435 lines
18 KiB
Python
435 lines
18 KiB
Python
"""
|
|
IHyperStrategy interface, hyperoptable Parameter class.
|
|
This module defines a base class for auto-hyperoptable strategies.
|
|
"""
|
|
import logging
|
|
from abc import ABC, abstractmethod
|
|
from contextlib import suppress
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
|
|
|
|
from freqtrade.misc import deep_merge_dicts, json_load
|
|
from freqtrade.optimize.hyperopt_tools import HyperoptTools
|
|
|
|
|
|
with suppress(ImportError):
|
|
from skopt.space import Integer, Real, Categorical
|
|
from freqtrade.optimize.space import SKDecimal
|
|
|
|
from freqtrade.enums import RunMode
|
|
from freqtrade.exceptions import OperationalException
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class BaseParameter(ABC):
|
|
"""
|
|
Defines a parameter that can be optimized by hyperopt.
|
|
"""
|
|
category: Optional[str]
|
|
default: Any
|
|
value: Any
|
|
in_space: bool = False
|
|
name: str
|
|
|
|
def __init__(self, *, default: Any, space: Optional[str] = None,
|
|
optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable parameter.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter field
|
|
name is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
|
|
"""
|
|
if 'name' in kwargs:
|
|
raise OperationalException(
|
|
'Name is determined by parameter field name and can not be specified manually.')
|
|
self.category = space
|
|
self._space_params = kwargs
|
|
self.value = default
|
|
self.optimize = optimize
|
|
self.load = load
|
|
|
|
def __repr__(self):
|
|
return f'{self.__class__.__name__}({self.value})'
|
|
|
|
@abstractmethod
|
|
def get_space(self, name: str) -> Union['Integer', 'Real', 'SKDecimal', 'Categorical']:
|
|
"""
|
|
Get-space - will be used by Hyperopt to get the hyperopt Space
|
|
"""
|
|
|
|
|
|
class NumericParameter(BaseParameter):
|
|
""" Internal parameter used for Numeric purposes """
|
|
float_or_int = Union[int, float]
|
|
default: float_or_int
|
|
value: float_or_int
|
|
|
|
def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
|
|
high: Optional[float_or_int] = None, *, default: float_or_int,
|
|
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable numeric parameter.
|
|
Cannot be instantiated, but provides the validation for other numeric parameters
|
|
:param low: Lower end (inclusive) of optimization space or [low, high].
|
|
:param high: Upper end (inclusive) of optimization space.
|
|
Must be none of entire range is passed first parameter.
|
|
:param default: A default value.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter fieldname is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.*.
|
|
"""
|
|
if high is not None and isinstance(low, Sequence):
|
|
raise OperationalException(f'{self.__class__.__name__} space invalid.')
|
|
if high is None or isinstance(low, Sequence):
|
|
if not isinstance(low, Sequence) or len(low) != 2:
|
|
raise OperationalException(f'{self.__class__.__name__} space must be [low, high]')
|
|
self.low, self.high = low
|
|
else:
|
|
self.low = low
|
|
self.high = high
|
|
|
|
super().__init__(default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
|
|
class IntParameter(NumericParameter):
|
|
default: int
|
|
value: int
|
|
|
|
def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
|
|
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable integer parameter.
|
|
:param low: Lower end (inclusive) of optimization space or [low, high].
|
|
:param high: Upper end (inclusive) of optimization space.
|
|
Must be none of entire range is passed first parameter.
|
|
:param default: A default value.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter fieldname is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.Integer.
|
|
"""
|
|
|
|
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
def get_space(self, name: str) -> 'Integer':
|
|
"""
|
|
Create skopt optimization space.
|
|
:param name: A name of parameter field.
|
|
"""
|
|
return Integer(low=self.low, high=self.high, name=name, **self._space_params)
|
|
|
|
@property
|
|
def range(self):
|
|
"""
|
|
Get each value in this space as list.
|
|
Returns a List from low to high (inclusive) in Hyperopt mode.
|
|
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
|
|
calculating 100ds of indicators.
|
|
"""
|
|
if self.in_space and self.optimize:
|
|
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
|
|
return range(self.low, self.high + 1)
|
|
else:
|
|
return range(self.value, self.value + 1)
|
|
|
|
|
|
class RealParameter(NumericParameter):
|
|
default: float
|
|
value: float
|
|
|
|
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
|
|
default: float, space: Optional[str] = None, optimize: bool = True,
|
|
load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable floating point parameter with unlimited precision.
|
|
:param low: Lower end (inclusive) of optimization space or [low, high].
