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>

CLAUDE.md

@@ -47,6 +47,11 @@ uv run python -m research.lab.fetch_cn_yahoo           # CSI 300/500 ever-member
 PYTHONPATH=. uv run python -m research.lab.factor_scan --market us       # or cn_hs300
 PYTHONPATH=. uv run python -m research.lab.stepwise --market us --objective maximin
 PYTHONPATH=. uv run python -m research.lab.evaluate --market us          # candidates vs champions
+uv run python -m research.lab.fetch_us_fundamentals   # SEC EDGAR point-in-time facts + Yahoo splits (US)
+PYTHONPATH=. uv run python -m research.lab.rotation --market us          # walk-forward factor weighting schemes
+PYTHONPATH=. uv run python -m research.lab.persistence                   # does last year's best combo persist?
+PYTHONPATH=. uv run --with numba python -m research.lab.us_sweep --part overlays   # construction / overlay sweeps
+PYTHONPATH=. uv run --with numba python -m research.lab.us_final         # 3-sleeve US portfolio check
 
 # Setup
 uv sync                                  # Install/sync dependencies
@@ -71,7 +76,7 @@ uv run python -m unittest discover tests
 
 **Backtest engine** (`main.py`): Orchestrates data loading, strategy execution, and visualization. `backtest()` has two modes. Close mode (no `open_data`, used by `main.py` and old research scripts) is vectorized and trades at the close the signal saw. Live mode (`open_data` given, used by the web UI through `backtest_runner.py`) keeps the book like `trader.py`: same signal timing (`strategy_positions`), holdings that drift, the $50/0.1% order floor, `trader.RESIZE_BAND`, fees on the orders placed (`simulate_live_book`).
 
-**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Orders: whole A-share lots are allocated nearest-lot within cash (`_allocate_lots`); a held name is resized only when ≥ `RESIZE_BAND` (10%) off target. Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`, 32 factor-combo strategies (`fc_{signal}_{freq}` — see `strategies/factor_combo.py`), and the robust-momentum bar strategies `overnight_recovery_top10`, `lowrisk_momentum_top15`, `lowbeta_sharpe_mom_top20`.
+**Daily trader** (`trader.py`): Live/forward-testing system with persistent portfolio state in `data/trader_{market}_{strategy}.json`. The `auto` subcommand runs both signal generation and execution in a single invocation — designed for cron. The `simulate` subcommand replays a date range day-by-day with realistic portfolio tracking (fractional shares, cash, commissions). Orders: whole A-share lots are allocated nearest-lot within cash (`_allocate_lots`); a held name is resized only when ≥ `RESIZE_BAND` (10%) off target. Available strategies: `recovery_mom_top10`, `recovery_mom_top20`, `momentum`, `momentum_quality`, `dual_momentum`, `inverse_vol`, `trend_following`, `buy_and_hold`, 32 factor-combo strategies (`fc_{signal}_{freq}` — see `strategies/factor_combo.py`), the robust-momentum bar strategies `overnight_recovery_top10`, `lowrisk_momentum_top15`, `lowbeta_sharpe_mom_top20`, `lowrisk_top20`, and the US sleeve portfolio `us_three_sleeve`.
 
 **Live books** (`trader.LIVE_BOOKS`): a book is a cohort of strategies, each with its own account, started on one date with one capital and a flat fee per order. The original book (every live strategy, `MONITOR_CAPITAL`, `MARKET_FEES`) keeps state in `data/trader_*.json`; a named book keeps it in `data/books/<book>/`. The monitor runs every book; `auto`/`status`/`log`/`compare`/`replay` and `bridge.py live` take `--book`, and `bridge.py books` lists them for the UI's book selector. `selected_2026_09` (29 strategies, US $10,000 / CN ¥100,000, $2 / ¥10 per order) starts on 2026-09-25 (US) and 2026-09-28 (CN).
 
@@ -88,7 +93,8 @@ uv run python -m unittest discover tests
 - `momentum_quality.py` — Momentum + return consistency + low drawdown
 - `adaptive_momentum.py` — Momentum weighted by inverse volatility
 - `recovery_momentum.py` — Recovery (price/63d low) + 12-1mo momentum composite. Best US performer.
-- `robust_momentum.py` — Bar strategies from the 2026-09 PIT research (report: https://claude.ai/artifact/Mo9nrzs5SWj5FfyshERyWS): US `overnight_recovery` (overnight-return momentum + distance from 126d low), CN `lowrisk_momentum` (amplitude-cut momentum + low beta + price-volume corr + momentum/vol) and close-only `lowbeta_sharpe_mom`. Top-N with rank hysteresis, rebalance every 10 days, weights drift between rebalances.
+- `robust_momentum.py` — Bar strategies from the 2026-09 PIT research (report: https://claude.ai/artifact/Mo9nrzs5SWj5FfyshERyWS): US `overnight_recovery` (overnight-return momentum + distance from 126d low), CN `lowrisk_momentum` (amplitude-cut momentum + low beta + price-volume corr + momentum/vol) and close-only `lowbeta_sharpe_mom`. Top-N with rank hysteresis, rebalance every 10 days, weights drift between rebalances. `low_risk` (US `lowrisk_top20`: idiosyncratic vol + low beta, top 20, monthly) is the defensive sleeve of the 2026-09-29 US research (report: https://claude.ai/artifact/VGhZvKkS9UBYZkLeqGjcNC).
+- `sleeve_portfolio.py` — Several strategies in one account with fixed capital shares. `us_three_sleeve` = 1/3 each of `overnight_recovery_top10`, `lowrisk_top20`, `trend_rider_v7_vt24` (a mixed stock+ETF strategy); sleeve shares drift with their returns and reset at a month start once any is >25% off. 2011-2026 research: Sharpe 1.21 vs 0.88-1.03 for the sleeves alone. Research finding behind it: choosing factors by their recent record (rotation, factor momentum, last year's winner) did not beat a fixed composite; construction knobs (N, weighting, dynamic N) barely matter.
 - `factor_combo.py` — Configurable factor-combination strategies with daily/weekly/biweekly/monthly rebalancing. US champions: `rec_mfilt+deep_upvol`, `ma200+mom7m+rec126`, `rec_mfilt+ma200`, `mom7m+rec126`. CN champions: `up_cap+quality_mom`, `down_resil+qual_mom`, `rec63+mom_gap`, `up_cap+mom_gap` — these were mined on today's CSI 300 members; on PIT members they earn ~3-7% a year (2016-2026). All registered in trader.py as `fc_{signal}_{freq}` (e.g., `fc_rec_mfilt_deep_upvol_monthly`). 32 new strategies total.
 
 **Metrics** (`metrics.py`): Standalone functions for portfolio analytics (Sharpe, Sortino, Calmar, max drawdown, etc.). `summary()` prints a formatted report and returns a dict.