mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 21:12:16 +00:00
32a2870c70
* fix a number of v5 bugs * fix lint error * more lint * fixes for quantitative, technical, charting, famafrench for version bumps --------- Co-authored-by: deeleeramone <>
217 lines
7.9 KiB
Python
217 lines
7.9 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import pytest
|
|
from statsmodels.stats.diagnostic import lilliefors
|
|
|
|
from openbb_quantitative import metrics, stats
|
|
|
|
MONTH_STARTS = pd.date_range("2023-02-01", periods=7, freq="MS")
|
|
MONTH_ENDS = MONTH_STARTS - pd.Timedelta(days=1)
|
|
|
|
|
|
def _month_end_prices(market_excess, risk_free, alpha=0.002, beta=1.5):
|
|
prices = [100.0]
|
|
for market, rf in zip(market_excess, risk_free):
|
|
prices.append(prices[-1] * (1 + rf + alpha + beta * market))
|
|
return [
|
|
{"date": day.date(), "close": price} for day, price in zip(MONTH_ENDS, prices)
|
|
]
|
|
|
|
|
|
def _factors(market_excess, risk_free, index):
|
|
return pd.DataFrame(
|
|
{"mkt_rf": market_excess, "smb": 0.0, "hml": 0.0, "rf": risk_free},
|
|
index=index,
|
|
)
|
|
|
|
|
|
def test_normality(prices_data):
|
|
out = metrics.normality(
|
|
metrics.NormalityQueryParams(data=prices_data, target="close")
|
|
)
|
|
dumped = out.results.model_dump()
|
|
for field in metrics.NormalityQueryParams.__output_columns__:
|
|
value = dumped[field]
|
|
assert isinstance(value, float)
|
|
assert np.isfinite(value)
|
|
|
|
|
|
def test_normality_non_numeric_raises():
|
|
data = [{"close": "a"}, {"close": "b"}, {"close": "c"}]
|
|
params = metrics.NormalityQueryParams(data=data, target="close")
|
|
with pytest.raises(ValueError, match="must be numeric"):
|
|
metrics.normality(params)
|
|
|
|
|
|
def test_capm(prices_data, monkeypatch):
|
|
def fake(start_date, end_date):
|
|
rng = np.random.default_rng(0)
|
|
index = pd.date_range(start_date, end_date, freq="MS")
|
|
return pd.DataFrame(
|
|
{
|
|
"mkt_rf": rng.normal(0.01, 0.04, len(index)),
|
|
"smb": rng.normal(0.0, 0.02, len(index)),
|
|
"hml": rng.normal(0.0, 0.02, len(index)),
|
|
"rf": rng.normal(0.002, 0.001, len(index)),
|
|
},
|
|
index=index,
|
|
)
|
|
|
|
monkeypatch.setattr("openbb_quantitative.helpers.get_fama_raw", fake)
|
|
out = metrics.capm(metrics.CapmQueryParams(data=prices_data, target="close"))
|
|
dumped = out.results.model_dump()
|
|
for field in metrics.CapmQueryParams.__output_columns__:
|
|
value = dumped[field]
|
|
assert isinstance(value, float)
|
|
assert np.isfinite(value)
|
|
|
|
|
|
def test_capm_recovers_known_beta(monkeypatch):
|
|
market_excess = [-0.03, -0.01, 0.01, 0.02, 0.04, -0.02]
|
|
risk_free = [0.001, 0.002, 0.001, 0.003, 0.002, 0.001]
|
|
factors = _factors(market_excess, risk_free, MONTH_STARTS[:6])
|
|
monkeypatch.setattr(
|
|
"openbb_quantitative.helpers.get_fama_raw", lambda start, end: factors
|
|
)
|
|
out = metrics.capm(
|
|
metrics.CapmQueryParams(
|
|
data=_month_end_prices(market_excess, risk_free), target="close"
|
|
)
|
|
).results
|
|
assert out.market_risk == pytest.approx(1.5)
|
|
assert out.systematic_risk == pytest.approx(1.0)
|
|
assert out.idiosyncratic_risk == pytest.approx(0.0, abs=1e-9)
|
|
|
|
|
|
def test_capm_single_month_of_daily_prices_raises():
|
|
days = pd.bdate_range("2023-03-01", "2023-03-31")
|
|
data = [{"date": day.date(), "close": 100.0 + i} for i, day in enumerate(days)]
|
|
with pytest.raises(
|
|
ValueError, match="at least 3 monthly returns; the data produced 0"
|
|
):
|
|
metrics.capm(metrics.CapmQueryParams(data=data, target="close"))
|
|
|
|
|
|
def test_capm_two_monthly_returns_raises():
|
|
data = _month_end_prices([0.01, 0.02], [0.001, 0.001])
|
|
with pytest.raises(
|
|
ValueError, match="at least 3 monthly returns; the data produced 2"
|
|
):
|
|
metrics.capm(metrics.CapmQueryParams(data=data, target="close"))
|
|
|
|
|
|
def test_capm_insufficient_factor_overlap_raises(monkeypatch):
|
|
market_excess = [-0.03, -0.01, 0.01, 0.02, 0.04, -0.02]
|
|
risk_free = [0.001] * 6
|
|
factors = _factors([0.01, 0.02], [0.001, 0.001], MONTH_STARTS[:2])
|
|
monkeypatch.setattr(
|
|
"openbb_quantitative.helpers.get_fama_raw", lambda start, end: factors
