Merge branch 'research/earnings-gap-and-sue' — near-close data fix + Task 2 closure

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.gitignore

@@ -46,3 +46,4 @@ backtest_snapshots/
 # Rebuildable pickle caches are local accelerators, not decision evidence.
 reports/*.pkl
 reports/*.pk1
+reports/.cache/

README.md

@@ -263,7 +263,7 @@ A systematic single-variable sweep (offline prod snapshot, production gate/rank/
 Two findings future sessions must not re-litigate:
 
 - **The "inverse-vol sizing win" (July 2026) was mis-attributed — do not resurrect.** The diagnostic sized `notional = equity × 1% / vol_6m`, and the 20% notional cap bound on 95% of entries, so it actually measured "~5 positions × 20% notional each" — a concentration/risk-appetite bump economically equivalent to raising risk to 1.5%, not vol-managed sizing. Genuine inverse-vol sizing (risk budget × median-vol/vol) cuts max drawdown to −18.2% but costs ~58pp total return at flat Sharpe: a risk-preference trade, not edge.
-- **`fip_id` — Da/Gurun/Warachka information discreteness over the 12-1 formation window — is the strongest cross-sectional signal measured on this universe: IC −0.045, t = −2.91, correct sign (continuous-information winners outperform).** It clears the iron-rule bar in isolation but does not improve this book (the momentum gate already captures the effect in-sample). It is the prime ranking/gate candidate **if the universe broadens** (e.g. `nasdaq_all`).
+- **`fip_id` — Da/Gurun/Warachka information discreteness over the 12-1 formation window — is the strongest cross-sectional signal on the *production* universe: IC −0.045, t = −2.91, correct sign (continuous-information winners outperform).** It clears the iron-rule bar in isolation but does not improve this book (the momentum gate already captures the effect in-sample). **Phase B (liquid-1500, research branch only):** unconditional fip fails iron rule (−0.017 / t −1.85); mom-conditional fip (−0.088 / t −4.58) is a *book-tilt candidate only* after a baseline breadth mom book is proven. Do **not** cite the orphaned 21:14 row (+0.0575) — it raced a partial `research.sqlite`. See `docs/research/fip-breadth-ic.md`.
 
 ### The iron rule for strategy changes
 
@@ -281,7 +281,7 @@ Corollaries: never let an unvalidated score gate setups; the outcome evaluator m
 
 1. **Forward monitor the promoted strategy** — the production UI now behaves like a portfolio monitor for the current strategy, with selectable lookbacks and SPY comparison. Forward paper-trade months are the only evidence the snapshot cannot provide; the July 2026 tuning pass closed every in-sample lead. (Trailing-stop sensitivity and the max-15 capacity check are done — see the tuning table above.)
 2. **Signal context snapshots** — accumulate point-in-time composite/sentiment/fundamental context for every new setup so the discretionary overlay can be tested forward-only.
-3. **More breadth, not more history** — widening the ranked universe (e.g. `nasdaq_all`) strengthens each week's cross-section and the IC t-stat, even if only the top slice is traded. Now doubly motivated: it is also where the strong `fip_id` signal (see tuning findings) could become tradeable. (Deeper history was considered and declined.)
+3. **Breadth is no longer free leverage** — Phase B found residual-mom t-stat *fell* on liquid-1500 vs the 505-name fingerprint (0.055/1.98 → 0.029/1.33). Any breadth book must clear a pre-registered baseline arm before fip tilts mean anything. (Deeper history was considered and declined.)
 
 ## Key Use Cases
 

app/main.py

@@ -3,56 +3,9 @@
 # ---------------------------------------------------------------------------
 # SSL + proxy injection — MUST happen before any HTTP client imports
 # ---------------------------------------------------------------------------
-import os as _os
-import ssl as _ssl
-from pathlib import Path as _Path
+from app.ssl_bootstrap import bootstrap_ssl
 
-_COMBINED_CERT = _Path(__file__).resolve().parent.parent / "combined-ca-bundle.pem"
-
-if _COMBINED_CERT.exists():
-    _cert_path = str(_COMBINED_CERT)
-    # Env vars for libraries that respect them (requests, urllib3)
-    _os.environ["SSL_CERT_FILE"] = _cert_path
-    _os.environ["REQUESTS_CA_BUNDLE"] = _cert_path
-    _os.environ["CURL_CA_BUNDLE"] = _cert_path
-
-    # Monkey-patch ssl.create_default_context so that ALL libraries
-    # (aiohttp, httpx, google-genai, alpaca-py, etc.) automatically
-    # use our combined CA bundle that includes the corporate root cert.
-    _original_create_default_context = _ssl.create_default_context
-
-    def _patched_create_default_context(
-        purpose=_ssl.Purpose.SERVER_AUTH, *, cafile=None, capath=None, cadata=None
-    ):
-        ctx = _original_create_default_context(
-            purpose, cafile=cafile, capath=capath, cadata=cadata
-        )
-        # Always load our combined bundle on top of whatever was loaded
-        ctx.load_verify_locations(cafile=_cert_path)
-        return ctx
-
-    _ssl.create_default_context = _patched_create_default_context
-
-    # Also patch aiohttp's cached SSL context objects directly, since
-    # aiohttp creates them at import time and may have already cached
-    # a context without our corporate CA bundle.
-    try:
-        import aiohttp.connector as _aio_conn
-        if hasattr(_aio_conn, '_SSL_CONTEXT_VERIFIED') and _aio_conn._SSL_CONTEXT_VERIFIED is not None:
-            _aio_conn._SSL_CONTEXT_VERIFIED.load_verify_locations(cafile=_cert_path)
-        if hasattr(_aio_conn, '_SSL_CONTEXT_UNVERIFIED') and _aio_conn._SSL_CONTEXT_UNVERIFIED is not None:
-            _aio_conn._SSL_CONTEXT_UNVERIFIED.load_verify_locations(cafile=_cert_path)
-    except ImportError:
-        pass
-
-# Corporate proxy — needed when Kiro spawns the process (no .zshrc sourced)
-# Only enable this if explicitly requested via environment variable.
-if _os.environ.get("USE_CORP_PROXY", "0") == "1":
-    _PROXY = "http://aproxy.corproot.net:8080"
-    _NO_PROXY = "corproot.net,sharedtcs.net,127.0.0.1,localhost,bix.swisscom.com,swisscom.com"
-    _os.environ.setdefault("HTTP_PROXY", _PROXY)
-    _os.environ.setdefault("HTTPS_PROXY", _PROXY)
-    _os.environ.setdefault("NO_PROXY", _NO_PROXY)
+bootstrap_ssl()
 
 import logging
 import sys

app/providers/alpaca.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import asyncio
 import logging
-from datetime import date
+from datetime import date, datetime, time, timedelta, timezone
 
 from alpaca.data.historical import StockHistoricalDataClient
 from alpaca.data.requests import StockBarsRequest
@@ -16,6 +16,11 @@ from app.providers.protocol import OHLCVData
 
 logger = logging.getLogger(__name__)
 
+# Free plans may not query data from the most recent ~15 minutes, and a window
+# reaching into it fails the *entire* request — which would silently leave the
+# near-close scan on yesterday's close. Margin over the documented boundary.
+_RECENT_DATA_CUTOFF = timedelta(minutes=20)
+
 
 class AlpacaOHLCVProvider:
     """Fetches daily OHLCV bars from Alpaca Markets Data API."""
@@ -25,6 +30,26 @@ class AlpacaOHLCVProvider:
             raise ProviderError("Alpaca API key and secret are required")
         self._client = StockHistoricalDataClient(api_key, api_secret)
 
+    @staticmethod
+    def _resolve_window(start_date: date, end_date: date) -> tuple[datetime, datetime]:
+        """Return the instants covering ``start_date``..``end_date`` inclusive.
+
+        Two boundaries have to be right or today's bar disappears:
+
+        * Daily bars are stamped at the session start in UTC (04:00Z under EDT),
+          so an ``end`` of midnight on ``end_date`` lands *before* that day's bar
+          and silently drops it — extend to the following midnight instead.
+        * The window must stay out of the delayed-data period, otherwise the
+          request is rejected outright with "subscription does not permit
+          querying recent SIP data". Clamping keeps today's in-progress bar
+          available, roughly 20 minutes behind live.
+        """
+        start = datetime.combine(start_date, time.min, tzinfo=timezone.utc)
+        end = datetime.combine(
+            end_date + timedelta(days=1), time.min, tzinfo=timezone.utc
+        )
+        return start, min(end, datetime.now(timezone.utc) - _RECENT_DATA_CUTOFF)
+
     @staticmethod
     def _to_alpaca_symbol(symbol: str) -> str:
         """Convert internal symbol format (BRK-B) to Alpaca format (BRK.B)."""
@@ -40,12 +65,15 @@ class AlpacaOHLCVProvider:
     ) -> list[OHLCVData]:
         """Fetch daily OHLCV bars for *ticker* between *start_date* and *end_date*."""
         alpaca_symbol = self._to_alpaca_symbol(ticker)
+        start, end = self._resolve_window(start_date, end_date)
+        if end <= start:
+            return []
         try:
             request = StockBarsRequest(
                 symbol_or_symbols=alpaca_symbol,
                 timeframe=TimeFrame.Day,
-                start=start_date,
-                end=end_date,
+                start=start,
+                end=end,
                 adjustment=Adjustment.SPLIT,
             )
 

app/scheduler.py

@@ -487,7 +487,12 @@ def _chunked(symbols: list[str], chunk_size: int) -> list[list[str]]:
 # ---------------------------------------------------------------------------
 
 
-async def collect_ohlcv(full_backfill: bool = False, job_name: str = "data_collector") -> None:
+async def collect_ohlcv(
+    full_backfill: bool = False,
+    job_name: str = "data_collector",
+    *,
+    refetch_days: int = 0,
+) -> None:
     """Fetch latest daily OHLCV for all tracked tickers.
 
     Uses AlpacaOHLCVProvider. Processes each ticker independently.
@@ -500,6 +505,10 @@ async def collect_ohlcv(full_backfill: bool = False, job_name: str = "data_colle
     ``settings.ohlcv_history_days`` window (ignoring incremental resume) — used by
     the manual data_backfill job to deepen shallow histories. ``job_name`` lets the
     backfill report its own runtime/resume state separate from data_collector.
+
+    ``refetch_days`` re-pulls the last N days regardless of ingestion progress —
+    the after-close run uses it to overwrite the day's partial intraday bar, which
+    resume logic would otherwise skip as "already up to date".
     """
     _log_event(logging.INFO, "job_start", job=job_name)
     _runtime_start(job_name)
@@ -536,11 +545,14 @@ async def collect_ohlcv(full_backfill: bool = False, job_name: str = "data_colle
             return
 
         end_date = date.today()
-        # Full backfill: pass an explicit start_date so fetch_and_ingest re-pulls
-        # the whole window instead of resuming from the last stored bar.
-        backfill_start = (
-            end_date - timedelta(days=settings.ohlcv_history_days) if full_backfill else None
-        )
+        # An explicit start_date makes fetch_and_ingest re-pull that window instead
+        # of resuming from the last stored bar (upsert overwrites, so this is safe).
+        if full_backfill:
+            backfill_start = end_date - timedelta(days=settings.ohlcv_history_days)
+        elif refetch_days:
+            backfill_start = end_date - timedelta(days=refetch_days)
+        else:
+            backfill_start = None
 
         for symbol in symbols:
             _runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
@@ -598,6 +610,18 @@ async def backfill_ohlcv() -> None:
     await collect_ohlcv(full_backfill=True, job_name="data_backfill")
 
 
+async def collect_ohlcv_final() -> None:
+    """After-close OHLCV refresh that replaces the day's partial bar.
+
+    Intraday runs store today's bar while the session is still open, so ingestion
+    progress already reads "today" and incremental resume would skip the day
+    entirely — leaving a partial bar as the permanent record. ``refetch_days``
+    forces the last few sessions to be re-pulled so outcome evaluation and
+    fill-quality checks grade against the real close.
+    """
+    await collect_ohlcv(refetch_days=_FINAL_REFETCH_DAYS)
+
+
 # ---------------------------------------------------------------------------
 # Job: Sentiment Collector
 # ---------------------------------------------------------------------------
@@ -1183,6 +1207,10 @@ async def sync_ticker_universe() -> None:
 # — the qualifying full-universe scan runs once near the US close so post-stop
 # gate-reset sees one observation per trading day (plus the trade_policy
 # distinct-day guard for manual re-scans).
+# Sessions re-pulled by the after-close fetch so the consolidated bar overwrites
+# the intraday partial one (covers a long weekend / holiday gap).
+_FINAL_REFETCH_DAYS = 5
+
 _DAILY_PIPELINE_STEPS = [
     ("data_collector", "collect_ohlcv"),
     ("benchmark_collector", "collect_benchmark"),
@@ -1206,6 +1234,8 @@ _DAILY_PIPELINE_STEPS = [
 # entries behave like stale_close (still acceptable per execution-recovery matrix).
 # No exchange calendar dependency.
 _NEAR_CLOSE_PIPELINE_STEPS = [
+    # Must land today's in-progress bar (~20 min behind live), or the scan falls
+    # back to the previous close and execution degrades to the stale_close floor.
     ("data_collector", "collect_ohlcv"),
     ("rr_scanner", "scan_rr"),
     ("alerts", "dispatch_alerts_job"),
@@ -1214,7 +1244,7 @@ _NEAR_CLOSE_PIPELINE_STEPS = [
 # After close (~16:45 ET Mon–Fri): fresh OHLCV fetch so outcomes resolve on the
 # final bar, not the near-close partial bar, then outcome/paper close.
 _AFTER_CLOSE_PIPELINE_STEPS = [
-    ("data_collector", "collect_ohlcv"),
+    ("data_collector", "collect_ohlcv_final"),
     ("outcome_evaluator", "evaluate_outcomes"),
 ]
 
