OpenBB/tests

1. OpenBB Terminal : Unit Testing

This document is part of the OpenBB Terminal library documentation.

It aims to provide necessary information in order to:

• Run unit tests
• Build unit tests
• Fix failed existing unit tests
• Maintain unit tests

1.1. Why having unit tests ?

Unit tests purpose is to allow developers to update without fear.

Writing tests that are easy to run will help the person modifying the code to do so quick and with more confidence. Having tests available also decreases the chances that bugs will make it into production and break other parts of the code.

To do that we need unit tests to be :

• fast to write
• fast to run

FAST TO WRITE.

The unit tests is used for testing a single functionality for its correctness of input and the corresponding output. Unit tests are about ensuring that low-level implementation details are rock solid.

FAST TO RUN.

Tests can be slow at times, due to the need to connect with external services. This can be sending a HTTP request or connecting to a database. We can speed this up by using mocks. In case this is not possible, make sure to marked it as slow

2. Run unit tests

In this section we will explain everything you need to run the unit tests on OpenBB Terminal.

2.1. How to install tests dependencies ?

To run the tests you will need first to install the dev-dependencies. By default poetry installs the dev-dependencies when you run this command :

poetry install

If you do not want to install the dev-dependencies you will have to add the option --no-dev like this :

poetry install --no-dev

2.2. How to run tests :

By module

You can run tests on a specific package/module by specifying the path of this package/module, as follows:

pytest tests/openbb_terminal/some_package
pytest tests/openbb_terminal/some_package/some_module.py

By test name

You can run tests by their name with the argument -k

pytest -k "test_function1"

By markers

You can run tests only on specific markers, like this :

pytest -m slow
pytest -k "not slow"
pytest -k "not slow and not network"

You can list the available markers using this command :

pytest --markers

2.3. How to skip tests

Skipping a specific test

You can use the decorator @pytest.mark.skip as below:

import pytest

@pytest.mark.skip
def test_some_function(mocker):
    pass

@pytest.mark.skip(reason="This time with a comment")
def test_another_function(mocker):
    pass

Skipping the entire test module

import pytest

pytest.skip(msg="Some optional comment.", allow_module_level=True)

def test_some_function(mocker):
    pass

3. How to build unit tests

When you contribute a new feature to the Terminal, it's important that tests are added for this particular feature. It is a part of the Checklist for the PR to be approved.

All the unit tests should be insides the tests folder. There should be at most one test module for each module of OpenBB Terminal.

Each test module should follow the same path of the module that it is testing.For instance,

• in order to test the following module openbb_terminal/stocks/due_diligence/dd_controller.py
• a test module should be added here: tests/openbb_terminal/stocks/due_diligence/test_dd_controller.py

Now that you know where to add tests, let's go through 3 types of unit tests that we have:

3.1. Model Test

As mentioned in the main repo's README, we are following the MVC pattern. The _model.py's job to get the required data, e.g. from an external APIs or scraping.

Therefore, in unit tests for _model.py, the function output is captured and saved when the test is first run. Then, the next time the test is run, the function's returned value is compared with the captured value in the first run, to ensure parity.

Depending on the data type of function output, we can generate the following files:

• csv : for pandas.DataFrame
• json : for python native data type (list, dict, tuple)
• txt : store str or printed output

Examples:

@pytest.fixture(scope="module")
def vcr_config():
    return {
        "filter_query_parameters": [
            ("token", "MOCK_TOKEN"),
        ],
    }


@pytest.mark.vcr
@pytest.mark.parametrize(
    "ticker",
    [
        ("BTC"),
        ("BTC-USD"),
    ],
)
def test_check_supported_ticker(ticker, recorder):
    df = sentimentinvestor_model.check_supported_ticker(ticker)
    recorder.capture(df)

Several things worth noting here:

