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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
"""Tests for ``openbb_quantitative.stats`` - summary statistics commands."""
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import math
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from openbb_quantitative import stats
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def test_skew(prices_data):
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"""The skew command returns a finite skewness value."""
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out = stats.skew(stats.StatsSkewQueryParams(data=prices_data, target="close"))
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result = out.results
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assert isinstance(result, stats.StatsSkewData)
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assert isinstance(result.skew, float)
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assert math.isfinite(result.skew)
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def test_variance(prices_data):
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"""The variance command returns a finite, non-negative variance value."""
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out = stats.variance(
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stats.StatsVarianceQueryParams(data=prices_data, target="close")
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)
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result = out.results
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assert isinstance(result, stats.StatsVarianceData)
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assert isinstance(result.variance, float)
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assert math.isfinite(result.variance)
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assert result.variance >= 0.0
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def test_stdev(prices_data):
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"""The stdev command returns a finite, non-negative standard deviation."""
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out = stats.stdev(stats.StatsStdevQueryParams(data=prices_data, target="close"))
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result = out.results
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assert isinstance(result, stats.StatsStdevData)
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assert isinstance(result.stdev, float)
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assert math.isfinite(result.stdev)
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assert result.stdev >= 0.0
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def test_kurtosis(prices_data):
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"""The kurtosis command returns a finite kurtosis value."""
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out = stats.kurtosis(
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stats.StatsKurtosisQueryParams(data=prices_data, target="close")
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)
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result = out.results
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assert isinstance(result, stats.StatsKurtosisData)
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assert isinstance(result.kurtosis, float)
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assert math.isfinite(result.kurtosis)
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def test_mean(prices_data):
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"""The mean command returns a finite arithmetic mean."""
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out = stats.mean(stats.StatsMeanQueryParams(data=prices_data, target="close"))
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result = out.results
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assert isinstance(result, stats.StatsMeanData)
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assert isinstance(result.mean, float)
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assert math.isfinite(result.mean)
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def test_quantile_default(prices_data):
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"""The quantile command defaults to the median (0.5 quantile)."""
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params = stats.StatsQuantileQueryParams(data=prices_data, target="close")
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assert params.quantile_pct == 0.5
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out = stats.quantile(params)
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result = out.results
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assert isinstance(result, stats.StatsQuantileData)
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assert isinstance(result.median, float)
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assert isinstance(result.quantile, float)
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assert math.isfinite(result.median)
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assert math.isfinite(result.quantile)
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assert result.quantile == result.median
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def test_quantile_explicit(prices_data):
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"""The quantile command honors an explicit quantile percentage."""
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params = stats.StatsQuantileQueryParams(
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data=prices_data, target="close", quantile_pct=0.9
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)
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assert params.quantile_pct == 0.9
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out = stats.quantile(params)
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result = out.results
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assert isinstance(result, stats.StatsQuantileData)
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assert math.isfinite(result.quantile)
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assert result.quantile >= result.median
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