@@ -1338,18 +1368,22 @@ def _parse_frequency(freq: str) -> dict[str, int]:
 # All wall times are America/New_York after the near-close execution cutover.
 # Stored SystemSetting values shadow these defaults — deploy migration 023
 # rewrites schedule_* keys so prod does not keep scanning at 07:00 Berlin.
+# DAY-OF-WEEK MUST BE NAMES, NEVER NUMBERS. APScheduler's from_crontab() passes
+# field 5 straight to its own day_of_week, where 0=Monday — so "1-5" resolves to
+# Tue–Sat, silently skipping every Monday and scanning on Saturdays. Names are
+# unambiguous in both dialects.
 SCHEDULE_DEFAULTS: dict[str, str] = {
     "schedule_timezone": "America/New_York",
     # Morning data/display refresh (no qualifying R:R scan).
     "schedule_daily_pipeline_cron": "0 2 * * *",
     # Fetch in-progress bars → scan → Telegram (manual MOC window).
-    "schedule_near_close_pipeline_cron": "30 15 * * 1-5",
+    "schedule_near_close_pipeline_cron": "30 15 * * mon-fri",
     # Fetch final bars → outcome eval (must not run on the partial near-close bar).
-    "schedule_after_close_pipeline_cron": "45 16 * * 1-5",
+    "schedule_after_close_pipeline_cron": "45 16 * * mon-fri",
     # Hourly mid-session price + outcome (10:00–15:00 ET Mon–Fri).
-    "schedule_intraday_pipeline_cron": "0 10-15 * * 1-5",
+    "schedule_intraday_pipeline_cron": "0 10-15 * * mon-fri",
     # Weekly fundamentals early Monday NY.
-    "schedule_fundamentals_cron": "0 1 * * 1",
+    "schedule_fundamentals_cron": "0 1 * * mon",
 }
 
 # job id -> schedule setting key

app/services/backtest_service.py

@@ -30,6 +30,11 @@ Environment variables (see also run_backtest_snapshot.py):
     BACKTEST_FALLBACK_CLEAR_AIR_ONLY=1
     BACKTEST_RESEARCH_EXITS=1
     BACKTEST_MIN_RR_SWEEP=1
+
+  Broad-universe signal research (local snapshots only; inert when unset):
+    BACKTEST_LIQUID_BREADTH=1500   # PIT top-N by 63d median $vol, price floor
+    BACKTEST_LIQUID_MIN_PRICE=5    # USD close floor at as-of (default 5)
+    BACKTEST_SIGNAL_EVAL_ONLY=1   # skip portfolio_sim / monitor (signal IC only)
 """
 
 from __future__ import annotations
@@ -808,7 +813,10 @@ def _residual_momentum_12_1(
     var_market = sum((x - mean_market) ** 2 for x in market_rets)
     if var_market <= 0:
         return None
-    cov = sum((stock_rets[k] - mean_stock) * (market_rets[k] - mean_market) for k in range(len(stock_rets)))
+    cov = sum(
+        (stock_rets[k] - mean_stock) * (market_rets[k] - mean_market)
+        for k in range(len(stock_rets))
+    )
     beta = cov / var_market
     return sum(stock_rets[k] - beta * market_rets[k] for k in range(len(stock_rets)))
 
@@ -876,20 +884,86 @@ def _signal_values(
     return out
 
 
+def _liquid_breadth_top_n() -> int:
+    """0 = off (production path). N > 0 enables PIT top-N $vol mask for signal IC."""
+    raw = os.getenv("BACKTEST_LIQUID_BREADTH", "").strip()
+    if not raw:
+        return 0
+    try:
+        return max(0, int(raw))
+    except ValueError:
+        return 0
+
+
+def _liquid_min_price() -> float:
+    raw = os.getenv("BACKTEST_LIQUID_MIN_PRICE", "5").strip() or "5"
+    try:
+        return max(0.0, float(raw))
+    except ValueError:
+        return 5.0
+
+
+def _signal_eval_only() -> bool:
+    return os.getenv("BACKTEST_SIGNAL_EVAL_ONLY", "").strip() in ("1", "true", "yes")
+
+
+async def _load_research_rank_only_symbols(db: AsyncSession) -> set[str]:
+    """Symbols that feed signal IC only (no GTL/candidate replay).
+
+    Optional side table ``research_rank_only`` on research snapshots. Missing
+    table → empty set (production path unchanged).
+    """
+    from sqlalchemy import text
+
+    try:
+        result = await db.execute(text("SELECT symbol FROM research_rank_only"))
+        return {str(row[0]).upper() for row in result.fetchall() if row[0]}
+    except Exception:
+        return set()
+
+
+def _median_dollar_vol_63(
+    closes: list[float], volumes: list[float], i: int, lookback: int = 63
+) -> float | None:
+    """Rolling median of close×volume over ``lookback`` bars ending at ``i`` (inclusive)."""
+    if i + 1 < lookback or lookback < 2:
+        return None
+    dvs: list[float] = []
+    for k in range(i - lookback + 1, i + 1):
+        if closes[k] > 0 and volumes[k] >= 0:
+            dvs.append(closes[k] * float(volumes[k]))
+    if len(dvs) < max(20, lookback // 2):
+        return None
+    dvs_sorted = sorted(dvs)
+    mid = len(dvs_sorted) // 2
+    if len(dvs_sorted) % 2:
+        return dvs_sorted[mid]
+    return 0.5 * (dvs_sorted[mid - 1] + dvs_sorted[mid])
+
+
 def _accumulate_signal_series(
     records: list,
     collected: dict,
     benchmark_closes: dict[date, float] | None = None,
+    *,
+    symbol: str | None = None,
 ) -> None:
     """For each weekly as-of bar, emit (signal, forward-return) pairs keyed by ISO
     week into ``collected[name][week_key]``. Forward return is close-to-close over
-    HORIZON trading days. Mutates ``collected`` (a dict of dict of list)."""
+    HORIZON trading days. Mutates ``collected`` (a dict of dict of list).
+
+    When ``BACKTEST_LIQUID_BREADTH`` is set, observations are dicts with PIT
+    liquidity fields for the mask; otherwise plain ``(val, fwd)`` tuples so the
+    production signal path stays unchanged.
+    """
     n = len(records)
     if n < HORIZON + 21:
         return
     closes = [float(r.close) for r in records]
     highs = [float(r.high) for r in records]
+    volumes = [float(getattr(r, "volume", 0) or 0) for r in records]
     dates = [r.date for r in records]
+    liquid_mode = _liquid_breadth_top_n() > 0
     for i in _weekly_asof_indices(records):
         j = i + HORIZON
         if j >= n or closes[i] <= 0:
@@ -897,8 +971,18 @@ def _accumulate_signal_series(
         fwd = closes[j] / closes[i] - 1.0
         iso = records[i].date.isocalendar()
         week_key = (iso[0], iso[1])
+        dvol = _median_dollar_vol_63(closes, volumes, i) if liquid_mode else None
         for name, val in _signal_values(dates, closes, highs, i, benchmark_closes).items():
-            collected[name][week_key].append((val, fwd))
+            if liquid_mode:
+                collected[name][week_key].append({
+                    "val": val,
+                    "fwd": fwd,
+                    "close": closes[i],
+                    "median_dvol_63": dvol,
+                    "symbol": symbol,
+                })
+            else:
+                collected[name][week_key].append((val, fwd))
 
 
 def _rank(xs: list[float]) -> list[float]:
@@ -937,6 +1021,110 @@ def _spearman(xs: list[float], ys: list[float]) -> float | None:
     return _pearson(_rank(xs), _rank(ys))
 
 
+def _obs_val_fwd(rec: object) -> tuple[float, float] | None:
+    """Unpack a signal observation: ``(val, fwd)`` or research dict form."""
+    if isinstance(rec, dict):
+        try:
+            return float(rec["val"]), float(rec["fwd"])
+        except (KeyError, TypeError, ValueError):
+            return None
+    if isinstance(rec, (tuple, list)) and len(rec) >= 2:
+        try:
+            return float(rec[0]), float(rec[1])
+        except (TypeError, ValueError):
+            return None
+    return None
+
+
+def _filter_liquid_breadth_week(
+    recs: list,
+    *,
+    top_n: int,
+    min_price: float,
+) -> list[tuple[float, float]]:
+    """Point-in-time top-N by median $vol among names with price ≥ floor.
+
+    Ranking is relative (IEX volume undercount is OK for order stats). Membership
+    is recomputed every week from as-of bars — never frozen from today's liquidity.
+    """
+    kept = _filter_liquid_breadth_week_rich(
+        recs, top_n=top_n, min_price=min_price
+    )
+    return [(float(r["val"]), float(r["fwd"])) for r in kept]
+
+
+def _filter_liquid_breadth_week_rich(
+    recs: list,
+    *,
+    top_n: int,
+    min_price: float,
+) -> list[dict]:
+    """Same mask as ``_filter_liquid_breadth_week``, returning rich rows.
+
+    Single source for harness IC and research diagnostics. Eligible pool =
+    dict observations with close ≥ min_price and median_dvol_63 > 0; then
+    keep top_n by dollar volume (highest first). Non-dict legacy tuples are
+    not eligible for the liquid mask (they have no dvol).
+    """
+    eligible: list[tuple[float, dict]] = []  # (-dvol, row)
+    for rec in recs:
+        if not isinstance(rec, dict):
+            continue
+        close = rec.get("close")
+        dvol = rec.get("median_dvol_63")
+        if close is None or float(close) < min_price:
+            continue
+        if dvol is None or float(dvol) <= 0:
+            continue
+        pair = _obs_val_fwd(rec)
+        if pair is None:
+            continue
+        row = {
+            "val": pair[0],
+            "fwd": pair[1],
+            "close": float(close),
+            "median_dvol_63": float(dvol),
+            "symbol": rec.get("symbol"),
+        }
+        # Preserve optional research fields for mom-conditional diagnostics.
+        for key in ("mom_12_1", "mom_12_1_resid", "vol_6m", "fip_id"):
+            if key in rec and rec[key] is not None:
+                row[key] = rec[key]
+        eligible.append((-float(dvol), row))
+    eligible.sort(key=lambda item: item[0])
+    return [row for _, row in eligible[:top_n]]
+
+
+def _liquid_breadth_week_stats(
+    recs: list,
+    *,
+    top_n: int,
+    min_price: float,
+) -> dict[str, int | bool]:
+    """Pre/post mask counts for reconciling avg_cross_section semantics."""
+    raw = len(recs)
+    eligible = 0
+    for rec in recs:
+        if not isinstance(rec, dict):
+            continue
+        close = rec.get("close")
+        dvol = rec.get("median_dvol_63")
+        if close is None or float(close) < min_price:
+            continue
+        if dvol is None or float(dvol) <= 0:
+            continue
+        if _obs_val_fwd(rec) is None:
+            continue
+        eligible += 1
+    post = min(eligible, top_n) if top_n > 0 else eligible
+    return {
+        "raw_pool": raw,
+        "eligible_pre_mask": eligible,
+        "post_mask": post,
+        "mask_binds": bool(top_n > 0 and eligible > top_n),
+    }
+
+
 def _quintile_spread(pairs: list[tuple[float, float]]) -> float | None:
     """Mean forward return of the top signal-quintile minus the bottom quintile."""
     n = len(pairs)
@@ -982,10 +1170,16 @@ def _signal_evaluation(collected: dict) -> list[dict]:
 
     IC is measured on NON-OVERLAPPING forward windows (weeks thinned to ~HORIZON
     apart) so the t-stat isn't inflated by autocorrelation. A signal with no edge
-    lands near IC 0 / spread 0; one with too few independent windows is flagged
+    lands near IC 0 / score 0; one with too few independent windows is flagged
     unreliable rather than trusted on a lucky handful.
+
+    When ``BACKTEST_LIQUID_BREADTH=N`` is set, each week's cross-section is first
+    restricted to the top-N names by point-in-time 63d median dollar volume
+    (price ≥ BACKTEST_LIQUID_MIN_PRICE). Production path (flag unset) is unchanged.
     """
     stride = max(1, round(HORIZON / 5))  # ISO weeks spanned by the forward window
+    top_n = _liquid_breadth_top_n()
+    min_price = _liquid_min_price()
     rows: list[dict] = []
     for name in sorted(collected):
         weeks_map = collected[name]
@@ -994,15 +1188,37 @@ def _signal_evaluation(collected: dict) -> list[dict]:
         ics: list[float] = []
         spreads: list[float] = []
         sizes: list[int] = []
+        raw_sizes: list[int] = []
+        eligible_sizes: list[int] = []
+        bind_flags: list[bool] = []
         for wk in kept:
             recs = weeks_map[wk]
-            ic = _spearman([r[0] for r in recs], [r[1] for r in recs])
+            if top_n > 0:
+                stats = _liquid_breadth_week_stats(
+                    recs, top_n=top_n, min_price=min_price
+                )
+                raw_sizes.append(int(stats["raw_pool"]))
+                eligible_sizes.append(int(stats["eligible_pre_mask"]))
+                bind_flags.append(bool(stats["mask_binds"]))
+                pairs = _filter_liquid_breadth_week(
+                    recs, top_n=top_n, min_price=min_price
+                )
+            else:
+                pairs = []
+                for rec in recs:
+                    pair = _obs_val_fwd(rec)
+                    if pair is not None:
+                        pairs.append(pair)
+            if len(pairs) < MIN_CROSS_SECTION:
+                continue
+            ic = _spearman([p[0] for p in pairs], [p[1] for p in pairs])
             if ic is not None:
                 ics.append(ic)
-            spread = _quintile_spread(recs)
+            spread = _quintile_spread(pairs)
             if spread is not None:
                 spreads.append(spread)
-            sizes.append(len(recs))
+            # avg_cross_section is ALWAYS post-mask pair count (the IC sample).
+            sizes.append(len(pairs))
         if not ics:
             continue
         mean_ic = sum(ics) / len(ics)
@@ -1011,7 +1227,7 @@ def _signal_evaluation(collected: dict) -> list[dict]:
         else:
             std = 0.0
         t_stat = mean_ic / std * math.sqrt(len(ics)) if std > 0 else None
-        rows.append({
+        row = {
             "signal": name,
             "weeks": len(ics),
             "avg_cross_section": round(sum(sizes) / len(sizes), 1) if sizes else None,
@@ -1020,16 +1236,36 @@ def _signal_evaluation(collected: dict) -> list[dict]:
             "ic_positive_pct": round(sum(1 for x in ics if x > 0) / len(ics) * 100, 1),
             "mean_quintile_spread": round(sum(spreads) / len(spreads), 4) if spreads else None,
             "reliable": len(ics) >= MIN_RELIABLE_PERIODS,
-        })
+        }
+        if top_n > 0:
+            row["liquid_breadth_top_n"] = top_n
+            row["liquid_min_price"] = min_price
+            # Explicit pre/post mask diagnostics (reconcile "did top-N bind?").
+            if raw_sizes:
+                row["avg_raw_pool"] = round(sum(raw_sizes) / len(raw_sizes), 1)
+            if eligible_sizes:
+                row["avg_eligible_pre_mask"] = round(
+                    sum(eligible_sizes) / len(eligible_sizes), 1
+                )
+            if bind_flags:
+                row["mask_binds_pct"] = round(
+                    sum(1 for b in bind_flags if b) / len(bind_flags) * 100, 1
+                )
+        rows.append(row)
     rows.sort(key=lambda r: r["mean_ic"], reverse=True)
     return rows
 
