feat: signal abstraction layer + sizer + HS300 universe + PnL/IC reports

tests/test_eval.py

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+"""Tests for cross-sectional IC evaluation."""
+import numpy as np
+import pandas as pd
+
+from eval.metrics import evaluate_cross_sectional
+
+
+def test_cross_sectional_keys_present():
+    dates = pd.date_range("2024-01-01", periods=10)
+    cols = ["a", "b", "c"]
+    rng = np.random.default_rng(0)
+    signals = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
+    returns = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
+    res = evaluate_cross_sectional(signals, returns)
+    for key in (
+        "ic_mean", "ic_std", "ir", "rank_ic_mean", "rank_ic_std",
+        "rank_ir", "hit_rate", "n_periods",
+    ):
+        assert key in res
+
+
+def test_perfect_signal_has_positive_rank_ic():
+    # When the signal equals next-period returns, rank IC should be ~1 each day.
+    dates = pd.date_range("2024-01-01", periods=8)
+    cols = ["a", "b", "c"]
+    rng = np.random.default_rng(42)
+    returns = pd.DataFrame(rng.standard_normal((8, 3)), index=dates, columns=cols)
+    signals = returns.copy()  # perfect foresight
+    res = evaluate_cross_sectional(signals, returns)
+    assert res["rank_ic_mean"] > 0.99
+    assert res["hit_rate"] == 1.0
+    assert res["n_periods"] == 8
+
+
+def test_inverted_signal_has_negative_rank_ic():
+    dates = pd.date_range("2024-01-01", periods=6)
+    cols = ["a", "b", "c"]
+    rng = np.random.default_rng(7)
+    returns = pd.DataFrame(rng.standard_normal((6, 3)), index=dates, columns=cols)
+    signals = -returns  # perfectly wrong
+    res = evaluate_cross_sectional(signals, returns)
+    assert res["rank_ic_mean"] < -0.99
+
+
+def test_single_stock_falls_back_to_rolling():
+    dates = pd.date_range("2024-01-01", periods=40)
+    rng = np.random.default_rng(1)
+    signals = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
+    returns = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
+    res = evaluate_cross_sectional(signals, returns)
+    # Rolling fallback still yields the standard metric keys.
+    assert "rank_ic_mean" in res
+    assert res["n_periods"] > 0