|
|
:param high: Upper end (inclusive) of optimization space.
|
|
Must be none if entire range is passed first parameter.
|
|
:param default: A default value.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter fieldname is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.Real.
|
|
"""
|
|
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
def get_space(self, name: str) -> 'Real':
|
|
"""
|
|
Create skopt optimization space.
|
|
:param name: A name of parameter field.
|
|
"""
|
|
return Real(low=self.low, high=self.high, name=name, **self._space_params)
|
|
|
|
|
|
class DecimalParameter(NumericParameter):
|
|
default: float
|
|
value: float
|
|
|
|
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
|
|
default: float, decimals: int = 3, space: Optional[str] = None,
|
|
optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable decimal parameter with a limited precision.
|
|
:param low: Lower end (inclusive) of optimization space or [low, high].
|
|
:param high: Upper end (inclusive) of optimization space.
|
|
Must be none if entire range is passed first parameter.
|
|
:param default: A default value.
|
|
:param decimals: A number of decimals after floating point to be included in testing.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter fieldname is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.Integer.
|
|
"""
|
|
self._decimals = decimals
|
|
default = round(default, self._decimals)
|
|
|
|
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
def get_space(self, name: str) -> 'SKDecimal':
|
|
"""
|
|
Create skopt optimization space.
|
|
:param name: A name of parameter field.
|
|
"""
|
|
return SKDecimal(low=self.low, high=self.high, decimals=self._decimals, name=name,
|
|
**self._space_params)
|
|
|
|
@property
|
|
def range(self):
|
|
"""
|
|
Get each value in this space as list.
|
|
Returns a List from low to high (inclusive) in Hyperopt mode.
|
|
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
|
|
calculating 100ds of indicators.
|
|
"""
|
|
if self.in_space and self.optimize:
|
|
low = int(self.low * pow(10, self._decimals))
|
|
high = int(self.high * pow(10, self._decimals)) + 1
|
|
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
|
|
else:
|
|
return [self.value]
|
|
|
|
|
|
class CategoricalParameter(BaseParameter):
|
|
default: Any
|
|
value: Any
|
|
opt_range: Sequence[Any]
|
|
|
|
def __init__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
|
|
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable parameter.
|
|
:param categories: Optimization space, [a, b, ...].
|
|
:param default: A default value. If not specified, first item from specified space will be
|
|
used.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter field
|
|
name is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.Categorical.
|
|
"""
|
|
if len(categories) < 2:
|
|
raise OperationalException(
|
|
'CategoricalParameter space must be [a, b, ...] (at least two parameters)')
|
|
self.opt_range = categories
|
|
super().__init__(default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
def get_space(self, name: str) -> 'Categorical':
|
|
"""
|
|
Create skopt optimization space.
|
|
:param name: A name of parameter field.
|
|
"""
|
|
return Categorical(self.opt_range, name=name, **self._space_params)
|
|
|
|
@property
|
|
def range(self):
|
|
"""
|
|
Get each value in this space as list.
|
|
Returns a List of categories in Hyperopt mode.
|
|
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
|
|
calculating 100ds of indicators.
|
|
"""
|
|
if self.in_space and self.optimize:
|
|
return self.opt_range
|
|
else:
|
|
return [self.value]
|
|
|
|
|
|
class BooleanParameter(CategoricalParameter):
|
|
|
|
def __init__(self, *, default: Optional[Any] = None,
|
|
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
|
|
"""
|
|
Initialize hyperopt-optimizable Boolean Parameter.
|
|
It's a shortcut to `CategoricalParameter([True, False])`.
|
|
:param default: A default value. If not specified, first item from specified space will be
|
|
used.
|
|
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
|
|
parameter field
|
|
name is prefixed with 'buy_' or 'sell_'.
|
|
:param optimize: Include parameter in hyperopt optimizations.
|
|
:param load: Load parameter value from {space}_params.
|
|
:param kwargs: Extra parameters to skopt.space.Categorical.
|
|
"""
|
|
|
|
categories = [True, False]
|
|
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
|
|
load=load, **kwargs)
|
|
|
|
|
|
class HyperStrategyMixin(object):
|
|
"""
|
|
A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
|
|
strategy logic.
|
|
"""
|
|
|
|
def __init__(self, config: Dict[str, Any], *args, **kwargs):
|
|
"""
|
|
Initialize hyperoptable strategy mixin.
|
|
"""
|
|
self.config = config
|
|
self.ft_buy_params: List[BaseParameter] = []
|
|
self.ft_sell_params: List[BaseParameter] = []
|
|
self.ft_protection_params: List[BaseParameter] = []
|
|
|
|
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
|
|
|
|
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
|
|
"""
|
|
Find all optimizable parameters and return (name, attr) iterator.