1. If the model utilizes an API key or token, make sure to filter it insider the vcr_config fixture inside your test module, similar to the example. This will hide your actual API key / token for security purposes.
2. Record HTTP requests using the decorator @pytest.mark.vcr
   1. When run the first time, the VCR will record the requests to cassettes/test_*.yml
   2. Run it again, and VCR will replay the recorded response when the HTTP request is made. This test is now fast (no real HTTP requests are made anymore), deterministic (the test will continue to pass, even if you have no internet connection or that external APIs go down) and accurate (the response will contain the same headers and body you get from a real request).
3. Test out multiple input variables to ensure tests cover multiple edge cases
   1. Use the decorator @pytest.mark.parametrizeto specify the inputs for your tests.
4. Capture the returned df using recorder.capture(<return_value>)
   1. Next time the test run, return output will be compared against the captured file
5. Each fixture / cassette file for a test module should be stored within the same folder. An example with tests/openbb_terminal/some_package/some_module.py :
   • tests/openbb_terminal/some_package/cassettes/some_module/test_function1.yaml
   • tests/openbb_terminal/some_package/txt/some_module/test_function1.csv
   • tests/openbb_terminal/some_package/csv/some_module/test_function1.csv
   • tests/openbb_terminal/some_package/json/some_module/test_function1.json
6. You can choose the cassette name using the @pytest.mark.default_cassette marker. Here is an example :

@pytest.mark.default_cassette("example.yaml")
@pytest.mark.vcr
def test_default():
    assert request("http://httpbin.org/get").text == '{"get": true}'

3.2. View Test

Different from the model.py, view.py module handles how data is displayed, e.g.as a chart or a table.

The view.py has no return statement, and simply prints to stdout. Hence, our unit tests will capture stdout values, and compare against the recorded fixture in subsequent runs.

Example:

@pytest.mark.vcr
@pytest.mark.record_stdout
def test_display_historical(mocker):
    # MOCK VISUALIZE_OUTPUT
    mocker.patch(target="openbb_terminal.helper_classes.TerminalStyle.visualize_output")
    sentimentinvestor_view.display_historical(
        ticker="AAPL", start="2021-12-12", end="2021-12-15", export="", raw=True
    )

Important notes to go through:

1. Use @pytest.mark.record_stdoutto capture sdtout.
   1. Recorded stdout is saved as fixtures in a txt file
   2. If your view function only plots a chart, and does not output anything in sdtout, you can skip unit testing your view.py
   3. Make sure to mock visualize_output method.
      1. Inside visualize_output, we use ion to enable interactive mode. However, under Windows, having ion enabled would make tests fail to run. Hence, you should always mock visualize_output, and hence ion

3.3. Controller Test

Unit test for _controller.py is the last component you would need to add. Most of the time you won't need to create a _controller.py from scratch. Hence, we will only cover how to add tests for a new command to an existing test_*_controller.py file

Test print help

This test in the test_*_controller.py captured the help text printed to stdout.

Example:

@pytest.mark.vcr(record_mode="none")
@pytest.mark.record_stdout
def test_print_help():
    controller = alt_controller.AlternativeDataController(queue=None)
    controller.print_help()

Adding an additional feature to the controller.py will alter the help text. Make sure to run the tests with the argument --rewrite-expected to re-generate the text file.

Example: pytest openbb_terminal.cryptocurrency.defi.defi_controller --rewrite-expected

Test call function

This test is to check when controller.py calls a command, the corresponding function from a view.py will be called with the correct arguments.

Example:

@pytest.mark.vcr(record_mode="none")
@pytest.mark.parametrize(
    "tested_func, other_args, mocked_func, called_args, called_kwargs",
    [
        (
            "call_aterra",
            [
                "ust",
                "--address=terra1wg2mlrxdmnnkkykgqg4znky86nyrtc45q336yv",  # pragma: allowlist secret
            ],
            "terraengineer_view.display_terra_asset_history",
            [],
            dict(),
        ),
        (
            "call_newsletter",
            [],
            "substack_view.display_newsletters",
            [],
            dict(),
        ),
    ],
)
def test_call_func(
    tested_func, mocked_func, other_args, called_args, called_kwargs, mocker
):
    path_controller = "openbb_terminal.cryptocurrency.defi.defi_controller"

    if mocked_func:
        mock = mocker.Mock()
        mocker.patch(
            target=f"{path_controller}.{mocked_func}",
            new=mock,
        )

        controller = defi_controller.DefiController(queue=None)
        getattr(controller, tested_func)(other_args)

        if called_args or called_kwargs:
            mock.assert_called_once_with(*called_args, **called_kwargs)
        else:
            mock.assert_called_once()
    else:
        controller = defi_controller.DefiController(queue=None)
        getattr(controller, tested_func)(other_args)

When adding a new feature to an existing menu, there should already be a test_*_controller.py file with the test_call_func() function. You can simply add another tuple under @pytest.mark.parametrize(), which contains:

1. call_<command> from the controller.py file
2. Arguments you want to pass in the call_<command>
3. The corresponding view function, which will be mocked (Here we only want check if the view function is called at least once. We mock it such that the function won't execute.)
4. Arguments you want to pass to the view function. Note that the arguments here need to be the same as arguments in (2). This is to ensure that arguments passed from the controller.py file are passed correctly to function in view.py

3.4. Fixing existing tests

It is not uncommon that your new feature or bug fixes break the existing code. Understanding a few concepts can help you navigate and fix most common cases.

