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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
"""Tests for ``openbb_quantitative.factors``."""
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from math import isfinite
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import pandas as pd
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import statsmodels.api as sm
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from openbb_core.app.utils import df_to_basemodel
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from openbb_quantitative import factors as factors_module
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def _params(target_data, factor_data, **kwargs):
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return factors_module.FactorRegressionQueryParams(
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data=target_data, factors_data=factor_data, target="close", **kwargs
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)
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def test_factors_happy_path(target_returns_data, factor_matrix_data):
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"""Returns one row per (period, regressor) with finite stats."""
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out = factors_module.factors(_params(target_returns_data, factor_matrix_data))
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results = out.results
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assert isinstance(results, list)
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assert results
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n_regressors = 4 # const + f1 + f2 + rf
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n_periods = len(set(r.period for r in results))
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assert len(results) == n_periods * n_regressors
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for row in results:
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assert isinstance(row, factors_module.FactorRegressionData)
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for field in ("coefficient", "p_value", "lower_ci", "upper_ci", "r_squared"):
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value = getattr(row, field)
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assert isinstance(value, float)
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assert isfinite(value)
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def test_factors_max_window_matches_statsmodels(
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target_returns_df, factor_matrix_df, target_returns_data, factor_matrix_data
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):
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"""The Max-period coefficients match a direct statsmodels OLS fit."""
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out = factors_module.factors(
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_params(target_returns_data, factor_matrix_data, risk_free_column="rf")
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)
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df = pd.DataFrame([r.model_dump() for r in out.results])
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max_rows = df[df.period == "Max"].set_index("factor")
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aligned = factor_matrix_df.set_index("date").drop(columns=["rf"]).astype(float)
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target = target_returns_df.set_index("date")["close"].astype(
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float
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) - factor_matrix_df.set_index("date")["rf"].astype(float)
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x = sm.add_constant(aligned)
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model = sm.OLS(target, x).fit()
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for name in model.params.index:
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assert max_rows.loc[name, "coefficient"] == pytest.approx( # type: ignore[name-defined]
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float(model.params[name]), abs=1e-9
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)
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def test_factors_with_risk_free(target_returns_data, factor_matrix_data):
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"""When ``risk_free_column`` is set the rf column drops out of the regressors."""
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out = factors_module.factors(
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_params(target_returns_data, factor_matrix_data, risk_free_column="rf")
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)
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factor_names = {r.factor for r in out.results}
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assert "rf" not in factor_names
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assert factor_names == {"const", "f1", "f2"}
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def test_factors_specific_periods(target_returns_data, factor_matrix_data):
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"""The ``periods`` parameter restricts which look-back windows are returned."""
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out = factors_module.factors(
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_params(target_returns_data, factor_matrix_data, periods=["Max"])
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)
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assert {r.period for r in out.results} == {"Max"}
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def test_factors_skips_too_short_windows(factor_matrix_df, factor_matrix_data):
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"""Periods whose slice contains fewer obs than regressors+1 are skipped."""
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# Build a target whose factor overlap is two days of business activity.
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short_dates = factor_matrix_df["date"].iloc[:2]
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short_target = df_to_basemodel(
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pd.DataFrame({"date": short_dates, "close": [0.01, -0.005]})
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)
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out = factors_module.factors(
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_params(short_target, factor_matrix_data, periods=["1 Month", "Max"])
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)
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# 2 observations cannot fit a 4-regressor model -> both periods are skipped.
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assert out.results == []
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def test_factors_default_periods():
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"""The default ``periods`` value enumerates the six named look-back windows."""
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params = factors_module.FactorRegressionQueryParams(
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data=[], factors_data=[], target="close"
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)
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assert params.periods == [
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"1 Month",
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"3 Month",
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"YTD",
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"1 Year",
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"3 Year",
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"Max",
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]
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# pytest is imported here (not at the top) so the local _params helper above
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# can reference it without surfacing in autocompletion.
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import pytest # noqa: E402
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