730 lines
31 KiB
Python
730 lines
31 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 locale
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import logging
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import random
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import warnings
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from math import ceil
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from collections import OrderedDict
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from operator import itemgetter
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from pathlib import Path
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from pprint import pformat
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from typing import Any, Dict, List, Optional
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import rapidjson
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from colorama import Fore, Style
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from joblib import (Parallel, cpu_count, delayed, dump, load,
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wrap_non_picklable_objects)
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from pandas import DataFrame, json_normalize, isna
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import progressbar
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import tabulate
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from os import path
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import io
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from freqtrade.data.converter import trim_dataframe
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from freqtrade.data.history import get_timerange
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import plural, round_dict
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
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from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
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HyperOptResolver)
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# Suppress scikit-learn FutureWarnings from skopt
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning)
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from skopt import Optimizer
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from skopt.space import Dimension
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progressbar.streams.wrap_stderr()
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progressbar.streams.wrap_stdout()
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logger = logging.getLogger(__name__)
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INITIAL_POINTS = 30
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# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
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# in the skopt model queue, to optimize memory consumption
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SKOPT_MODEL_QUEUE_SIZE = 10
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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class Hyperopt:
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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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self.config = config
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self.backtesting = Backtesting(self.config)
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self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
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self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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self.results_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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self.total_epochs = config.get('epochs', 0)
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self.current_best_loss = 100
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if not self.config.get('hyperopt_continue'):
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self.clean_hyperopt()
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else:
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logger.info("Continuing on previous hyperopt results.")
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self.num_epochs_saved = 0
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# Previous evaluations
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self.epochs: List = []
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# Populate functions here (hasattr is slow so should not be run during "regular" operations)
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if hasattr(self.custom_hyperopt, 'populate_indicators'):
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self.backtesting.strategy.advise_indicators = \
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self.custom_hyperopt.populate_indicators # type: ignore
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if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
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self.backtesting.strategy.advise_buy = \
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self.custom_hyperopt.populate_buy_trend # type: ignore
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if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
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self.backtesting.strategy.advise_sell = \
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self.custom_hyperopt.populate_sell_trend # type: ignore
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# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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self.max_open_trades = self.config['max_open_trades']
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else:
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logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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self.max_open_trades = 0
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self.position_stacking = self.config.get('position_stacking', False)
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if self.has_space('sell'):
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# Make sure use_sell_signal is enabled
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if 'ask_strategy' not in self.config:
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self.config['ask_strategy'] = {}
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self.config['ask_strategy']['use_sell_signal'] = True
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self.print_all = self.config.get('print_all', False)
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self.hyperopt_table_header = 0
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self.print_colorized = self.config.get('print_colorized', False)
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self.print_json = self.config.get('print_json', False)
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@staticmethod
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def get_lock_filename(config: Dict[str, Any]) -> str:
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return str(config['user_data_dir'] / 'hyperopt.lock')
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def clean_hyperopt(self) -> None:
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"""
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Remove hyperopt pickle files to restart hyperopt.
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"""
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for f in [self.data_pickle_file, self.results_file]:
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p = Path(f)
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if p.is_file():
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logger.info(f"Removing `{p}`.")
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p.unlink()
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def _get_params_dict(self, raw_params: List[Any]) -> Dict:
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dimensions: List[Dimension] = self.dimensions
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# Ensure the number of dimensions match
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# the number of parameters in the list.
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if len(raw_params) != len(dimensions):
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raise ValueError('Mismatch in number of search-space dimensions.')
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# Return a dict where the keys are the names of the dimensions
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# and the values are taken from the list of parameters.
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return {d.name: v for d, v in zip(dimensions, raw_params)}
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def _save_results(self) -> None:
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"""
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Save hyperopt results to file
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"""
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num_epochs = len(self.epochs)
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if num_epochs > self.num_epochs_saved:
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logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
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dump(self.epochs, self.results_file)
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self.num_epochs_saved = num_epochs
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logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
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f"saved to '{self.results_file}'.")
