openbb_platform/CONTRIBUTING.md

Contributing to the OpenBB Platform

• Contributing to the OpenBB Platform
  • Introduction
    • Quick look into the OpenBB Platform
      • What is the Standardization Framework?
        • Standardization Caveats
        • Standard QueryParams Example
        • Standard Data Example
      • What is an extension?
        • Types of extensions
  • Dependency Management
    • High-Level Overview
    • Core Dependency Management
      • Installation
      • Using Poetry
    • Core and Extensions
      • Installation
      • Dependency Management with Poetry
  • Developer Guidelines
    • Expectations for Developers
    • How to build OpenBB extensions?
    • Building Extensions: Best Practices
    • How to add a new data point?
      • Identify which type of data you want to add
      • Check if the standard model exists
        • Create Query Parameters model
        • Create Data Output model
        • Build the Fetcher
      • Make the provider visible
    • How to add custom data sources?
      • OpenBB Platform commands
    • Architectural considerations
      • Important classes
      • Import statements
      • The TET pattern
      • Error
      • Data processing commands
        • Python Interface
        • API Interface
  • Contributor Guidelines
    • Expectations for Contributors
    • Quality Assurance
      • Unit tests
      • Integration tests
      • Import time
    • Sharing your extension
      • Publish your extension to PyPI
        • Setup
        • Release
        • Publish
    • Manage extensions
      • Add an extension as a dependency
    • Write code and commit
      • How to create a PR?
        • Branch Naming Conventions

Introduction

This document provides guidelines for contributing to the OpenBB Platform. Throughout this document, we will be differentiating between two types of contributors: Developers and Contributors.

1. Developers: Those who are building new features or extensions for the OpenBB Platform or leveraging the OpenBB Platform.
2. Contributors: Those who contribute to the existing codebase, by opening a Pull Request thus giving back to the community.

Why is this distinction important?

The OpenBB Platform is designed as a foundation for further development. We anticipate a wide range of creative use cases for it. Some use cases may be highly specific or detail-oriented, solving particular problems that may not necessarily fit within the OpenBB Platform Github repository. This is entirely acceptable and even encouraged. This document provides a comprehensive guide on how to build your own extensions, add new data points, and more.

The Developer role, as defined in this document, can be thought of as the foundational role. Developers are those who use the OpenBB Platform as is or build upon it.

Conversely, the Contributor role refers to those who enhance the OpenBB Platform codebase (either by directly adding to the OpenBB Platform or by extending the extension repository). Contributors are willing to go the extra mile, spending additional time on quality assurance, testing, or collaborating with the OpenBB development team to ensure adherence to standards, thereby giving back to the community.

Quick look into the OpenBB Platform

The OpenBB Platform is built by the Open-Source community and is characterized by its core and extensions. The core handles data integration and standardization, while the extensions enable customization and advanced functionalities. The OpenBB Platform is designed to be used both from a Python interface and a REST API.

The REST API is built on top of FastAPI and can be started by running the following command from the root:

uvicorn openbb_platform.core.openbb_core.api.rest_api:app --host 0.0.0.0 --port 8000 --reload

The Python interfaces we provide to users is the openbb python package.

The code you will find in this package is generated from a script and it is just a wrapper around the openbb-core and any installed extensions.

When the user runs import openbb, from openbb import obb or other variants, the script that generates the packaged code is triggered. It detects if there are new extensions installed in the environment and rebuilds the packaged code accordingly. If new extensions are not found, it just uses the current packaged version.

When you are developing chances are you want to manually trigger the package rebuild.

You can do that with:

python -c "import openbb; openbb.build()"

The Python interface can be imported with:

from openbb import obb

This document will take you through two types of contributions:

1. Building a custom extension
2. Contributing directly to the OpenBB Platform

Before moving forward, please take a look at the high-level view of the OpenBB Platform architecture. We will go over each bit in this document.

What is the Standardization Framework?

The Standardization Framework is a set of tools and guidelines that enable the user to query and obtain data in a consistent way across multiple providers.

Each data model should inherit from a standard data model that is already defined inside the OpenBB Platform. All standard models are created and maintained by the OpenBB team.

Usage of these models will unlock a set of perks that are only available to standardized data, namely:

• Can query and output data in a standardized way.
• Can expect extensions that follow standardization to work out-of-the-box.
• Can expect transparently defined schemas for the data that is returned by the API.
• Can expect consistent data types and validation.
• Will work seamlessly with other providers that use the same standard model.

