406 lines
16 KiB
Python
406 lines
16 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 contextlib import suppress
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from pathlib import Path
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from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
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from freqtrade.misc import deep_merge_dicts, json_load
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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with suppress(ImportError):
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from skopt.space import Integer, Real, Categorical
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from freqtrade.optimize.space import SKDecimal
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from freqtrade.enums import RunMode
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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: Optional[str]
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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__(self, *, default: Any, space: Optional[str] = None,
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optimize: bool = True, load: bool = True, **kwargs):
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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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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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class NumericParameter(BaseParameter):
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""" Internal parameter used for Numeric purposes """
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float_or_int = Union[int, float]
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default: float_or_int
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value: float_or_int
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def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
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high: Optional[float_or_int] = None, *, default: float_or_int,
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space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
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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,
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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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def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
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space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
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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__(low=low, high=high, default=default, space=space, optimize=optimize,
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load=load, **kwargs)
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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.in_space and self.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__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
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default: float, space: Optional[str] = None, optimize: bool = True,
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load: bool = True, **kwargs):
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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__(low=low, high=high, default=default, space=space, optimize=optimize,
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load=load, **kwargs)
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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__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
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default: float, decimals: int = 3, space: Optional[str] = None,
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optimize: bool = True, load: bool = True, **kwargs):
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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__(low=low, high=high, default=default, space=space, optimize=optimize,
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load=load, **kwargs)
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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(low=self.low, high=self.high, decimals=self._decimals, name=name,
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**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.in_space and self.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__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
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space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
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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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self.opt_range = categories
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super().__init__(default=default, space=space, optimize=optimize,
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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.in_space and self.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 HyperStrategyMixin(object):
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"""
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A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
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strategy logic.
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"""
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def __init__(self, config: Dict[str, Any], *args, **kwargs):
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"""
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Initialize hyperoptable strategy mixin.
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"""
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self.config = config
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self.ft_buy_params: List[BaseParameter] = []
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self.ft_sell_params: List[BaseParameter] = []
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self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
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def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
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"""
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Find all optimizable parameters and return (name, attr) iterator.
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:param category:
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:return:
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"""
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if category not in ('buy', 'sell', None):
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raise OperationalException('Category must be one of: "buy", "sell", None.')
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if category is None:
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params = self.ft_buy_params + self.ft_sell_params
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else:
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params = getattr(self, f"ft_{category}_params")
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for par in params:
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yield par.name, par
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@classmethod
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def detect_parameters(cls, category: str) -> Iterator[Tuple[str, BaseParameter]]:
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""" Detect all parameters for 'category' """
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for attr_name in dir(cls):
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if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
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attr = getattr(cls, attr_name)
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if issubclass(attr.__class__, BaseParameter):
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if (attr_name.startswith(category + '_')
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and attr.category is not None and attr.category != category):
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raise OperationalException(
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f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
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if (category == attr.category or
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(attr_name.startswith(category + '_') and attr.category is None)):
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yield attr_name, attr
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@classmethod
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def detect_all_parameters(cls) -> Dict:
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""" Detect all parameters and return them as a list"""
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params: Dict = {
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'buy': list(cls.detect_parameters('buy')),
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'sell': list(cls.detect_parameters('sell')),
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}
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params.update({
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'count': len(params['buy'] + params['sell'])
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})
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return params
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def _load_hyper_params(self, hyperopt: bool = False) -> None:
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"""
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Load Hyperoptable parameters
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"""
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params = self.load_params_from_file()
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params = params.get('params', {})
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self._ft_params_from_file = params
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buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
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sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
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self._load_params(buy_params, 'buy', hyperopt)
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self._load_params(sell_params, 'sell', hyperopt)
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def load_params_from_file(self) -> Dict:
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filename_str = getattr(self, '__file__', '')
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if not filename_str:
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return {}
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filename = Path(filename_str).with_suffix('.json')
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if filename.is_file():
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logger.info(f"Loading parameters from file {filename}")
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try:
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params = json_load(filename.open('r'))
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if params.get('strategy_name') != self.__class__.__name__:
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raise OperationalException('Invalid parameter file provided.')
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return params
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except ValueError:
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logger.warning("Invalid parameter file format.")
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return {}
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logger.info("Found no parameter file.")
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return {}
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def _load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
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"""
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Set optimizable parameter values.
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:param params: Dictionary with new parameter values.
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"""
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if not params:
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logger.info(f"No params for {space} found, using default values.")
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param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
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for attr_name, attr in self.detect_parameters(space):
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attr.name = attr_name
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attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
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if not attr.category:
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attr.category = space
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param_container.append(attr)
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if params and attr_name in params:
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if attr.load:
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attr.value = params[attr_name]
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logger.info(f'Strategy Parameter: {attr_name} = {attr.value}')
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else:
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logger.warning(f'Parameter "{attr_name}" exists, but is disabled. '
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f'Default value "{attr.value}" used.')
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else:
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logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
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def get_no_optimize_params(self):
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"""
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Returns list of Parameters that are not part of the current optimize job
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"""
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params = {
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'buy': {},
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'sell': {}
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}
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for name, p in self.enumerate_parameters():
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if not p.optimize or not p.in_space:
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params[p.category][name] = p.value
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return params
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