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@staticmethod
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def _read_results(results_file: Path) -> List:
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"""
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Read hyperopt results from file
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"""
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logger.info("Reading epochs from '%s'", results_file)
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data = load(results_file)
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return data
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def _get_params_details(self, params: Dict) -> Dict:
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"""
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Return the params for each space
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"""
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result: Dict = {}
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if self.has_space('buy'):
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result['buy'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('buy')}
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if self.has_space('sell'):
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result['sell'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('sell')}
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if self.has_space('roi'):
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result['roi'] = self.custom_hyperopt.generate_roi_table(params)
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if self.has_space('stoploss'):
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result['stoploss'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('stoploss')}
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if self.has_space('trailing'):
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result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
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return result
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@staticmethod
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def print_epoch_details(results, total_epochs: int, print_json: bool,
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no_header: bool = False, header_str: str = None) -> None:
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"""
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Display details of the hyperopt result
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"""
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params = results.get('params_details', {})
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# Default header string
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if header_str is None:
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header_str = "Best result"
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if not no_header:
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explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
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print(f"\n{header_str}:\n\n{explanation_str}\n")
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if print_json:
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result_dict: Dict = {}
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for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
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Hyperopt._params_update_for_json(result_dict, params, s)
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print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
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else:
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Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
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Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
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Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
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Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
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Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
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@staticmethod
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def _params_update_for_json(result_dict, params, space: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space)
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if space in ['buy', 'sell']:
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result_dict.setdefault('params', {}).update(space_params)
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elif space == 'roi':
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# TODO: get rid of OrderedDict when support for python 3.6 will be
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# dropped (dicts keep the order as the language feature)
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# Convert keys in min_roi dict to strings because
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# rapidjson cannot dump dicts with integer keys...
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# OrderedDict is used to keep the numeric order of the items
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# in the dict.
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result_dict['minimal_roi'] = OrderedDict(
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(str(k), v) for k, v in space_params.items()
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)
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else: # 'stoploss', 'trailing'
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result_dict.update(space_params)
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@staticmethod
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def _params_pretty_print(params, space: str, header: str) -> None:
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if space in params:
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space_params = Hyperopt._space_params(params, space, 5)
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params_result = f"\n# {header}\n"
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if space == 'stoploss':
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params_result += f"stoploss = {space_params.get('stoploss')}"
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elif space == 'roi':
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minimal_roi_result = rapidjson.dumps(
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# TODO: get rid of OrderedDict when support for python 3.6 will be
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# dropped (dicts keep the order as the language feature)
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OrderedDict(
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(str(k), v) for k, v in space_params.items()
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),
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default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
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params_result += f"minimal_roi = {minimal_roi_result}"
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else:
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params_result += f"{space}_params = {pformat(space_params, indent=4)}"
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params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
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params_result = params_result.replace("\n", "\n ")
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print(params_result)
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@staticmethod
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def _space_params(params, space: str, r: int = None) -> Dict:
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d = params[space]
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# Round floats to `r` digits after the decimal point if requested
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return round_dict(d, r) if r else d
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@staticmethod
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def is_best_loss(results, current_best_loss: float) -> bool:
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return results['loss'] < current_best_loss
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def print_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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is_best = results['is_best']
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if self.print_all or is_best:
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print(
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self.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header
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)
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)
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self.hyperopt_table_header = 2
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@staticmethod
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def _format_explanation_string(results, total_epochs) -> str:
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return (("*" if results['is_initial_point'] else " ") +
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f"{results['current_epoch']:5d}/{total_epochs}: " +
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f"{results['results_explanation']} " +
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f"Objective: {results['loss']:.5f}")
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@staticmethod
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def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool, remove_header: int) -> str:
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"""
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Log result table
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"""
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if not results:
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return ''
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
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'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '* '
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Epoch'] = trials['Epoch'].apply(
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lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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)
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trials['Profit'] = trials.apply(
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lambda x: '{:,.8f} {} {}'.format(
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x['Total profit'], config['stake_currency'],
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'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
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).rjust(25+len(config['stake_currency']))
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if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
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axis=1
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)
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trials = trials.drop(columns=['Total profit'])
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if print_colorized:
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for i in range(len(trials)):
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if trials.loc[i]['is_profit']:
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][j]), Fore.RESET)
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if trials.loc[i]['is_best'] and highlight_best:
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for j in range(len(trials.loc[i])-3):
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trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][j]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='orgtbl',
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headers='keys', stralign="right"
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)
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table = table.split("\n", remove_header)[remove_header]
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elif remove_header < 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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table = "\n".join(table.split("\n")[0:remove_header])
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else:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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return table
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@staticmethod
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def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
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csv_file: str) -> None:
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"""
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Log result to csv-file
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"""
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if not results:
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return
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# Verification for overwrite
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if path.isfile(csv_file):
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logger.error(f"CSV file already exists: {csv_file}")
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return
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try:
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io.open(csv_file, 'w+').close()
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except IOError:
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logger.error(f"Failed to create CSV file: {csv_file}")
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return
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials['Stake currency'] = config['stake_currency']
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base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.avg_profit', 'results_metrics.total_profit',
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'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']
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param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
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trials = trials[base_metrics + param_metrics]
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base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
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'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
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param_columns = list(results[0]['params_dict'].keys())
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trials.columns = base_columns + param_columns
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '*'
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Epoch'] = trials['Epoch'].astype(str)
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Total profit'] = trials['Total profit'].apply(
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lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
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)
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trials['Profit'] = trials['Profit'].apply(
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lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
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)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
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logger.info(f"CSV file created: {csv_file}")
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def has_space(self, space: str) -> bool:
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"""
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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 = \
|
|
self.custom_hyperopt.generate_roi_table(params_dict)
|
|
|
|
if self.has_space('buy'):
|
|
self.backtesting.strategy.advise_buy = \
|
|
self.custom_hyperopt.buy_strategy_generator(params_dict)
|
|
|
|
if self.has_space('sell'):
|
|
self.backtesting.strategy.advise_sell = \
|
|
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,
|
|
stake_amount=self.config['stake_amount'],
|
|
start_date=min_date,
|
|
end_date=max_date,
|
|
max_open_trades=self.max_open_trades,
|
|
position_stacking=self.position_stacking,
|
|
)
|
|
return self._get_results_dict(backtesting_results, min_date, max_date,
|
|
params_dict, params_details)
|
|
|
|
def _get_results_dict(self, backtesting_results, min_date, max_date,
|
|
params_dict, params_details):
|
|
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)
|
|
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:
|
|
return {
|
|
'trade_count': len(backtesting_results.index),
|
|
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
|
|
'total_profit': backtesting_results.profit_abs.sum(),
|
|
'profit': backtesting_results.profit_percent.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"Avg profit {results_metrics['avg_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)
|
|
|
|
@staticmethod
|
|
def load_previous_results(results_file: Path) -> List:
|
|
"""
|
|
Load data for epochs from the file if we have one
|
|
"""
|
|
epochs: List = []
|
|
if results_file.is_file() and results_file.stat().st_size > 0:
|
|
epochs = Hyperopt._read_results(results_file)
|
|
# Detection of some old format, without 'is_best' field saved
|
|
if epochs[0].get('is_best') is None:
|
|
raise OperationalException(
|
|
"The file with Hyperopt results is incompatible with this version "
|
|
"of Freqtrade and cannot be loaded.")
|
|
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
|
|
return epochs
|
|
|
|
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(data)
|
|
|
|
logger.info(
|
|
'Hyperopting with data from %s up to %s (%s days)..',
|
|
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
|
|
)
|
|
dump(preprocessed, self.data_pickle_file)
|
|
|
|
# We don't need exchange instance anymore while running hyperopt
|
|
self.backtesting.exchange = None # type: ignore
|
|
self.backtesting.pairlists = None # type: ignore
|
|
|
|
self.epochs = self.load_previous_results(self.results_file)
|
|
|
|
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)
|
|
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 = self.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]
|
|
self.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.")
|