The standard models are defined under the /OpenBB/openbb_platform/core/openbb_core/provider/standard_models directory.

They define the QueryParams and Data models, which are used to query and output data. They are pydantic and you can leverage all the pydantic features such as validators.

Standardization Caveats

The standardization framework is a very powerful tool, but it has some caveats that you should be aware of:

• We standardize fields that are shared between two or more providers. If there is a third provider that doesn't share the same fields, we will declare it as an Optional field.
• When mapping the column names from a provider-specific model to the standard model, the CamelCase to snake_case conversion is done automatically. If the column names are not the same, you'll need to manually map them. (e.g. o -> open)
• The standard models are created and maintained by the OpenBB team. If you want to add a new field to a standard model, you'll need to open a PR to the OpenBB Platform.

Standard QueryParams Example

class EquityHistoricalQueryParams(QueryParams):
    """Equity Historical end of day Query."""
    symbol: str = Field(description=QUERY_DESCRIPTIONS.get("symbol", ""))
    start_date: Optional[date] = Field(
        description=QUERY_DESCRIPTIONS.get("start_date", ""), default=None
    )
    end_date: Optional[date] = Field(
        description=QUERY_DESCRIPTIONS.get("end_date", ""), default=None
    )

The QueryParams is an abstract class that just tells us that we are dealing with query parameters

The OpenBB Platform dynamically knows where the standard models begin in the inheritance tree, so you don't need to worry about it.

Standard Data Example

class EquityHistoricalData(Data):
    """Equity Historical end of day price Data."""

    date: datetime = Field(description=DATA_DESCRIPTIONS.get("date", ""))
    open: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("open", ""))
    high: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("high", ""))
    low: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("low", ""))
    close: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("close", ""))
    volume: float = Field(description=DATA_DESCRIPTIONS.get("volume", ""))
    vwap: Optional[PositiveFloat] = Field(description=DATA_DESCRIPTIONS.get("vwap", ""), default=None)

The Data class is an abstract class that tells us the expected output data. Here we can see a vwap field that is Optional. This is because not all providers share this field while it is shared between two or more providers.

What is an extension?

An extension adds functionality to the OpenBB Platform. It can be a new data source, a new command, a new visualization, etc.

Types of extensions

We primarily have 3 types of extensions:

1. OpenBB Extensions - built and maintained by the OpenBB team (e.g. openbb-equity)
2. Community Extensions - built by anyone and primarily maintained by OpenBB (e.g. openbb-yfinance)
3. Independent Extensions - built and maintained independently by anyone

If your extension is of high quality and you think that it would be a good community extension, you can open a PR to the OpenBB Platform repository and we'll review it.

We encourage independent extensions to be shared with the community by publishing them to PyPI.

Dependency Management

High-Level Overview

• Provider: The base package with no dependencies on other openbb packages.
• Core: Depends on the Provider and serves as the main infrastructural package.
• Extensions: Utility packages that leverage Core's infrastructure. Each extension is its own package.
• Providers: Utility packages extending functionality to different providers, where each provider is its own package.

Dependency Management

Using Poetry

Ensure you're in a fresh conda environment before adjusting dependencies. Dependencies are manages with poetry. Install poetry with pip install poetry

• Add a Dependency: poetry add <my-dependency>
• Update Dependencies:
  • All: poetry update
  • Specific: poetry update <my-dependency>
• Remove a Dependency: poetry remove <my-dependency>

Core and Extensions

Installation

For development setup, use the provided script to install all extensions and their dependencies:

• From the root of the repo call python dev_install.py --extras

Note: If developing an extension, you can avoid installing all extensions to prevent unnecessary overhead.

Dependency Management with Poetry

• Add Platform Extension: poetry add openbb-extension-name [--dev]
• Resolve Conflicts: Adjust versions in pyproject.toml if notified by Poetry.
• Update Dependencies Lock File: poetry lock
• Update Platform: poetry update openbb-platform
• Documentation: Maintain pyproject.toml and poetry.lock for a clear record of dependencies.

Developer Guidelines

Expectations for Developers

1. Use Cases:

   • Ensure that your extensions or features align with the broader goals of the application.
   • Understand that the OpenBB Platform is designed to be foundational; build in a way that complements and doesn't conflict with its core functionalities.

2. Documentation:

   • Provide clear and comprehensive documentation for any new feature or extension you develop.