Before continuing this section, please have a look at our FIXTURES documentation.

TL;DR:

You can run unit tests under a few mode:

• --rewrite-expected will re-generate the fixtures (csv, json and txt files) if there's detected change.
• --record-mode will impact both the cassette (yaml) and fixtures (csv, json, txt files)
  1. --record-mode=none : searches for existing cassette / fixtures. If there's none, an exception will be thrown.
  2. --record-mode=once : creates new fixtures and cassette if they did not exist. If there are already fixtures or cassette, it will take the existing one.
  3. --record-mode=rewrite : this rewrites both the cassette and fixtures, even if they already exist.

Scenario 1: Update Fixtures

Let's say you just updated the code in _model.py file, which then altered the recorded output. You can update the fixture files by running the tests with --rewrite-expected argument. This would update the fixtures with latest changes.

Example: pytest openbb_terminal.cryptocurrency.defi.defi_controller --rewrite-expected

Scenario 2: Update both Fixtures and Cassettes

If you want to update existing cassettes and fixtures, use the argument --record-mode=rewrite when running tests.

As existing cassettes and fixtures will be overwritten (if there's any), make sure that you check both, and that they yield expected output.

3.5. How to mock ?

PYTEST-MOCK

A mocker fixture is available through the package pytest-mock.

Here is an example on how to mocker fixture can be used :

def test_something(mocker):
    mocker.patch("some_package.some_module")

More information about pytest-mock are available here :

• https://pypi.org/project/pytest-mock/

MOCKEYPATCH

There is also a monkeypatch fixture available by default inside pytest.

def test_double(monkeypatch):
    monkeypatch.setattr("some_package.some_module", "Some value")

More information about monkeypatch are available here :

• https://docs.pytest.org/en/6.2.x/monkeypatch.html

3.6. Which helpers are available ?

You can find the available helpers inside the following package/module :

• tests/helpers/
• tests/conftest.py

See also the pytest fixtures which are autoloaded helpers.

3.7. Which fixtures are available ?

You can list all the available pytest fixtures using the following command :

pytest --fixtures

More on custom fixtures here :

• FIXTURES

3.8. Which markers are available ?

You can list the available markers using this command :

pytest --markers

More information on markers location are available in pytest documentation :

• https://docs.pytest.org/en/6.2.x/mark.html#mark

3.9. Known issue / solution

ION Usage

As mentioned earlier, if you are testing for a _view.py module that uses ION, you would need to mock it. With Windows, the charts / graphs are unable to close the graph applications. This would make the tests failed when running with Windows.

You can use the following example below. Once you have mocked the graphs / charts, simply call the display function, which is now a mocked object.

@pytest.mark.vcr
@pytest.mark.record_stdout
def test_display_defi_tvl(mocker):
    # MOCK EXPORT_DATA
    mocker.patch(target="openbb_terminal.cryptocurrency.defi.llama_view.export_data")

    # MOCK VISUALIZE_OUTPUT
    mocker.patch(target="openbb_terminal.helper_classes.TerminalStyle.visualize_output")

    llama_view.display_defi_tvl(20)

YFINANCE

If a method is using the yfinance library but do not let you pick the start/end dates it will pick the current date each time.

So your test might fail the next days.

To solve that you can filter the start/end date like this :

@pytest.fixture(scope="module")
def vcr_config():
    return {
        "filter_headers": [("User-Agent", None)],
        "filter_query_parameters": [
            ("period1", "MOCK_PERIOD_1"),
            ("period2", "MOCK_PERIOD_2"),
            ("date", "MOCK_DATE"),
        ],
    }

You can also refactor this method to let access to stard/end dates.

USER-AGENT

Some function uses a random User-Agent on the HTTP HEADER when fetching data from an API.

Here is how to filter this random User-Agent.

@pytest.fixture(scope="module")
def vcr_config():
    return {
        "filter_headers": [("User-Agent", None)],
    }

YFINANCE

If you do something like this with yfinance library.

import yfinance as yf

yf.download(tickers="VCYT QSI")

Chances are you requests will be multi-threaded.

