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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
"""Tests for ``openbb_quantitative.attribution``."""
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from math import isnan
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import numpy as np
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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 attribution as att_module
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def _params(target_data, factor_data, **kwargs):
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return att_module.ReturnAttributionQueryParams(
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data=target_data, factors_data=factor_data, target="close", **kwargs
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)
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def test_attribution_happy_path(target_returns_data, factor_matrix_data):
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"""Returns rows for every regressor plus Alpha and Residual per period."""
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out = att_module.attribution(_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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names = [r.factor for r in results if r.period == period]
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# const is hidden behind the Alpha label; rf is in the factor set unless
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# risk_free_column is provided.
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assert set(names) == {"f1", "f2", "rf", "Alpha", "Residual"}
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def test_attribution_contributions_sum_to_total(
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target_returns_df, factor_matrix_df, target_returns_data, factor_matrix_data
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):
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"""Per-period contributions sum to the period's total target return."""
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out = att_module.attribution(
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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")["contribution"].sum()
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# Recompute the expected per-period target totals (excess of rf).
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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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max_total = float(target.sum())
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assert sums["Max"] == pytest.approx(max_total, abs=1e-9)
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def test_attribution_zero_total_uses_nan_share(factor_matrix_df, factor_matrix_data):
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"""When the period's total return is zero, share is NaN (no divide-by-zero)."""
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n = len(factor_matrix_df)
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half = n // 2
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# Alternating +eps / -eps gives an exactly zero sum.
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series = np.tile([1e-6, -1e-6], half + 1)[:n]
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zero_target = df_to_basemodel(
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pd.DataFrame({"date": factor_matrix_df["date"], "close": series})
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)
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out = att_module.attribution(
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_params(zero_target, factor_matrix_data, periods=["Max"])
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)
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shares = [r.share for r in out.results]
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assert shares
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assert all(isnan(s) for s in shares)
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def test_attribution_with_risk_free_drops_rf(target_returns_data, factor_matrix_data):
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"""The risk-free column is removed from the factor set when provided."""
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out = att_module.attribution(
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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", "Alpha", "Residual"}.issubset(factors)
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def test_attribution_skips_too_short_window(factor_matrix_df, factor_matrix_data):
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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 = att_module.attribution(
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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_attribution_default_periods():
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"""The default ``periods`` value enumerates the six named look-back windows."""
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params = att_module.ReturnAttributionQueryParams(
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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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