feat: US 3-sleeve portfolio and lowrisk_top20; US portfolio research harness

Register two live strategies from the 2026-09-29 US research:
- lowrisk_top20: idiosyncratic vol + low beta, top 20, monthly, buffer 2
  (new `low_risk` signal in strategies/robust_momentum.py)
- us_three_sleeve: 1/3 each of overnight_recovery_top10, lowrisk_top20 and
  trend_rider_v7_vt24 in one account (strategies/sleeve_portfolio.py, a
  mixed stock+ETF strategy). Sleeve shares drift with their returns and reset
  at a month start once any is >25% off its share.

Research (research/lab, S&P 500 PIT, next-open fills):
- rotation.py / persistence.py: walk-forward factor weighting (factor
  momentum, rolling IC, market state) and last-year's-winner selection do not
  beat a fixed composite
- us_sweep.py: holdings count, z/vol weights, dynamic N, tranching and market
  overlays (Faber SMA, de-risk vol target, statistical jump model,
  momentum state); engine.py gains zvol weighting and dyn_n
- fetch_us_fundamentals.py / fundamentals.py / fund_eval.py: SEC EDGAR
  point-in-time fundamentals (first-filed values) and factor tests
- trend_rider_long.py / us_final.py: 2010-2026 TrendRider curves and the
  3-sleeve check (2011-2026 Sharpe 1.21, CAGR 21.9%, MaxDD -23%)

Web-UI engine, 2017-09..2026-09, $2/order: us_three_sleeve CAGR 28.9%,
Sharpe 1.49, MaxDD -24.8%.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

tests/test_sleeve_portfolio.py

@@ -0,0 +1,93 @@
+"""Contracts of the sleeve portfolio (us_three_sleeve): causal targets, sleeves
+kept to their own columns, shares that drift like separate accounts, and the
+live trader's session timing."""
+
+import unittest
+
+import numpy as np
+import pandas as pd
+
+import trader
+from strategies.permanent import ETF_UNIVERSE
+from strategies.robust_momentum import RobustMomentumStrategy
+from strategies.sleeve_portfolio import (SleevePortfolioStrategy, decisions, drifting_shares,
+                                         us_three_sleeve)
+
+
+def _bars(n_days: int = 520, n_stocks: int = 30, seed: int = 1) -> dict[str, pd.DataFrame]:
+    rng = np.random.default_rng(seed)
+    idx = pd.bdate_range("2024-01-01", periods=n_days)
+    cols = [f"S{i:02d}" for i in range(n_stocks)] + sorted(set(ETF_UNIVERSE))
+    r = rng.normal(0.0005, 0.015, (n_days, len(cols)))
+    close = pd.DataFrame(100 * np.exp(np.cumsum(r, axis=0)), index=idx, columns=cols)
+    opens = close.shift(1).bfill() * np.exp(rng.normal(0, 0.004, close.shape))
+    return {"close": close, "open": opens}
+
+
+class SleevePortfolioTests(unittest.TestCase):
+    def test_targets_on_a_day_ignore_that_day_and_later_bars(self):
+        bars = _bars()
+        strat = us_three_sleeve()
+        cut = bars["close"].index[450]
+        base = strat.generate_signals(bars)
+        shocked = {f: b.copy() for f, b in bars.items()}
+        for f in shocked:
+            shocked[f].loc[cut:] *= 1.7
+        again = strat.generate_signals(shocked)
+        pd.testing.assert_frame_equal(base.loc[:cut], again.loc[:cut])
+
+    def test_stock_sleeves_hold_no_etfs_and_the_book_is_not_levered(self):
+        bars = _bars()
+        strat = us_three_sleeve()
+        weights = strat.generate_signals(bars)
+        etfs = set(ETF_UNIVERSE)
+        self.assertLessEqual(weights.sum(axis=1).max(), 1.0 + 1e-9)
+        # After warm-up the stock part is two sleeves' worth of whole names only.
+        late = weights.iloc[-1]
+        stocks = late[[c for c in late.index if c not in etfs]]
+        self.assertEqual(int((stocks > 0).sum()) <= 30, True)
+        self.assertGreater(stocks.sum(), 0.5)
+        _, shares = strat.target_weights(bars)
+        self.assertTrue(np.allclose(shares.sum(axis=1), 1.0))
+
+    def test_decisions_are_the_strategy_weights_one_row_earlier(self):
+        bars = _bars()
+        strat = RobustMomentumStrategy("low_risk", top_n=5, rebal_freq=5, buffer=1.0)
+        stock = {f: b[[c for c in b.columns if c.startswith("S")]] for f, b in bars.items()}
+        d = decisions(strat, stock)
+        w = strat.generate_signals(stock)
+        pd.testing.assert_frame_equal(d.iloc[:-1], w.shift(-1).iloc[:-1].fillna(0.0))
+        self.assertAlmostEqual(d.iloc[-1].sum(), 1.0)
+
+    def test_shares_drift_with_sleeve_returns_and_reset_past_the_band(self):
+        # Two sleeves fully in one asset each; A doubles on day 1, B is flat.
+        returns = np.array([[0.0, 0.0], [1.0, 0.0], [0.0, 0.0], [0.0, 0.0]])
+        wa = np.array([[1.0, 0.0]] * 4)
+        wb = np.array([[0.0, 1.0]] * 4)
+        base = np.array([0.5, 0.5])
+        month_start = np.array([False, False, False, True])
+        shares = drifting_shares([wa, wb], returns, base, month_start, band=0.25)
+        np.testing.assert_allclose(shares[1], [2 / 3, 1 / 3])     # drifted like two accounts
+        np.testing.assert_allclose(shares[2], [2 / 3, 1 / 3])     # no reset mid-month
+        np.testing.assert_allclose(shares[3], [0.5, 0.5])         # 33% off base: reset
+        kept = drifting_shares([wa, wb], returns, base, month_start, band=0.5)
+        np.testing.assert_allclose(kept[3], [2 / 3, 1 / 3])       # inside a 50% band
+
+    def test_trader_session_targets_use_bars_through_the_previous_day(self):
+        bars = _bars()
+        strat = us_three_sleeve()
+        days = bars["close"].index[-2:]
+        full = strat.generate_signals(bars).loc[days]
+        got = trader.bar_session_weights(strat, bars, days)
+        pd.testing.assert_frame_equal(got, full)
+
+    def test_registered_as_a_us_mixed_strategy(self):
+        self.assertIsInstance(trader.STRATEGY_REGISTRY["us_three_sleeve"](), SleevePortfolioStrategy)
+        self.assertEqual(set(trader.strategy_extra_tickers("us_three_sleeve")), set(ETF_UNIVERSE))
+        self.assertTrue(trader.strategy_supports_market("us_three_sleeve", "us"))
+        self.assertFalse(trader.strategy_supports_market("us_three_sleeve", "cn"))
+        self.assertEqual(trader.strategy_fields("us_three_sleeve"), ("close", "open"))
+
+
+if __name__ == "__main__":
+    unittest.main()