# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement """ This module contains the hyperopt logic """ import json import logging import os import pickle import signal import sys from argparse import Namespace from functools import reduce from math import exp from operator import itemgetter from typing import Dict, Any, Callable import numpy import talib.abstract as ta from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe from hyperopt.mongoexp import MongoTrials from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.arguments import Arguments from freqtrade.configuration import Configuration from freqtrade.optimize import load_data from freqtrade.optimize.backtesting import Backtesting from user_data.hyperopt_conf import hyperopt_optimize_conf logger = logging.getLogger(__name__) class Hyperopt(Backtesting): """ Hyperopt class, this class contains all the logic to run a hyperopt simulation To run a backtest: hyperopt = Hyperopt(config) hyperopt.start() """ def __init__(self, config: Dict[str, Any]) -> None: super().__init__(config) # set TARGET_TRADES to suit your number concurrent trades so its realistic # to the number of days self.target_trades = 600 self.total_tries = config.get('epochs', 0) self.current_tries = 0 self.current_best_loss = 100 # max average trade duration in minutes # if eval ends with higher value, we consider it a failed eval self.max_accepted_trade_duration = 300 # this is expexted avg profit * expected trade count # for example 3.5%, 1100 trades, self.expected_max_profit = 3.85 # check that the reported Σ% values do not exceed this! self.expected_max_profit = 3.0 # Configuration and data used by hyperopt self.processed = None # Hyperopt Trials self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle') self.trials = Trials() @staticmethod def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['adx'] = ta.ADX(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe def save_trials(self) -> None: """ Save hyperopt trials to file """ logger.info('Saving Trials to \'%s\'', self.trials_file) pickle.dump(self.trials, open(self.trials_file, 'wb')) def read_trials(self) -> Trials: """ Read hyperopt trials file """ logger.info('Reading Trials from \'%s\'', self.trials_file) trials = pickle.load(open(self.trials_file, 'rb')) os.remove(self.trials_file) return trials def log_trials_result(self) -> None: """ Display Best hyperopt result """ vals = json.dumps(self.trials.best_trial['misc']['vals'], indent=4) results = self.trials.best_trial['result']['result'] logger.info('Best result:\n%s\nwith values:\n%s', results, vals) def log_results(self, results) -> None: """ Log results if it is better than any previous evaluation """ if results['loss'] < self.current_best_loss: self.current_best_loss = results['loss'] log_msg = '\n{:5d}/{}: {}. Loss {:.5f}'.format( results['current_tries'], results['total_tries'], results['result'], results['loss'] ) print(log_msg) else: print('.', end='') sys.stdout.flush() def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float: """ Objective function, returns smaller number for more optimal results """ trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8) profit_loss = max(0, 1 - total_profit / self.expected_max_profit) duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1) return trade_loss + profit_loss + duration_loss @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: """ Generate the ROI table thqt 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 roi_space() -> Dict[str, Any]: """ Values to search for each ROI steps """ return { 'roi_t1': hp.quniform('roi_t1', 10, 120, 20), 'roi_t2': hp.quniform('roi_t2', 10, 60, 15), 'roi_t3': hp.quniform('roi_t3', 10, 40, 10), 'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01), 'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01), 'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01), } @staticmethod def stoploss_space() -> Dict[str, Any]: """ Stoploss Value to search """ return { 'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02), } @staticmethod def indicator_space() -> Dict[str, Any]: """ Define your Hyperopt space for searching strategy parameters """ return { 'macd_below_zero': hp.choice('macd_below_zero', [ {'enabled': False}, {'enabled': True} ]), 'mfi': hp.choice('mfi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)} ]), 'fastd': hp.choice('fastd', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)} ]), 'adx': hp.choice('adx', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)} ]), 'rsi': hp.choice('rsi', [ {'enabled': False}, {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)} ]), 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ {'enabled': False}, {'enabled': True} ]), 'over_sar': hp.choice('over_sar', [ {'enabled': False}, {'enabled': True} ]), 'green_candle': hp.choice('green_candle', [ {'enabled': False}, {'enabled': True} ]), 'uptrend_sma': hp.choice('uptrend_sma', [ {'enabled': False}, {'enabled': True} ]), 'trigger': hp.choice('trigger', [ {'type': 'lower_bb'}, {'type': 'lower_bb_tema'}, {'type': 'faststoch10'}, {'type': 'ao_cross_zero'}, {'type': 'ema3_cross_ema10'}, {'type': 'macd_cross_signal'}, {'type': 'sar_reversal'}, {'type': 'ht_sine'}, {'type': 'heiken_reversal_bull'}, {'type': 'di_cross'}, ]), } def has_space(self, space: str) -> bool: """ Tell if a space value is contained in the configuration """ if space in self.config['spaces'] or 'all' in self.config['spaces']: return True return False def hyperopt_space(self) -> Dict[str, Any]: """ Return the space to use during Hyperopt """ spaces = {} if self.has_space('buy'): spaces = {**spaces, **Hyperopt.indicator_space()} if self.has_space('roi'): spaces = {**spaces, **Hyperopt.roi_space()} if self.has_space('stoploss'): spaces = {**spaces, **Hyperopt.stoploss_space()} return spaces @staticmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the buy strategy parameters to be used by hyperopt """ def populate_buy_trend(dataframe: DataFrame) -> DataFrame: """ Buy strategy Hyperopt will build and use """ conditions = [] # GUARDS AND TRENDS if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']: conditions.append(dataframe['ema50'] > dataframe['ema100']) if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: conditions.append(dataframe['macd'] < 0) if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']: conditions.append(dataframe['ema5'] > dataframe['ema10']) if 'mfi' in params and params['mfi']['enabled']: conditions.append(dataframe['mfi'] < params['mfi']['value']) if 'fastd' in params and params['fastd']['enabled']: conditions.append(dataframe['fastd'] < params['fastd']['value']) if 'adx' in params and params['adx']['enabled']: conditions.append(dataframe['adx'] > params['adx']['value']) if 'rsi' in params and params['rsi']['enabled']: conditions.append(dataframe['rsi'] < params['rsi']['value']) if 'over_sar' in params and params['over_sar']['enabled']: conditions.append(dataframe['close'] > dataframe['sar']) if 'green_candle' in params and params['green_candle']['enabled']: conditions.append(dataframe['close'] > dataframe['open']) if 'uptrend_sma' in params and params['uptrend_sma']['enabled']: prevsma = dataframe['sma'].shift(1) conditions.append(dataframe['sma'] > prevsma) # TRIGGERS triggers = { 'lower_bb': ( dataframe['close'] < dataframe['bb_lowerband'] ), 'lower_bb_tema': ( dataframe['tema'] < dataframe['bb_lowerband'] ), 'faststoch10': (qtpylib.crossed_above( dataframe['fastd'], 10.0 )), 'ao_cross_zero': (qtpylib.crossed_above( dataframe['ao'], 0.0 )), 'ema3_cross_ema10': (qtpylib.crossed_above( dataframe['ema3'], dataframe['ema10'] )), 'macd_cross_signal': (qtpylib.crossed_above( dataframe['macd'], dataframe['macdsignal'] )), 'sar_reversal': (qtpylib.crossed_above( dataframe['close'], dataframe['sar'] )), 'ht_sine': (qtpylib.crossed_above( dataframe['htleadsine'], dataframe['htsine'] )), 'heiken_reversal_bull': ( (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & (dataframe['ha_low'] == dataframe['ha_open']) ), 'di_cross': (qtpylib.crossed_above( dataframe['plus_di'], dataframe['minus_di'] )), } conditions.append(triggers.get(params['trigger']['type'])) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe return populate_buy_trend def generate_optimizer(self, params: Dict) -> Dict: if self.has_space('roi'): self.analyze.strategy.minimal_roi = self.generate_roi_table(params) if self.has_space('buy'): 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, } def format_results(self, results: DataFrame) -> str: """ Return the format result in a string """ return ('{:6d} trades. Avg profit {: 5.2f}%. ' 'Total profit {: 11.8f} {} ({:.4f}Σ%). Avg duration {:5.1f} mins.').format( len(results.index), results.profit_percent.mean() * 100.0, results.profit_BTC.sum(), self.config['stake_currency'], 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 configuration # Monkey patch the configuration with hyperopt_conf.py configuration = Configuration(args) logger.info('Starting freqtrade in Hyperopt mode') 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()