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

tests/test_signals.py

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+"""Tests for alpha signal computation."""
+import pandas as pd
+
+from signals.reversal import ReversalSignal
+
+
+def _make_df(closes):
+    return pd.DataFrame({"close": closes})
+
+
+def test_reversal_name():
+    assert ReversalSignal(lookback=5).name == "reversal_5d"
+    assert ReversalSignal(lookback=10).name == "reversal_10d"
+
+
+def test_reversal_is_negative_trailing_return():
+    # Monotonically rising prices -> negative (bearish) reversal signal.
+    df = _make_df([10.0, 11.0, 12.0, 13.0, 14.0, 15.0])
+    sig = ReversalSignal(lookback=5).compute(df)
+    # First 5 values are NaN (insufficient lookback).
+    assert sig.iloc[:5].isna().all()
+    # 15/10 - 1 = 0.5 return -> signal = -0.5
+    assert abs(sig.iloc[5] - (-0.5)) < 1e-9
+
+
+def test_reversal_oversold_is_positive():
+    # Falling prices -> positive (bullish) reversal signal.
+    df = _make_df([20.0, 18.0, 16.0, 14.0, 12.0, 10.0])
+    sig = ReversalSignal(lookback=5).compute(df)
+    assert sig.iloc[5] > 0
+    # 10/20 - 1 = -0.5 -> signal = +0.5
+    assert abs(sig.iloc[5] - 0.5) < 1e-9
+
+
+def test_reversal_output_length_matches_input():
+    df = _make_df([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+    sig = ReversalSignal(lookback=3).compute(df)
+    assert len(sig) == len(df)