bayesline.api.equity.AsyncRiskDatasetLoaderApi

class bayesline.api.equity.AsyncRiskDatasetLoaderApi

API for loading and managing risk datasets.

This class provides functionality for loading, creating, and managing risk datasets through a registry-based approach. self.settings is a registry view over the discriminated union AnyRiskDatasetSettings — both reference-based DerivedRiskDatasetSettings and root RootRiskDatasetSettings are addressable through the same handle.

__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_dataset_status(name)	Return the status of a single dataset.
list_riskdatasets([name, df])	List all available risk datasets.
load(ref_or_settings, *args, **kwargs)	Load an API instance from settings or reference.
validate(settings)	Validate settings against the menu for its dataset.

Attributes

settings

Get the settings registry.

abstract async 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.

Datasets owned by the current user are exposed under both their email-qualified name (alice@example.com/MyDS) and a bare alias (MyDS), mapping to the same status. This is intentional — it lets callers look up a dataset by either form against the returned dict (the status-fetch helpers in calendar, universe, exposures, portfolios rely on this). Callers iterating len(...), sorted(...), or building counts from the result should treat the dual-key entries as one logical dataset.

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.

async get_dataset_status(name: str) → Literal['ready', 'data_stale', 'needs_update', 'not_ready', 'corrupt']

Return the status of a single dataset.

Defaults to indexing get_dataset_names(); implementations may override to resolve a single dataset’s status without computing the status of every dataset.

Parameters

namestr

Name of the dataset (bare alias or email-qualified).

Returns

DatasetStatus

The status of the dataset. See get_dataset_names() for the meaning of each value.

Raises

KeyError

If name is not a known dataset.

abstract async 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.

async create_or_replace_dataset(name: str, settings: Annotated[DerivedRiskDatasetSettings | RootRiskDatasetSettings, Discriminator(discriminator=kind, custom_error_type=None, custom_error_message=None, custom_error_context=None)]) → AsyncRiskDatasetApi

Create a new dataset or replace an existing one.

Parameters

namestr

The name of the dataset to create or replace.

settingsAnyRiskDatasetSettings

The settings for the dataset. Pass DerivedRiskDatasetSettings for a reference-based dataset; pass RootRiskDatasetSettings for a root dataset built from upload sources.

Returns

AsyncRiskDatasetApi

The API for the created or replaced dataset.

abstract async create_dataset(name: str, settings: Annotated[DerivedRiskDatasetSettings | RootRiskDatasetSettings, Discriminator(discriminator=kind, custom_error_type=None, custom_error_message=None, custom_error_context=None)]) → AsyncRiskDatasetApi

Create a new dataset with the given name and settings.

Parameters

namestr

The name of the dataset to create.

settingsAnyRiskDatasetSettings

The settings for the dataset to create. Pass DerivedRiskDatasetSettings for a reference-based dataset; pass RootRiskDatasetSettings for a root dataset built from upload sources. Dispatch is by isinstance of the settings type.

Raises

DatasetError

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

Returns

AsyncRiskDatasetApi

The API of the newly created dataset.

abstract async create_dataset_as_task(name: str, settings: Annotated[DerivedRiskDatasetSettings | RootRiskDatasetSettings, Discriminator(discriminator=kind, custom_error_type=None, custom_error_message=None, custom_error_context=None)]) → AsyncTask[AsyncRiskDatasetApi]

abstract async 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.

async 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.