Docs

Read Data

Yahoo data API only supported as a programmtic means for querying data. Users can provide their own file locally if they want.

class rescaledranges.tasks.read_data_tasks.DataReader

Bases: prefect.core.task.Task

DataReader Subclass of Prefect Task class for reading either Dask or P

Example

>>> data_reader = DataReader()
>>> data_reader
>>> <Task: DataReader>
>>> data_reader.run()

run(data_frame_type, data_type, ticker)

[summary]

Parameters

• data_frame_type ([type]) – [description]

• data_type ([type]) – [description]

• ticker ([type]) – [description]

Raises

ValueError – [description]

Returns

[description]

Return type

[type]

Data PreProcessing

class rescaledranges.tasks.preprocess_tasks.PreProcess

Bases: prefect.core.task.Task

PreProcess Subclass of Prefect Task class.

Example

>>> prep = PreProcess()
>>> prep.run()

run(data)

The run() method is called (with arguments, if appropriate) to run a task.

Note: The implemented run method cannot have *args in its signature. In addition, the following keywords are reserved: upstream_tasks, task_args and mapped.

If a task has arguments in its run() method, these can be bound either by using the functional API and _calling_ the task instance, or by using self.bind directly.

In addition to running arbitrary functions, tasks can interact with Prefect in a few ways: <ul><li> Return an optional result. When this function runs successfully,

the task is considered successful and the result (if any) can be made available to downstream tasks. </li>

<li> Raise an error. Errors are interpreted as failure. </li> <li> Raise a [signal](../engine/signals.html). Signals can include FAIL, SUCCESS,

RETRY, SKIP, etc. and indicate that the task should be put in the indicated state.

<ul> <li> FAIL will lead to retries if appropriate </li> <li> SUCCESS will cause the task to be marked successful </li> <li> RETRY will cause the task to be marked for retry, even if max_retries

has been exceeded </li>

<li> SKIP will skip the task and possibly propogate the skip state through the

flow, depending on whether downstream tasks have skip_on_upstream_skip=True.

</li></ul>

</li></ul>

Rescaled Range Calculations

This code is the guts of the package.

class rescaledranges.tasks.rescaled_range_tasks.RescaledRange

Bases: prefect.core.task.Task

RescaledRange Subclass of Prefect Task Class.

Example

>>> rescaled_ranges = RescaledRange()
>>> rescaled_ranges.run(data)

calc_r(column_name)

cummean(column_name)

cumstd()

mean_adjust(column_name)

run(data)

[summary]

Parameters

data ([type]) – [description]

Returns

[description]

Return type

[type]

Visualization Functionalities

Still a work in progress.

class rescaledranges.tasks.visual_tasks.Visualize

Bases: prefect.core.task.Task

Visualize Subclass of Prefect Task class.

Example

>>> viz = Visualize()
>>> viz
>>> <Task: Visualize>

plot(ticker_data)

run(ticker_data)

The run() method is called (with arguments, if appropriate) to run a task.

Note: The implemented run method cannot have *args in its signature. In addition, the following keywords are reserved: upstream_tasks, task_args and mapped.

If a task has arguments in its run() method, these can be bound either by using the functional API and _calling_ the task instance, or by using self.bind directly.

In addition to running arbitrary functions, tasks can interact with Prefect in a few ways: <ul><li> Return an optional result. When this function runs successfully,

the task is considered successful and the result (if any) can be made available to downstream tasks. </li>

<li> Raise an error. Errors are interpreted as failure. </li> <li> Raise a [signal](../engine/signals.html). Signals can include FAIL, SUCCESS,

RETRY, SKIP, etc. and indicate that the task should be put in the indicated state.

<ul> <li> FAIL will lead to retries if appropriate </li> <li> SUCCESS will cause the task to be marked successful </li> <li> RETRY will cause the task to be marked for retry, even if max_retries

has been exceeded </li>

<li> SKIP will skip the task and possibly propogate the skip state through the

flow, depending on whether downstream tasks have skip_on_upstream_skip=True.

</li></ul>

</li></ul>

signal_summary()

visualize_graph(flow)