# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement """ This module contains the hyperopt logic """ import logging import os import sys from operator import itemgetter from pathlib import Path from pprint import pprint from typing import Any, Dict, List, Optional from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count from pandas import DataFrame from skopt import Optimizer from skopt.space import Dimension from freqtrade.configuration import Arguments from freqtrade.data.history import load_data, get_timeframe from freqtrade.optimize.backtesting import Backtesting # Import IHyperOptLoss to allow users import from this file from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4 from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver logger = logging.getLogger(__name__) INITIAL_POINTS = 30 MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl') TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle') HYPEROPT_LOCKFILE = os.path.join('user_data', 'hyperopt.lock') 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) self.custom_hyperopt = HyperOptResolver(self.config).hyperopt self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function self.total_epochs = config.get('epochs', 0) self.current_best_loss = 100 if not self.config.get('hyperopt_continue'): self.clean_hyperopt() else: logger.info("Continuing on previous hyperopt results.") # Previous evaluations self.trials_file = TRIALSDATA_PICKLE self.trials: List = [] # Populate functions here (hasattr is slow so should not be run during "regular" operations) if hasattr(self.custom_hyperopt, 'populate_buy_trend'): self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore if hasattr(self.custom_hyperopt, 'populate_sell_trend'): self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): self.max_open_trades = self.config['max_open_trades'] else: logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...') self.max_open_trades = 0 self.position_stacking = self.config.get('position_stacking', False), if self.has_space('sell'): # Make sure experimental is enabled if 'experimental' not in self.config: self.config['experimental'] = {} self.config['experimental']['use_sell_signal'] = True def clean_hyperopt(self): """ Remove hyperopt pickle files to restart hyperopt. """ for f in [TICKERDATA_PICKLE, TRIALSDATA_PICKLE]: p = Path(f) if p.is_file(): logger.info(f"Removing `{p}`.") p.unlink() def get_args(self, params): dimensions = self.hyperopt_space() # Ensure the number of dimensions match # the number of parameters in the list x. if len(params) != len(dimensions): raise ValueError('Mismatch in number of search-space dimensions. ' f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}') # Create a dict where the keys are the names of the dimensions # and the values are taken from the list of parameters x. arg_dict = {dim.name: value for dim, value in zip(dimensions, params)} return arg_dict def save_trials(self) -> None: """ Save hyperopt trials to file """ if self.trials: logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file) dump(self.trials, self.trials_file) def read_trials(self) -> List: """ Read hyperopt trials file """ logger.info('Reading Trials from \'%s\'', self.trials_file) trials = load(self.trials_file) os.remove(self.trials_file) return trials def log_trials_result(self) -> None: """ Display Best hyperopt result """ results = sorted(self.trials, key=itemgetter('loss')) best_result = results[0] params = best_result['params'] log_str = self.format_results_logstring(best_result) print(f"\nBest result:\n\n{log_str}\n") if self.has_space('buy'): print('Buy hyperspace params:') pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')}, indent=4) if self.has_space('sell'): print('Sell hyperspace params:') pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')}, indent=4) if self.has_space('roi'): print("ROI table:") pprint(self.custom_hyperopt.generate_roi_table(params), indent=4) if self.has_space('stoploss'): print(f"Stoploss: {params.get('stoploss')}") def log_results(self, results) -> None: """ Log results if it is better than any previous evaluation """ print_all = self.config.get('print_all', False) if print_all or results['loss'] < self.current_best_loss: log_str = self.format_results_logstring(results) if print_all: print(log_str) else: print('\n' + log_str) else: print('.', end='') sys.stdout.flush() def format_results_logstring(self, results) -> str: # Output human-friendly index here (starting from 1) current = results['current_epoch'] + 1 total = self.total_epochs res = results['results_explanation'] loss = results['loss'] self.current_best_loss = results['loss'] log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}' log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}' return log_str def has_space(self, space: str) -> bool: """ Tell if a space value is contained in the configuration """ return any(s in self.config['spaces'] for s in [space, 'all']) def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]: """ Return the dimensions in the hyperoptimization space. :param space: Defines hyperspace to return dimensions for. If None, then the self.has_space() will be used to return dimensions for all hyperspaces used. """ spaces: List[Dimension] = [] if