 
-def _signal_series(records: list, benchmark_closes: dict[date, float] | None = None) -> dict:
+def _signal_series(
+    records: list,
+    benchmark_closes: dict[date, float] | None = None,
+    *,
+    symbol: str | None = None,
+) -> dict:
     """Per-ticker signal/forward-return series as a PLAIN (picklable) nested dict
     — no defaultdict/lambda — so it can cross a process boundary."""
     tmp: dict = defaultdict(lambda: defaultdict(list))
-    _accumulate_signal_series(records, tmp, benchmark_closes)
+    _accumulate_signal_series(records, tmp, benchmark_closes, symbol=symbol)
     return {name: dict(weeks) for name, weeks in tmp.items()}
 
 
@@ -1041,10 +1277,15 @@ def _replay_and_signals(
     benchmark_closes: dict[date, float] | None = None,
     target_model: str = PRODUCTION_GTL_TARGET_MODEL,
     cadence: str = DEFAULT_BACKTEST_CADENCE,
+    signal_only: bool = False,
 ) -> tuple[list[dict], dict]:
     """The CPU-bound per-ticker work, as a top-level (picklable) function so it can
     run in a worker process. Takes primitive column arrays (cheap to pickle),
-    rebuilds bar objects, and returns (candidates, signal_series)."""
+    rebuilds bar objects, and returns (candidates, signal_series).
+
+    ``signal_only=True`` (research rank-only names): skip GTL/candidate replay so
+    the production portfolio book is never polluted by broad-universe tickers.
+    """
     date_ords, opens, highs, lows, closes, volumes = columns
     bars = [
         SimpleNamespace(
@@ -1052,8 +1293,9 @@ def _replay_and_signals(
         )
         for o, op, hi, lo, cl, vo in zip(date_ords, opens, highs, lows, closes, volumes)
     ]
-    return (
-        _replay_ticker(
+    candidates: list[dict] = []
+    if not signal_only:
+        candidates = _replay_ticker(
             symbol,
             bars,
             config,
@@ -1061,8 +1303,10 @@ def _replay_and_signals(
             benchmark_closes,
             target_model,
             cadence,
-        ),
-        _signal_series(bars, benchmark_closes),
+        )
+    return (
+        candidates,
+        _signal_series(bars, benchmark_closes, symbol=symbol),
     )
 
 
@@ -3789,6 +4033,12 @@ async def run_backtest(
     result = await db.execute(select(Ticker).order_by(Ticker.symbol))
     tickers = list(result.scalars().all())
     total = len(tickers)
+    rank_only_symbols = await _load_research_rank_only_symbols(db)
+    if rank_only_symbols:
+        logger.info(json.dumps({
+            "event": "backtest_rank_only_loaded",
+            "count": len(rank_only_symbols),
+        }))
 
     candidates: list[dict] = []
     # Signal IC remains a weekly, non-overlapping diagnostic regardless of the
@@ -3847,10 +4097,16 @@ async def run_backtest(
                         continue
                     if columns is not None:
                         futures.append(loop.run_in_executor(
-                            pool, _replay_and_signals, ticker.symbol, columns, config, activation,
+                            pool,
+                            _replay_and_signals,
+                            ticker.symbol,
+                            columns,
+                            config,
+                            activation,
                             benchmark_closes,
                             target_model,
                             cadence,
+                            ticker.symbol in rank_only_symbols,
                         ))
                 for result in await asyncio.gather(*futures, return_exceptions=True):
                     if isinstance(result, Exception):
@@ -3870,10 +4126,15 @@ async def run_backtest(
                 columns = await _fetch_columns(db, ticker.symbol)
                 if columns is not None:
                     _merge(await asyncio.to_thread(
-                        _replay_and_signals, ticker.symbol, columns, config, activation,
+                        _replay_and_signals,
+                        ticker.symbol,
+                        columns,
+                        config,
+                        activation,
                         benchmark_closes,
                         target_model,
                         cadence,
+                        ticker.symbol in rank_only_symbols,
                     ))
             except Exception:
                 logger.exception("Backtest replay failed for %s", ticker.symbol)
@@ -3916,73 +4177,75 @@ async def run_backtest(
     portfolio_monitor_report: dict | None = None
     holdout_report: dict | None = None
     min_rr_sweep_report: dict | None = None
-    try:
-        qual_symbols = sorted({
-            c["symbol"]
-            for c in candidates
-            if c.get("qualified")
-            or any(_qualifies_strategy_variant(c, cfg) for cfg in STRATEGY_VARIANTS)
-        })
-        price_columns: dict[str, tuple] = {}
-        for sym in qual_symbols:
-            cols = await _fetch_columns(db, sym)
-            if cols is not None:
-                price_columns[sym] = cols
-
-        spy_closes: dict | None = None
+    if not _signal_eval_only():
         try:
-            oldest = min((cols[0][0] for cols in price_columns.values()), default=None)
-            days_needed = None
-            if oldest is not None and not _offline_snapshot_mode():
-                days_needed = (date.today() - date.fromordinal(oldest)).days + 30
-            spy_closes = await _load_benchmark_closes_for_backtest(
-                db, days=days_needed, refresh=oldest is not None
-            )
-        except Exception:
-            logger.exception("Benchmark load for the portfolio sim failed")
+            qual_symbols = sorted({
+                c["symbol"]
+                for c in candidates
+                if c.get("qualified")
+                or any(_qualifies_strategy_variant(c, cfg) for cfg in STRATEGY_VARIANTS)
+            })
+            price_columns: dict[str, tuple] = {}
+            for sym in qual_symbols:
+                cols = await _fetch_columns(db, sym)
+                if cols is not None:
+                    price_columns[sym] = cols
 
-        for policy in ("target", "hold"):
-            sim = _simulate_portfolio(
-                candidates, price_columns, spy_closes, policy, hold_horizon
-            )
-            if sim is not None:
-                sim_policies.append({"policy": policy, **sim})
-        strategy_variant_rows = _strategy_variant_sims(
-            candidates, price_columns, spy_closes, hold_horizon
-        )
-        exit_policy_rows = _exit_policy_sims(
-            candidates, price_columns, spy_closes, hold_horizon
-        )
-        live_exit_policy: dict | None = None
-        try:
-            from app.services.paper_trade_service import get_exit_policy
+            spy_closes: dict | None = None
+            try:
+                oldest = min((cols[0][0] for cols in price_columns.values()), default=None)
+                days_needed = None
+                if oldest is not None and not _offline_snapshot_mode():
+                    days_needed = (date.today() - date.fromordinal(oldest)).days + 30
+                spy_closes = await _load_benchmark_closes_for_backtest(
+                    db, days=days_needed, refresh=oldest is not None
+                )
+            except Exception:
+                logger.exception("Benchmark load for the portfolio sim failed")
 
-            live_exit_policy = await get_exit_policy(db)
-        except Exception:
-            logger.exception("Live exit policy load failed; monitor uses defaults")
-        portfolio_monitor_report = _portfolio_monitor(
-            candidates, price_columns, spy_closes, hold_horizon,
-            live_exit_policy=live_exit_policy,
-            cadence=cadence,
-        )
-        split = _holdout_split()
-        if split is not None:
-            holdout_report = _holdout_evaluation(
-                candidates, price_columns, spy_closes, hold_horizon, split,
+            for policy in ("target", "hold"):
+                sim = _simulate_portfolio(
+                    candidates, price_columns, spy_closes, policy, hold_horizon
+                )
+                if sim is not None:
+                    sim_policies.append({"policy": policy, **sim})
+            strategy_variant_rows = _strategy_variant_sims(
+                candidates, price_columns, spy_closes, hold_horizon
+            )
+            exit_policy_rows = _exit_policy_sims(
+                candidates, price_columns, spy_closes, hold_horizon
+            )
+            live_exit_policy: dict | None = None
+            try:
+                from app.services.paper_trade_service import get_exit_policy
+
+                live_exit_policy = await get_exit_policy(db)
+            except Exception:
+                logger.exception("Live exit policy load failed; monitor uses defaults")
+            portfolio_monitor_report = _portfolio_monitor(
+                candidates, price_columns, spy_closes, hold_horizon,
                 live_exit_policy=live_exit_policy,
                 cadence=cadence,
             )
-        if _min_rr_sweep_enabled():
-            min_rr_sweep_report = _min_rr_sweep(
-                candidates, price_columns, spy_closes, activation, current_min_pct,
-                hold_horizon, live_exit_policy=live_exit_policy, cadence=cadence,
-            )
-    except Exception:
-        logger.exception("Portfolio simulation failed")
+            split = _holdout_split()
+            if split is not None:
+                holdout_report = _holdout_evaluation(
+                    candidates, price_columns, spy_closes, hold_horizon, split,
+                    live_exit_policy=live_exit_policy,
+                    cadence=cadence,
+                )
+            if _min_rr_sweep_enabled():
+                min_rr_sweep_report = _min_rr_sweep(
+                    candidates, price_columns, spy_closes, activation, current_min_pct,
+                    hold_horizon, live_exit_policy=live_exit_policy, cadence=cadence,
+                )
+        except Exception:
+            logger.exception("Portfolio simulation failed")
 
     report = {
         "generated_at": datetime.now(timezone.utc).isoformat(),
         "tickers": total,
+        "rank_only_tickers": len(rank_only_symbols),
         "candidates": len(candidates),
         "qualified": len(qualified),
         "params": {
@@ -3999,6 +4262,9 @@ async def run_backtest(
             "target_model_label": BACKTEST_TARGET_MODELS[target_model],
             "is_production_target_model": target_model == PRODUCTION_GTL_TARGET_MODEL,
             "production_reentry_policy": PRODUCTION_REENTRY_POLICY,
+            "liquid_breadth_top_n": _liquid_breadth_top_n() or None,
+            "liquid_min_price": _liquid_min_price() if _liquid_breadth_top_n() else None,
+            "signal_eval_only": _signal_eval_only(),
         },
         "activation": activation,
         "overall_qualified": _bucket_stats(qualified),

