research: lagged futures signals for stock exposure vs matched ETF rules (none passes, retain baseline)

128 fixed runs (32 profiles x daily ledger / overnight-only x 2 costs) plus 12
post-hoc availability diagnostics on overnight_recovery_top10, 2006-10 to 2026-09.
Futures trend/shock/rates rules do not beat the same rules on SPY/QQQ/IEF or a
static 75% exposure; all six default schemes fail the gates.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tests/test_futures_stock_signals.py

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+import unittest
+
+import numpy as np
+import pandas as pd
+
+from research.lab.futures_stock_signals import configurations, overnight_exposure, risk_exposure
+
+
+class FuturesTimingTests(unittest.TestCase):
+    def setUp(self):
+        dates = pd.bdate_range("2020-01-01", periods=180)
+        t = np.arange(len(dates))
+        equity = 100 * np.exp(.001 * t + .002 * np.sin(t))
+        rates = 100 * np.exp(.0001 * t + .0002 * np.sin(t))
+        self.prices = pd.DataFrame({"ES=F": equity, "NQ=F": equity, "ZN=F": rates,
+                                    "SPY": equity, "QQQ": equity, "IEF": rates}, index=dates)
+
+    def test_future_prices_cannot_change_earlier_decisions(self):
+        changed = self.prices.copy()
+        changed.iloc[110:, :3] *= .7
+        for config in configurations():
+            original, known = risk_exposure(self.prices, config)
+            modified, modified_known = risk_exposure(changed, config)
+            last = 110 + config.get("lag", 1)
+            with self.subTest(config=config["name"]):
+                pd.testing.assert_series_equal(original.iloc[:last], modified.iloc[:last])
+                pd.testing.assert_series_equal(known.iloc[:last], modified_known.iloc[:last])
+
+    def test_same_day_futures_crash_is_not_usable_for_entry_or_preceding_night(self):
+        config = next(c for c in configurations() if c["name"] == "futures_equity_shock_default")
+        changed = self.prices.copy()
+        changed.iloc[110:, :2] *= .7
+        entry, _ = risk_exposure(changed, config)
+        self.assertEqual(entry.iloc[110], 1.0)
+        self.assertEqual(entry.iloc[111], .5)
+        night = overnight_exposure(entry)
+        self.assertEqual(night.iloc[111], 1.0)
+        self.assertEqual(night.iloc[112], .5)
+
+    def test_identical_etf_and_futures_inputs_have_identical_rules(self):
+        configs = {c["name"]: c for c in configurations()}
+        for name, config in configs.items():
+            if not name.startswith("futures_"):
+                continue
+            with self.subTest(config=name):
+                a, av = risk_exposure(self.prices, config)
+                b, bv = risk_exposure(self.prices, configs[name.replace("futures_", "etf_", 1)])
+                pd.testing.assert_series_equal(a, b, check_names=False)
+                pd.testing.assert_series_equal(av, bv, check_names=False)
+
+    def test_unavailable_prices_are_not_backward_filled_from_future(self):
+        config = next(c for c in configurations() if c["name"] == "futures_equity_shock_default")
+        missing = self.prices.copy()
+        missing.iloc[110, :2] = np.nan
+        exposure, known = risk_exposure(missing, config)
+        self.assertFalse(known.iloc[111])
+        self.assertEqual(exposure.iloc[111], 1.0)
+        prefix, prefix_known = risk_exposure(missing.iloc[:112], config)
+        pd.testing.assert_series_equal(exposure.iloc[:112], prefix)
+        pd.testing.assert_series_equal(known.iloc[:112], prefix_known)
+
+
+if __name__ == "__main__":
+    unittest.main()