|
|
)
|
|
with pytest.raises(
|
|
ValueError, match="overlapping the Fama-French factors; found 2"
|
|
):
|
|
metrics.capm(
|
|
metrics.CapmQueryParams(
|
|
data=_month_end_prices(market_excess, risk_free), target="close"
|
|
)
|
|
)
|
|
|
|
|
|
def test_normality_kolmogorov_smirnov_uses_sample_moments():
|
|
sample = np.random.default_rng(3).normal(100.0, 2.0, 400)
|
|
data = [{"close": float(value)} for value in sample]
|
|
out = metrics.normality(
|
|
metrics.NormalityQueryParams(data=data, target="close")
|
|
).results
|
|
statistic, p_value = lilliefors(sample, dist="norm")
|
|
assert out.kolmogorov_smirnov_statistic == pytest.approx(statistic)
|
|
assert out.kolmogorov_smirnov_p_value == pytest.approx(p_value)
|
|
assert out.kolmogorov_smirnov_p_value > 0.05
|
|
|
|
|
|
def test_normality_kolmogorov_smirnov_rejects_heavy_tails():
|
|
sample = np.random.default_rng(3).standard_t(2, 400)
|
|
data = [{"close": float(value)} for value in sample]
|
|
out = metrics.normality(
|
|
metrics.NormalityQueryParams(data=data, target="close")
|
|
).results
|
|
assert out.kolmogorov_smirnov_p_value < 0.05
|
|
|
|
|
|
def test_stats_dispersion_matches_summary():
|
|
data = [{"close": float(value)} for value in [1, 2, 3, 4, 5]]
|
|
summary = metrics.summary(metrics.SummaryQueryParams(data=data, target="close"))
|
|
stdev = stats.stdev(stats.StatsStdevQueryParams(data=data, target="close"))
|
|
variance = stats.variance(stats.StatsVarianceQueryParams(data=data, target="close"))
|
|
assert stdev.results.stdev == pytest.approx(summary.results.std)
|
|
assert variance.results.variance == pytest.approx(summary.results.var)
|
|
assert summary.results.var == pytest.approx(2.5)
|
|
|
|
|
|
def test_unitroot_test(prices_data):
|
|
out = metrics.unitroot_test(
|
|
metrics.UnitRootTestQueryParams(data=prices_data, target="close")
|
|
)
|
|
params = metrics.UnitRootTestQueryParams(data=prices_data, target="close")
|
|
assert params.fuller_reg == "c"
|
|
assert params.kpss_reg == "c"
|
|
assert params.maxlag is None
|
|
assert params.autolag == "AIC"
|
|
assert params.nlags == "auto"
|
|
dumped = out.results.model_dump()
|
|
assert isinstance(dumped["adf_statistic"], float)
|
|
assert np.isfinite(dumped["adf_statistic"])
|
|
assert isinstance(dumped["adf_p_value"], float)
|
|
assert isinstance(dumped["adf_nlags"], int)
|
|
assert isinstance(dumped["adf_nobs"], int)
|
|
assert isinstance(dumped["adf_icbest"], float)
|
|
assert isinstance(dumped["kpss_statistic"], float)
|
|
assert isinstance(dumped["kpss_p_value"], float)
|
|
assert isinstance(dumped["kpss_nlags"], int)
|
|
assert isinstance(dumped["kpss_p_value_interpolated"], bool)
|
|
|
|
|
|
def test_unitroot_test_regression_options(prices_data):
|
|
out = metrics.unitroot_test(
|
|
metrics.UnitRootTestQueryParams(
|
|
data=prices_data,
|
|
target="close",
|
|
fuller_reg="ct",
|
|
kpss_reg="ct",
|
|
)
|
|
)
|
|
dumped = out.results.model_dump()
|
|
assert np.isfinite(dumped["adf_statistic"])
|
|
assert np.isfinite(dumped["kpss_statistic"])
|
|
|
|
|
|
def test_unitroot_test_no_autolag(prices_data):
|
|
out = metrics.unitroot_test(
|
|
metrics.UnitRootTestQueryParams(
|
|
data=prices_data, target="close", maxlag=4, autolag=None
|
|
)
|
|
)
|
|
dumped = out.results.model_dump()
|
|
assert dumped["adf_nlags"] == 4
|
|
assert dumped["adf_icbest"] is None
|
|
|
|
|
|
def test_unitroot_test_explicit_kpss_nlags(prices_data):
|
|
out = metrics.unitroot_test(
|
|
metrics.UnitRootTestQueryParams(data=prices_data, target="close", nlags=6)
|
|
)
|
|
assert out.results.kpss_nlags == 6
|
|
|
|
|
|
def test_summary(prices_data):
|
|
out = metrics.summary(metrics.SummaryQueryParams(data=prices_data, target="close"))
|
|
dumped = out.results.model_dump()
|
|
assert isinstance(dumped["count"], int)
|
|
assert dumped["count"] == 250
|
|
for field in ("mean", "std", "var", "min", "p25", "p50", "p75", "max"):
|
|
value = dumped[field]
|
|
assert isinstance(value, float)
|
|
assert np.isfinite(value)
|
|
assert dumped["min"] <= dumped["p25"] <= dumped["p50"] <= dumped["p75"]
|
|
assert dumped["p75"] <= dumped["max"]
|