def build_graph(self, entity: Entity, cell,
queries: List[Union[DataQueryInfo, MeasureQueryInfo]],
rdate_entity_map: Dict[str, Set[Tuple]],
overrides: Optional[List]):
""" Generates the nested cell graph and keeps a map of leaf data queries to processors"""
self.data_cell = cell
self.__add_required_rdates(entity, rdate_entity_map)
attributes = self.__dict__
if self.measure_processor:
queries.append(
MeasureQueryInfo(attr='a', processor=self, entity=entity))
for attr_name, child in self.children.items():
if isinstance(child, DataCoordinate):
# Override coordinate dimensions
if overrides:
override_dimensions = list(
filter(lambda x: x.coordinate == child, overrides))
if len(override_dimensions):
use_default = True
for override in overrides:
if override.coordinate_id == child.id:
child.set_dimensions(override.dimensions)
use_default = False
break
if use_default and overrides[0].coordinate_id is None:
child.set_dimensions(overrides[0].dimensions)
if child.frequency == DataFrequency.DAILY:
query = DataQuery(coordinate=child,
start=attributes.get('start'),
end=attributes.get('end'))
else:
query = DataQuery(coordinate=child,
query_type=DataQueryType.LAST)
# track the leaf data query
queries.append(
DataQueryInfo(attr=attr_name,
processor=self,
query=query,
entity=entity))
elif isinstance(child, BaseProcessor):
# Set the children's parent fields
child.parent = self
child.parent_attr = attr_name
child.build_graph(entity, cell, queries, rdate_entity_map,
overrides)
elif isinstance(child, DataQueryInfo):
child.parent = self
child.parent_attr = attr_name
child.processor = self
queries.append(child)
def get_benchmark_coordinate(self) -> DataQueryInfo:
coordinate = DataCoordinate(measure=DataMeasure.CLOSE_PRICE,
frequency=DataFrequency.DAILY)
data_query = DataQuery(coordinate=coordinate,
start=self.start,
end=self.end)
return DataQueryInfo('benchmark', None, data_query, self.benchmark)
def build_graph(self, entity: Entity, cell, queries: List[DataQueryInfo],
overrides: Optional[List]):
""" Generates the nested cell graph and keeps a map of leaf data queries to processors"""
self.data_cell = cell
attributes = self.__dict__
for attr_name, child in self.children.items():
if isinstance(child, DataCoordinate):
# Override coordinate dimensions
if overrides:
override_dimensions = list(
filter(lambda x: x.coordinate == child, overrides))
if len(override_dimensions):
child.set_dimensions(overrides[0].dimensions)
if child.frequency == DataFrequency.DAILY:
query = DataQuery(coordinate=child,
start=attributes.get('start'),
end=attributes.get('end'))
else:
query = DataQuery(coordinate=child,
query_type=DataQueryType.LAST)
# track the leaf data query
queries.append(
DataQueryInfo(attr=attr_name,
processor=self,
query=query,
entity=entity))
elif isinstance(child, BaseProcessor):
# Set the children's parent fields
child.parent = self
child.parent_attr = attr_name
child.build_graph(entity, cell, queries, overrides)
elif isinstance(child, DataQueryInfo):
child.parent = self
child.parent_attr = attr_name
child.processor = self
queries.append(child)
def get_excess_returns_query(self) -> DataQueryInfo:
marquee_id = SharpeAssets[self.currency.value].value
entity = Stock(marquee_id, "", "")
coordinate: DataCoordinate = DataCoordinate(
measure=DataMeasure.CLOSE_PRICE,
frequency=DataFrequency.DAILY
)
data_query: DataQuery = DataQuery(coordinate=coordinate, start=self.start, end=self.end)
return DataQueryInfo('excess_returns', None, data_query, entity)
