dau_core.workflows
==================

.. py:module:: dau_core.workflows

.. autoapi-nested-parse::

   Registrable ccflow workflows over the temporal goldens.

   ``HeroWorkflow`` is the composable form of
   :func:`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
   :class:`dau_core.temporal.MarketStream` carried in a ``GenericContext``.
   The result is the typed :class:`dau_core.hero.HeroResult`.



Classes
-------

.. autoapisummary::

   dau_core.workflows.HeroWorkflow


Module Contents
---------------

.. py:class:: HeroWorkflow(/, **data: Any)

   Bases: :py:obj:`dau_core.models.CheckedMixin`, :py:obj:`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.


   .. py:attribute:: model_config
      :type:  ClassVar

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


   .. py:attribute:: bar_width
      :type:  int


   .. py:attribute:: roll_window
      :type:  int


   .. py:attribute:: tolerance
      :type:  int | None
      :value: None



   .. py:attribute:: roll_value
      :type:  str
      :value: 'close'



   .. py:method:: 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.



   .. py:method:: __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.