stable/freqtrade/resolvers/hyperopt_resolver.py

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