bayesline.api.equity.RiskDatasetLoaderApi

class bayesline.api.equity.RiskDatasetLoaderApi

API for loading and managing risk datasets.

This class provides functionality for loading, creating, and managing risk datasets through a registry-based approach.

__init__()

Methods

__init__()
create_dataset(name, settings)	Create a new dataset with the given name and settings.
create_dataset_as_task(name, settings)
create_or_replace_dataset(name, settings)	Create a new dataset or replace an existing one.
delete_dataset(name)	Delete the given dataset.
delete_dataset_if_exists(name)	Delete the given dataset if it exists.
get_dataset_names(*[, mode])	Get the names of all available datasets.
get_default_dataset_name()	Get the default dataset name.
list_riskdatasets([name, df])	List all available risk datasets.
load(ref_or_settings, *args, **kwargs)	Load an API instance from settings or reference.

Attributes

settings

Get the settings registry.

abstract get_default_dataset_name() → str

Get the default dataset name.

Returns

str

The default dataset name that will be populated into settings if no dataset name is provided.

abstract get_dataset_names(*, mode: Literal['System', 'User'] | None = None) → dict[str, Literal['ready', 'data_stale', 'needs_update', 'not_ready', 'corrupt']]

Get the names of all available datasets.

Parameters

modeLiteral[“System”, “User”] | None, default=None

System: only system wide datasets (available to all users and provided by the system). User: only user specific datasets (available to the current user). None: all datasets (system wide and user specific).

Returns

dict[str, DatasetStatus]

The names of all available datasets and their status. “ready” means the dataset is ready to use. “data_stale” means the dataset is ready to use but newer data is available. “needs_update” means local data needs to be computed/updated. “not_ready” means the reference dataset is not in a usable state. “corrupt” means the underlying settings are corrupted and need to be repaired.

abstract list_riskdatasets(name: str | None = None, *, df: bool = False) → list[RiskDatasetMetadata] | DataFrame

List all available risk datasets.

Parameters

namestr | None, default=None

The name of the dataset to filter by. If not given, all datasets are returned.

dfbool, default=False

If True, return a polars DataFrame instead of a list of metadata objects.

Returns

list[RiskDatasetMetadata] | pl.DataFrame

The metadata of all available datasets, either as a list of objects or as a DataFrame depending on the df parameter.

create_or_replace_dataset(name: str, settings: RiskDatasetSettings) → RiskDatasetApi

Create a new dataset or replace an existing one.

Parameters

namestr

The name of the dataset to create or replace.

settingsRiskDatasetSettings

The settings for the dataset.

Returns

RiskDatasetApi

The API for the created or replaced dataset.

abstract create_dataset(name: str, settings: RiskDatasetSettings) → RiskDatasetApi

Create a new dataset with the given name and settings.

Parameters

namestr

The name of the dataset to create.

settingsRiskDatasetSettings

The settings for the dataset to create.

Raises

DatasetError

If a dataset with the given name already exists or if the settings are otherwise invalid.

Returns

RiskDatasetApi

The API of the newly created dataset.

abstract create_dataset_as_task(name: str, settings: RiskDatasetSettings) → Task[RiskDatasetApi]

abstract delete_dataset(name: str) → RawSettings

Delete the given dataset.

Parameters

namestr

The name of the dataset to delete.

Raises

KeyError

If the dataset does not exist.

DatasetError

If the dataset could not be deleted.

Returns

RawSettings

The raw settings of the deleted dataset.

delete_dataset_if_exists(name: str) → RawSettings | None

Delete the given dataset if it exists.

Parameters

namestr

The name of the dataset to delete.

Raises

DatasetError

If the dataset could not be deleted.

Returns

RawSettings | None

The raw settings of the deleted dataset if dataset existed.