mirror of
https://github.com/freqtrade/freqtrade.git
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370 lines
13 KiB
Python
370 lines
13 KiB
Python
"""
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IHyperStrategy interface, hyperoptable Parameter class.
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This module defines a base class for auto-hyperoptable strategies.
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"""
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import logging
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from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from contextlib import suppress
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from typing import Any, Union
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from freqtrade.enums import HyperoptState
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from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
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with suppress(ImportError):
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from skopt.space import Categorical, Integer, Real
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from freqtrade.optimize.space import SKDecimal
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from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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class BaseParameter(ABC):
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"""
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Defines a parameter that can be optimized by hyperopt.
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"""
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category: str | None
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default: Any
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value: Any
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in_space: bool = False
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name: str
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def __init__(
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self,
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*,
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default: Any,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable parameter.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter field
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name is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
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"""
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if "name" in kwargs:
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raise OperationalException(
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"Name is determined by parameter field name and can not be specified manually."
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)
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self.category = space
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self._space_params = kwargs
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self.value = default
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self.optimize = optimize
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self.load = load
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def __repr__(self):
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return f"{self.__class__.__name__}({self.value})"
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@abstractmethod
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def get_space(self, name: str) -> Union["Integer", "Real", "SKDecimal", "Categorical"]:
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"""
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Get-space - will be used by Hyperopt to get the hyperopt Space
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"""
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def can_optimize(self):
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return (
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self.in_space
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and self.optimize
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and HyperoptStateContainer.state != HyperoptState.OPTIMIZE
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)
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class NumericParameter(BaseParameter):
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"""Internal parameter used for Numeric purposes"""
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float_or_int = int | float
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default: float_or_int
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value: float_or_int
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def __init__(
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self,
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low: float_or_int | Sequence[float_or_int],
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high: float_or_int | None = None,
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*,
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default: float_or_int,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable numeric parameter.
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Cannot be instantiated, but provides the validation for other numeric parameters
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:param low: Lower end (inclusive) of optimization space or [low, high].
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:param high: Upper end (inclusive) of optimization space.
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Must be none of entire range is passed first parameter.
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:param default: A default value.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter fieldname is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.*.
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"""
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if high is not None and isinstance(low, Sequence):
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raise OperationalException(f"{self.__class__.__name__} space invalid.")
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if high is None or isinstance(low, Sequence):
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if not isinstance(low, Sequence) or len(low) != 2:
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raise OperationalException(f"{self.__class__.__name__} space must be [low, high]")
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self.low, self.high = low
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else:
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self.low = low
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self.high = high
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super().__init__(default=default, space=space, optimize=optimize, load=load, **kwargs)
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class IntParameter(NumericParameter):
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default: int
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value: int
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low: int
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high: int
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def __init__(
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self,
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low: int | Sequence[int],
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high: int | None = None,
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*,
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default: int,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable integer parameter.
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:param low: Lower end (inclusive) of optimization space or [low, high].
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:param high: Upper end (inclusive) of optimization space.
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Must be none of entire range is passed first parameter.
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:param default: A default value.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter fieldname is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.Integer.
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"""
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super().__init__(
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low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
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)
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def get_space(self, name: str) -> "Integer":
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"""
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Create skopt optimization space.
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:param name: A name of parameter field.
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"""
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return Integer(low=self.low, high=self.high, name=name, **self._space_params)
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@property
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def range(self):
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"""
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Get each value in this space as list.
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Returns a List from low to high (inclusive) in Hyperopt mode.
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Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
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calculating 100ds of indicators.
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"""
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if self.can_optimize():
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# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
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return range(self.low, self.high + 1)
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else:
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return range(self.value, self.value + 1)
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class RealParameter(NumericParameter):
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default: float
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value: float
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def __init__(
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self,
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low: float | Sequence[float],
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high: float | None = None,
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*,
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default: float,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable floating point parameter with unlimited precision.
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:param low: Lower end (inclusive) of optimization space or [low, high].
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:param high: Upper end (inclusive) of optimization space.
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Must be none if entire range is passed first parameter.
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:param default: A default value.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter fieldname is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.Real.
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"""
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super().__init__(
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low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
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)
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def get_space(self, name: str) -> "Real":
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"""
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Create skopt optimization space.
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:param name: A name of parameter field.
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"""
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return Real(low=self.low, high=self.high, name=name, **self._space_params)
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class DecimalParameter(NumericParameter):
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default: float
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value: float
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def __init__(
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self,
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low: float | Sequence[float],
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high: float | None = None,
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*,
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default: float,
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decimals: int = 3,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable decimal parameter with a limited precision.
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:param low: Lower end (inclusive) of optimization space or [low, high].
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:param high: Upper end (inclusive) of optimization space.
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Must be none if entire range is passed first parameter.
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:param default: A default value.
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:param decimals: A number of decimals after floating point to be included in testing.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter fieldname is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.Integer.
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"""
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self._decimals = decimals
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default = round(default, self._decimals)
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super().__init__(
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low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
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)
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def get_space(self, name: str) -> "SKDecimal":
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"""
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Create skopt optimization space.
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:param name: A name of parameter field.
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"""
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return SKDecimal(
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low=self.low, high=self.high, decimals=self._decimals, name=name, **self._space_params
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)
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@property
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def range(self):
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"""
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Get each value in this space as list.
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Returns a List from low to high (inclusive) in Hyperopt mode.
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Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
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calculating 100ds of indicators.
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"""
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if self.can_optimize():
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low = int(self.low * pow(10, self._decimals))
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high = int(self.high * pow(10, self._decimals)) + 1
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return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
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else:
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return [self.value]
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class CategoricalParameter(BaseParameter):
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default: Any
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value: Any
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opt_range: Sequence[Any]
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def __init__(
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self,
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categories: Sequence[Any],
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*,
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default: Any | None = None,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable parameter.
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:param categories: Optimization space, [a, b, ...].
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:param default: A default value. If not specified, first item from specified space will be
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used.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter field
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name is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.Categorical.
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"""
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if len(categories) < 2:
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raise OperationalException(
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"CategoricalParameter space must be [a, b, ...] (at least two parameters)"
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)
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self.opt_range = categories
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super().__init__(default=default, space=space, optimize=optimize, load=load, **kwargs)
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def get_space(self, name: str) -> "Categorical":
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"""
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Create skopt optimization space.
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:param name: A name of parameter field.
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"""
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return Categorical(self.opt_range, name=name, **self._space_params)
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@property
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def range(self):
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"""
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Get each value in this space as list.
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Returns a List of categories in Hyperopt mode.
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Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
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calculating 100ds of indicators.
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"""
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if self.can_optimize():
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return self.opt_range
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else:
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return [self.value]
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class BooleanParameter(CategoricalParameter):
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def __init__(
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self,
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*,
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default: Any | None = None,
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space: str | None = None,
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optimize: bool = True,
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load: bool = True,
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**kwargs,
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):
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"""
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Initialize hyperopt-optimizable Boolean Parameter.
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It's a shortcut to `CategoricalParameter([True, False])`.
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:param default: A default value. If not specified, first item from specified space will be
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used.
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:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
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parameter field
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name is prefixed with 'buy_' or 'sell_'.
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:param optimize: Include parameter in hyperopt optimizations.
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:param load: Load parameter value from {space}_params.
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:param kwargs: Extra parameters to skopt.space.Categorical.
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"""
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categories = [True, False]
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super().__init__(
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categories=categories,
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default=default,
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space=space,
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optimize=optimize,
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load=load,
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**kwargs,
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)
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