mirror of
https://github.com/OpenBB-finance/OpenBB.git
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3cc6025ab4
* Split out a rolling submenu for the rolling functions * Make a performance and a stats submenu. * Test the statistics functions * lint * lint * dupe test * pylint * ruff * Try tests quick * black magic signature funcs * fix my custom tests * Fix the existing imports/urls * push the api update * okay I figured out whats going on * this should be all of them * Correct docstringing examples --------- Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com> Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com>
545 lines
13 KiB
Python
545 lines
13 KiB
Python
"""Test qa extension."""
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import random
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from typing import Literal
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import pytest
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from extensions.tests.conftest import parametrize
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from openbb_core.app.model.obbject import OBBject
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# pylint:disable=inconsistent-return-statements
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@pytest.fixture(scope="session")
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def obb(pytestconfig):
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"""Fixture to setup obb."""
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if pytestconfig.getoption("markexpr") != "not integration":
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import openbb # pylint:disable=import-outside-toplevel
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return openbb.obb
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# pylint:disable=redefined-outer-name
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data: dict = {}
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def get_stocks_data():
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import openbb # pylint:disable=import-outside-toplevel
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if "stocks_data" in data:
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return data["stocks_data"]
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symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311
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provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311
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data["stocks_data"] = openbb.obb.equity.price.historical(
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symbol=symbol, provider=provider
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).results
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return data["stocks_data"]
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def get_crypto_data():
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import openbb # pylint:disable=import-outside-toplevel
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if "crypto_data" in data:
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return data["crypto_data"]
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# TODO : add more crypto providers and symbols
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symbol = random.choice(["BTC"]) # noqa: S311
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provider = random.choice(["fmp"]) # noqa: S311
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data["crypto_data"] = openbb.obb.crypto.price.historical(
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symbol=symbol, provider=provider
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).results
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return data["crypto_data"]
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def get_data(menu: Literal["equity", "crypto"]):
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funcs = {"equity": get_stocks_data, "crypto": get_crypto_data}
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return funcs[menu]()
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_normality(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.normality(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_capm(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.capm(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"threshold_start": "",
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"threshold_end": "",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"threshold_start": "0.1",
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"threshold_end": "1.6",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_performance_omega_ratio(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.performance.omega_ratio(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close", "window": "5", "index": "date"}, "equity"),
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({"data": "", "target": "high", "window": "10", "index": "date"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_kurtosis(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.kurtosis(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"fuller_reg": "c",
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"kpss_reg": "ct",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"fuller_reg": "ct",
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"kpss_reg": "c",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_unitroot_test(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.unitroot_test(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"rfr": "",
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"window": "100",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"rfr": "0.5",
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"window": "100",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_performance_sharpe_ratio(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.performance.sharpe_ratio(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"target_return": "",
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"window": "100",
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"adjusted": "",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "close",
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"target_return": "",
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"window": "100",
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"adjusted": "true",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_performance_sortino_ratio(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.performance.sortino_ratio(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_skew(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.skew(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"window": "10",
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"quantile_pct": "",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"window": "50",
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"quantile_pct": "0.6",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_quantile(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.quantile(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_summary(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.summary(**params)
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assert result
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assert isinstance(result, OBBject)
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"window": "10",
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"quantile_pct": "",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"window": "50",
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"quantile_pct": "0.6",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_stdev(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.stdev(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"window": "10",
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"quantile_pct": "",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"window": "50",
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"quantile_pct": "0.6",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_mean(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.mean(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"window": "10",
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"quantile_pct": "",
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"index": "date",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"window": "50",
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"quantile_pct": "0.6",
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"index": "date",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_rolling_variance(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.rolling.variance(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_skew(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.skew(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_kurtosis(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.kurtosis(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_variance(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.variance(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_stdev(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.stdev(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_mean(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.mean(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"quantile_pct": "",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "close",
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"quantile_pct": "0.6",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_stats_quantile(params, data_type, obb):
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params = {p: v for p, v in params.items() if v}
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params["data"] = get_data(data_type)
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result = obb.quantitative.stats.quantile(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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