app/ssl_bootstrap.py

@@ -0,0 +1,136 @@
+"""TLS / corporate-proxy bootstrap for CLI scripts and the API.
+
+Must run **before** httpx / alpaca / aiohttp open connections.
+
+Resolution order for the CA bundle:
+1. ``combined-ca-bundle.pem`` in the repo root (gitignored corporate bundle)
+2. ``$HOME/combined-ca-bundle.pem`` (MacBook path used by existing tooling)
+3. ``SSL_CERT_FILE`` / ``REQUESTS_CA_BUNDLE`` if already set and present
+4. ``certifi.where()`` when the package is installed
+5. System defaults (no patch)
+
+Optional corporate proxy (Swisscom-style) when ``USE_CORP_PROXY=1``.
+"""
+
+from __future__ import annotations
+
+import os
+import ssl
+from pathlib import Path
+
+_BOOTSTRAPPED = False
+
+
+def _candidate_ca_paths() -> list[Path]:
+    root = Path(__file__).resolve().parent.parent
+    home = Path.home()
+    env_paths = [
+        os.environ.get("SSL_CERT_FILE", ""),
+        os.environ.get("REQUESTS_CA_BUNDLE", ""),
+        os.environ.get("CURL_CA_BUNDLE", ""),
+    ]
+    paths = [
+        root / "combined-ca-bundle.pem",
+        home / "combined-ca-bundle.pem",
+        *[Path(p) for p in env_paths if p],
+    ]
+    try:
+        import certifi
+
+        paths.append(Path(certifi.where()))
+    except Exception:
+        pass
+    return paths
+
+
+def resolve_ca_bundle() -> str | None:
+    for path in _candidate_ca_paths():
+        try:
+            if path.is_file() and path.stat().st_size > 0:
+                return str(path.resolve())
+        except OSError:
+            continue
+    return None
+
+
+def apply_corp_proxy_if_requested() -> None:
+    if os.environ.get("USE_CORP_PROXY", "0") != "1":
+        return
+    proxy = os.environ.get("CORP_HTTP_PROXY", "http://aproxy.corproot.net:8080")
+    no_proxy = os.environ.get(
+        "CORP_NO_PROXY",
+        "corproot.net,sharedtcs.net,127.0.0.1,localhost,bix.swisscom.com,swisscom.com",
+    )
+    os.environ.setdefault("HTTP_PROXY", proxy)
+    os.environ.setdefault("HTTPS_PROXY", proxy)
+    os.environ.setdefault("NO_PROXY", no_proxy)
+    os.environ.setdefault("http_proxy", proxy)
+    os.environ.setdefault("https_proxy", proxy)
+    os.environ.setdefault("no_proxy", no_proxy)
+
+
+def bootstrap_ssl(*, force: bool = False) -> str | None:
+    """Install CA env vars + patch ``ssl.create_default_context``.
+
+    Returns the CA path used, or None if nothing was applied.
+    Safe to call multiple times.
+    """
+    global _BOOTSTRAPPED
+    if _BOOTSTRAPPED and not force:
+        return os.environ.get("SSL_CERT_FILE") or None
+
+    apply_corp_proxy_if_requested()
+
+    cert_path = resolve_ca_bundle()
+    if not cert_path:
+        _BOOTSTRAPPED = True
+        return None
+
+    os.environ["SSL_CERT_FILE"] = cert_path
+    os.environ["REQUESTS_CA_BUNDLE"] = cert_path
+    os.environ["CURL_CA_BUNDLE"] = cert_path
+
+    original = ssl.create_default_context
+
+    def _patched(
+        purpose=ssl.Purpose.SERVER_AUTH, *, cafile=None, capath=None, cadata=None
+    ):
+        ctx = original(purpose, cafile=cafile, capath=capath, cadata=cadata)
+        try:
+            ctx.load_verify_locations(cafile=cert_path)
+        except Exception:
+            pass
+        return ctx
+
+    ssl.create_default_context = _patched  # type: ignore[assignment]
+
+    # aiohttp may cache SSL contexts at import time.
+    try:
+        import aiohttp.connector as aio_conn
+
+        for attr in ("_SSL_CONTEXT_VERIFIED", "_SSL_CONTEXT_UNVERIFIED"):
+            ctx = getattr(aio_conn, attr, None)
+            if ctx is not None:
+                try:
+                    ctx.load_verify_locations(cafile=cert_path)
+                except Exception:
+                    pass
+    except ImportError:
+        pass
+
+    _BOOTSTRAPPED = True
+    return cert_path
+
+
+def ssl_status() -> dict:
+    """Diagnostic blob for research scripts / MacBook troubleshooting."""
+    ca = resolve_ca_bundle()
+    return {
+        "ca_bundle": ca,
+        "ssl_cert_file_env": os.environ.get("SSL_CERT_FILE"),
+        "use_corp_proxy": os.environ.get("USE_CORP_PROXY", "0"),
+        "http_proxy": os.environ.get("HTTPS_PROXY") or os.environ.get("HTTP_PROXY"),
+        "candidates_exist": {
+            str(p): p.is_file() for p in _candidate_ca_paths()[:4]
+        },
+    }

docs/research/README.md

@@ -47,6 +47,7 @@ score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is
 | 10 | **Inverse-vol position sizing** | The apparent "win" was **mis-attributed**: the 20% notional cap bound on 95% of entries, so it measured concentration, not vol-sizing. Genuine inverse-vol cuts DD to −18.2% but costs ~58pp return at flat Sharpe | **Rejected** as edge; it's a risk-preference trade | `backtest-20260709-position-sizing*.json` |
 | 11 | **FIP path-smoothness** as tie-breaker/filter | Non-monotonic within the qualified set; thinning the entry stream costs more compounding than the tilt returns | **Rejected as a filter** — but see §4, it's the strongest raw signal we've measured | — |
 | 12 | **Fixed take-profit sweep** (R-multiples) | No interior optimum ever found — the best TP is "no TP" | **Rejected.** Momentum's edge lives in the right tail | `backtest_service.py:450` |
+| 13 | **Sector-residual 12-1** (`mom_12_1_sector_resid` / sector demean) as replacement for market residual | Short-window IC/A/B looked knife-edge green; deep repaired + **liquid-1500** retest: weeks 83, mild +IC **0.027** / t 1.69, **below iron bar 0.03** (FAIL). Demean already weaker | **Rejected / closed.** Keep production market residual. Do not resurrect without a new pre-registered protocol | [sector-residual-momentum.md](sector-residual-momentum.md) · `sector-resid-deep-20260719-113319.json` · history-depth supersession note |
 
 ---
 
@@ -140,9 +141,9 @@ knobs.
 
 | Lead | Why it's interesting | Blocker |
 |---|---|---|
-| **Near-close / MOC execution (ops)** | Recovers overnight momentum drift left on the table by a morning EU scan; evidence closed | Implement schedule + partial-bar scan path; one qualifying scan/day only |
-| **`fip_id`** (information discreteness over the 12-1 window) | **Strongest cross-sectional signal measured on this universe** — IC −0.045, t = −2.91, correct sign; re-derived fingerprint matched Phase A | Doesn't improve *this* book. Revisit when the universe broadens — **after** execution path is decided |
-| **Broader universe** (`nasdaq_all`) | Strengthens every week's cross-section and the IC t-stat | Grade under the fill mode you will trade |
+| **Near-close / MOC execution (ops)** | Recovers overnight momentum drift left on the table by a morning EU scan; evidence closed | Schedule + fill_mode shipped; live paper validation ongoing |
+| **`fip_id` / liquid breadth** | Fingerprint −0.045 / t −2.91; liquid unconditional **−0.017 / t −1.85** (not green); mom-conditional **−0.088 / t −4.58** | **Parked.** Orphan +0.0575 died (snapshot race). Breadth did not strengthen resid-mom t-stat. Optional reopen = pre-registered two-arm liquid-1500 book first. See [fip-breadth-ic.md](fip-breadth-ic.md) |
+| **Broader universe** | Composition changes factor signs (fip tug-of-war); vol-tilt on breadth is only a **directional hypothesis** (auth. −0.048 / t −1.36) | Any prod broaden must re-validate 80/20 tilt; offline research only; research.sqlite requires completion manifest |
 | **Forward paper-trade record** | The only true out-of-sample evidence the snapshot cannot give | Time; mark entries at actual near-close fill once ops ships |
 | **Better target model for clear-air names** | The return is demonstrably there (#2 wins on raw CAGR in *both* train and test); it's the *flat* 3× ATR target that makes it too expensive in risk | Needs a per-name model, not a constant k×ATR |
 
@@ -169,6 +170,11 @@ knobs.
 6. **Fill timing is part of the strategy.** Close-fill reports are not deployable
    numbers for an overnight scanner. Grade promotion under the fill mode you will
    actually trade.
+7. **Incomplete research artifacts are not results.** The Phase B +0.0575 / t +5.12
+   liquid-fip row was orphaned within hours: it raced a partially built
+   `research.sqlite`. Extender now writes a completion manifest; breadth mode
+   refuses without a match. Same class of protection as calendar-truncation
+   asserts — do not re-mythologize numbers computed on half a universe.
 
 ---
 

docs/research/earnings-gap-and-sue.md

@@ -0,0 +1,167 @@
+# Earnings gap diagnostic + SUE / PEAD (Tier-1 alpha research)
+
+**Status:** **CLOSED — SUE DEAD**.
+**Branch:** `research/earnings-gap-and-sue`
+**Production impact:** none. Local research only; no earnings filter or SUE integration is shipped.
+
+---
+
+## Pre-registration (locked before the final research run)
+
+### Data
+
+- Historical earnings announcements for the production universe, stored in the
+  real `earnings_events` table and deduplicated on symbol + announcement date.
+- The originally requested 2016 start is amended, with user approval, to the
+  public source's announcement coverage start of 2020-01-22. Earlier EPS-period
+  history may scale later surprises but may never activate a live signal.
+- Report coverage, pairing, duplicates/restatements, annual-rate sanity, and
+  announcement-session quality before either experiment.
+- Point-in-time: an earnings surprise is usable only from announcement date +1
+  trading day. Same-day use is forbidden.
+
+### Experiment 2a — earnings-gap risk (defense, report-only)
+
+Run the production-config book on the approximately 505-name production
+universe with close fills and 0.001 transaction cost per side. Join simulated
+trades to earnings by symbol and date.
+
+1. Among closed trades with realized net R ≤ -1.0, report the fraction with an
+   announcement strictly after entry and before exit, alongside the base rate
+   for all trades.
+2. Compare entries within three trading sessions before an announcement with
+   all other entries: count, mean/median R, win rate, p05, and p95.
+3. Compare stops within one trading session after an announcement with all
+   other stops and exits.
+
+Verdict is always `INFORMATIONAL`. Report only: no filter arm, recommendation,
+or implementation. The right tail must be shown alongside the left tail.
+
+### Experiment 2b — SUE / PEAD (offense)
+
+Signal `sue_latest`:
+
+\[
+\text{SUE} = \frac{\text{actual} - \text{estimate}}
+{\sigma(\text{trailing 8 surprises})}
+\]
+
+Use at least four trailing surprises; if estimate history fails the registered
+quality gate, use `(actual - estimate) / price` and name that fallback. Activate
+at announcement date +1 trading day, carry for 63 trading days, then drop the
+symbol from the cross-section.
+
+Evaluate mean weekly Spearman IC on the existing non-overlapping-window harness.
+Always report `sue_latest`, `mom_12_1`, and `mom_12_1_resid` on identical
+week-symbol-forward-return cells, plus SUE inside the top momentum quintile.
+
+### Mechanical verdict rule
+
+- **PASS** only if unconditional `sue_latest` has mean IC ≥ +0.03,
+  `reliable: true` (at least 12 windows), and positive signs in both the pre-2021
+  and post-2021 eras.
+- **FAIL** otherwise, with terminal verdict `SUE DEAD for this stack`.
+- PASS stops at `SUE PASS→PENDING_HUMAN`; integration design remains a separate
+  human decision. FAIL is terminal and no variants are proposed.
+
+---
+
+## Results
+
+### Data quality gate
+
+Approved earnings window: 2020-01-22 to 2026-07-17. Source mode: dolthub_public_bulk_clone.
+
+| check | result |
+|---|---:|
+| Prod symbols requested / tradable | 506 / 505 |
+| Manifest complete + live counts match | True |
+| Prod symbols with pre-2021 bars | 491 (97.2%) |
+| SPY benchmark depth | 2649 rows, 2016-01-04 to 2026-07-17 |
+| Snapshot depth gate | True |
+| Bulk source windows / requests logged | 1/1 / 1 |
+| Source repository / pinned commit | https://www.dolthub.com/repositories/post-no-preference/earnings @ 9n0et3hpj9j7vue8f3qsldon3qa5sdjj |
+| Source license / upstream provider documented | CC-BY-SA-4.0 / False |
+| Existing-source conflicts preserved | 940 rows / 1526 fields |
+| Symbols with >=8 announcements | 498 (98.6%) |
+| Symbols with >=8 paired announcements | 495 (98.0%) |
+| Events with estimate + actual | 12311/12414 (99.2%) |
+| Duplicate rows in keyed table | 0 |
+| Duplicate / restated payload rows fetched | 0 / 940 |
+| Mean announcements per active symbol-year | 4.08 (expected about 4) |
+| Symbols far off (<2 or >6/year, incl. zero) | 1 |
+| Recognised BMO/AMC/during | 92.8% (reliable=True) |
+| Point-in-time policy | announce_date_plus_1_trading_day_for_all_events |
+| SUE price fallback | not_used |
+
+Deduplication: UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment
+
+Far-off announcement-rate symbols: SPCX
+
+### Experiment 2a - earnings-gap risk diagnostic
+
+Verdict: **INFORMATIONAL**. Report-only; no filter arm or implementation.
+
+Trade cohort is restricted to the approved earnings-coverage window 2020-01-22 to 2026-07-17; 0 simulated trades outside that window were excluded.
+
+| cohort | count | fraction |
+|---|---:|---:|
+| Realized net R <= -1.0 | 266 | - |
+| Losses with announcement strictly inside hold | 23 | 0.0865 |
+| All trades with announcement strictly inside hold | 115 | 0.2003 |
+
+| Entry cohort | count | mean R | median R | win rate | p05 R | p95 R |
+|---|---:|---:|---:|---:|---:|---:|
+| Within 3 sessions before earnings | 27 | 0.4837 | -1.0265 | 0.3333 | -1.1463 | 5.981 |
+| All other entries | 547 | 0.2734 | -0.8316 | 0.3565 | -1.1228 | 4.5888 |
+
+Tail deltas (pre minus other): p05=-0.0235, p95=1.3922.
+
+Registered directional tail condition is not present.
+
+| Exit cohort | count | mean R | median R | win rate | p05 R | p95 R |
+|---|---:|---:|---:|---:|---:|---:|
+| Stops within 1 session after earnings | 26 | -0.6434 | -0.9753 | 0.2308 | -2.4614 | 1.1142 |
+| All other stops | 433 | -0.5035 | -1.0278 | 0.1963 | -1.1373 | 1.3591 |
+| All other exits | 548 | 0.3273 | -0.8361 | 0.3613 | -1.0644 | 4.7224 |
+
+### Experiment 2b - SUE / post-earnings drift
+
+Mechanical verdict: **FAIL** - SUE DEAD for this stack
+
+Identical cross-sections:
+
+| signal | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| sue_latest | 0.0148 | 1.27 | 56 | 450.9 | 51.8 | true |
+| mom_12_1 | 0.0195 | 0.74 | 56 | 450.9 | 58.9 | true |
+| mom_12_1_resid | 0.0262 | 1.07 | 56 | 450.9 | 55.4 | true |
+
+Unconditional SUE grade row:
+
+| signal | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| sue_latest | 0.0151 | 1.29 | 56 | 451.4 | 51.8 | true |
+
+Era stability:
+
+| era | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| pre-2021 | 0.0286 | 0.7 | 9 | 398.3 | 55.6 | false |
+| post-2021 | 0.0172 | 1.34 | 48 | 461.9 | 64.6 | true |
+
+Coverage: 501 symbols with live SUE; avg weekly N=453.1; scored non-overlap avg N=451.4.
+
+Cross-section is not flagged thin at the registered <100-name read.
+
+Momentum-conditional top-quintile SUE: mean IC=0.0213, t=1.3, windows=56, avg N=89.8.
+
+## Artifacts
+
+- `reports/earnings-2a-gap-20260720-dolthub-final.json` and companion Markdown
+- `reports/earnings-2b-sue-20260720-dolthub-final.json` and companion Markdown
+- `reports/earnings-backfill-status.json`
+
+Production changes: **none**. No earnings filter or SUE integration was implemented.
+
+## Final status: **Task 2 CLOSED (SUE DEAD)**

