# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement """ This module contains the hyperopt logic """ import logging import random import warnings from datetime import datetime, timezone from math import ceil from pathlib import Path from typing import Any, Dict, List, Optional import progressbar import rapidjson 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, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN from freqtrade.data.converter import trim_dataframes from freqtrade.data.history import get_timerange from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, 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, hyperopt_serializer from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver # 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.buy_space: List[Dimension] = [] self.sell_space: List[Dimension] = [] self.protection_space: List[Dimension] = [] self.roi_space: List[Dimension] = [] self.stoploss_space: List[Dimension] = [] self.trailing_space: List[Dimension] = [] self.dimensions: List[Dimension] = [] self.config = config self.backtesting = Backtesting(self.config) self.pairlist = self.backtesting.pairlists.whitelist if not self.config.get('hyperopt'): self.custom_hyperopt = HyperOptAuto(self.config) else: raise OperationalException( "Using separate Hyperopt files has been removed in 2021.9. Please convert " "your existing Hyperopt file to the new Hyperoptable strategy interface") self.backtesting._set_strategy(self.backtesting.strategylist[0]) 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: Path = (self.config['user_data_dir'] / 'hyperopt_results' / f'strategy_{strategy}_{time_now}.fthypt') 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 self.current_best_epoch: Optional[Dict[str, Any]] = None # 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 HyperoptTools.has_space(self.config, '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, dimensions: List[Dimension], raw_params: List[Any]) -> Dict: # 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_result(self, epoch: Dict) -> None: """ Save hyperopt results to file Store one line per epoch. While not a valid json object - this allows appending easily. :param epoch: result dictionary for this epoch. """ epoch[FTHYPT_FILEVERSION] = 2 with self.results_file.open('a') as f: rapidjson.dump(epoch, f, default=hyperopt_serializer, number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN) f.write("\n") self.num_epochs_saved += 1 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 HyperoptTools.has_space(self.config, 'buy'): result['buy'] = {p.name: params.get(p.name) for p in self.buy_space} if HyperoptTools.has_space(self.config, 'sell'): result['sell'] = {p.name: params.get(p.name) for p in self.sell_space} if HyperoptTools.has_space(self.config, 'protection'): result['protection'] = {p.name: params.get(p.name) for p in self.protection_space} if HyperoptTools.has_space(self.config, 'roi'): result['roi'] = {str(k): v for k, v in self.custom_hyperopt.generate_roi_table(params).items()} if HyperoptTools.has_space(self.config, 'stoploss'): result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space} if HyperoptTools.has_space(self.config, 'trailing'): result['trailing'] = self.custom_hyperopt.generate_trailing_params(params) return result def _get_no_optimize_details(self) -> Dict[str, Any]: """ Get non-optimized parameters """ result: Dict[str, Any] = {} strategy = self.backtesting.strategy if not HyperoptTools.has_space(self.config, 'roi'): result['roi'] = {str(k): v for k, v in strategy.minimal_roi.items()} if not HyperoptTools.has_space(self.config, 'stoploss'): result['stoploss'] = {'stoploss': strategy.stoploss} if not HyperoptTools.has_space(self.config, 'trailing'): result['trailing'] = { 'trailing_stop': strategy.trailing_stop, 'trailing_stop_positive': strategy.trailing_stop_positive, 'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset, 'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached, } 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 init_spaces(self): """ Assign the dimensions in the hyperoptimization space. """ if HyperoptTools.has_space(self.config, 'protection'): # Protections can only be optimized when using the Parameter interface logger.debug("Hyperopt has 'protection' space") # Enable Protections if protection space is selected. self.config['enable_protections'] = True self.protection_space = self.custom_hyperopt.protection_space() if HyperoptTools.has_space(self.config, 'buy'): logger.debug("Hyperopt has 'buy' space") self.buy_space = self.custom_hyperopt.buy_indicator_space() if HyperoptTools.has_space(self.config, 'sell'): logger.debug("Hyperopt has 'sell' space") self.sell_space = self.custom_hyperopt.sell_indicator_space() if HyperoptTools.has_space(self.config, 'roi'): logger.debug("Hyperopt has 'roi' space") self.roi_space = self.custom_hyperopt.roi_space() if HyperoptTools.has_space(self.config, 'stoploss'): logger.debug("Hyperopt has 'stoploss' space") self.stoploss_space = self.custom_hyperopt.stoploss_space() if HyperoptTools.has_space(self.config, 'trailing'): logger.debug("Hyperopt has 'trailing' space") self.trailing_space = self.custom_hyperopt.trailing_space() self.dimensions = (self.buy_space + self.sell_space + self.protection_space + self.roi_space + self.stoploss_space + self.trailing_space) def assign_params(self, params_dict: Dict, category: str) -> None: """ Assign hyperoptable parameters """ for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category): if attr.optimize: # noinspection PyProtectedMember attr.value = params_dict[attr_name] 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! """ backtest_start_time = datetime.now(timezone.utc) params_dict = self._get_params_dict(self.dimensions, raw_params) # Apply parameters if HyperoptTools.has_space(self.config, 'buy'): self.assign_params(params_dict, 'buy') if HyperoptTools.has_space(self.config, 'sell'): self.assign_params(params_dict, 'sell') if HyperoptTools.has_space(self.config, 'protection'): self.assign_params(params_dict, 'protection') if HyperoptTools.has_space(self.config, 'roi'): self.backtesting.strategy.minimal_roi = ( # type: ignore self.custom_hyperopt.generate_roi_table(params_dict)) if HyperoptTools.has_space(self.config, 'stoploss'): self.backtesting.strategy.stoploss = params_dict['stoploss'] if HyperoptTools.has_space(self.config, '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'] with self.data_pickle_file.open('rb') as f: processed = load(f, mmap_mode='r') bt_results = self.backtesting.backtest( processed=processed, start_date=self.min_date, end_date=self.max_date, max_open_trades=self.max_open_trades, position_stacking=self.position_stacking, enable_protections=self.config.get('enable_protections', False), ) backtest_end_time = datetime.now(timezone.utc) bt_results.update({ 'backtest_start_time': int(backtest_start_time.timestamp()), 'backtest_end_time': int(backtest_end_time.timestamp()), }) return self._get_results_dict(bt_results, self.min_date, self.max_date, params_dict, processed=processed) def _get_results_dict(self, backtesting_results, min_date, max_date, params_dict, processed: Dict[str, DataFrame] ) -> Dict[str, Any]: params_details = self._get_params_details(params_dict) strat_stats = generate_strategy_stats( self.pairlist, self.backtesting.strategy.get_strategy_name(), backtesting_results, min_date, max_date, market_change=0 ) results_explanation = HyperoptTools.format_results_explanation_string( strat_stats, self.config['stake_currency']) not_optimized = self.backtesting.strategy.get_no_optimize_params() not_optimized = deep_merge_dicts(not_optimized, self._get_no_optimize_details()) trade_count = strat_stats['total_trades'] total_profit = strat_stats['profit_total'] # 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['results'], trade_count=trade_count, min_date=min_date, max_date=max_date, config=self.config, processed=processed, backtest_stats=strat_stats) return { 'loss': loss, 'params_dict': params_dict, 'params_details': params_details, 'params_not_optimized': not_optimized, 'results_metrics': strat_stats, 'results_explanation': results_explanation, 'total_profit': total_profit, } def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer: estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions) acq_optimizer = "sampling" if isinstance(estimator, str): if estimator not in ("GP", "RF", "ET", "GBRT"): raise OperationalException(f"Estimator {estimator} not supported.") else: acq_optimizer = "auto" logger.info(f"Using estimator {estimator}.") return Optimizer( dimensions, base_estimator=estimator, acq_optimizer=acq_optimizer, 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 prepare_hyperopt_data(self) -> None: data, timerange = self.backtesting.load_bt_data() logger.info("Dataload complete. Calculating indicators") preprocessed = self.backtesting.strategy.advise_all_indicators(data) # Trim startup period from analyzed dataframe to get correct dates for output. processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup) self.min_date, self.max_date = get_timerange(processed) logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} ' f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} ' f'({(self.max_date - self.min_date).days} days)..') # Store non-trimmed data - will be trimmed after signal generation. dump(preprocessed, self.data_pickle_file) def get_asked_points(self, n_points: int) -> List[List[Any]]: ''' Enforce points returned from `self.opt.ask` have not been already evaluated Steps: 1. Try to get points using `self.opt.ask` first 2. Discard the points that have already been evaluated 3. Retry using `self.opt.ask` up to 3 times 4. If still some points are missing in respect to `n_points`, random sample some points 5. Repeat until at least `n_points` points in the `asked_non_tried` list 6. Return a list with length truncated at `n_points` ''' i = 0 asked_non_tried: List[List[Any]] = [] while i < 100 and len(asked_non_tried) < n_points: if i < 3: asked = self.opt.ask(n_points=n_points) else: asked = self.opt.space.rvs(n_samples=n_points * 5) asked_non_tried += [x for x in asked if x not in self.opt.Xi and x not in asked_non_tried] i += 1 return asked_non_tried[:n_points] 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 # Initialize spaces ... self.init_spaces() self.prepare_hyperopt_data() # 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.loop = None # type: ignore # self.backtesting.exchange = None # type: ignore self.backtesting.pairlists = 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.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.get_asked_points(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.current_best_epoch = val self._save_result(val) pbar.update(current) except KeyboardInterrupt: print('User interrupted..') logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} " f"saved to '{self.results_file}'.") if self.current_best_epoch: HyperoptTools.try_export_params( self.config, self.backtesting.strategy.get_strategy_name(), self.current_best_epoch) HyperoptTools.show_epoch_details(self.current_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.")