3. Code Quality:

   • Adhere to the coding standards and conventions of the OpenBB Platform.
   • Ensure your code is maintainable, well-organized, and commented where necessary.

4. Testing:

   • Thoroughly test any new feature or extension to ensure it works as expected.

5. Performance:

   • Ensure that your extensions or features do not adversely affect the performance of the OpenBB Platform.
   • Optimize for scalability, especially if you anticipate high demand for your feature.

6. Collaboration:

   • Engage with the OpenBB community to gather feedback on your developments.

How to build OpenBB extensions?

We have a Cookiecutter template that will help you get started. It serves as a jumpstart for your extension development, so you can focus on the data and not on the boilerplate.

Please refer to the Cookiecutter template and follow the instructions there.

This document will walk you through the steps of adding a new extension to the OpenBB Platform.

The high level steps are:

• Generate the extension structure
• Install your dependencies
• Install your new package
• Use your extension (either from Python or the API interface)
• QA your extension
• Share your extension with the community

Building Extensions: Best Practices

1. Review Platform Dependencies: Before adding any dependency, ensure it aligns with the Platform's existing dependencies.
2. Use Loose Versioning: If possible, specify a range to maintain compatibility. E.g., >=1.4,<1.5.
3. Testing: Test your extension with the Platform's core to avoid conflicts. Both unit and integration tests are recommended.
4. Document Dependencies: Use pyproject.toml and poetry.lock for clear, up-to-date records.

How to add a new data point?

In this section, we'll be adding a new data point to the OpenBB Platform. We will add a new provider with an existing standard data model.

Identify which type of data you want to add

In this example, we'll be adding OHLC stock data that is used by the obb.equity.price.historical command.

Note that, if no command exists for your data, we need to add one under the right router. Each router is categorized under different extensions (equity, currency, crypto, etc.).

Check if the standard model exists

Given the fact that there's already an endpoint for OHLCV stock data, we can check if the standard exists.

In this case, it's EquityHistorical which can be found in /OpenBB/openbb_platform/core/openbb_core/provider/standard_models/equity_historical.

If the standard model doesn't exist:

• you won't need to inherit from it in the next steps.
• all your provider query parameters will be under the **kwargs in the python interface.
• it might not work out-of-the box with other extensions that follow standardization e.g. the charting extension

Create Query Parameters model

Query Parameters are the parameters that are passed to the API endpoint in order to make the request.

For the EquityHistorical example, this would look like the following:

class <ProviderName>EquityHistoricalQueryParams(EquityHistoricalQueryParams):
    """<ProviderName> Equity Historical Query.

    Source: https://www.<provider_name>.co/documentation/
    """

    # provider specific query parameters if any

Create Data Output model

The data output is the data that is returned by the API endpoint. For the EquityHistorical example, this would look like the following:

class <ProviderName>EquityHistoricalData(EquityHistoricalData):
    """<ProviderName> Equity Historical Data.

    Source: https://www.<provider_name>.co/documentation/
    """

    # provider specific data output fields if any

Note that, since EquityHistoricalData inherits from pydantic's BaseModel, we can leverage validators to perform additional checks on the output model. A very good example of this, would be to transform a string date into a datetime object.

Build the Fetcher

The Fetcher class is responsible for making the request to the API endpoint and providing the output.

It will receive the query parameters, and it will return the output while leveraging the pydantic model schemas.

For the EquityHistorical example, this would look like the following:

class <ProviderName>EquityHistoricalFetcher(
    Fetcher[
        <ProviderName>EquityHistoricalQueryParams,
        List[<ProviderName>EquityHistoricalData],
    ]
):
    """Transform the query, extract and transform the data."""

    @staticmethod
    def transform_query(params: Dict[str, Any]) -> <ProviderName>EquityHistoricalQueryParams:
        """Transform the query parameters."""

        return <ProviderName>EquityHistoricalQueryParams(**transformed_params)

    @staticmethod
    def extract_data(
        query: <ProviderName>EquityHistoricalQueryParams,
        credentials: Optional[Dict[str, str]],
        **kwargs: Any,
    ) -> dict:
        """Return the raw data from the endpoint."""

        obtained_data = my_request(query, credentials, **kwargs)

        return obtained_data

    @staticmethod
    def transform_data(
        query: <ProviderName>EquityHistoricalQueryParams,
        data: dict,
        **kwargs: Any,
    ) -> List[<ProviderName>EquityHistoricalData]:
        """Transform the data to the standard format."""

        return [<ProviderName>EquityHistoricalData.model_validate(d) for d in data]

Make sure that you're following the TET pattern when building a Fetcher - Transform, Extract, Transform. See more on this here.