docs/research/fip-breadth-ic.md

@@ -0,0 +1,216 @@
+# Broad-universe fip_id IC research (Phase B)
+
+**Status:** **Parked / closed for now.** Unconditional fip not green; mom-conditional lead logged; breadth-momentum thesis challenged. No book sim until reopen.  
+**Production impact:** none. Display card remains context-only. No deploy from this work.  
+**Artifacts:** research log + compact reports + env-gated harness hooks; tooling stays for a future reopen.
+
+## Scope
+
+- Research only — production universe, gate, scanner, schedule unchanged.
+- Snapshot: `research.sqlite` (~4,650 tickers = prod + nasdaq_all extend).
+- Liquid mask: top **1,500** by point-in-time 63d median $vol, price ≥ **$5**/week.
+- **Completion manifest required:** extender writes `<snapshot>.manifest.json`; breadth runners refuse without a matching complete manifest (see §Race guard).
+
+## Caveats
+
+- Survivorship bias (today’s constituents, history backfilled).
+- IEX volume undercount → relative $vol rank only.
+- Pool skew: Nasdaq-heavy; missing pure NYSE mid-caps.
+- Do not mix multi-signal tables across universe baselines.
+- **Do not cite orphaned 21:14 numbers** (see below).
+
+---
+
+## Fingerprint (505-name prod)
+
+| | Expected | Observed |
+|---|---:|---:|
+| mean IC | −0.045 | **−0.045** |
+| t-stat | −2.9 | **−2.91** |
+| weeks / N / reliable | ≥12 / ~500 / true | 35 / 497.7 / true |
+
+**Pass.** Formula + pipeline trustworthy.
+
+Residual momentum on the same fingerprint (what the production book ranks on): **IC +0.055 / t +1.98**.
+
+---
+
+## The orphan (21:14) — root cause
+
+| Source | fip IC (liquid ~1500) | t |
+|---|---:|---:|
+| Orphan run 21:14 (removed from tree; was `fip-breadth-20260718-211440-breadth.json`) | **+0.0575** | **+5.12** |
+| Single-sourced recompute on complete snapshot (2026-07-19) | **−0.0168** | **−1.85** |
+
+That is a **sign disagreement** on the same intended quantity. Method rule: the number you cannot reconcile is the number you cannot use.
+
+### Verdict: orphaned — raced the snapshot build
+
+**Not** “orphaned, unexplained.” The mechanism is derivable from the table itself:
+
+1. **Code was not the difference.** Reconcile shows the old harness path and the new shared filter produce **identical** results on current data (−0.0168 / −1.85). The implementation fork is closed.
+2. **Data was the difference.** On today’s complete snapshot the liquid mask **binds in 97.1% of weeks** at top-N = 1,500. Dense signals (e.g. `vol_6m`) post-mask at **exactly 1,500**. The orphaned report’s `vol_6m` averaged **~1,475** cross-section — a masked run on complete data cannot do that. At 21:14 the eligible pool was smaller than 1,500 and the mask never bound.
+3. **Timeline fits.** Extender fixes landed ~20:32 / 20:34; full fetch takes ~30 minutes; breadth run fired **21:14** against a partially built `research.sqlite`. Every number in that report was computed on an incomplete universe.
+
+**Do not cite +0.0575 / t +5.12.** It survived less than six hours of contact with project discipline — that is the system working, not time wasted. The orphan JSON was **deleted from the tree** (still in Git history) so it cannot be re-imported as evidence.
+
+**Kept artifacts**
+
+| File | Role |
+|---|---|
+| `reports/fip-reconcile-20260719-000520.json` | Authoritative single-sourced ICs (compact; membership dumps stripped) |
+| `reports/fip-breadth-20260718-211440-fingerprint.json` | Prod fingerprint pass |
+
+### Race guard (same class as calendar truncation)
+
+| Piece | Behavior |
+|---|---|
+| `extend_snapshot_universe.py` | Clears any prior manifest on start; on full completion writes `<output>.manifest.json` with `complete=true`, ticker / OHLCV / rank_only counts, `finished_at`. `--limit` smoke runs write `complete=false`. |
+| `run_fip_breadth_research.py` / `run_fip_breadth_diagnostics.py` | **Refuse** breadth mode unless a matching complete manifest exists and live counts equal the recorded totals. |
+
+Helper: `scripts/research_snapshot_manifest.py`.
+
+---
+
+## Authoritative unconditional liquid fip (post-reconciliation)
+
+| metric | value |
+|---|---:|
+| mean_ic | **−0.0168** |
+| ic_t_stat | **−1.85** |
+| weeks | 35 |
+| avg_cross_section (**post-mask IC sample**) | 1471.2 |
+| avg_raw_pool | 3214.4 |
+| avg_eligible_pre_mask | **2338.4** |
+| mask_binds_pct | **97.1%** |
+| reliable | true |
+
+**Mask binds hard** on complete data (eligible ≫ 1500). Post-mask IC N for fip is ~1471 because not every liquid name has a valid 12-1 fip path — that is signal availability, not a non-binding mask. Contrast orphan `vol_6m` avg N ~1475 vs complete-data `vol_6m` avg N **1500**.
+
+Harness `_signal_evaluation` vs manual IC through the same filter: **exact match** (−0.0168 / −1.85).
+
+**Iron rule unconditional:** **not green** (|IC| 0.017 < 0.03), correct mild-negative sign.
+
+---
+
+## Context table (orphaned 21:14 vs authoritative) — kill the myth numbers
+
+The context table died with the orphan. **−0.16 must not survive in the log.**
+
+| signal (liquid ~1500) | orphaned (21:14) | authoritative (shared filter) | consequence |
+|---|---:|---:|---|
+| **vol_6m** | −0.16 / t **−6.1** | **−0.048 / t −1.36** | “High-vol tilt harmful on breadth” **downgrades from finding to directional hypothesis** — not significant |
+| **raw mom** (`mom_12_1`) | +0.10 / t +4.6 | **+0.046 / t +1.91** | Below iron-rule bar on this pool |
+| **resid mom** (`mom_12_1_resid`) | +0.04 / t +2.3 | **+0.029 / t +1.33** | Ditto, and weaker than raw |
+
+### Breadth-momentum thesis — challenged
+
+That last pair is the sobering one. Momentum on liquid breadth is **marginal**. The “more breadth strengthens the momentum t-stat” thesis that motivated Phase B is **empirically wrong on this pool**: same 35 weeks, triple the names, residual-mom t-stat **fell** versus the 505-name fingerprint (**0.055 / 1.98** → **0.029 / 1.33**). The clean momentum edge lives in the large-cap universe already traded.
+
+Meanwhile the strongest reliable signal on liquid breadth is now **mom-conditional fip** (−0.088 / −4.58) — but a fip tilt presupposes a breadth momentum book worth tilting, and that is no longer free.
+
+---
+
+## Compositional story (supported)
+
+`fip_id = sign(PRET)×(%neg−%pos)` pools:
+
+- **Continuous winners** → want **negative** IC  
+- **Continuous bleeders** → want **positive** IC  
+
+| check | IC | t | read |
+|---|---:|---:|---|
+| Prod-universe subset inside liquid | **−0.044** | **−2.88** | Matches fingerprint → compositional, not regime change |
+| Tier 1–800 (senior) | **−0.035** | **−2.99** | Winner leg |
+| Tier 801–1500 (junior) | **+0.014** | +1.25 | More bleeder / junk weight |
+| Lagged membership (prior-week $vol) | −0.010 | −0.93 | Same sign as same-week; not a +5σ leak artifact |
+
+**Do not log “on Nasdaq, jumpy paths outperform.”** That would mythologize an orphaned +0.06.
+
+---
+
+## Platform-relevant test: momentum-conditional fip
+
+Among liquid top-1500, keep **mom_12_1 ≥ P80** (~294 names/week):
+
+| metric | value |
+|---|---:|
+| mean_ic | **−0.0879** |
+| ic_t_stat | **−4.58** |
+| ic_positive_pct | 22.9% |
+| weeks | 35 |
+| reliable | **true** |
+
+Computed on the **same single-sourced path** as the authoritative −0.017. This is the paper’s claim and the only version a gate could consume.
+
+| Decision | |
+|---|---|
+| Unconditional fip | **Closed** for production |
+| Mom-conditional fip | **Alive as book-tilt candidate only** — and only after a baseline breadth book proves itself |
+| Display card | Stays |
+| Production change | **None** |
+
+---
+
+## Vol-tilt warning (softened)
+
+| signal (liquid, single-sourced) | IC | t |
+|---|---:|---:|
+| vol_6m | −0.048 | **−1.36** |
+| mom_12_1 | +0.046 | +1.91 |
+| mom_12_1_resid | +0.029 | +1.33 |
+
+High-vol names **tend** to underperform on this pool relative to a clean S&P-like book — that is a **directional hypothesis**, not a finding. Production **80/20 high-vol tilt** was validated on S&P-like names. If the universe ever broadens in production, re-validate that tilt; do not treat the orphaned −0.16 / t −6.1 as evidence.
+
+---
+
+## What this means for the book experiment
+
+A fip tilt presupposes a breadth momentum book worth tilting — **that is no longer free.**
+
+**Caution against over-reacting the other way:** modest cross-sectional IC does not preclude a good book. The 505-name book turns resid-mom IC ~0.055 into Sharpe ~2 because the gate trades the **extreme tail**, not the linear sort. The breadth book might still work; it just has to **prove it** before the fip arm means anything. If the baseline cannot clearly beat the existing production book’s territory, fip’s future is a footnote regardless of −4.58.
+
+### Parked next step (if reopened): pre-registered two-arm design
+
+Not started — **design only**, pre-register before any sim:
+
+| Arm | Definition |
+|---|---|
+| **A — baseline** | Top-quintile residual (or raw — pick one and lock) momentum book on liquid-1500; **no fip**; honest costs; next-open or near-close fills; production-like capacity / risk / stops |
+| **B — +fip tilt** | Same book + mom-conditional fip tilt (among mom winners, prefer smoother paths / negative fip_id) |
+
+| Grade on | Spec |
+|---|---|
+| Split | Entry-date train / validation (`BACKTEST_HOLDOUT_SPLIT` naming — not pristine holdout) |
+| Metrics | Sharpe + Mertens/Lo SE, PSR, **DSR**; max DD; turnover; cost drag |
+| Promote bar | Arm A must be in production-book territory first; Arm B must beat A on validation with DSR-aware multiple-testing honesty |
+| Fail-closed | If A fails, fip is a footnote; do not shop tilts on a dead baseline |
+
+---
+
+## How to re-run (research branch only)
+
+```powershell
+# 1) Full extend writes completion manifest (required)
+.\.venv\Scripts\python.exe scripts\extend_snapshot_universe.py `
+  --source backtest_snapshots\prod.sqlite `
+  --output backtest_snapshots\research.sqlite
+
+# 2) Breadth / diagnostics refuse without matching manifest
+.\.venv\Scripts\python.exe scripts\run_fip_breadth_diagnostics.py `
+  --research-snapshot backtest_snapshots\research.sqlite `
+  --prod-snapshot backtest_snapshots\prod.sqlite `
+  --workers 6 --allow-spawn
+```
+
+---
+
+## Bottom line
+
+1. Formal iron-rule screen: **not green** either before or after reconciliation.  
+2. **+0.0575 / +5.12 is orphaned: raced the snapshot build** — authoritative unconditional liquid fip is **−0.017 / −1.9**; mask binds (~97%) on complete data.  
+3. Context-table myths die with the orphan: **vol −0.16 is not real**; authoritative vol is **−0.048 / t −1.36** (directional only).  
+4. Compositional tug-of-war is the right story; jumpiness premium is not.  
+5. **Breadth does not strengthen residual-mom t-stat** on this pool (0.055/1.98 → 0.029/1.33).  
+6. **Mom-conditional −0.088 / −4.6 stands** on the single-sourced path → optional next step is a **pre-registered two-arm breadth book** (baseline first), not a gate wire-in.  
+7. Manifest guard is in place so the race cannot recur silently.

