feat(quant): consolidate research artifact contract (#7)

tests/test_metrics.py

@@ -0,0 +1,163 @@
+"""Mathematical contracts for the standard performance metrics."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from quant_engine.metrics import (
+    TRADING_DAYS_PER_YEAR,
+    annualized_return,
+    annualized_volatility,
+    benchmark_summary,
+    calmar_ratio,
+    max_drawdown,
+    sharpe_ratio,
+    sortino_ratio,
+    summary,
+    win_rate,
+)
+
+
+def test_annualized_return_uses_compounded_simple_returns() -> None:
+    returns = pd.Series([0.10, -0.10])
+    expected = 0.99 ** (TRADING_DAYS_PER_YEAR / 2) - 1.0
+
+    assert annualized_return(returns) == pytest.approx(expected)
+
+
+def test_annualized_volatility_uses_sample_standard_deviation() -> None:
+    returns = pd.Series([0.01, 0.03, 0.02])
+
+    assert annualized_volatility(returns) == pytest.approx(
+        returns.std() * np.sqrt(TRADING_DAYS_PER_YEAR)
+    )
+
+
+def test_sharpe_ratio_subtracts_annual_risk_free_rate() -> None:
+    returns = pd.Series([0.01, -0.005, 0.02, 0.0])
+
+    result = sharpe_ratio(returns, rf=0.02)
+
+    assert result == pytest.approx(
+        (annualized_return(returns) - 0.02) / annualized_volatility(returns)
+    )
+
+
+def test_zero_volatility_metrics_return_zero() -> None:
+    returns = pd.Series([0.0, 0.0, 0.0])
+
+    assert sharpe_ratio(returns) == 0.0
+    assert sortino_ratio(returns) == 0.0
+    assert calmar_ratio(returns) == 0.0
+
+
+def test_sortino_ratio_uses_all_sessions_for_downside_deviation() -> None:
+    returns = pd.Series([0.02, -0.01, 0.0, -0.03])
+    downside = np.minimum(returns.to_numpy(), 0.0)
+    downside_deviation = np.sqrt(np.mean(np.square(downside))) * np.sqrt(
+        TRADING_DAYS_PER_YEAR
+    )
+
+    assert sortino_ratio(returns) == pytest.approx(
+        annualized_return(returns) / downside_deviation
+    )
+
+
+def test_max_drawdown_includes_loss_from_initial_capital() -> None:
+    returns = pd.Series([-0.20, 0.0])
+
+    assert max_drawdown(returns) == pytest.approx(-0.20)
+
+
+def test_max_drawdown_tracks_peak_to_trough_loss() -> None:
+    returns = pd.Series([0.10, -0.20, 0.05])
+
+    assert max_drawdown(returns) == pytest.approx(-0.20)
+
+
+def test_metrics_clean_nan_and_infinite_values() -> None:
+    returns = pd.Series([0.10, np.nan, np.inf, -0.05, -np.inf])
+
+    assert win_rate(returns) == 0.5
+    assert summary(returns)["n_days"] == 2
+
+
+def test_summary_aliases_match_canonical_fields() -> None:
+    result = summary(pd.Series([0.01, -0.02, 0.03]))
+
+    assert result["annual_yield"] == result["ann_return"]
+    assert result["annual_sd"] == result["ann_volatility"]
+    assert result["drawback"] == result["max_drawdown"]
+
+
+@pytest.mark.parametrize(
+    "metric",
+    [
+        annualized_return,
+        annualized_volatility,
+        sharpe_ratio,
+        sortino_ratio,
+        max_drawdown,
+        calmar_ratio,
+        win_rate,
+    ],
+)
+def test_metrics_reject_non_series_input(metric) -> None:
+    with pytest.raises(TypeError, match=r"expected pd\.Series"):
+        metric([0.01, 0.02])
+
+
+def test_short_and_empty_series_return_zero() -> None:
+    assert annualized_return(pd.Series(dtype=float)) == 0.0
+    assert annualized_volatility(pd.Series([0.01])) == 0.0
+    assert max_drawdown(pd.Series([0.01])) == 0.0
+    assert win_rate(pd.Series(dtype=float)) == 0.0
+
+
+def test_benchmark_summary_uses_aligned_active_returns_and_regression() -> None:
+    dates = pd.date_range("2026-01-05", periods=4, freq="B")
+    benchmark = pd.Series([-0.01, 0.0, 0.01, 0.02], index=dates)
+    portfolio = 0.001 + 1.5 * benchmark
+    active = portfolio - benchmark
+
+    result = benchmark_summary(portfolio, benchmark)
+
+    assert result["n_observations"] == 4
+    assert result["tracking_error"] == pytest.approx(
+        active.std() * np.sqrt(TRADING_DAYS_PER_YEAR)
+    )
+    assert result["information_ratio"] == pytest.approx(
+        active.mean() / active.std() * np.sqrt(TRADING_DAYS_PER_YEAR)
+    )
+    assert result["beta"] == pytest.approx(1.5)
+    assert result["alpha"] == pytest.approx(1.001**TRADING_DAYS_PER_YEAR - 1.0)
+
+
+def test_benchmark_summary_rejects_silent_calendar_alignment() -> None:
+    portfolio = pd.Series([0.01, 0.02], index=pd.date_range("2026-01-05", periods=2))
+    benchmark = pd.Series([0.01, 0.02], index=pd.date_range("2026-01-06", periods=2))
+
+    with pytest.raises(ValueError, match="matching indexes"):
+        benchmark_summary(portfolio, benchmark)
+
+
+def test_benchmark_summary_rejects_missing_observations() -> None:
+    dates = pd.date_range("2026-01-05", periods=2)
+    portfolio = pd.Series([0.01, np.nan], index=dates)
+    benchmark = pd.Series([0.0, 0.01], index=dates)
+
+    with pytest.raises(ValueError, match="finite"):
+        benchmark_summary(portfolio, benchmark)
+
+
+def test_benchmark_summary_marks_constant_benchmark_regression_unestimable() -> None:
+    dates = pd.date_range("2026-01-05", periods=3)
+    portfolio = pd.Series([0.01, -0.01, 0.02], index=dates)
+    benchmark = pd.Series([0.0, 0.0, 0.0], index=dates)
+
+    result = benchmark_summary(portfolio, benchmark)
+
+    assert np.isnan(result["alpha"])
+    assert np.isnan(result["beta"])