|
|
:param category:
|
|
:return:
|
|
"""
|
|
if category not in ('buy', 'sell', 'protection', None):
|
|
raise OperationalException(
|
|
'Category must be one of: "buy", "sell", "protection", None.')
|
|
|
|
if category is None:
|
|
params = self.ft_buy_params + self.ft_sell_params + self.ft_protection_params
|
|
else:
|
|
params = getattr(self, f"ft_{category}_params")
|
|
|
|
for par in params:
|
|
yield par.name, par
|
|
|
|
@classmethod
|
|
def detect_parameters(cls, category: str) -> Iterator[Tuple[str, BaseParameter]]:
|
|
""" Detect all parameters for 'category' """
|
|
for attr_name in dir(cls):
|
|
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
|
|
attr = getattr(cls, attr_name)
|
|
if issubclass(attr.__class__, BaseParameter):
|
|
if (attr_name.startswith(category + '_')
|
|
and attr.category is not None and attr.category != category):
|
|
raise OperationalException(
|
|
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
|
|
if (category == attr.category or
|
|
(attr_name.startswith(category + '_') and attr.category is None)):
|
|
yield attr_name, attr
|
|
|
|
@classmethod
|
|
def detect_all_parameters(cls) -> Dict:
|
|
""" Detect all parameters and return them as a list"""
|
|
params: Dict = {
|
|
'buy': list(cls.detect_parameters('buy')),
|
|
'sell': list(cls.detect_parameters('sell')),
|
|
'protection': list(cls.detect_parameters('protection')),
|
|
}
|
|
params.update({
|
|
'count': len(params['buy'] + params['sell'] + params['protection'])
|
|
})
|
|
|
|
return params
|
|
|
|
def _load_hyper_params(self, hyperopt: bool = False) -> None:
|
|
"""
|
|
Load Hyperoptable parameters
|
|
"""
|
|
params = self.load_params_from_file()
|
|
params = params.get('params', {})
|
|
self._ft_params_from_file = params
|
|
buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
|
|
sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
|
|
protection_params = deep_merge_dicts(params.get('protection', {}),
|
|
getattr(self, 'protection_params', {}))
|
|
|
|
self._load_params(buy_params, 'buy', hyperopt)
|
|
self._load_params(sell_params, 'sell', hyperopt)
|
|
self._load_params(protection_params, 'protection', hyperopt)
|
|
|
|
def load_params_from_file(self) -> Dict:
|
|
filename_str = getattr(self, '__file__', '')
|
|
if not filename_str:
|
|
return {}
|
|
filename = Path(filename_str).with_suffix('.json')
|
|
|
|
if filename.is_file():
|
|
logger.info(f"Loading parameters from file {filename}")
|
|
try:
|
|
params = json_load(filename.open('r'))
|
|
if params.get('strategy_name') != self.__class__.__name__:
|
|
raise OperationalException('Invalid parameter file provided.')
|
|
return params
|
|
except ValueError:
|
|
logger.warning("Invalid parameter file format.")
|
|
return {}
|
|
logger.info("Found no parameter file.")
|
|
|
|
return {}
|
|
|
|
def _load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
|
|
"""
|
|
Set optimizable parameter values.
|
|
:param params: Dictionary with new parameter values.
|
|
"""
|
|
if not params:
|
|
logger.info(f"No params for {space} found, using default values.")
|
|
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
|
|
|
|
for attr_name, attr in self.detect_parameters(space):
|
|
attr.name = attr_name
|
|
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
|
|
if not attr.category:
|
|
attr.category = space
|
|
|
|
param_container.append(attr)
|
|
|
|
if params and attr_name in params:
|
|
if attr.load:
|
|
attr.value = params[attr_name]
|
|
logger.info(f'Strategy Parameter: {attr_name} = {attr.value}')
|
|
else:
|
|
logger.warning(f'Parameter "{attr_name}" exists, but is disabled. '
|
|
f'Default value "{attr.value}" used.')
|
|
else:
|
|
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
|
|
|
|
def get_no_optimize_params(self):
|
|
"""
|
|
Returns list of Parameters that are not part of the current optimize job
|
|
"""
|
|
params = {
|
|
'buy': {},
|
|
'sell': {},
|
|
'protection': {},
|
|
}
|
|
for name, p in self.enumerate_parameters():
|
|
if not p.optimize or not p.in_space:
|
|
params[p.category][name] = p.value
|
|
return params
|