# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement """ This module contains the hyperopt logic """ import locale import logging import random import warnings from datetime import datetime from math import ceil from operator import itemgetter from pathlib import Path from typing import Any, Dict, List, Optional import progressbar from colorama import Fore, Style from colorama import init as colorama_init from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects from pandas import DataFrame from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN from freqtrade.data.converter import trim_dataframe from freqtrade.data.history import get_timerange from freqtrade.misc import file_dump_json, plural from freqtrade.optimize.backtesting import Backtesting # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules from freqtrade.optimize.hyperopt_auto import HyperOptAuto from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401 from freqtrade.optimize.hyperopt_tools import HyperoptTools from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver from freqtrade.strategy import IStrategy # Suppress scikit-learn FutureWarnings from skopt with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=FutureWarning) from skopt import Optimizer from skopt.space import Dimension progressbar.streams.wrap_stderr() progressbar.streams.wrap_stdout() logger = logging.getLogger(__name__) INITIAL_POINTS = 30 # Keep no more than SKOPT_MODEL_QUEUE_SIZE models # in the skopt model queue, to optimize memory consumption SKOPT_MODEL_QUEUE_SIZE = 10 MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization class Hyperopt: """ Hyperopt class, this class contains all the logic to run a hyperopt simulation To run a backtest: hyperopt = Hyperopt(config) hyperopt.start() """ custom_hyperopt: IHyperOpt def __init__(self, config: Dict[str, Any]) -> None: self.config = config self.backtesting = Backtesting(self.config) if not self.config.get('hyperopt'): self.custom_hyperopt = HyperOptAuto(self.config) else: self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config) self.custom_hyperopt.strategy = self.backtesting.strategy self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config) self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") strategy = str(self.config['strategy']) self.results_file = (self.config['user_data_dir'] / 'hyperopt_results' / f'strategy_{strategy}_hyperopt_results_{time_now}.pickle') self.data_pickle_file = (self.config['user_data_dir'] / 'hyperopt_results' / 'hyperopt_tickerdata.pkl') self.total_epochs = config.get('epochs', 0) self.current_best_loss = 100 self.clean_hyperopt() self.num_epochs_saved = 0 # Previous evaluations self.epochs: List = [] # Populate functions here (hasattr is slow so should not be run during "regular" operations) if hasattr(self.custom_hyperopt, 'populate_indicators'): self.backtesting.strategy.advise_indicators = ( # type: ignore self.custom_hyperopt.populate_indicators) # type: ignore if hasattr(self.custom_hyperopt, 'populate_buy_trend'): self.backtesting.strategy.advise_buy = ( # type: ignore self.custom_hyperopt.populate_buy_trend) # type: ignore if hasattr(self.custom_hyperopt, 'populate_sell_trend'): self.backtesting.strategy.advise_sell = ( # type: ignore 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 use_sell_signal is enabled if 'ask_strategy' not in self.config: self.config['ask_strategy'] = {} self.config['ask_strategy']['use_sell_signal'] = True self.print_all = self.config.get('print_all', False) self.hyperopt_table_header = 0 self.print_colorized = self.config.get('print_colorized', False) self.print_json = self.config.get('print_json', False) @staticmethod def get_lock_filename(config: Dict[str, Any]) -> str: return str(config['user_data_dir'] / 'hyperopt.lock') def clean_hyperopt(self) -> None: """ Remove hyperopt pickle files to restart hyperopt. """ for f in [self.data_pickle_file, self.results_file]: p = Path(f) if p.is_file(): logger.info(f"Removing `{p}`.") p.unlink() def _get_params_dict(self, raw_params: List[Any]) -> Dict: dimensions: List[Dimension] = self.dimensions # Ensure the number of dimensions match # the number of parameters in the list. if len(raw_params) != len(dimensions): raise ValueError('Mismatch in number of search-space dimensions.') # Return a dict where the keys are the names of the dimensions # and the values are taken from the list of parameters. return {d.name: v for d, v in zip(dimensions, raw_params)} def _save_results(self) -> None: """ Save hyperopt results to file """ num_epochs = len(self.epochs) if num_epochs > self.num_epochs_saved: logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.") dump(self.epochs, self.results_file) self.num_epochs_saved = num_epochs logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} " f"saved to '{self.results_file}'.") # Store hyperopt filename latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN) file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)}, log=False) def _get_params_details(self, params: Dict) -> Dict: """ Return the params for each space """ result: Dict = {} if self.has_space('buy'): result['buy'] = {p.name: params.get(p.name) for p in self.hyperopt_space('buy')} if self.has_space('sell'): result['sell'] = {p.name: params.get(p.name) for p in self.hyperopt_space('sell')} if self.has_space('roi'): result['roi'] = self.custom_hyperopt.generate_roi_table(params) if self.has_space('stoploss'): result['stoploss'] = {p.name: params.get(p.name) for p in self.hyperopt_space('stoploss')} if self.has_space('trailing'): result['trailing'] = self.custom_hyperopt.generate_trailing_params(params) return result def print_results(self, results) -> None: """ Log results if it is better than any previous evaluation TODO: this should be moved to HyperoptTools too """ is_best = results['is_best'] if self.print_all or is_best: print( HyperoptTools.get_result_table( self.config, results, self.total_epochs, self.print_all, self.print_colorized, self.hyperopt_table_header ) ) self.hyperopt_table_header = 2 def has_space(self, space: str) -> bool: """ Tell if the space value is contained in the configuration """ # The 'trailing' space is not included in the 'default' set of spaces if space == 'trailing': return any(s in self.config['spaces'] for s in [space, 'all']) else: return any(s in self.config['spaces'] for s in [space, 'all', 'default']) 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() if space == 'trailing' or (space is None and self.has_space('trailing')): logger.debug("Hyperopt has 'trailing' space") spaces += self.custom_hyperopt.trailing_space() return spaces def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict: """ Used Optimize function. Called once per epoch to optimize whatever is configured. Keep this function as optimized as possible! """ params_dict = self._get_params_dict(raw_params) params_details = self._get_params_details(params_dict) if self.has_space('roi'): self.backtesting.strategy.minimal_roi = ( # type: ignore self.custom_hyperopt.generate_roi_table(params_dict)) if self.has_space('buy'): self.backtesting.strategy.advise_buy = ( # type: ignore self.custom_hyperopt.buy_strategy_generator(params_dict)) if self.has_space('sell'): self.backtesting.strategy.advise_sell = ( # type: ignore self.custom_hyperopt.sell_strategy_generator(params_dict)) if self.has_space('stoploss'): self.backtesting.strategy.stoploss = params_dict['stoploss'] if self.has_space('trailing'): d = self.custom_hyperopt.generate_trailing_params(params_dict) self.backtesting.strategy.trailing_stop = d['trailing_stop'] self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive'] self.backtesting.strategy.trailing_stop_positive_offset = \ d['trailing_stop_positive_offset'] self.backtesting.strategy.trailing_only_offset_is_reached = \ d['trailing_only_offset_is_reached'] processed = load(self.data_pickle_file) min_date, max_date = get_timerange(processed) backtesting_results = self.backtesting.backtest( processed=processed, start_date=min_date.datetime, end_date=max_date.datetime, max_open_trades=self.max_open_trades, position_stacking=self.position_stacking, enable_protections=self.config.get('enable_protections', False), ) return self._get_results_dict(backtesting_results, min_date, max_date, params_dict, params_details, processed=processed) def _get_results_dict(self, backtesting_results, min_date, max_date, params_dict, params_details, processed: Dict[str, DataFrame]): results_metrics = self._calculate_results_metrics(backtesting_results) results_explanation = self._format_results_explanation_string(results_metrics) trade_count = results_metrics['trade_count'] total_profit = results_metrics['total_profit'] # 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. loss: float = MAX_LOSS if trade_count >= self.config['hyperopt_min_trades']: loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count, min_date=min_date.datetime, max_date=max_date.datetime, config=self.config, processed=processed) return { 'loss': loss, 'params_dict': params_dict, 'params_details': params_details, 'results_metrics': results_metrics, 'results_explanation': results_explanation, 'total_profit': total_profit, } def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict: wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0]) draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0]) losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0]) return { 'trade_count': len(backtesting_results.index), 'wins': wins, 'draws': draws, 'losses': losses, 'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}", 'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0, 'median_profit': backtesting_results['profit_ratio'].median() * 100.0, 'total_profit': backtesting_results['profit_abs'].sum(), 'profit': backtesting_results['profit_ratio'].sum() * 100.0, 'duration': backtesting_results['trade_duration'].mean(), } def _format_results_explanation_string(self, results_metrics: Dict) -> str: """ Return the formatted results explanation in a string """ stake_cur = self.config['stake_currency'] return (f"{results_metrics['trade_count']:6d} trades. " f"{results_metrics['wins']}/{results_metrics['draws']}" f"/{results_metrics['losses']} Wins/Draws/Losses. " f"Avg profit {results_metrics['avg_profit']: 6.2f}%. " f"Median profit {results_metrics['median_profit']: 6.2f}%. " f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} " f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). " f"Avg duration {results_metrics['duration']:5.1f} min." ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8') def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer: return Optimizer( dimensions, base_estimator="ET", acq_optimizer="auto", n_initial_points=INITIAL_POINTS, acq_optimizer_kwargs={'n_jobs': cpu_count}, random_state=self.random_state, model_queue_size=SKOPT_MODEL_QUEUE_SIZE, ) def run_optimizer_parallel(self, parallel, asked, i) -> List: return parallel(delayed( wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked) def _set_random_state(self, random_state: Optional[int]) -> int: return random_state or random.randint(1, 2**16 - 1) def start(self) -> None: self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None)) logger.info(f"Using optimizer random state: {self.random_state}") self.hyperopt_table_header = -1 data, timerange = self.backtesting.load_bt_data() preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data) # Trim startup period from analyzed dataframe for pair, df in preprocessed.items(): preprocessed[pair] = trim_dataframe(df, timerange) min_date, max_date = get_timerange(preprocessed) logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'({(max_date - min_date).days} days)..') dump(preprocessed, self.data_pickle_file) # We don't need exchange instance anymore while running hyperopt self.backtesting.exchange.close() self.backtesting.exchange._api = None # type: ignore self.backtesting.exchange._api_async = None # type: ignore # self.backtesting.exchange = None # type: ignore self.backtesting.pairlists = None # type: ignore self.backtesting.strategy.dp = None # type: ignore IStrategy.dp = None # type: ignore 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}') self.dimensions: List[Dimension] = self.hyperopt_space() self.opt = self.get_optimizer(self.dimensions, config_jobs) if self.print_colorized: colorama_init(autoreset=True) try: with Parallel(n_jobs=config_jobs) as parallel: jobs = parallel._effective_n_jobs() logger.info(f'Effective number of parallel workers used: {jobs}') # Define progressbar if self.print_colorized: widgets = [ ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs), ' (', progressbar.Percentage(), ')] ', progressbar.Bar(marker=progressbar.AnimatedMarker( fill='\N{FULL BLOCK}', fill_wrap=Fore.GREEN + '{}' + Fore.RESET, marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL, )), ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']', ] else: widgets = [ ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs), ' (', progressbar.Percentage(), ')] ', progressbar.Bar(marker=progressbar.AnimatedMarker( fill='\N{FULL BLOCK}', )), ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']', ] with progressbar.ProgressBar( max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False, widgets=widgets ) as pbar: EVALS = ceil(self.total_epochs / jobs) for i in range(EVALS): # Correct the number of epochs to be processed for the last # iteration (should not exceed self.total_epochs in total) n_rest = (i + 1) * jobs - self.total_epochs current_jobs = jobs - n_rest if n_rest > 0 else jobs asked = self.opt.ask(n_points=current_jobs) f_val = self.run_optimizer_parallel(parallel, asked, i) self.opt.tell(asked, [v['loss'] for v in f_val]) # Calculate progressbar outputs for j, val in enumerate(f_val): # Use human-friendly indexes here (starting from 1) current = i * jobs + j + 1 val['current_epoch'] = current val['is_initial_point'] = current <= INITIAL_POINTS logger.debug(f"Optimizer epoch evaluated: {val}") is_best = HyperoptTools.is_best_loss(val, self.current_best_loss) # This value is assigned here and not in the optimization method # to keep proper order in the list of results. That's because # evaluations can take different time. Here they are aligned in the # order they will be shown to the user. val['is_best'] = is_best self.print_results(val) if is_best: self.current_best_loss = val['loss'] self.epochs.append(val) # Save results after each best epoch and every 100 epochs if is_best or current % 100 == 0: self._save_results() pbar.update(current) except KeyboardInterrupt: print('User interrupted..') self._save_results() logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} " f"saved to '{self.results_file}'.") if self.epochs: sorted_epochs = sorted(self.epochs, key=itemgetter('loss')) best_epoch = sorted_epochs[0] HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json) else: # This is printed when Ctrl+C is pressed quickly, before first epochs have # a chance to be evaluated. print("No epochs evaluated yet, no best result.")