bayesline.api.equity.AsyncUniverseApi

class bayesline.api.equity.AsyncUniverseApi

Provide access to universe data and operations.

A universe represents a filtered set of assets with specific properties.

__init__()

Methods

__init__()
counts([dates, ...])	Get universe counts with optional grouping and aggregation.
counts_as_task([dates, ...])
coverage([id_type])	Get the list of all asset IDs this universe covers.
coverage_as_task([id_type])
dates(*[, range_only, trade_only])	Get the list of all dates this universe covers.
get(*[, start, end, id_type, filter_tradedays])	Get universe data for a specific date range.
get_as_task(*[, start, end, id_type, ...])
input_id_mapping(*[, id_type, filter_mode, mode])	Get input ID mappings with various filtering and output modes.

Attributes

id_types	Get the supported ID types for this universe.
settings	Get the settings used to create this universe.

abstract property settings: UniverseSettings

Get the settings used to create this universe.

Returns

UniverseSettings

The settings used to create this universe.

abstract property id_types: list[Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name']]

Get the supported ID types for this universe.

Returns

list[IdType]

Supported ID types for this universe.

abstract async coverage(id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None) → list[str]

Get the list of all asset IDs this universe covers.

Parameters

id_typeIdType | None, default=None

The ID type to return asset IDs in, e.g. ticker.

Raises

ValueError

If the given ID type is not supported.

Returns

list[str]

List of all asset IDs this universe covers, in the given ID type.

abstract async coverage_as_task(id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None) → AsyncTask[list[str]]

abstract async dates(*, range_only: bool = False, trade_only: bool = False) → list[date]

Get the list of all dates this universe covers.

Parameters

range_onlybool, default=False

If True, returns the first and last date only.

trade_onlybool, default=False

If True, filter down the dates to trade dates only.

Returns

list[dt.date]

List of all dates this universe covers.

abstract async counts(dates: bool = True, categorical_hierarchy_levels: dict[str, int] | None = None, id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None, labels: bool = True) → DataFrame

Get universe counts with optional grouping and aggregation.

Parameters

datesbool, default=True

If True, groups by dates.

categorical_hierarchy_levelsdict[str, int] | None, default=None

The level of categorical aggregation to group by. The key is the hierarchy name, the value is the level. If None, no categorical aggregation is done. A level of -1 means to use all levels.

id_typeIdType | None, default=None

The ID type to calculate the daily stats for, e.g. ticker, which is relevant as the coverage may differ by ID type. The given ID type must be supported, i.e. in id_types.

labelsbool, default=True

If True, return labels for the counts, otherwise use the codes.

Returns

pl.DataFrame

Universe counts. If grouped by dates then the count will be given. If not grouped by dates then the mean/min/max across all dates will be given.

abstract async counts_as_task(dates: bool = True, categorical_hierarchy_levels: dict[str, int] | None = None, id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None, labels: bool = True) → AsyncTask[DataFrame]

abstract async input_id_mapping(*, id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None, filter_mode: Literal['all', 'mapped', 'unmapped'] = 'all', mode: Literal['all', 'daily-counts', 'input-asset-counts', 'latest-name'] = 'all') → DataFrame

Get input ID mappings with various filtering and output modes.

Parameters

id_typeIdType | None, default=None

The ID type to return asset IDs in, e.g. ticker, or the default ID type of the universe if None.

filter_modeLiteral[“all”, “mapped”, “unmapped”], default=”all”

If mapped will only consider assets that could be mapped. If unmapped will only consider assets that could not be mapped.

modeLiteral[“all”, “daily-counts”, “input-asset-counts”, “latest-name”], default=”all” # noqa: B950

If all, returns all dated mappings. If daily-counts, returns the daily counts of mapped assets. If input-asset-counts, returns the total counts of input assets. If latest-name, returns the latest name of mapped assets.

Returns

pl.DataFrame

If mode is all, a DataFrame with date, input_asset_id, input_asset_id_type, output_asset_id, output_asset_id_type and name columns. It contains the original input ID space and the mapped IDs. The mapped IDs will be None if for the given date and input ID the asset cannot be mapped. If mode is daily-counts, a DataFrame with date and count columns. If mode is input-asset-counts, a DataFrame with input_asset_id and count columns. If mode is latest-name, a DataFrame with asset_id and name columns.

abstract async get(*, start: str | date | datetime | None = None, end: str | date | datetime | None = None, id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None, filter_tradedays: bool = False) → DataFrame

Get universe data for a specific date range.

Parameters

startDateLike | None, default=None

The start date of the universe to return, inclusive.

endDateLike | None, default=None

The end date of the data to return, inclusive.

id_typeIdType | None, default=None

The ID type to return asset IDs in, e.g. ticker. The given ID type must be supported, i.e. in id_types.

filter_tradedaysbool, default=False

If True, filter down the data to trade dates only.

Raises

ValueError

If the given ID type is not supported or date range is invalid.

Returns

pl.DataFrame

The data for the given date range.

abstract async get_as_task(*, start: str | date | datetime | None = None, end: str | date | datetime | None = None, id_type: Literal['bayesid', 'bayesid_core', 'ticker', 'composite_figi', 'cik', 'cusip8', 'cusip9', 'isin', 'sedol6', 'sedol7', 'proxy', 'name'] | None = None, filter_tradedays: bool = False) → AsyncTask[DataFrame]