feat: us_momentum_qqq_trend; alpha-vs-QQQ research across new data sources

Register us_momentum_qqq_trend: half overnight_recovery_top10, half a QQQ
trend sleeve (strategies/qqq_trend.py: 200-day switch times a 30% vol target,
held as TQQQ + SHY). Mixed stock+ETF, US only (extra tickers QQQ, SHY, TQQQ).

Research (research/lab/): alpha against QQQ with a 2006-16 design / 2017-26
holdout split (alpha.py, qqq_scan.py, beat_qqq.py); leverage and drawdown
frontier (levered_book.py, us_target50.py); synthetic 3x ETFs from 1990 and
TrendRider/QQQ-trend stress tests (fetch_long_history.py, letf_history.py,
vol_trend.py); short-horizon sleeves, diversifiers, industry momentum,
earnings-announcement returns; new point-in-time data sources: SEC Form 4
insider trades (fetch_insider.py, insider.py), 10-K/10-Q text similarity,
8-K earnings dates and SIC codes (fetch_lazy_prices.py, lazy_prices.py), and
an S&P 400/600 universe from iShares holdings (fetch_us_mid.py, panel_mid.py).

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

research/lab/fetch_long_history.py

@@ -0,0 +1,107 @@
+"""
+Long daily history for the leveraged-ETF and cross-asset research
+(research/lab/letf_history.py): stock indices from 1927/1985, the ETFs the
+trend strategies trade, gold futures, bitcoin, and Treasury yields.
+
+    PYTHONPATH=. uv run python -m research.lab.fetch_long_history [--no-proxy]
+
+Yahoo is fetched ticker by ticker (batched requests fail intermittently)
+and each ticker is cached in data/research/ext/yahoo/ as it arrives, so a
+re-run after rate limiting fetches only what is missing. The fetch stops
+after three tickers fail in a row: Yahoo is rate limiting, and the live
+monitor needs it too. FRED yields come from the public fredgraph CSV
+endpoint. Writes data/research/long_history.pkl: {"close", "open"}
+DataFrames (adjusted for splits and dividends) and "fred" (yields in
+percent, and the Nasdaq-100 level).
+"""
+
+from __future__ import annotations
+
+import argparse
+import io
+import os
+import pickle
+import time
+import urllib.request
+import warnings
+
+import pandas as pd
+import yfinance as yf
+
+from research.lab.fetch_us import _proxy_env
+
+warnings.filterwarnings("ignore")
+OUT = os.path.join("data", "research", "long_history.pkl")
+YAHOO = [
+    "^GSPC", "^NDX", "^VIX",                                   # indices (price only)
+    "SPY", "QQQ", "IWM", "EFA", "EEM", "VNQ", "TLT", "IEF", "SHY", "GLD", "DBC", "GSG", "UUP",
+    "TQQQ", "UPRO", "QLD", "SSO", "SOXL", "TMF", "UGL",        # leveraged ETFs (for validation)
+    "VFINX", "VUSTX",                                           # mutual funds with dividends, 1980s-
+    "GC=F", "BTC-USD", "ETH-USD", "IBIT",
+]
+FRED = ["DTB3", "DGS2", "DGS10", "DGS30", "NASDAQ100"]
+
+
+CACHE = os.path.join("data", "research", "ext", "yahoo")
+
+
+def _cached(t: str) -> str:
+    return os.path.join(CACHE, t.replace("^", "_").replace("=", "_") + ".csv")
+
+
+def fetch_yahoo(tickers: list[str], start: str = "1927-01-01") -> tuple[pd.DataFrame, pd.DataFrame]:
+    os.makedirs(CACHE, exist_ok=True)
+    fails = 0
+    for t in tickers:
+        if os.path.exists(_cached(t)) or fails >= 3:
+            continue
+        for attempt in range(2):
+            try:
+                h = yf.Ticker(t).history(start=start, auto_adjust=True)
+                if len(h):
+                    h.index = h.index.tz_localize(None).normalize()
+                    h[["Open", "Close"]].to_csv(_cached(t))
+                    break
+            except Exception as exc:  # noqa: BLE001 — retry transient Yahoo errors
+                print(f"  {t}: {type(exc).__name__}", flush=True)
+            time.sleep(20)
+        fails = 0 if os.path.exists(_cached(t)) else fails + 1
+        print(f"{t:8s} {'ok' if os.path.exists(_cached(t)) else 'MISSING'}", flush=True)
+        time.sleep(2)
+    close, opens = {}, {}
+    for t in tickers:
+        if os.path.exists(_cached(t)):
+            h = pd.read_csv(_cached(t), index_col=0, parse_dates=True)
+            close[t], opens[t] = h["Close"], h["Open"]
+    missing = [t for t in tickers if t not in close]
+    if missing:
+        print("missing (re-run later):", missing)
+    return pd.DataFrame(close).sort_index(), pd.DataFrame(opens).sort_index()
+
+
+def fetch_fred(series: list[str]) -> pd.DataFrame:
+    out = {}
+    for s in series:
+        url = f"https://fred.stlouisfed.org/graph/fredgraph.csv?id={s}"
+        with urllib.request.urlopen(url, timeout=60) as fh:
+            df = pd.read_csv(io.BytesIO(fh.read()), index_col=0, parse_dates=True)
+        out[s] = pd.to_numeric(df.iloc[:, 0], errors="coerce")
+    return pd.DataFrame(out)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--no-proxy", action="store_true",
+                    help="connect directly (when the proxy's IP is rate limited by Yahoo)")
+    if not ap.parse_args().no_proxy:
+        _proxy_env()
+    close, opens = fetch_yahoo(YAHOO)
+    fred = fetch_fred(FRED)
+    os.makedirs(os.path.dirname(OUT), exist_ok=True)
+    with open(OUT, "wb") as fh:
+        pickle.dump({"close": close, "open": opens, "fred": fred}, fh)
+    print(f"Saved {OUT}: {close.shape[1]} series, FRED {list(fred.columns)}")
+
+
+if __name__ == "__main__":
+    main()