By default the credentials declared on each Provider are required. This means that before a query is executed, we check that all the credentials are present and if not an exception is raised. If you want to make credentials optional on a given fetcher, even though they are declared on the Provider, you can add require_credentials=False to the Fetcher class. See the following example:

class <ProviderName>EquityHistoricalFetcher(
    Fetcher[
        <ProviderName>EquityHistoricalQueryParams,
        List[<ProviderName>EquityHistoricalData],
    ]
):
    """Transform the query, extract and transform the data."""

    require_credentials = False

    ...

Make the provider visible

In order to make the new provider visible to the OpenBB Platform, you'll need to add it to the __init__.py file of the providers/<provider_name>/openbb_<provider_name>/ folder.

"""<Provider Name> Provider module."""
from openbb_core.provider.abstract.provider import Provider

from openbb_<provider_name>.models.equity_historical import <ProviderName>EquityHistoricalFetcher

<provider_name>_provider = Provider(
    name="<provider_name>",
    website="<URL to the provider website>",
    description="Provider description goes here",
    credentials=["api_key"],
    fetcher_dict={
        "EquityHistorical": <ProviderName>EquityHistoricalFetcher,
    },
)

If the provider does not require any credentials, you can remove that parameter. On the other hand, if it requires more than 2 items to authenticate, you can add a list of all the required items to the credentials list.

After running pip install . on openbb_platform/providers/<provider_name> your provider should be ready for usage, both from the Python interface and the API.

How to add custom data sources?

You will get your data either from a CSV file, local database or from an API endpoint.

If you don't want or don't need to partake in the data standardization framework, you have the option to add all the logic straight inside the router file. This is usually the case when you are returning custom data from your local CSV file, or similar. Keep in mind that we also serve the REST API and that you shouldn't send non-serializable objects as a response (e.g. a pandas dataframe).

Saying that, we highly recommend following the standardization framework, as it will make your life easier in the long run and unlock a set of features that are only available to standardized data.

When standardizing, all data is defined using two different pydantic models:

1. Define the query parameters model.
2. Define the resulting data schema model.

The models can be entirely custom, or inherit from the OpenBB standardized models. They enforce a safe and consistent data structure, validation and type checking.

We call this the Know-Your-Data principle.

After you've defined both models, you'll need to define a Fetcher class which contains three methods:

1. transform_query - transforms the query parameters to the format of the API endpoint.
2. extract_data - makes the request to the API endpoint and returns the raw data.
3. transform_data - transforms the raw data into the defined data model.

Note that the Fetcher should inherit from the Fetcher class, which is a generic class that receives the query parameters and the data model as type parameters.

After finalizing your models, you need to make them visible to the Openbb Platform. This is done by adding the Fetcher to the __init__.py file of the <your_package_name>/<your_module_name> folder as part of the Provider.

Any command, that uses the Fetcher class you've just defined, will be calling the transform_query, extract_data and transform_data methods under the hood in order to get the data and output it do the end user.

If you're not sure what's a command and why is it even using the Fetcher class, follow along!

OpenBB Platform commands

The OpenBB Platform will enable you to query and output your data in a very simple way.

Any Platform endpoint will be available both from a Python interface and the API.

The command definition on the Platform follows FastAPI conventions, meaning that you'll be creating endpoints.

The Cookiecutter template generates for you a router.py file with a set of examples that you can follow, namely:

• Perform a simple GET and POST request - without worrying on any custom data definition.
• Using a custom data definition so you get your data the exact way you want it.

You can expect the following endpoint structure when using a Fetcher to serve the data:

@router.command(model="Example")
async def model_example(    # create an async endpoint
    cc: CommandContext,
    provider_choices: ProviderChoices,
    standard_params: StandardParams,
    extra_params: ExtraParams,
) -> OBBject:
    """Example Data."""
    return await OBBject.from_query(Query(**locals()))

Let's break it down:

• @router.command(...) - this tells the OpenBB Platform that this is a command.
• model="Example" - this is the name of the Fetcher dictionary key that you've defined in the __init__.py file of the <your_package_name>/<your_module_name> folder.
• cc: CommandContext - this contains a set of user and system settings that is useful during the execution of the command - eg. api keys.
• provider_choices: ProviderChoices - all the providers that implement the Example Fetcher.
• standard_params: StandardParams - standardized parameters that are common to all providers that implement the Example Fetcher.
• extra_params: ExtraParams - it contains the provider specific arguments that are not standardized.

