bayesline.api.equity.TSBetaExposureGroupSettings#

model TSBetaExposureGroupSettings#

Exposure group settings for time-series beta exposures from uploaded time-series.

Computes rolling betas between asset returns and uploaded factor time-series at report time, without requiring a risk dataset rebuild.

Fields#
Field	Type	Required	Default	Constraints
`currency`	`str`	No	`'USD'`
`exclude`	`list`[`str`]	No	factory
`exposure_type`	`Literal`[‘tsbeta’]	No	`'tsbeta'`
`factor_group`	`str`	Yes
`gaussianize`	`bool`	No	`True`
`gaussianize_maintain_zeros`	`bool`	No	`False`
`include`	`Literal`[‘All’] \| `list`[`str`]	No	`'All'`
`net_of`	`list`[`str`]	No	factory
`return_clip_bounds`	`tuple`[`float` \| `None`, `float` \| `None`]	No	`(None, None)`
`rolling_regression`	`RollingBetaSettings` \| `RollingHuberBetaSettings`	No	factory
`tsfactors_source`	`str`	Yes
`window`	`int`	No	`252`	ge=2

field exclude: list[str] [Optional]#: Factor names to exclude from the uploaded dataset.

field factor_group: str [Required]#: The output factor group name for these exposures.

field gaussianize: bool [Optional]#: Whether to gaussianize the resulting exposures.

field gaussianize_maintain_zeros: bool [Optional]#: Whether to maintain zeros when gaussianizing the exposures.

field include: Literal['All'] | list[str] [Optional]#: Factor names to include from the uploaded dataset. ‘All’ includes all.

field net_of: list[str] [Optional]#: Factor groups this group’s exposures are taken net of (weighted-orthogonalized against) under the exposure settings’ orthogonalization_weights, e.g. a style block net of [‘market’, ‘industry’]. See orthogonalization_weights for what moves in a consuming model and the covariance caveat.

field return_clip_bounds: tuple[float | None, float | None] [Optional]#: Clip asset returns to (lower, upper) before regression. None means no clip.

field rolling_regression: RollingBetaSettings | RollingHuberBetaSettings [Optional]#: Rolling regression method for beta computation.

field tsfactors_source: str [Required]#: The name of the uploaded time-series factors dataset.

field window: int [Optional]#

Rolling window size for time-series beta computation.

Constraints: