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Split "enabled" to "load" and "optimize" parameters.
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6954a1e029
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@ -25,7 +25,8 @@ class HyperOptAuto(IHyperOpt):
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def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
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def populate_buy_trend(dataframe: DataFrame, metadata: dict):
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for attr_name, attr in self.strategy.enumerate_parameters('buy'):
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attr.value = params[attr_name]
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if attr.optimize:
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attr.value = params[attr_name]
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return self.strategy.populate_buy_trend(dataframe, metadata)
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return populate_buy_trend
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@ -33,7 +34,8 @@ class HyperOptAuto(IHyperOpt):
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def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
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def populate_buy_trend(dataframe: DataFrame, metadata: dict):
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for attr_name, attr in self.strategy.enumerate_parameters('sell'):
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attr.value = params[attr_name]
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if attr.optimize:
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attr.value = params[attr_name]
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return self.strategy.populate_sell_trend(dataframe, metadata)
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return populate_buy_trend
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@ -53,7 +55,7 @@ class HyperOptAuto(IHyperOpt):
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def _generate_indicator_space(self, category):
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for attr_name, attr in self.strategy.enumerate_parameters(category):
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if attr.enabled:
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if attr.optimize:
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yield attr.get_space(attr_name)
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def _get_indicator_space(self, category, fallback_method_name):
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@ -27,12 +27,14 @@ class BaseParameter(ABC):
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opt_range: Sequence[Any]
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def __init__(self, *, opt_range: Sequence[Any], default: Any, space: Optional[str] = None,
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enabled: bool = True, **kwargs):
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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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@ -42,7 +44,8 @@ class BaseParameter(ABC):
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self._space_params = kwargs
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self.value = default
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self.opt_range = opt_range
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self.enabled = enabled
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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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@ -60,7 +63,7 @@ class IntParameter(BaseParameter):
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opt_range: Sequence[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, enabled: bool = True, **kwargs):
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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 low: Lower end (inclusive) of optimization space or [low, high].
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@ -69,6 +72,8 @@ class IntParameter(BaseParameter):
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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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if high is not None and isinstance(low, Sequence):
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@ -79,8 +84,8 @@ class IntParameter(BaseParameter):
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opt_range = low
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else:
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opt_range = [low, high]
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super().__init__(opt_range=opt_range, default=default, space=space, enabled=enabled,
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**kwargs)
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super().__init__(opt_range=opt_range, 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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@ -96,7 +101,7 @@ class FloatParameter(BaseParameter):
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opt_range: Sequence[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, enabled: bool = True, **kwargs):
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default: float, 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 low: Lower end (inclusive) of optimization space or [low, high].
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@ -105,6 +110,8 @@ class FloatParameter(BaseParameter):
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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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if high is not None and isinstance(low, Sequence):
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@ -115,8 +122,8 @@ class FloatParameter(BaseParameter):
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opt_range = low
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else:
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opt_range = [low, high]
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super().__init__(opt_range=opt_range, default=default, space=space, enabled=enabled,
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**kwargs)
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super().__init__(opt_range=opt_range, 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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@ -132,7 +139,7 @@ class CategoricalParameter(BaseParameter):
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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, enabled: bool = True, **kwargs):
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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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@ -141,13 +148,15 @@ class CategoricalParameter(BaseParameter):
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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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super().__init__(opt_range=categories, default=default, space=space, enabled=enabled,
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**kwargs)
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super().__init__(opt_range=categories, 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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@ -184,8 +193,7 @@ class HyperStrategyMixin(object):
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if issubclass(attr.__class__, BaseParameter):
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if category is None or category == attr.category or \
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attr_name.startswith(category + '_'):
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if attr.enabled:
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yield attr_name, attr
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yield attr_name, attr
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def _load_params(self, params: dict) -> None:
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"""
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@ -196,7 +204,7 @@ class HyperStrategyMixin(object):
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return
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for attr_name, attr in self.enumerate_parameters():
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if attr_name in params:
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if attr.enabled:
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if attr.load:
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attr.value = params[attr_name]
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logger.info(f'attr_name = {attr.value}')
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else:
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