dau_core.workflows

Registrable ccflow workflows over the temporal goldens.

HeroWorkflow is the composable form of dau_core.hero.run_hero_workflow(): the bar clock, rolling window, tolerance, and rolled field are configuration; the market stream under evaluation is the runtime input, a hashable dau_core.temporal.MarketStream carried in a GenericContext. The result is the typed dau_core.hero.HeroResult.

Classes

HeroWorkflow

The predeclared align -> derive -> bar -> roll pipeline over the

Module Contents

class dau_core.workflows.HeroWorkflow(/, **data: Any)

Bases: dau_core.models.CheckedMixin, ccflow.CallableModel

The predeclared align -> derive -> bar -> roll pipeline over the temporal goldens, recording the cardinality funnel and stage outputs.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

bar_width: int

roll_window: int

tolerance: int | None = None

roll_value: str = 'close'

model_post_init(_context: object) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

__call__(context: ccflow.GenericContext[dau_core.temporal.MarketStream]) → dau_core.hero.HeroResult

This method produces the result for the given context.

Instead of passing the context, one can pass an object that pydantic will try to validate as the context. Additionally, if kwargs are passed instead of the context, it will use these to construct the context.