docs/research/history-depth-extension.md

@@ -0,0 +1,206 @@
+# History-depth extension (Tier-1 alpha research)
+
+**Status:** **CLOSED.** Sector-residual deep test **FAIL** — Task 1 archived as rejected (see supersession).  
+**Branch:** `research/earnings-gap-and-sue`  
+**Superseded artifact (do not cite):** `reports/history-depth-20260719-103315.json` — **UNMASKED, TWO-TIER SNAPSHOT**  
+**Authoritative sector grade:** `reports/sector-resid-deep-20260719-113319.json`  
+**Production impact:** none. **Do not retune any production knob on deep history.**
+
+---
+
+## Pre-registration (locked before rebuild)
+
+### Motivation
+
+All current conclusions rest on ~35 non-overlapping weekly windows in essentially
+one post-2021 regime. Extending history toward max Alpaca daily-bar depth adds
+the 2018 vol shock and full 2020 crash (where the feed allows).
+
+### Protocol
+
+1. **Empirical coverage first** — bars per calendar year per symbol; document
+   where the feed thins out. Do **not** assume a uniform start date.
+2. **Rebuild the research snapshot completely** from prod source + max history
+   per symbol (`Adjustment.SPLIT`, ~200 req/min pacing via existing extender).
+3. **Race guard (rule 6)** — refuse analysis until completion manifest is
+   `complete=true` and live counts match.
+4. **Re-run full signal harness** (all existing signals incl. sector residual /
+   SUE if present) on the extended window.
+5. **Report per signal:** mean IC, t, window count, and **era split**
+   (pre-/post-2021) — diagnostic only, **not a tuning input**.
+6. **Log prominently:** survivorship bias grows with depth (today’s constituents
+   backfilled). Absolute Sharpe/CAGR on deep history is optimistic; payload is
+   **relative** signal comparisons and IC stability, not levels.
+7. **Do not retune** production knobs. If a knob’s confirmation looks
+   overturned on deep history → report only; human decides.
+
+### Success / interpretation (not promotion of a new signal)
+
+| outcome | meaning |
+|---|---|
+| Sector residual still ≥ market residual on deep IC + stable sign | strengthens Task 1 PROMOTE case |
+| Sector residual collapses pre-2021 | **PARK** Task 1 wire-in |
+| SUE remains weak after full earnings + depth | **DEAD** SUE for this stack |
+| Any production knob looks worse deep | report; no auto-retune |
+
+---
+
+## Data provenance
+
+| check | result |
+|---|---|
+| Snapshot | MacBook `research.sqlite` |
+| Manifest `complete` | **true** (finished 2026-07-19T08:19Z) |
+| Live counts match | yes — 4655 tickers / 6,609,926 OHLCV / 4149 rank_only |
+| `history_days` | 5000 |
+| fetch_ok / fail | 4152 / 0 |
+| Race guard | **pass** |
+
+> **SURVIVORSHIP BIAS:** today’s constituents backfilled historically. Absolute
+> Sharpe/CAGR levels on deep history are optimistic. Use **relative** signal IC
+> comparisons and era stability only — not levels.
+
+**Coverage JSON was empty in the auto-written doc** (harness-only phase after
+rebuild). Manifest is the race-guard source of truth for this run.
+
+Earlier MacBook files `history-depth-20260719-093853` … `095156` are intermediate
+/ incomplete passes — **do not cite**. Only **103315** is authoritative.
+
+---
+
+## Results (authoritative: 103315)
+
+### Full-window signal IC (broad research universe, deep bars)
+
+| signal | mean_ic | t | weeks | avg_N | notes |
+|---|---:|---:|---:|---:|---|
+| high_52w | **0.111** | **6.41** | 84 | 2280 | strong on deep breadth |
+| mom_12_1 | **0.066** | **5.11** | 83 | 2278 | raw momentum strong |
+| trend_200 | 0.055 | 4.30 | 85 | 2308 | |
+| mom_6_1 | 0.049 | 4.91 | 88 | 2376 | |
+| mom_3_1 | 0.036 | 3.58 | 90 | 2426 | |
+| fip_id | **+0.027** | **3.25** | 83 | 2278 | **sign flip vs prod fingerprint** |
+| mom_12_1_resid | 0.026 | 2.21 | 83 | 2278 | market residual still + but weaker than raw |
+| reversal_1m | ~0 | 0.31 | 91 | 2443 | dead |
+| vol_6m | **−0.123** | **−6.34** | 88 | 2376 | low-vol anomaly strong |
+| mom_12_1_sector_resid | 0.058 | 2.34 | **35** | **498** | **not deep-sample — see caveats** |
+| mom_12_1_sector_demeaned | 0.034 | 1.32 | **35** | **497** | same short fingerprint |
+
+### Era split (diagnostic only — not a tuning input)
+
+| signal | pre-2021 IC / t / w / N | post-2021 IC / t / w / N |
+|---|---|---|
+| mom_12_1 | +0.041 / 2.94 / 36 / 1425 | +0.079 / 3.64 / 48 / 2913 |
+| mom_12_1_resid | +0.023 / 1.68 / 36 / 1425 | +0.027 / 1.37 / 48 / 2913 |
+| fip_id | +0.012 / 1.35 / 36 / 1425 | +0.037 / 2.91 / 48 / 2913 |
+| vol_6m | −0.056 / −2.16 / 40 / 1461 | −0.162 / −4.94 / 48 / 3123 |
+| high_52w | +0.049 / 2.0 / 36 / 1422 | +0.138 / 4.34 / 48 / 2915 |
+| **sector_resid** | **absent** | 0.058 / 2.34 / 35 / 498 (short only) |
+| **sector_demeaned** | **absent** | 0.034 / 1.32 / 35 / 497 (short only) |
+
+---
+
+## Critical caveats (must read)
+
+### 1. Sector residual did **not** get a deep-history stress test
+
+`mom_12_1_sector_resid` / `_demeaned` still show **exactly** the Task‑1 short-window
+fingerprint: **35 weeks, N≈498, IC 0.0578, t 2.34**.
+
+On the same run, raw `mom_12_1` has **83 weeks, N≈2278**. So depth worked for
+price-only signals, but sector residual is still limited to the **~505 labeled
+prod names × short factor calendar** (sector map only covers prod; and/or sector
+ETF / two-factor path did not extend usable residual weeks).
+
+**Pre-registered rule:** “Sector residual collapses pre-2021 → PARK Task 1
+wire-in.” Pre-2021 sector residual is **absent** from the era table. That is a
+**PARK**, not a confirmation of the short-window PROMOTE.
+
+Do **not** claim “sector residual beats market residual on deep history” from
+this table — the two rows are **not the same cross-section or window count**.
+
+### 2. `fip_id` sign flips vs production fingerprint
+
+| sample | fip mean IC | t |
+|---|---:|---:|
+| Prod 505, ~5y (fingerprint) | **−0.045** | −2.91 |
+| Research breadth, deep (this run) | **+0.027** | +3.25 |
+
+This does **not** authorize resurrecting unconditional FIP as a book filter. It
+confirms earlier Phase‑B caution: FIP edge is **universe- and sample-dependent**.
+Production display card can stay context-only. Nested lookbacks still not OOS.
+
+### 3. Market residual vs raw momentum on deep breadth
+
+On deep broad IC, **raw 12‑1 (0.066 / t 5.1) ≫ market residual (0.026 / t 2.2)**.
+That does **not** by itself overturn production residual ranking (book A/B was
+on 505 + GTL gate, not pure factor IC), but it is a yellow flag for “residual is
+always the better rank key” stories on broad history. **No auto-retune.**
+
+### 4. Low-vol anomaly is the cleanest deep-history result
+
+`vol_6m` IC −0.12 / t −6.3 full; stronger post-2021. Consistent sign across eras.
+Production already blends **high**-vol (not low-vol) into the 80/20 rank — this
+report does not change that without a separate A/B. Flag for human awareness only.
+
+---
+
+## Verdicts (vs pre-registration)
+
+| question | verdict |
+|---|---|
+| Task 1 sector residual wire-in | **PARK** — no pre-2021 sector residual; deep-sample IC not established; short-window PROMOTE stays “human design only,” **not strengthened** by this run |
+| Sector demean | still **DEAD** for promotion (t 1.32, short only) |
+| SUE | **not re-scored here** (no `sue_latest` in harness table) — leave Task 2 **PARK** until full earnings backfill |
+| fip unconditional book filter | remains **rejected / parked** despite sign flip on broad deep sample |
+| Production residual / 80/20 / trail knobs | **no retune** from this report |
+| Overall Task 3 | **COMPLETE as diagnostic** — payload is relative IC + caveats above |
+
+---
+
+## What a human must decide next
+
+1. **Sector residual — decided:** CLOSED / REJECTED (archive complete). No wire-in.
+2. **Do not** retune residual vs raw, FIP, or vol blend from deep IC tables
+   without a pre-registered book A/B on the intended universe.
+3. Optional (separate threads only): finish earnings backfill and re-run SUE;
+   snapshot per-symbol depth guard as tooling.
+
+---
+
+## Artifacts
+
+| file | role |
+|---|---|
+| `reports/history-depth-20260719-103315.json` | Superseded unmasked/two-tier IC dump (do not cite for sector residual) |
+| `reports/sector-resid-deep-20260719-113319.json` | Authoritative sector-resid deep grade |
+| `reports/prod-book-universe-horizon-20260719-140737.json` | 505 vs liquid × horizon book matrix |
+
+Intermediate history-depth partials (093853–095156) and SANITY-FAIL noise were
+removed in branch cleanup.
+
+---
+
+## Supersession notice (2026-07-19 sector-resid deep test)
+
+The table and interpretation from **`history-depth-20260719-103315`** are **UNMASKED, TWO-TIER SNAPSHOT — superseded, directional only, do not cite**. Prod-universe names (and sector residual coverage) were left shallow while breadth names were deepened; sector residual weeks=35 was a data gap.
+
+### Sector-residual deep test outcome: **FAIL** (archived)
+
+**Task 1 CLOSED / REJECTED** — sector residual dead on deep evidence. Archived in
+the research log rejected table (#13). Do not resurrect without a new
+pre-registered protocol.
+
+| check | result |
+|---|---|
+| weeks | **83** (data fix worked) |
+| mean IC | **0.0268** (below 0.03 bar) → FAIL |
+| t vs resid same CS | 1.69 ≥ 1.30 pass |
+| era signs | both + pass |
+
+- Artifact: `reports/sector-resid-deep-20260719-113319.json`
+- Summary write-up: [sector-residual-momentum.md](sector-residual-momentum.md)
+
+**Future snapshot rebuilds must verify per-symbol depth** (earliest-bar
+uniformity across the intended universe) — guard is a to-do, not part of this
+order.

docs/research/prod-book-universe-horizon.md

@@ -0,0 +1,127 @@
+# Production book × universe × horizon matrix
+
+**Status:** PRE-REGISTERED — prepare / MacBook run; no production changes.  
+**Branch:** `research/earnings-gap-and-sue`  
+**Runner:** `scripts/run_prod_book_universe_matrix.py`
+
+---
+
+## Question
+
+How does the **live production book** (unchanged knobs) behave when we only vary:
+
+1. **History length** used for entries (≈4y vs since 2016-07)  
+2. **Tradable universe** (prod ~505 vs 505 + PIT liquid Nasdaq/breadth)
+
+No strategy modifications: same residual gate, 80/20 high-vol rank, GTL entry
+machinery, 3× ATR trail, 30d max hold, gate-reset re-entry, `fill_mode=close`,
+cost 10 bps/side, max 10, 1% risk.
+
+---
+
+## Pre-registered arms (locked)
+
+| id | label | Entry start | Tradable universe |
+|---|---|---|---|
+| **A** | prod_4y_505 | **2022-07-01** | Prod ~505 only |
+| **B** | prod_4y_505_liquid | **2022-07-01** | Prod ∪ liquid top-1500 |
+| **C** | prod_2016_505 | **2016-07-01** | Prod ~505 only |
+| **D** | prod_2016_505_liquid | **2016-07-01** | Prod ∪ liquid top-1500 |
+
+- **End:** last available bar in snapshot (no artificial end).  
+- **4y start** chosen to align with recent Phase‑A / book baselines (~mid‑2022 → mid‑2026).  
+- **2016-07-01** = first full month after typical Alpaca floor (~2016-01); residual 12‑1 needs ~1y bars so first residual ranks appear mid‑2017 where feed allows.
+
+### Universe definitions
+
+| set | definition |
+|---|---|
+| **Prod ~505** | Symbols **not** in `research_rank_only` on the research snapshot (the original prod-universe copy). |
+| **Liquid top-1500** | Point-in-time: among names with as-of close ≥ **$5** and valid 63d median $vol, keep top **1500** by that $vol. Same definition as breadth IC research. |
+| **Prod ∪ liquid** | A name may enter the book on date *t* if it is prod **or** in the liquid top-1500 at *t*. |
+
+Cross-sectional residual / vol / 80/20 ranks are **recomputed inside each arm’s
+eligible candidate set** that period (so breadth arms are not ranked against
+non-eligible thin names).
+
+### Explicit non-goals
+
+- No sector residual, SUE, FIP filter, gap-cap, take-profit, vol-target, corr-cap  
+- No retune of trail / cutoff / min_rr  
+- Survivorship: report levels with the standard caveat; **compare arms relatively**
+
+### Reporting (required table)
+
+Per arm: Sharpe, Sharpe SE (Mertens), CAGR %, max DD %, total return %, trades,
+win rate if available, start/end, n qualified longs. One markdown table + JSON.
+
+**No promotion rule** — descriptive matrix only. Human decides whether breadth
+or depth changes the risk story.
+
+---
+
+## Snapshot requirements
+
+- Prefer MacBook **deep** `research.sqlite` after sector-resid deepen (prod names
+  from ~2016, breadth deep, completion manifest `complete=true`).  
+- Race-guard before run.  
+- Sector map / sector ETFs optional (not used for ranking).
+
+---
+
+## Results
+
+Generated: `2026-07-19T14:07:37` · artifact
+`reports/prod-book-universe-horizon-20260719-140737.json`  
+Snapshot: MacBook deep `research.sqlite` (506 prod + breadth prices; 2.39M raw
+GTL candidates). Strategy knobs = live production (residual 80, 80/20 high-vol
+rank, ATR trail 3×, hold 30, gate-reset, `fill_mode=close`).
+
+> Survivorship: today's constituents backfilled. **Compare arms relatively.**
+> Absolute deep CAGR/Sharpe are not deployable forecasts.
+
+| arm | universe | entries from | Sharpe | SE | CAGR % | max DD % | total ret % | trades | win % | vs SPY |
+|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| **A** | 505 only | 2022-07-01 | **1.32** | 0.49 | **31.8** | **18.9** | +205 | 374 | 35.6 | +96.5 |
+| **B** | 505 + liquid 1500 | 2022-07-01 | 0.14 | 0.50 | −1.8 | 55.3 | −7 | 706 | 29.3 | +95.0 |
+| **C** | 505 only | 2016-07-01 | **0.88** | 0.31 | **16.7** | **24.4** | +369 | 763 | 36.7 | +257 |
+| **D** | 505 + liquid 1500 | 2016-07-01 | −0.06 | 0.32 | −7.0 | 73.9 | −52 | 1567 | 28.0 | +254 |
+
+Qualified longs: A 1 448 · B 6 587 · C 2 450 · D 11 551.
+
+### Read (relative only)
+
+1. **Same strategy, broader liquid universe kills the book** (A→B and C→D).
+   Sharpe collapses; DD roughly triples; win rate drops ~6–8pp; trade count
+   ~doubles. This matches earlier breadth IC work: the production residual +
+   high-vol package is a **large-cap / prod-universe** edge, not a
+   “more names = better” edge.
+
+2. **Longer history on 505 stays positive but softer** (A→C). Sharpe 1.32 → 0.88,
+   CAGR 32% → 17%, DD 19% → 24%. Still well above the liquid-breadth arms.
+   Levels are optimistic (survivorship); the useful message is “edge does not
+   vanish when 2018/2020 are included,” not “expect 17% CAGR forever.”
+
+3. **Arm A vs older Phase‑A / short-window controls** (~Sharpe 1.7–2.1): this
+   matrix re-ranked on deep research.sqlite with a fixed entry start; numbers
+   need not match prior reports row-for-row. Use **this table for A–D
+   comparisons**, not for rewriting the production baseline number.
+
+4. **No production change implied.** Keep the live ~505 universe. Do not broaden
+   the tradable set to liquid Nasdaq under current knobs without a new
+   pre-registered design (and almost certainly a different rank/tilt package).
+
+## Verdict
+
+**Descriptive matrix complete.**
+
+| question | answer from this matrix |
+|---|---|
+| Prod book @ ~4y / 505 | Positive (arm A) |
+| Same + liquid Nasdaq | **No** — large degradation (arm B) |
+| Prod book since 2016 / 505 | Still positive, milder (arm C) |
+| Same + liquid Nasdaq deep | **No** — worst arm (arm D) |
+
+**PENDING_HUMAN** only for whether to log “universe broaden under current knobs”
+as rejected in the main research index. Strategy knobs unchanged either way.
+

