# pragma pylint: disable=attribute-defined-outside-init """ This module load custom hyperopt """ import logging from pathlib import Path from typing import Dict from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS from freqtrade.exceptions import OperationalException from freqtrade.optimize.hyperopt_interface import IHyperOpt from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss from freqtrade.resolvers import IResolver logger = logging.getLogger(__name__) class HyperOptResolver(IResolver): """ This class contains all the logic to load custom hyperopt class """ object_type = IHyperOpt object_type_str = "Hyperopt" user_subdir = USERPATH_HYPEROPTS initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve() @staticmethod def load_hyperopt(config: Dict) -> IHyperOpt: """ Load the custom hyperopt class from config parameter :param config: configuration dictionary """ if not config.get('hyperopt'): raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify " "the Hyperopt class to use.") hyperopt_name = config['hyperopt'] hyperopt = HyperOptResolver.load_object(hyperopt_name, config, kwargs={'config': config}, extra_dir=config.get('hyperopt_path')) if not hasattr(hyperopt, 'populate_indicators'): logger.warning("Hyperopt class does not provide populate_indicators() method. " "Using populate_indicators from the strategy.") if not hasattr(hyperopt, 'populate_buy_trend'): logger.warning("Hyperopt class does not provide populate_buy_trend() method. " "Using populate_buy_trend from the strategy.") if not hasattr(hyperopt, 'populate_sell_trend'): logger.warning("Hyperopt class does not provide populate_sell_trend() method. " "Using populate_sell_trend from the strategy.") return hyperopt class HyperOptLossResolver(IResolver): """ This class contains all the logic to load custom hyperopt loss class """ object_type = IHyperOptLoss object_type_str = "HyperoptLoss" user_subdir = USERPATH_HYPEROPTS initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve() @staticmethod def load_hyperoptloss(config: Dict) -> IHyperOptLoss: """ Load the custom class from config parameter :param config: configuration dictionary """ # Verify the hyperopt_loss is in the configuration, otherwise fallback to the # default hyperopt loss hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS hyperoptloss = HyperOptLossResolver.load_object(hyperoptloss_name, config, kwargs={}, extra_dir=config.get('hyperopt_path')) # Assign timeframe to be used in hyperopt hyperoptloss.__class__.ticker_interval = str(config['timeframe']) hyperoptloss.__class__.timeframe = str(config['timeframe']) if not hasattr(hyperoptloss, 'hyperopt_loss_function'): raise OperationalException( f"Found HyperoptLoss class {hyperoptloss_name} does not " "implement `hyperopt_loss_function`.") return hyperoptloss