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612 lines
23 KiB
Python
612 lines
23 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
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"""
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This module contains the hyperopt logic
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"""
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import json
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import logging
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import os
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import pickle
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import signal
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import sys
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from argparse import Namespace
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from functools import reduce
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from math import exp
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from operator import itemgetter
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from typing import Dict, Any, Callable
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import numpy
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import talib.abstract as ta
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from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
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from hyperopt.mongoexp import MongoTrials
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from pandas import DataFrame
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.logger import Logger
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from freqtrade.optimize import load_data
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from freqtrade.optimize.backtesting import Backtesting
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from user_data.hyperopt_conf import hyperopt_optimize_conf
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logger = logging.getLogger(__name__)
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class Hyperopt(Backtesting):
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"""
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Hyperopt class, this class contains all the logic to run a hyperopt simulation
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To run a backtest:
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hyperopt = Hyperopt(config)
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hyperopt.start()
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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super().__init__(config)
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# set TARGET_TRADES to suit your number concurrent trades so its realistic
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# to the number of days
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self.target_trades = 600
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self.total_tries = config.get('epochs', 0)
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self.current_tries = 0
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self.current_best_loss = 100
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# max average trade duration in minutes
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# if eval ends with higher value, we consider it a failed eval
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self.max_accepted_trade_duration = 300
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# this is expexted avg profit * expected trade count
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# for example 3.5%, 1100 trades, self.expected_max_profit = 3.85
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# check that the reported Σ% values do not exceed this!
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self.expected_max_profit = 3.0
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# Configuration and data used by hyperopt
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self.processed = None
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# Hyperopt Trials
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self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
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self.trials = Trials()
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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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def save_trials(self) -> None:
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"""
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Save hyperopt trials to file
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"""
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logger.info('Saving Trials to \'%s\'', self.trials_file)
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pickle.dump(self.trials, open(self.trials_file, 'wb'))
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def read_trials(self) -> Trials:
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"""
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Read hyperopt trials file
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"""
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logger.info('Reading Trials from \'%s\'', self.trials_file)
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trials = pickle.load(open(self.trials_file, 'rb'))
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os.remove(self.trials_file)
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return trials
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def log_trials_result(self) -> None:
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"""
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Display Best hyperopt result
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"""
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vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4)
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results = self.trials.best_trial['result']['result']
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logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
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def log_results(self, results) -> None:
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"""
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Log results if it is better than any previous evaluation
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"""
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if results['loss'] < self.current_best_loss:
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self.current_best_loss = results['loss']
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log_msg = '{:5d}/{}: {}. Loss {:.5f}'.format(
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results['current_tries'],
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results['total_tries'],
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results['result'],
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results['loss']
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)
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logger.info(log_msg)
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else:
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print('.', end='')
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sys.stdout.flush()
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def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
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"""
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Objective function, returns smaller number for more optimal results
