132 lines
5.4 KiB
Python
132 lines
5.4 KiB
Python
# pragma pylint: disable=attribute-defined-outside-init
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"""
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This module load custom hyperopt
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"""
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import logging
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from pathlib import Path
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from typing import Optional, Dict
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from freqtrade import OperationalException
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from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
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from freqtrade.resolvers import IResolver
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logger = logging.getLogger(__name__)
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class HyperOptResolver(IResolver):
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"""
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This class contains all the logic to load custom hyperopt class
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"""
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object_type = IHyperOpt
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@staticmethod
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def load_hyperopt(config: Dict) -> IHyperOpt:
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"""
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Load the custom hyperopt class from config parameter
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:param config: configuration dictionary
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"""
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if not config.get('hyperopt'):
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raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify "
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"the Hyperopt class to use.")
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hyperopt_name = config['hyperopt']
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hyperopt = HyperOptResolver._load_hyperopt(hyperopt_name, config,
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extra_dir=config.get('hyperopt_path'))
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if not hasattr(hyperopt, 'populate_indicators'):
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logger.warning("Hyperopt class does not provide populate_indicators() method. "
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"Using populate_indicators from the strategy.")
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if not hasattr(hyperopt, 'populate_buy_trend'):
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logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
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"Using populate_buy_trend from the strategy.")
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if not hasattr(hyperopt, 'populate_sell_trend'):
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logger.warning("Hyperopt class does not provide populate_sell_trend() method. "
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"Using populate_sell_trend from the strategy.")
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return hyperopt
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@staticmethod
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def _load_hyperopt(
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hyperopt_name: str, config: Dict, extra_dir: Optional[str] = None) -> IHyperOpt:
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"""
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Search and loads the specified hyperopt.
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:param hyperopt_name: name of the module to import
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:param config: configuration dictionary
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:param extra_dir: additional directory to search for the given hyperopt
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:return: HyperOpt instance or None
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"""
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current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
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abs_paths = HyperOptResolver.build_search_paths(config, current_path=current_path,
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user_subdir=USERPATH_HYPEROPTS,
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extra_dir=extra_dir)
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hyperopt = HyperOptResolver._load_object(paths=abs_paths,
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object_name=hyperopt_name,
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kwargs={'config': config})
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if hyperopt:
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return hyperopt
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raise OperationalException(
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f"Impossible to load Hyperopt '{hyperopt_name}'. This class does not exist "
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"or contains Python code errors."
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)
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class HyperOptLossResolver(IResolver):
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"""
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This class contains all the logic to load custom hyperopt loss class
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"""
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object_type = IHyperOptLoss
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@staticmethod
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def load_hyperoptloss(config: Dict) -> IHyperOptLoss:
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"""
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Load the custom class from config parameter
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:param config: configuration dictionary
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"""
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# Verify the hyperopt_loss is in the configuration, otherwise fallback to the
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# default hyperopt loss
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hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
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hyperoptloss = HyperOptLossResolver._load_hyperoptloss(
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hyperoptloss_name, config, extra_dir=config.get('hyperopt_path'))
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# Assign ticker_interval to be used in hyperopt
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hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
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if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
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raise OperationalException(
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f"Found HyperoptLoss class {hyperoptloss_name} does not "
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"implement `hyperopt_loss_function`.")
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return hyperoptloss
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@staticmethod
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def _load_hyperoptloss(hyper_loss_name: str, config: Dict,
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extra_dir: Optional[str] = None) -> IHyperOptLoss:
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"""
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Search and loads the specified hyperopt loss class.
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:param hyper_loss_name: name of the module to import
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:param config: configuration dictionary
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:param extra_dir: additional directory to search for the given hyperopt
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:return: HyperOptLoss instance or None
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"""
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current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
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abs_paths = HyperOptLossResolver.build_search_paths(config, current_path=current_path,
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user_subdir=USERPATH_HYPEROPTS,
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extra_dir=extra_dir)
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hyperoptloss = HyperOptLossResolver._load_object(paths=abs_paths,
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object_name=hyper_loss_name)
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if hyperoptloss:
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return hyperoptloss
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raise OperationalException(
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f"Impossible to load HyperoptLoss '{hyper_loss_name}'. This class does not exist "
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"or contains Python code errors."
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)
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