You only need to change the model parameter to the name of the Fetcher dictionary key and everything else will be handled by the OpenBB Platform.

Architectural considerations

Important classes

Import statements

# The `Data` class
from openbb_core.provider.abstract.data import Data

# The `QueryParams` class
from openbb_core.provider.abstract.query_params import QueryParams

# The `Fetcher` class
from openbb_core.provider.abstract.fetcher import Fetcher

# The `OBBject` class
from openbb_core.app.model.obbject import OBBject

# The `Router` class
from openbb_core.app.router import Router

The TET pattern

The TET pattern is a pattern that we use to build the Fetcher classes. It stands for Transform, Extract, Transform. As the OpenBB Platform has its own standardization framework and the data fetcher are a very important part of it, we need to ensure that the data is transformed and extracted in a consistent way, to help us do that, we came up with the TET pattern, which helps us build and ship faster as we have a clear structure on how to build the Fetcher classes.

1. Transform - transform_query(params: Dict[str, Any]): transforms the query parameters. Given a params dictionary this method should return the transformed query parameters as a QueryParams child so that we can leverage the pydantic model schemas and validation into the next step. This might also be the place do perform some transformations on any given parameter, i.e., if you want to transform an empty date into a datetime.now().date().
2. Extract - extract_data(query: ExampleQueryParams,credentials: Optional[Dict[str, str]],**kwargs: Any,) -> Dict: makes the request to the API endpoint and returns the raw data. Given the transformed query parameters, the credentials and any other extra arguments, this method should return the raw data as a dictionary.
3. Transform - transform_data(query: ExampleQueryParams, data: Dict, **kwargs: Any) -> List[ExampleHistoricalData]: transforms the raw data into the defined data model. Given the transformed query parameters (might be useful for some filtering), the raw data and any other extra arguments, this method should return the transformed data as a list of Data children.

Errors

To ensure a consistent error handling behavior our API relies on the convention below.

Status code	Exception	Detail	Description
400	OpenBBError or child of OpenBBError	Custom message.	Use this to explicitly raise custom exceptions, like EmptyDataError.
422	ValidationError	Pydantic errors dict message.	Automatically raised to inform the user about query validation errors. ValidationErrors outside of the query are treated with status code 500 by default.
500	Any exception not covered above, eg ValueError, ZeroDivisionError	Unexpected error.	Unexpected exceptions, most likely a bug.

Data processing commands

The data processing commands are commands that are used to process the data that may or may not come from the OpenBB Platform. In order to create a data processing framework general enough to be used by any extension, we've created a special abstract class called Data which all standardized (and consequently its child classes) will inherit from.

Why is this important? So that we can ensure that all OBBject.results will share a common ground on which we can apply out-of-the-box data processing commands, such as the ta, qa or the econometrics menus.

But what's really the Data class? It's a pydantic model that inherits from the BaseModel and can contain any given number of extra fields. In practice, it looks as follows:

>>> res = obb.equity.price.historical("AAPL")
>>> res.results[0]

AVEquityHistoricalData(date=2023-11-03 00:00:00, open=174.24, high=176.82, low=173.35, close=176.65, volume=79829246.0, vwap=None, adj_close=None, dividend_amount=None, split_coefficient=None)

The AVEquityHistoricalData class, is a child class of the Data class.

Note how we've indexed to get only the first element of the results list (which represents a single row, if we want to think about it as a tabular output). This simply means that we are getting a List of AVEquityHistoricalData from the obb.equity.price.historical command. Or, we can also say that that's equivalent to List[Data]!

This is very powerful, as we can now apply any data processing command to the results list, without worrying about the underlying data structure. That's why, on data processing commands (such as the ta menu) we will find on its function signature the following:

def ema(
        self,
        data: Union[List[Data], pandas.DataFrame],
        target: str = "close",
        index: str = "date",
        length: int = 50,
        offset: int = 0,
        chart: bool = False,
    ) -> OBBject[List[Data]]:

    ...

Note that data can actually be a different type, but we'll focus on the List[Data] case for now.