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"""
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trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
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profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
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duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
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return trade_loss + profit_loss + duration_loss
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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 thqt 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 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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@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 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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def has_space(self, space: str) -> bool:
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"""
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Tell if a space value is contained in the configuration
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"""
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if space in self.config['spaces'] or 'all' in self.config['spaces']:
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return True
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return False
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def hyperopt_space(self) -> Dict[str, Any]:
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"""
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Return the space to use during Hyperopt
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"""
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spaces = {}
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if self.has_space('buy'):
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spaces = {**spaces, **Hyperopt.indicator_space()}
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if self.has_space('roi'):
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spaces = {**spaces, **Hyperopt.roi_space()}
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if self.has_space('stoploss'):
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spaces = {**spaces, **Hyperopt.stoploss_space()}
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return spaces
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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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def generate_optimizer(self, params: Dict) -> Dict:
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if self.has_space('roi'):
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self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
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if self.has_space('buy'):
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self.populate_buy_trend = self.buy_strategy_generator(params)
|
|
|
|
if self.has_space('stoploss'):
|
|
self.analyze.strategy.stoploss = params['stoploss']
|
|
|
|
results = self.backtest(
|
|
{
|
|
'stake_amount': self.config['stake_amount'],
|
|
'processed': self.processed,
|
|
'realistic': self.config.get('realistic_simulation', False),
|
|
}
|
|
)
|
|
result_explanation = self.format_results(results)
|
|
|
|
total_profit = results.profit_percent.sum()
|
|
trade_count = len(results.index)
|
|
trade_duration = results.duration.mean()
|
|
|
|
if trade_count == 0 or trade_duration > self.max_accepted_trade_duration:
|
|
print('.', end='')
|
|
return {
|
|
'status': STATUS_FAIL,
|
|
'loss': float('inf')
|
|
}
|
|
|
|
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
|
|
|
|
self.current_tries += 1
|
|
|
|
self.log_results(
|
|
{
|
|
'loss': loss,
|
|
'current_tries': self.current_tries,
|
|
'total_tries': self.total_tries,
|
|
'result': result_explanation,
|
|
}
|
|
)
|
|
|
|
return {
|
|
'loss': loss,
|
|
'status': STATUS_OK,
|
|
'result': result_explanation,
|
|
}
|
|
|
|
@staticmethod
|
|
def format_results(results: DataFrame) -> str:
|
|
"""
|
|
Return the format result in a string
|
|
"""
|
|
return ('{:6d} trades. Avg profit {: 5.2f}%. '
|
|
'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
|
|
len(results.index),
|
|
results.profit_percent.mean() * 100.0,
|
|
results.profit_BTC.sum(),
|
|
results.profit_percent.sum(),
|
|
results.duration.mean(),
|
|
)
|
|
|
|
def start(self) -> None:
|
|
timerange = Arguments.parse_timerange(self.config.get('timerange'))
|
|
data = load_data(
|
|
datadir=self.config.get('datadir'),
|
|
pairs=self.config['exchange']['pair_whitelist'],
|
|
ticker_interval=self.ticker_interval,
|
|
timerange=timerange
|
|
)
|
|
|
|
if self.has_space('buy'):
|
|
self.analyze.populate_indicators = Hyperopt.populate_indicators
|
|
self.processed = self.tickerdata_to_dataframe(data)
|
|
|
|
if self.config.get('mongodb'):
|
|
logger.info('Using mongodb ...')
|
|
logger.info(
|
|
'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
|
|
)
|
|
|
|
db_name = 'freqtrade_hyperopt'
|
|
self.trials = MongoTrials(
|
|
arg='mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
|
|
exp_key='exp1'
|
|
)
|
|
else:
|
|
logger.info('Preparing Trials..')
|
|
signal.signal(signal.SIGINT, self.signal_handler)
|
|
# read trials file if we have one
|
|
if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
|
|
self.trials = self.read_trials()
|
|
|
|
self.current_tries = len(self.trials.results)
|
|
self.total_tries += self.current_tries
|
|
logger.info(
|
|
'Continuing with trials. Current: %d, Total: %d',
|
|
self.current_tries,
|
|
self.total_tries
|
|
)
|
|
|
|
try:
|
|
best_parameters = fmin(
|
|
fn=self.generate_optimizer,
|
|
space=self.hyperopt_space(),
|
|
algo=tpe.suggest,
|
|
max_evals=self.total_tries,
|
|
trials=self.trials
|
|
)
|
|
|
|
results = sorted(self.trials.results, key=itemgetter('loss'))
|
|
best_result = results[0]['result']
|
|
|
|
except ValueError:
|
|
best_parameters = {}
|
|
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
|
|
'try with more epochs (param: -e).'
|
|
|
|
# Improve best parameter logging display
|
|
if best_parameters:
|
|
best_parameters = space_eval(
|
|
self.hyperopt_space(),
|
|
best_parameters
|
|
)
|
|
|
|
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
|
|
if 'roi_t1' in best_parameters:
|
|
logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
|
|
|
|
logger.info('Best Result:\n%s', best_result)
|
|
|
|
# Store trials result to file to resume next time
|
|
self.save_trials()
|
|
|
|
def signal_handler(self, sig, frame) -> None:
|
|
"""
|
|
Hyperopt SIGINT handler
|
|
"""
|
|
logger.info(
|
|
'Hyperopt received %s',
|
|
signal.Signals(sig).name
|
|
)
|
|
|
|
self.save_trials()
|
|
self.log_trials_result()
|
|
sys.exit(0)
|
|
|
|
|
|
def start(args: Namespace) -> None:
|
|
"""
|
|
Start Backtesting script
|
|
:param args: Cli args from Arguments()
|
|
:return: None
|
|
"""
|
|
|
|
# Remove noisy log messages
|
|
logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
|
|
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
|
|
|
|
# Initialize logger
|
|
logger = Logger(__name__).get_logger()
|
|
logger.info('Starting freqtrade in Hyperopt mode')
|
|
|
|
# Initialize configuration
|
|
# Monkey patch the configuration with hyperopt_conf.py
|
|
configuration = Configuration(args)
|
|
optimize_config = hyperopt_optimize_conf()
|
|
config = configuration._load_common_config(optimize_config)
|
|
config = configuration._load_backtesting_config(config)
|
|
config = configuration._load_hyperopt_config(config)
|
|
config['exchange']['key'] = ''
|
|
config['exchange']['secret'] = ''
|
|
|
|
# Initialize backtesting object
|
|
hyperopt = Hyperopt(config)
|
|
hyperopt.start()
|