space == 'buy' or (space is None and self.has_space('buy')): logger.debug("Hyperopt has 'buy' space") spaces += self.custom_hyperopt.indicator_space() if space == 'sell' or (space is None and self.has_space('sell')): logger.debug("Hyperopt has 'sell' space") spaces += self.custom_hyperopt.sell_indicator_space() if space == 'roi' or (space is None and self.has_space('roi')): logger.debug("Hyperopt has 'roi' space") spaces += self.custom_hyperopt.roi_space() if space == 'stoploss' or (space is None and self.has_space('stoploss')): logger.debug("Hyperopt has 'stoploss' space") spaces += self.custom_hyperopt.stoploss_space() return spaces def generate_optimizer(self, _params: Dict) -> Dict: """ Used Optimize function. Called once per epoch to optimize whatever is configured. Keep this function as optimized as possible! """ params = self.get_args(_params) if self.has_space('roi'): self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params) if self.has_space('buy'): self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params) if self.has_space('sell'): self.advise_sell = self.custom_hyperopt.sell_strategy_generator(params) if self.has_space('stoploss'): self.strategy.stoploss = params['stoploss'] processed = load(TICKERDATA_PICKLE) min_date, max_date = get_timeframe(processed) results = self.backtest( { 'stake_amount': self.config['stake_amount'], 'processed': processed, 'max_open_trades': self.max_open_trades, 'position_stacking': self.position_stacking, 'start_date': min_date, 'end_date': max_date, } ) results_explanation = self.format_results(results) trade_count = len(results.index) # If this evaluation contains too short amount of trades to be # interesting -- consider it as 'bad' (assigned max. loss value) # in order to cast this hyperspace point away from optimization # path. We do not want to optimize 'hodl' strategies. if trade_count < self.config['hyperopt_min_trades']: return { 'loss': MAX_LOSS, 'params': params, 'results_explanation': results_explanation, } loss = self.calculate_loss(results=results, trade_count=trade_count, min_date=min_date.datetime, max_date=max_date.datetime) return { 'loss': loss, 'params': params, 'results_explanation': results_explanation, } def format_results(self, results: DataFrame) -> str: """ Return the formatted results explanation in a string """ trades = len(results.index) avg_profit = results.profit_percent.mean() * 100.0 total_profit = results.profit_abs.sum() stake_cur = self.config['stake_currency'] profit = results.profit_percent.sum() * 100.0 duration = results.trade_duration.mean() return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. ' f'Total profit {total_profit: 11.8f} {stake_cur} ' f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.') def get_optimizer(self, cpu_count) -> Optimizer: return Optimizer( self.hyperopt_space(), base_estimator="ET", acq_optimizer="auto", n_initial_points=INITIAL_POINTS, acq_optimizer_kwargs={'n_jobs': cpu_count}, random_state=self.config.get('hyperopt_random_state', None) ) def run_optimizer_parallel(self, parallel, asked) -> List: return parallel(delayed( wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked) def load_previous_results(self): """ 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() logger.info( 'Loaded %d previous evaluations from disk.', len(self.trials) ) def start(self) -> None: timerange = Arguments.parse_timerange(None if self.config.get( 'timerange') is None else str(self.config.get('timerange'))) data = load_data( datadir=Path(self.config['datadir']) if self.config.get('datadir') else None, pairs=self.config['exchange']['pair_whitelist'], ticker_interval=self.ticker_interval, refresh_pairs=self.config.get('refresh_pairs', False), exchange=self.exchange, timerange=timerange ) if not data: logger.critical("No data found. Terminating.") return min_date, max_date = get_timeframe(data) logger.info( 'Hyperopting with data from %s up to %s (%s days)..', min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days ) self.strategy.advise_indicators = \ self.custom_hyperopt.populate_indicators # type: ignore preprocessed = self.strategy.tickerdata_to_dataframe(data) dump(preprocessed, TICKERDATA_PICKLE) # We don't need exchange instance anymore while running hyperopt self.exchange = None # type: ignore self.load_previous_results() cpus = cpu_count() logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!') config_jobs = self.config.get('hyperopt_jobs', -1) logger.info(f'Number of parallel jobs set as: {config_jobs}') opt = self.get_optimizer(config_jobs) try: with Parallel(n_jobs=config_jobs) as parallel: jobs = parallel._effective_n_jobs() logger.info(f'Effective number of parallel workers used: {jobs}') EVALS = max(self.total_epochs // jobs, 1) for i in range(EVALS): asked = opt.ask(n_points=jobs) f_val = self.run_optimizer_parallel(parallel, asked) opt.tell(asked, [v['loss'] for v in f_val]) for j in range(jobs): current = i * jobs + j val = f_val[j] val['current_epoch'] = current val['is_initial_point'] = current < INITIAL_POINTS self.log_results(val) self.trials.append(val) logger.debug(f"Optimizer epoch evaluated: {val}") except KeyboardInterrupt: print('User interrupted..') self.save_trials() self.log_trials_result()