Does that mean that I can only use the data processing commands if I instantiate a class that inherits from Data? Not at all! Consider the following example:

>>> from openbb_core.provider.abstract.data import Data
>>> my_data_item_1 = {"open": 1, "high": 2, "low": 3, "close": 4, "volume": 5, "date": "2020-01-01"}
>>> my_data_item_1_as_data = Data.model_validate(my_data_item_1)
>>> my_data_item_1_as_data

Data(open=1, high=2, low=3, close=4, volume=5, date=2020-01-01)

This means that the Data class is clever enough to understand that you are passing a dictionary and it will try to validate it for you. In other words, if you're using data that doesn't come from the OpenBBPlatform, you only need to ensure it's parsable by the Data class and you'll be able to use the data processing commands. In other words, imagine you have a dataframe that you want to use with the ta menu. You can do the following:

>>> res = obb.equity.price.historical("AAPL")
>>> my_df = res.to_dataframe() # yes, you can convert your OBBject.results into a dataframe out-of-the-box!
>>> my_records = df.to_dict(orient="records")

>>> obb.ta.ema(data=my_record)

OBBject

results: [{'close': 77.62, 'close_EMA_50': None}, {'close': 80.25, 'close_EMA_50': ... # this is a `List[Data]` yet again

Note that that for this example we've used the OBBject.to_dataframe() method to have an example dataframe, but it could be any other dataframe that you have.

Python Interface

When using the OpenBB Platform on a Python Interface, docstrings and type hints are your best friends as they provides plenty of context on how to use the commands. Looking at an example on the ta menu:

def ema(
        self,
        data: Union[List[Data], pandas.DataFrame],
        target: str = "close",
        index: str = "date",
        length: int = 50,
        offset: int = 0,
        chart: bool = False,
    ) -> OBBject[List[Data]]:

    ...

We can easily deduct that the ema command accept data in the formats of List[Data] or pandas.DataFrame.

Note that other types might be added in the future.

API Interface

When using the OpenBB Platform on a API Interface, the types are a bit more limited than on the Python one, as, for example, we can't use pandas.DataFrame as a type. However the same principles apply for what Data means, i.e., any given data processing command, which are characterized as POST endpoints on the API, will accept data as a list of records on the request body, i.e.:

[
    {
        "open": 80,
        "high": 80.69,
        "low": 77.37,
        "close": 77.62,
        "volume": 2487300
    }
    ...
]

Contributor Guidelines

The Contributor Guidelines are intended to be a continuation of the Developer Guidelines. They are not a replacement, but rather an expansion, focusing specifically on those who seek to directly enhance the OpenBB Platform's codebase. It's crucial for Contributors to be familiar with both sets of guidelines to ensure a harmonious and productive engagement with the OpenBB Platform.

There are many ways to contribute to the OpenBB Platform. You can add a new data point, add a new command, add a new visualization, add a new extension, fix a bug, improve or create documentation, etc.

Expectations for Contributors

1. Use Cases:

   • Ensure that your contributions directly enhance the OpenBB Platform's functionality or extension ecosystem.

2. Documentation:

   • All code contributions should come with relevant documentation, including the purpose of the contribution, how it works, and any changes it makes to existing functionalities.
   • Update any existing documentation if your contribution alters the behavior of the OpenBB Platform.

3. Code Quality:

   • Your code should adhere strictly to the OpenBB Platform's coding standards and conventions.
   • Ensure clarity, maintainability, and proper organization in your code.

4. Testing:

   • All contributions must be thoroughly tested to avoid introducing bugs to the OpenBB Platform.
   • Contributions should include relevant automated tests (unit and integration), and any new feature should come with its test cases.

5. Performance:

   • Your contributions should be optimized for performance and should not degrade the overall efficiency of the OpenBB Platform.
   • Address any potential bottlenecks and ensure scalability.

6. Collaboration:

   • Engage actively with the OpenBB development team to ensure that your contributions align with the platform's roadmap and standards.
   • Welcome feedback and be open to making revisions based on reviews and suggestions from the community.

Quality Assurance

We are strong believers in the Quality Assurance (QA) process and we want to make sure that all the extensions that are added to the OpenBB Platform are of high quality. To ensure this, we have a set of QA tools that you can use to test your extension.

Primarily, we have tools that semi-automate the creation of unit and integration tests.

The QA tools are still in development and we are constantly improving them.

Unit tests

Each Fetcher comes equipped with a test method that will ensure that it is implemented correctly and that it is returning the expected data. It also ensures that all types are correct and that the data is valid.

