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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
"""Tests for ``openbb_quantitative.risk_decomposition``."""
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import pandas as pd
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import pytest
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from openbb_core.app.utils import df_to_basemodel
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from openbb_quantitative import risk_decomposition as rd_module
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def _params(target_data, factor_data, **kwargs):
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return rd_module.RiskDecompositionQueryParams(
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data=target_data, factors_data=factor_data, target="close", **kwargs
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)
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def test_risk_decomposition_happy_path(target_returns_data, factor_matrix_data):
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"""Returns rows for every factor plus a Residual row per period."""
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out = rd_module.risk_decomposition(_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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periods = {r.period for r in results}
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for period in periods:
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period_factors = [r.factor for r in results if r.period == period]
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# f1, f2, rf, Residual
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assert set(period_factors) == {"f1", "f2", "rf", "Residual"}
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def test_risk_decomposition_shares_sum_to_one(target_returns_data, factor_matrix_data):
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"""Per-period shares sum to 1.0 within floating-point tolerance."""
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out = rd_module.risk_decomposition(
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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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sums = df.groupby("period")["share"].sum()
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for period_sum in sums:
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assert period_sum == pytest.approx(1.0, abs=1e-9)
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def test_risk_decomposition_with_risk_free(target_returns_data, factor_matrix_data):
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"""The risk-free column is dropped from the factor list when provided."""
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out = rd_module.risk_decomposition(
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_params(target_returns_data, factor_matrix_data, risk_free_column="rf")
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)
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factors = {r.factor for r in out.results}
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assert "rf" not in factors
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assert {"f1", "f2", "Residual"}.issubset(factors)
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def test_risk_decomposition_constant_target_skipped(
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factor_matrix_df, factor_matrix_data
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):
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"""Periods where Var(target) is zero are skipped, not divided by zero."""
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constant_target = df_to_basemodel(
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pd.DataFrame(
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{"date": factor_matrix_df["date"], "close": [1.0] * len(factor_matrix_df)}
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)
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)
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out = rd_module.risk_decomposition(
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_params(constant_target, factor_matrix_data, periods=["Max"])
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)
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assert out.results == []
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def test_risk_decomposition_skips_too_short_window(
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factor_matrix_df, factor_matrix_data
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):
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"""Windows with too few observations for OLS are skipped cleanly."""
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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 = rd_module.risk_decomposition(
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_params(short_target, factor_matrix_data, periods=["Max"])
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)
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assert out.results == []
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def test_risk_decomposition_default_periods():
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"""The default ``periods`` value enumerates the six named look-back windows."""
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params = rd_module.RiskDecompositionQueryParams(
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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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