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* merge branch develop with main * bump desktop version * fix the merge * sync Cargo.lock to desktop 2.0.0 * fix cli conftest.py --------- Co-authored-by: deeleeramone <>
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
"""Tests for ``openbb_econometrics.tools`` - correlation matrix and statistics."""
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import pytest
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from openbb_econometrics import tools
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@pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"])
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def test_correlation_matrix_methods(timeseries_data, method):
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"""The correlation matrix returns one row per numeric column for each method."""
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out = tools.correlation_matrix(
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tools.CorrelationMatrixQueryParams(data=timeseries_data, method=method)
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)
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results = out.results
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assert isinstance(results, list)
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# open/high/low/close/volume -> 5 numeric columns.
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assert len(results) == 5
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comp_to = {row.comp_to for row in results}
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assert comp_to == {"open", "high", "low", "close", "volume"}
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for row in results:
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dumped = row.model_dump()
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assert dumped["comp_to"] in comp_to
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# The self-correlation is 1.0.
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assert dumped[row.comp_to] == pytest.approx(1.0)
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def test_correlation_matrix_defaults(timeseries_data):
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"""The correlation matrix defaults to the pearson method with min_periods of 1."""
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params = tools.CorrelationMatrixQueryParams(data=timeseries_data)
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assert params.method == "pearson"
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assert params.min_periods == 1
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out = tools.correlation_matrix(params)
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assert len(out.results) == 5
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def test_correlation_matrix_min_periods(timeseries_data):
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"""The correlation matrix honours an explicit min_periods threshold."""
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out = tools.correlation_matrix(
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tools.CorrelationMatrixQueryParams(data=timeseries_data, min_periods=10)
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)
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assert len(out.results) == 5
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def test_correlation_matrix_symbol_pivot(symbol_data):
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"""A dataset with a ``symbol`` column and >1 symbol pivots on ``close``."""
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out = tools.correlation_matrix(tools.CorrelationMatrixQueryParams(data=symbol_data))
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results = out.results
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# Pivoted on the three symbols.
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assert len(results) == 3
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assert {row.comp_to for row in results} == {"AAA", "BBB", "CCC"}
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def test_summary_statistics(timeseries_data):
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"""Summary statistics returns one fully-populated row per numeric column."""
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out = tools.summary_statistics(
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tools.SummaryStatisticsQueryParams(data=timeseries_data)
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)
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results = out.results
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assert len(results) == 5
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columns = {row.column for row in results}
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assert columns == {"open", "high", "low", "close", "volume"}
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for row in results:
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dumped = row.model_dump()
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assert dumped["count"] > 0
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assert dumped["min"] <= dumped["median"] <= dumped["max"]
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assert dumped["p25"] <= dumped["p75"]
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assert isinstance(dumped["skew"], float)
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assert isinstance(dumped["kurtosis"], float)
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