Issues : as for now vcrpy seems to be incompatible with multi-threading.

The library vcrpy is used to record cassettes (network calls into yaml files).

Here is a solution to still combine yfinance and vcrpy :

import pytest
import yfinance

yf_download = yf.download
def mock_yf_download(*args, **kwargs):
    kwargs["threads"] = False
    return yf_download(*args, **kwargs)

@pytest.mark.vcr
def test_ark_orders_view(kwargs_dict, mocker, use_color):
    mocker.patch("yfinance.download", side_effect=mock_yf_download)

    yf.download(tickers="VCYT QSI")

BROTLI

The library requests doesn't support : brotli comnpression.

Unless brotli library is installed in the environment.

Plus brotli is not in OpenBBTerminal requirements.

So if both of these conditions are fulfilled :

• one generates a cassette with brotli installed in his/her environment
• the server choose to send brotli compressed data

Then the test might work in local but crash during PullRequest.

BEFORE_RECORD_RESPONSE

The library vcrpy has a before_record_response which accept a filtering function that can be used to filter your cassette content :

import pytest

def my_custom_filter(response):
    return response

@pytest.mark.vcr(before_record_response=my_custom_filter)
def test_simple(recorder):
    pass

For instance this can be used to reduce the size of a cassette if it's too heavy.

Or to filter sensitive data in the response.

The issue with this before_record_response : it isn't launched at the first run of the test.

More on this here : https://github.com/kevin1024/vcrpy/pull/594

A solution for now is to run this command while initializing the cassettes :

# THE SAME COMMAND NEEDS TO BE RUN TWICE
pytest tests/.../test_some_test_module.py --record-mode=once --rewrite-expected
pytest tests/.../test_some_test_module.py --record-mode=once --rewrite-expected

3.10. List of useful vscode tools for unit tests

VSCODE TESTING

The default testing tool from vscode should let you add breakpoints and run debug on a specific test.

It's a convenient way to see what's inside your test while running.

More information on this tool are available here :

• https://code.visualstudio.com/docs/python/testing

3.11. How to handle dev-dependencies ?

UPDATE PYPROJECT

If a library need to be added or removed from dev-dependencies this can be done directly in the pyproject.toml file.

All the dev-dependencies should be under the tool.poetry.dev-dependencies section, like in this example :

[tool.poetry.dev-dependencies]
pytest = "^6.2.2"
pylint = "^2.7.2"
flake8 = "^3.9.0"
...

EXPORT REQUIREMENTS

After updating the pyproject.toml you will have to export the requirements files using the following commands :

poetry export -f requirements.txt  -o requirements.txt --without-hashes --with dev
poetry export -f requirements.txt  -o requirements-full.txt --extras forecast --extras optimization --without-hashes --with dev

4. Maintain unit tests

4.1. What is the PR process ? (in progress)

Here are the steps to write proper tests for Gamestonk :

A. Find right place
B. Verify coverage is above 90%
C. Set the right markers

A. Find right place

Put the code following the same module and package structure than openbb_terminal package.

B. Verify coverage is above 90%

Once you made your PullRequest an automation will let you know whether or not you have the proper amount of tests coverage.

You can also run the following command to check your coverage manually :

• pytest --cov --cov-fail-under=90

C. Set the right markers

If parts of your have specificities like :

• being slow
• requiring network connectivity
• requiring authentication to APIs

Then the proper markers should be sent on each test.

The rests of this document is there to provide a deeper comprehension of this steps.

A tests update might be asked whenever the answer to one of the following question is yes :

A. Is this PR fixing bug which was not detected by the current tests ?
B. Is this PR reducing code coverage ?
C. Is this PR adding features ?
D. Is this PR updating features ?

4.2. Which automations are available ?

PULL REQUEST

A github action should comment every Pull Request with the code coverage.

This automation will not enforce any rules regarding tests.

4.3. How to check code coverage ?

PULL REQUEST AUTOMATION

You should be able to see the code coverage on comment of your Pull Requests.

MANUALLY

This is how to manually check code coverage.

pytest --cov=openbb_terminal --cov=terminal --cov-report term-missing

You can also select a specific package/module with the option --cov.

Here is an example where we select only the package stocks\due_diligence :

pytest --cov=openbb_terminal\stocks\due_diligence --cov-report term-missing

PRE-COMMIT

Code coverage and unit tests are not run on pre-commit hooks since it will slow down each commit.