To create unit tests for your Fetchers, you can run the following command:

python openbb_platform/providers/tests/utils/unit_tests_generator.py

Note that you should be running this file from the root of the repository. Note that the tests folder must exist in order to generate the tests.

The automatic unit test generation will add unit tests for all the fetchers available in a given provider.

To record the unit tests, you can run the following command:

pytest <path_to_the_unit_test_file> --record=all

Note that sometimes manual intervention is needed. For example, adjusting out-of-top level imports or adding specific arguments for a given fetcher.

Integration tests

The integration tests are a bit more complex than the unit tests, as we want to test both the Python interface and the API interface. For this, we have two scripts that will help you generate the integration tests.

To generate the integration tests for the Python interface, you can run the following command:

python openbb_platform/extensions/tests/utils/integration_tests_generator.py

To generate the integration tests for the API interface, you can run the following command:

python openbb_platform/extensions/tests/utils/integration_tests_api_generator.py

When testing the API interface, you'll need to run the OpenBB Platform locally before running the tests. To do so, you can run the following command:

uvicorn openbb_platform.core.openbb_core.api.rest_api:app --host 0.0.0.0 --port 8000 --reload

These automated tests are a great way to reduce the amount of code you need to write, but they are not a replacement for manual testing and might require tweaking. That's why we have unit tests that test the generated integration tests to ensure they cover all providers and parameters.

To run the tests we can do:

• Unit tests only:

pytest openbb_platform -m "not integration"

• Integration tests only:

pytest openbb_platform -m integration

• Both integration and unit tests:

pytest openbb_platform

Import time

We aim to have a short import time for the package. To measure that we use tuna.

• https://pypi.org/project/tuna/

To visualize the import time breakdown by module and find potential bottlenecks, run the following commands from openbb_platform directory:

pip install tuna
python -X importtime openbb/__init__.py 2> import.log
tuna import.log

Sharing your extension

We encourage you to share your extension with the community. You can do that by publishing it to PyPI.

Publish your extension to PyPI

To publish your extension to PyPI, you'll need to have a PyPI account and a PyPI API token.

Setup

Create an account and get an API token from https://pypi.org/manage/account/token/ Store the token with

poetry config pypi-token.pypi pypi-YYYYYYYY

Release

cd into the directory where your extension pyproject.toml lives and make sure that the pyproject.toml specifies the version tag you want to release and run.

poetry build

This will create a /dist folder in the directory, which will contain the .whl and tar.gz files matching the version to release.

If you want to test your package locally you can do it with

pip install dist/openbb_[FILE_NAME].whl

Publish

To publish your package to PyPI run:

poetry publish

Now, you can pip install your package from PyPI with:

pip install openbb-some_ext

Manage extensions

To install an extension hosted on PyPI, use the pip install <extension> command.

To install an extension that is developed locally, ensure that it contains a pyproject.toml file and then use the pip install <extension> command.

To install the extension in editable mode using pip, add the -e argument.

Alternatively, for local extensions, you can add this line in the LOCAL_DEPS variable in dev_install.py file:

# If this is a community dependency, add this under "Community dependencies",
# with additional argument optional = true
openbb-extension = { path = "<relative-path-to-the-extension>", develop = true }

Now you can use the python dev_install.py [-e] command to install the local extension.

Add an extension as a dependency

To add the openbb-qa extension as a dependency, you'll need to add it to the pyproject.toml file:

[tool.poetry.dependencies]
openbb-qa = "^0.0.0a2"

Then you can follow the same process as above to install the extension.

Write code and commit

How to create a PR?

To create a PR to the OpenBB Platform, you'll need to fork the repository and create a new branch.

1. Create your Feature Branch, e.g. git checkout -b feature/AmazingFeature
2. Check the files you have touched using git status
3. Stage the files you want to commit, e.g. git add openbb_platform/platform/core/openbb_core/app/constants.py. Note: DON'T add any files with personal information.
4. Write a concise commit message under 50 characters, e.g. git commit -m "meaningful commit message". If your PR solves an issue raised by a user, you may specify such an issue by adding #ISSUE_NUMBER to the commit message, so that these get linked. Note: If you installed pre-commit hooks and one of the formatters re-formats your code, you'll need to go back to step 3 to add these.

Branch Naming Conventions

The accepted branch naming conventions are:

• feature/feature-name
• hotfix/hotfix-name

These branches can only have PRs pointing to the develop branch.