
|||
|
|
@ -0,0 +1,138 @@
|
|||
# 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, List
|
||||
from functools import reduce
|
||||
|
||||
import numpy
|
||||
from skopt.space import Categorical, Dimension, Integer, Real
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.optimize.interface import IHyperOpt
|
||||
|
||||
class_name = 'SampleHyperOpts'
|
||||
|
||||
|
||||
# This class is a sample. Feel free to customize it.
|
||||
class SampleHyperOpts(IHyperOpt):
|
||||
"""
|
||||
This is a test hyperopt to inspire you.
|
||||
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
|
||||
You can:
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your hyperopt
|
||||
- Add any lib you need to build your hyperopt
|
||||
You must keep:
|
||||
- the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
|
||||
roi_space, generate_roi_table, stoploss_space
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
# Bollinger bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe['bb_lowerband'] = bollinger['lower']
|
||||
dataframe['sar'] = ta.SAR(dataframe)
|
||||
return dataframe
|
||||
|
||||
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Buy strategy Hyperopt will build and use
|
||||
"""
|
||||
conditions = []
|
||||
# GUARDS AND TRENDS
|
||||
if 'mfi-enabled' in params and params['mfi-enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi-value'])
|
||||
if 'fastd-enabled' in params and params['fastd-enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd-value'])
|
||||
if 'adx-enabled' in params and params['adx-enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx-value'])
|
||||
if 'rsi-enabled' in params and params['rsi-enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi-value'])
|
||||
|
||||
# TRIGGERS
|
||||
if params['trigger'] == 'bb_lower':
|
||||
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
|
||||
if params['trigger'] == 'macd_cross_signal':
|
||||
conditions.append(qtpylib.crossed_above(
|
||||
dataframe['macd'], dataframe['macdsignal']
|
||||
))
|
||||
if params['trigger'] == 'sar_reversal':
|
||||
conditions.append(qtpylib.crossed_above(
|
||||
dataframe['close'], dataframe['sar']
|
||||
))
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
||||
@staticmethod
|
||||
def indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Define your Hyperopt space for searching strategy parameters
|
||||
"""
|
||||
return [
|
||||
Integer(10, 25, name='mfi-value'),
|
||||
Integer(15, 45, name='fastd-value'),
|
||||
Integer(20, 50, name='adx-value'),
|
||||
Integer(20, 40, name='rsi-value'),
|
||||
Categorical([True, False], name='mfi-enabled'),
|
||||
Categorical([True, False], name='fastd-enabled'),
|
||||
Categorical([True, False], name='adx-enabled'),
|
||||
Categorical([True, False], name='rsi-enabled'),
|
||||
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
|
||||
]
|
||||
|
||||
@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() -> List[Dimension]:
|
||||
"""
|
||||
Stoploss Value to search
|
||||
"""
|
||||
return [
|
||||
Real(-0.5, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Values to search for each ROI steps
|
||||
"""
|
||||
return [
|
||||
Integer(10, 120, name='roi_t1'),
|
||||
Integer(10, 60, name='roi_t2'),
|
||||
Integer(10, 40, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
|
@ -1,279 +0,0 @@
|
|||
# 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
|
||||
from math import exp
|
||||
|
||||
import numpy
|
||||
import talib.abstract as ta
|
||||
from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.indicator_helpers import fishers_inverse
|
||||
from freqtrade.optimize.interface import IHyperOpt
|
||||
|
||||
|
||||
# This class is a sample. Feel free to customize it.
|
||||
class TestHyperOpt(IHyperOpt):
|
||||
"""
|
||||
This is a test hyperopt to inspire you.
|
||||
More information in https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md
|
||||
|
||||
You can:
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your hyperopt
|
||||
- Add any lib you need to build your hyperopt
|
||||
|
||||
You must keep:
|
||||
- the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
|
||||
roi_space, generate_roi_table, stoploss_space
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def populate_indicators(dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
"""
|
||||
|
||||
# Momentum Indicator
|
||||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
|
||||
"""
|
||||
# Awesome oscillator
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||||
|
||||
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
|
||||
dataframe['cci'] = ta.CCI(dataframe)
|
||||
|
||||
# MACD
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['macdhist'] = macd['macdhist']
|
||||
|
||||
# MFI
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
|
||||
# Minus Directional Indicator / Movement
|
||||
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# Plus Directional Indicator / Movement
|
||||
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||||
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# ROC
|
||||
dataframe['roc'] = ta.ROC(dataframe)
|
||||
|
||||
# RSI
|
||||
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']
|
||||
"""
|
||||
|
||||
# Overlap Studies
|
||||
# ------------------------------------
|
||||
|
||||
# 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 Parabol
|
||||
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)
|
||||
|
||||
# Cycle Indicator
|
||||
# ------------------------------------
|
||||
# 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 indicator_space() -> Dict[str, Any]:
|
||||
"""
|
||||
Define your Hyperopt space for searching strategy parameters
|
||||
"""
|
||||
return {
|
||||
'adx': hp.choice('adx', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('adx-value', 50, 80, 5)}
|
||||
]),
|
||||
'uptrend_tema': hp.choice('uptrend_tema', [
|
||||
{'enabled': False},
|
||||
{'enabled': True}
|
||||
]),
|
||||
'trigger': hp.choice('trigger', [
|
||||
{'type': 'middle_bb_tema'},
|
||||
]),
|
||||
}
|
||||
|
||||
@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:
|
||||
conditions = []
|
||||
# GUARDS AND TRENDS
|
||||
if 'adx' in params and params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if 'uptrend_tema' in params and params['uptrend_tema']['enabled']:
|
||||
prevtema = dataframe['tema'].shift(1)
|
||||
conditions.append(dataframe['tema'] > prevtema)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'middle_bb_tema': (
|
||||
dataframe['tema'] > dataframe['bb_middleband']
|
||||
),
|
||||
}
|
||||
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 roi_space() -> Dict[str, Any]:
|
||||
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),
|
||||
}
|
||||
|
||||
@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]:
|
||||
return {
|
||||
'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
|
||||
}
|
||||
Loading…
Reference in New Issue