docs/research/sector-residual-momentum.md

@@ -0,0 +1,235 @@
+# Sector-residual momentum (Tier-1 alpha research)
+
+**Status:** **CLOSED / REJECTED** — do not resurrect without a new pre-registered protocol.  
+**Branch:** `research/earnings-gap-and-sue` (final grade) · earlier short-window work on `research/sector-residual-momentum`  
+**Production impact:** none. Market residual 12-1 remains the production momentum leg.  
+**Authoritative deep grade:** `reports/sector-resid-deep-20260719-113319.json` (**FAIL**)  
+**Short-window A/B (superseded for promotion):** `reports/sector-residual-20260719-083356.json` — knife-edge only; not decisive after deep masked retest.
+
+### Closure (2026-07-19)
+
+Pre-registered deep test on repaired snapshot + liquid-1500 mask:
+
+| check | result |
+|---|---|
+| weeks extended (≫ 35) | pass (83) |
+| sign +, reliable, eras both + | pass |
+| t ≥ `mom_12_1_resid` same CS | pass (1.69 ≥ 1.30) |
+| \|mean IC\| ≥ 0.03 | **fail (0.0268)** |
+
+**Verdict:** Task 1 CLOSED — sector residual dead on deep evidence.  
+`mom_12_1_sector_demeaned` remains DEAD for promotion. No further sector-residual variants from this thread.
+
+---
+
+## Pre-registration (locked before first research run)
+
+### Hypothesis
+
+Residualizing 12–1 momentum against the sector, not only the market, reduces
+factor volatility at similar return (Blitz / Huij / Martens-style) → higher
+Sharpe on the production book when the residual replaces market-only residual
+as the momentum leg.
+
+### Signals (candidates)
+
+| signal | construction |
+|---|---|
+| `mom_12_1_sector_resid` | Two-factor residual vs SPY + ticker’s sector ETF. Same window as `mom_12_1_resid`: ≥100 daily obs, 252-bar lookback, 21-bar skip; two-factor OLS betas **without intercept**; cumulate residual returns over the formation window. |
+| `mom_12_1_sector_demeaned` | Plain `mom_12_1` minus the **cross-sectional** mean of `mom_12_1` within the same GICS sector that week (≥2 names in sector). No regression. |
+
+### Baselines (same run, same cross-sections — iron rule)
+
+Always report side-by-side with:
+
+- `mom_12_1`
+- `mom_12_1_resid`
+
+Computed on the **identical** weekly non-overlapping cross-sections in this run.
+Never compare against IC numbers from another report.
+
+### Iron rule (IC harness)
+
+Source of truth: `_signal_evaluation` in `app/services/backtest_service.py`.
+
+- Mean weekly Spearman IC on **non-overlapping** weekly windows  
+- Bar: \|mean IC\| ≥ ~0.03, **consistent positive sign**, `reliable: true` (≥ 12 windows)
+
+### Promotion to portfolio A/B (candidate → book)
+
+A candidate promotes to A/B **only if**:
+
+1. It clears the iron-rule bar **and**
+2. Its IC **t-stat ≥** that of `mom_12_1_resid` on the same cross-sections.
+
+### Portfolio A/B grading (if and only if IC promotion fires)
+
+- Swap candidate in as the **momentum leg** of the production 80/20 momentum/vol
+  rank **and** as the gate-percentile signal.
+- `fill_mode=close`, `COST_PER_SIDE = 0.001`, full config otherwise unchanged.
+- Validation window = entries ≥ **2024-07-01** (call it **validation**, not
+  holdout — contaminated by prior experiments).
+- Pre-registered promotion bar:
+  - validation Sharpe ≥ control − 0.5·SE  
+  - full-period Sharpe and max-DD **not worse** than control  
+- Report Lo / Mertens-adjusted SEs.
+
+### Optional sector-cap sub-experiment
+
+Only if labels are in **and** A/B ran: max **3** positions per sector in the
+10-slot book. Same A/B grading. **Tail-trim presumption of guilt** (rule 4):
+report entry counts and both tails of the R distribution. Rising win rate with
+falling Sharpe/CAGR = red flag → do not promote.
+
+**This run:** sector-cap arm **not executed** (optional; A/B unconstrained book
+only). Can be a human-approved follow-up.
+
+### Verdict labels
+
+| label | meaning |
+|---|---|
+| **PROMOTE** | Clears pre-registered bar; human decides next (wire design separate) |
+| **PARK** | Inconclusive / weak; keep machinery, no book change |
+| **DEAD** | Failed iron rule or worse than residual baseline with clear sign |
+
+### Explicit non-goals
+
+- No production deploy from this doc  
+- Do not resurrect: take-profit exits, EV gate, regime entry-blocking,
+  inverse-vol sizing, gap-caps, unconditional FIP filter
+
+---
+
+## Data provenance
+
+### Snapshot race guard
+
+| check | result |
+|---|---|
+| Snapshot path | `backtest_snapshots/prod.sqlite` |
+| Manifest | none (expected for prod snapshot); bar-count sanity applied |
+| Tickers / OHLCV | **506** / **629,263** |
+| Bars min / avg / max | 14 / 1246.1 / 1261 |
+| OHLCV range | 2021-06-24 → 2026-07-02 |
+| Partial-build red flags | none (avg bars healthy) |
+
+Integrity fingerprint on same run: `fip_id` mean IC **−0.045** / t **−2.91**
+(35 weeks, N≈498) — matches the established prod fingerprint.
+
+### Sector labels
+
+| source | count |
+|---|---:|
+| Public S&P 500 GICS CSV | 496 newly filled |
+| FMP profile requests | 10 (all missing after CSV) |
+| Mapped / universe | **505 / 506 (99.8%)** |
+| With mappable ETF | 505 |
+| Still missing | **RHM** only |
+
+Persist path: `data/research/ticker_sector_map.json`.
+
+FMP aliases (`Technology`, `Consumer Defensive`, `Financial Services`) map to
+SPDRs via the alias table in `app/services/sector_map.py`.
+
+### Sector ETFs in `benchmark_prices` (auxiliary only — not tradable)
+
+| symbol | bars | min date | max date |
+|---|---:|---|---|
+| SPY | 1516 | 2020-07-06 | 2026-07-17 |
+| XLB…XLY (11) | 1512 each | 2020-07-10 | 2026-07-17 |
+
+Fetched via Alpaca `Adjustment.SPLIT` into **`benchmark_prices`** (same table as
+SPY) so they never enter the ticker universe or candidate replay.
+
+---
+
+## Results
+
+Generated: `2026-07-19T08:33:56`
+
+### IC harness (identical cross-sections, production 506-name universe)
+
+| signal | mean_ic | ic_t_stat | weeks | avg_N | reliable | ic+_pct | quintile spread |
+|---|---:|---:|---:|---:|---|---:|---:|
+| **mom_12_1_sector_resid** | **0.0578** | **2.34** | 35 | 497.7 | true | 65.7 | 0.0245 |
+| mom_12_1_resid | 0.0552 | 1.98 | 35 | 497.7 | true | 60.0 | 0.0207 |
+| mom_12_1 | 0.0531 | 1.61 | 35 | 497.7 | true | 65.7 | 0.0206 |
+| mom_12_1_sector_demeaned | 0.0340 | 1.32 | 35 | 496.7 | true | 62.9 | 0.0154 |
+
+### IC promotion grades
+
+| candidate | iron rule | t ≥ resid | promote_to_ab |
+|---|---|---|---|
+| `mom_12_1_sector_resid` | pass (IC 0.058, +sign, reliable) | **yes** (2.34 ≥ 1.98) | **yes** |
+| `mom_12_1_sector_demeaned` | pass (IC 0.034, +sign, reliable) | **no** (1.32 < 1.98) | **no** |
+
+### Portfolio A/B — `mom_12_1_sector_resid` as residual leg
+
+Config: production 80/20 residual/high-vol rank + gate percentile, `fill_mode=close`,
+cost 10 bps/side, ATR trail / gate-reset re-entry as live. Validation split
+2024-07-01.
+
+| window | arm | Sharpe | Sharpe SE (Mertens) | CAGR % | max DD % | trades | n_days |
+|---|---|---:|---:|---:|---:|---:|---:|
+| train | control (resid) | 1.30 | 0.685 | 29.2 | 21.4 | 176 | 525 |
+| train | treatment (sector resid) | **1.57** | 0.677 | **35.5** | **19.8** | 176 | 530 |
+| validation | control | **2.92** | 0.709 | **76.3** | **11.7** | 150 | 501 |
+| validation | treatment | 2.57 | 0.701 | 66.3 | 14.8 | 163 | 501 |
+| full | control | 2.09 | 0.497 | 51.6 | 21.4 | 322 | 1000 |
+| full | treatment | 2.09 | 0.491 | 51.0 | **19.8** | 337 | 1005 |
+
+**Pre-registered A/B checks**
+
+| check | result |
+|---|---|
+| val Sharpe ≥ control − 0.5·SE | **pass** (2.57 ≥ 2.92 − 0.5×0.701 = 2.5695) — **knife-edge** |
+| full Sharpe not worse | **pass** (2.09 = 2.09) |
+| full max DD not worse | **pass** (19.8 < 21.4) |
+
+Qualified long candidates: control 1086 vs treatment 1210 (sector residual
+gates a slightly larger set).
+
+---
+
+## Verdict (final — archived)
+
+| signal | verdict | note |
+|---|---|---|
+| **`mom_12_1_sector_resid`** | **CLOSED / REJECTED** | Deep masked IC 0.0268 &lt; 0.03 bar (`sector-resid-deep-20260719-113319`). Short-window PROMOTE superseded. |
+| **`mom_12_1_sector_demeaned`** | **DEAD** | Never cleared t vs market residual; stays dead. |
+
+Short-window evidence below is **historical only** (pre-deep retest). Do not use it
+to reopen promotion.
+
+### Read carefully (archived context)
+
+1. Short-window IC (0.058 / t 2.34 vs resid 0.055 / t 1.98 on 35 weeks) and knife-edge
+   A/B looked openable — that was the data gap era (shallow prod bars).
+2. Deep repaired + liquid-1500 retest closed the case: weeks 83, mild +IC, **below bar**.
+3. Production keeps **market** residual 12-1. Research harness may still *emit*
+   sector residual for diagnostics; it is not a promotion candidate.
+4. **Do not resurrect** without a new pre-registered protocol and new data.
+
+---
+
+## What a human must decide next
+
+**Nothing on Task 1** — archived. Optional: leave research machinery in tree
+(harmless) or delete later as cleanup; not a strategy decision.
+
+---
+
+## Implementation notes
+
+Research runners and sector-residual harness hooks were **removed after close**
+(2026-07-19 cleanup). Evidence remains in the report artifacts below. Do not
+re-add without a new pre-registered protocol.
+
+---
+
+## Artifacts
+
+| file | role |
+|---|---|
+| `reports/sector-resid-deep-20260719-113319.json` | **Authoritative deep FAIL** |
+| `reports/sector-residual-20260719-083356.json` | Short-window IC/A/B (superseded for promotion) |

reports/README.md

@@ -41,3 +41,23 @@ rejected stop-adjustment path, and add no decision evidence beyond the final
 daily matrix and narrative. Their matching one-off runners were removed too.
 All remain recoverable from Git history. Rebuildable candidate pickle caches
 are intentionally ignored and must not be committed.
+
+### Phase B fip breadth IC (2026-07-18/19) — compact evidence
+
+Canonical artifacts:
+
+- `fip-reconcile-20260719-000520.json` — single-sourced authoritative ICs
+  (unconditional liquid fip, tiers, prod-subset, mom-conditional, context
+  signals). Membership symbol dumps stripped after the decision; narrative in
+  [`docs/research/fip-breadth-ic.md`](../docs/research/fip-breadth-ic.md).
+- `fip-breadth-20260718-211440-fingerprint.json` — prod-snapshot fingerprint
+  pass (fip IC −0.045 / t −2.91).
+
+Removed as superseded / dangerous intermediate noise (recoverable from Git):
+
+- `fip-breadth-20260718-211440-breadth.json` (+ wrapper) — **orphaned** +0.0575
+  / t +5.12 from racing a partial `research.sqlite`. Kept out of the tree so it
+  cannot be re-mythologized.
+- `fip-breadth-20260718-194828*.json` — fingerprint-only partial run.
+- `fip-breadth-diagnostics-20260718-213705.json` and `…-213908.json` — dual-path
+  diagnostics superseded by the single-sourced reconcile.

