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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
"""Tests for ``openbb_quantitative.metrics`` - quantitative analysis commands."""
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import numpy as np
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import pandas as pd
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import pytest
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from openbb_quantitative import metrics
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def test_normality(prices_data):
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"""Normality returns finite test statistics and p-values for every test."""
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out = metrics.normality(
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metrics.NormalityQueryParams(data=prices_data, target="close")
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)
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dumped = out.results.model_dump()
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for field in metrics.NormalityQueryParams.__output_columns__:
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value = dumped[field]
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assert isinstance(value, float)
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assert np.isfinite(value)
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def test_normality_non_numeric_raises():
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"""A non-numeric target column raises a clear ValueError."""
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data = [{"close": "a"}, {"close": "b"}, {"close": "c"}]
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params = metrics.NormalityQueryParams(data=data, target="close")
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with pytest.raises(ValueError, match="must be numeric"):
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metrics.normality(params)
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def test_capm(prices_data, monkeypatch):
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"""CAPM reports finite risk measures from mocked monthly Fama-French factors."""
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def fake(start_date, end_date):
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rng = np.random.default_rng(0)
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index = pd.date_range(start_date, end_date, freq="MS")
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return pd.DataFrame(
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{
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"mkt_rf": rng.normal(0.01, 0.04, len(index)),
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"smb": rng.normal(0.0, 0.02, len(index)),
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"hml": rng.normal(0.0, 0.02, len(index)),
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"rf": rng.normal(0.002, 0.001, len(index)),
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},
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index=index,
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)
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monkeypatch.setattr("openbb_quantitative.helpers.get_fama_raw", fake)
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out = metrics.capm(metrics.CapmQueryParams(data=prices_data, target="close"))
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dumped = out.results.model_dump()
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for field in metrics.CapmQueryParams.__output_columns__:
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value = dumped[field]
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assert isinstance(value, float)
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assert np.isfinite(value)
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def test_unitroot_test(prices_data):
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"""The unit root test reports finite ADF and KPSS statistics."""
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out = metrics.unitroot_test(
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metrics.UnitRootTestQueryParams(data=prices_data, target="close")
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)
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params = metrics.UnitRootTestQueryParams(data=prices_data, target="close")
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assert params.fuller_reg == "c"
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assert params.kpss_reg == "c"
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assert params.maxlag is None
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assert params.autolag == "AIC"
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assert params.nlags == "auto"
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dumped = out.results.model_dump()
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assert isinstance(dumped["adf_statistic"], float)
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assert np.isfinite(dumped["adf_statistic"])
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assert isinstance(dumped["adf_p_value"], float)
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assert isinstance(dumped["adf_nlags"], int)
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assert isinstance(dumped["adf_nobs"], int)
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assert isinstance(dumped["adf_icbest"], float)
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assert isinstance(dumped["kpss_statistic"], float)
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assert isinstance(dumped["kpss_p_value"], float)
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assert isinstance(dumped["kpss_nlags"], int)
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assert isinstance(dumped["kpss_p_value_interpolated"], bool)
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def test_unitroot_test_regression_options(prices_data):
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"""The unit root test accepts non-default ADF and KPSS regression terms."""
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out = metrics.unitroot_test(
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metrics.UnitRootTestQueryParams(
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data=prices_data,
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target="close",
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fuller_reg="ct",
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kpss_reg="ct",
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)
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)
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dumped = out.results.model_dump()
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assert np.isfinite(dumped["adf_statistic"])
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assert np.isfinite(dumped["kpss_statistic"])
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def test_unitroot_test_no_autolag(prices_data):
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"""With autolag disabled the ADF test uses maxlag directly and has no icbest."""
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out = metrics.unitroot_test(
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metrics.UnitRootTestQueryParams(
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data=prices_data, target="close", maxlag=4, autolag=None
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)
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)
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dumped = out.results.model_dump()
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assert dumped["adf_nlags"] == 4
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assert dumped["adf_icbest"] is None
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def test_unitroot_test_explicit_kpss_nlags(prices_data):
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"""The KPSS test accepts an explicit integer lag count."""
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out = metrics.unitroot_test(
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metrics.UnitRootTestQueryParams(data=prices_data, target="close", nlags=6)
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)
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assert out.results.kpss_nlags == 6
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def test_summary(prices_data):
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"""Summary reports finite descriptive statistics for the target column."""
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out = metrics.summary(metrics.SummaryQueryParams(data=prices_data, target="close"))
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dumped = out.results.model_dump()
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assert isinstance(dumped["count"], int)
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assert dumped["count"] == 250
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for field in ("mean", "std", "var", "min", "p25", "p50", "p75", "max"):
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value = dumped[field]
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assert isinstance(value, float)
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assert np.isfinite(value)
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assert dumped["min"] <= dumped["p25"] <= dumped["p50"] <= dumped["p75"]
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assert dumped["p75"] <= dumped["max"]
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