feat(research): Tiingo backfill of delisted names; honest PIT panels

Yahoo purges delisted members: the S&P research panel was missing 209
ever-members (AGN/CELG/ESRX/ATVI/TWTR/XLNX/...), the production store 89, and
the S&P 400/600 panels ~180 more. The gap inflated momentum-family factors
(overnight_mom holdout 16.9->14.8%/yr, rec_126 14.7->10.7 on the research
panel; factor_panel_check).

research/lab/fetch_tiingo.py pulls full EOD histories (free tier: 500 unique
symbols/month, 50/hour; paced, resume-safe, key never logged).
research/lab/tiingo_merge.py overlays the caches on the research S&P panel at
build time (>=50% PIT-coverage gate, median-overlap cross-check, stub-column
replacement, rename splices GPS<-GAP, DISCA<-WBD, WLTW<-WTW, ADS<-BFH,
CCE<-CCEP, ESV<-VAL, FBHS<-FBIN, HFC<-DINO) and can apply the same to the
production store (--production [--apply]; data/us*.csv, .bak-tiingo backups,
under the panel lock). load_us() rebuilds when the overlay changes
(_tiingo_sig); fetch_us_mid.load_bars falls back to converted CSVs.

Round 1 (2026-10-08): 165/209 S&P names and 183 mid/small PIT members
restored (2011-19 snapshots were 15-20% short of PIT members). prod_impact
A/B on registered replays: overnight_recovery_top10 23.8%/0.83/-38.3% vs
27.8%/0.88/-47.3% backfill-off; lowrisk_top20 6.1%/0.58 vs 8.6%/0.60 -
honest long returns with shallower drawdowns; trend_rider_v10 (ETF path)
unchanged at 55.0%/1.64. Official precompute refreshed (backup at
data/precomputed/backtest_results.json.pretiingo). midsmall_scan on the
corrected panel: no factor beats the EW benchmark (0.60 holdout Sharpe vs
0.51-0.52 best) - no mid/small sleeve.

research/lab/panel.py

@@ -18,6 +18,7 @@ import numpy as np
 import pandas as pd
 
 from research.lab.engine import Costs, Panel
+from research.lab.tiingo_merge import apply_sp500
 
 RAW_DIR = os.path.join("data", "research")
 US_ETFS = {"SPY", "RSP", "QQQ", "SHY", "BIL", "IEF", "TLT", "GLD",
@@ -54,6 +55,7 @@ def _us_membership(dates: pd.DatetimeIndex, tickers: list[str]) -> pd.DataFrame:
 def _build_us() -> Panel:
     with open(os.path.join(RAW_DIR, "us_ohlcv.pkl"), "rb") as fh:
         raw = pickle.load(fh)
+    raw = apply_sp500(raw)
     close = raw["close"]
     # keep rows where most listed tickers printed (drops stray holiday rows)
     alive = close.notna().sum(axis=1)
@@ -92,8 +94,31 @@ def _us_sectors(stocks: list[str]) -> pd.Series:
     return sec.reindex(stocks)
 
 
+def _tiingo_sig() -> list:
+    d = os.path.join(RAW_DIR, "tiingo")
+    if not os.path.isdir(d):
+        return [0, 0]
+    files = [f for f in os.listdir(d) if f.endswith(".csv")]
+    mtime = max((os.path.getmtime(os.path.join(d, f)) for f in files), default=0)
+    return [len(files), round(mtime, 3)]
+
+
 def load_us() -> Panel:
-    return _cached("us", _build_us)
+    """Cached US panel, rebuilt whenever the Tiingo overlay changes."""
+    path = os.path.join(RAW_DIR, "us.panel.pkl")
+    key = os.path.join(RAW_DIR, "us.panel.key")
+    sig = _tiingo_sig()
+    if os.path.exists(path) and os.path.exists(key):
+        with open(key) as fh:
+            if json.load(fh) == sig:
+                with open(path, "rb") as fh:
+                    return pickle.load(fh)
+    p = _build_us()
+    with open(path, "wb") as fh:
+        pickle.dump(p, fh)
+    with open(key, "w") as fh:
+        json.dump(sig, fh)
+    return p
 
 
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