reports/earnings-2a-gap-20260720-dolthub-final.json

@@ -0,0 +1,348 @@
+{
+  "generated_at": "2026-07-20T07:02:10.934892+00:00",
+  "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\research.sqlite",
+  "snapshot_depth": {
+    "manifest": {
+      "schema_version": 1,
+      "snapshot": "research.sqlite",
+      "snapshot_resolved": "C:\\Workspace\\signal-platform\\backtest_snapshots\\research.sqlite",
+      "complete": true,
+      "finished_at": "2026-07-19T14:22:15.706192+00:00",
+      "ticker_count": 4650,
+      "ohlcv_row_count": 5081073,
+      "rank_only_count": 4144,
+      "sources": {
+        "pool": "source_snapshot"
+      },
+      "history_days": 5000,
+      "min_bars": 1262,
+      "fetch_ok": 505,
+      "fetch_fail": 1,
+      "limit": null,
+      "extra": {
+        "prod_symbols_at_start": 506,
+        "pool_size": 506,
+        "to_fetch": 506,
+        "source_symbols_only": true,
+        "benchmark_spy_rows": 2649
+      },
+      "live_counts": {
+        "ticker_count": 4650,
+        "ohlcv_row_count": 5081073,
+        "rank_only_count": 4144
+      }
+    },
+    "requested_symbols": 506,
+    "tradable_symbols": 505,
+    "missing_symbols": [],
+    "zero_bar_symbols": [
+      "RHM"
+    ],
+    "bar_count": {
+      "min": 24,
+      "median": 2649,
+      "max": 2649
+    },
+    "symbols_with_pre2021_bars": 491,
+    "symbols_with_pre2021_bars_pct": 97.2,
+    "shallow_symbols_lt_1000_bars": [
+      {
+        "symbol": "SPCX",
+        "first_bar": "2026-06-12",
+        "last_bar": "2026-07-17",
+        "bars": 24
+      },
+      {
+        "symbol": "Q",
+        "first_bar": "2025-11-03",
+        "last_bar": "2026-07-17",
+        "bars": 176
+      },
+      {
+        "symbol": "PSKY",
+        "first_bar": "2025-08-07",
+        "last_bar": "2026-07-17",
+        "bars": 237
+      },
+      {
+        "symbol": "SNDK",
+        "first_bar": "2025-02-13",
+        "last_bar": "2026-07-17",
+        "bars": 357
+      },
+      {
+        "symbol": "GEV",
+        "first_bar": "2024-04-02",
+        "last_bar": "2026-07-17",
+        "bars": 575
+      },
+      {
+        "symbol": "SOLV",
+        "first_bar": "2024-04-01",
+        "last_bar": "2026-07-17",
+        "bars": 576
+      },
+      {
+        "symbol": "VLTO",
+        "first_bar": "2023-10-02",
+        "last_bar": "2026-07-17",
+        "bars": 700
+      },
+      {
+        "symbol": "KVUE",
+        "first_bar": "2023-05-04",
+        "last_bar": "2026-07-17",
+        "bars": 803
+      },
+      {
+        "symbol": "GEHC",
+        "first_bar": "2022-12-15",
+        "last_bar": "2026-07-17",
+        "bars": 898
+      }
+    ],
+    "price_window": {
+      "min": "2016-01-04",
+      "max": "2026-07-17"
+    },
+    "benchmark_spy": {
+      "rows": 2649,
+      "min": "2016-01-04",
+      "max": "2026-07-17"
+    },
+    "gate_threshold": {
+      "min_tradable_symbols": 505,
+      "max_missing_or_zero_bar": 1,
+      "min_symbols_with_pre2021_bars_pct": 80.0,
+      "benchmark_min_rows": 1000,
+      "benchmark_must_begin_pre2021": true
+    },
+    "gate_pass": true
+  },
+  "data_quality": {
+    "window": {
+      "from": "2020-01-22",
+      "to": "2026-07-17"
+    },
+    "prod_symbols": 505,
+    "events": 12414,
+    "symbols_with_any_event": 504,
+    "symbols_with_ge8_announcements": 498,
+    "symbols_with_ge8_announcements_pct": 98.6,
+    "symbols_with_ge8_paired_announcements": 495,
+    "symbols_with_ge8_paired_announcements_pct": 98.0,
+    "events_with_actual_and_estimate": 12311,
+    "events_with_actual_and_estimate_pct": 99.2,
+    "duplicate_rows_in_table": 0,
+    "duplicate_rows_fetched": 0,
+    "restated_rows_fetched": 940,
+    "dedupe_policy": "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment",
+    "events_per_symbol_year": {
+      "mean_active_span_rate": 4.08,
+      "expected": "approximately 4",
+      "far_off_rule": "active-span rate <2 or >6, plus zero-event symbols",
+      "far_off_count": 1,
+      "far_off_symbols": [
+        {
+          "symbol": "SPCX",
+          "events": 0,
+          "events_per_year": 0.0
+        }
+      ]
+    },
+    "announcement_session": {
+      "recognised_bmo_amc_or_during": 11520,
+      "recognised_pct": 92.8,
+      "reliable": true,
+      "assessment": "usable"
+    },
+    "point_in_time_policy": "announce_date_plus_1_trading_day_for_all_events",
+    "sue_scaling": {
+      "primary": "eps_surprise_over_stdev_of_prior_8_surprises_min_4",
+      "fallback_trigger": "paired event coverage <50% or symbols with >=8 paired events <50%",
+      "fallback_needed": false,
+      "fallback_name": "not_used"
+    },
+    "backfill": {
+      "mode": "dolthub_public_bulk_clone",
+      "window": {
+        "from": "2020-01-22",
+        "to": "2026-07-17"
+      },
+      "coverage_amendment": {
+        "approved_by_user": true,
+        "reason": "FMP free tier blocks historical bulk earnings",
+        "original_start": "2016-01-04",
+        "amended_announcement_start": "2020-01-22"
+      },
+      "source": {
+        "repository": "https://www.dolthub.com/repositories/post-no-preference/earnings",
+        "commit": "9n0et3hpj9j7vue8f3qsldon3qa5sdjj",
+        "license": "CC-BY-SA-4.0",
+        "upstream_provider_documented": false
+      },
+      "bulk_windows_total": 1,
+      "bulk_windows_done": 1,
+      "bulk_requests_logged_total": 1,
+      "bulk_exports": 2,
+      "calendar": {
+        "raw_rows": 117482,
+        "universe_rows_in_window": 12342,
+        "deduped_rows_in_window": 12342,
+        "duplicate_rows": 0,
+        "restated_rows": 0
+      },
+      "eps_history": {
+        "raw_rows": 165050,
+        "universe_rows": 18515,
+        "deduped_rows": 18515,
+        "duplicate_rows": 0,
+        "restated_rows": 0,
+        "complete_actual_and_estimate": 18304
+      },
+      "pairing": {
+        "method": "minimum-cost monotonic alignment per symbol",
+        "allowed_announce_minus_period_end_days": [
+          -14,
+          90
+        ],
+        "matched_calendar_events": 12271,
+        "unmatched_calendar_events": 71,
+        "unmatched_periods_in_pairing_window": 538,
+        "announce_minus_period_end_days": {
+          "min": -10,
+          "median": 30,
+          "max": 89
+        },
+        "pre_2020_eps_history_use": "trailing_surprise_stdev_only; never treated as an announcement or live signal event"
+      },
+      "duplicate_rows_logged_total": 0,
+      "restated_rows_logged_total": 940,
+      "conflicting_existing_rows": 940,
+      "conflicting_existing_fields": 1526,
+      "preserved_existing_fields": 2945,
+      "existing_enrichment_events_not_in_dolthub_calendar": 72,
+      "dedupe_policy": "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment",
+      "events_in_window": 12414,
+      "events_with_actual_and_estimate": 12311,
+      "symbols_done": 506,
+      "symbols_universe": 506,
+      "symbols_with_dolthub_calendar": 504,
+      "symbols_without_dolthub_calendar": [
+        "RHM",
+        "SPCX"
+      ],
+      "announce_date_range": {
+        "min": "2020-01-22",
+        "max": "2026-07-17"
+      },
+      "complete": true
+    }
+  },
+  "production_impact": "none",
+  "experiment": "2a",
+  "result": {
+    "verdict": "INFORMATIONAL",
+    "costs": {
+      "per_side": 0.001,
+      "r_is_net_of_round_trip_costs": true
+    },
+    "closed_trades": 574,
+    "q1_loss_concentration": {
+      "loss_definition": "realized_net_R <= -1.0",
+      "holding_period_definition": "announcement strictly after entry and before exit",
+      "losses_count": 266,
+      "losses_with_announcement_count": 23,
+      "losses_with_announcement_fraction": 0.0865,
+      "all_trades_with_announcement_count": 115,
+      "all_trades_with_announcement_fraction": 0.2003
+    },
+    "q2_entries_within_3_trading_days_before_announcement": {
+      "pre_earnings": {
+        "count": 27,
+        "mean_r": 0.4837,
+        "median_r": -1.0265,
+        "win_rate": 0.3333,
+        "p05_r": -1.1463,
+        "p95_r": 5.981,
+        "min_r": -1.2449,
+        "max_r": 8.8996
+      },
+      "all_other_entries": {
+        "count": 547,
+        "mean_r": 0.2734,
+        "median_r": -0.8316,
+        "win_rate": 0.3565,
+        "p05_r": -1.1228,
+        "p95_r": 4.5888,
+        "min_r": -6.0161,
+        "max_r": 19.98
+      },
+      "tail_deltas_pre_minus_other": {
+        "p05_r": -0.0235,
+        "p95_r": 1.3922
+      },
+      "directional_tail_condition_present": false,
+      "tail_read": "Registered directional tail condition is not present."
+    },
+    "q3_stop_exits_within_1_trading_day_after_announcement": {
+      "stops_after_earnings": {
+        "count": 26,
+        "mean_r": -0.6434,
+        "median_r": -0.9753,
+        "win_rate": 0.2308,
+        "p05_r": -2.4614,
+        "p95_r": 1.1142,
+        "min_r": -2.6413,
+        "max_r": 1.7012
+      },
+      "all_other_stops": {
+        "count": 433,
+        "mean_r": -0.5035,
+        "median_r": -1.0278,
+        "win_rate": 0.1963,
+        "p05_r": -1.1373,
+        "p95_r": 1.3591,
+        "min_r": -6.0161,
+        "max_r": 19.98
+      },
+      "all_other_exits": {
+        "count": 548,
+        "mean_r": 0.3273,
+        "median_r": -0.8361,
+        "win_rate": 0.3613,
+        "p05_r": -1.0644,
+        "p95_r": 4.7224,
+        "min_r": -6.0161,
+        "max_r": 19.98
+      }
+    },
+    "implementation": "REPORT_ONLY_NO_FILTER_ARM_NO_FILTER_CHANGE",
+    "analysis_window": {
+      "from": "2020-01-22",
+      "to": "2026-07-17",
+      "rule": "entry_on_or_after_start_and_exit_on_or_before_end",
+      "simulation_trades_total": 574,
+      "trades_excluded_outside_earnings_coverage": 0
+    },
+    "run_config": {
+      "universe_symbols": 505,
+      "fill_mode": "close",
+      "cost_per_side": 0.001,
+      "momentum_cutoff": 80.0,
+      "exit_policy": "atr_trail3",
+      "hold_days": 30,
+      "max_positions": 10,
+      "risk_per_trade": 0.01
+    },
+    "sim_summary": {
+      "start_date": "2020-01-22",
+      "end_date": "2026-07-13",
+      "trades": 574,
+      "sharpe": 1.03,
+      "cagr_pct": 22.9,
+      "max_drawdown_pct": 26.6,
+      "total_return_pct": 280.6
+    }
+  }
+}

reports/earnings-2a-gap-20260720-dolthub-final.md

@@ -0,0 +1,58 @@
+# Earnings Task 2a - gap diagnostic
+
+### Data quality gate
+
+Approved earnings window: 2020-01-22 to 2026-07-17. Source mode: dolthub_public_bulk_clone.
+
+| check | result |
+|---|---:|
+| Prod symbols requested / tradable | 506 / 505 |
+| Manifest complete + live counts match | True |
+| Prod symbols with pre-2021 bars | 491 (97.2%) |
+| SPY benchmark depth | 2649 rows, 2016-01-04 to 2026-07-17 |
+| Snapshot depth gate | True |
+| Bulk source windows / requests logged | 1/1 / 1 |
+| Source repository / pinned commit | https://www.dolthub.com/repositories/post-no-preference/earnings @ 9n0et3hpj9j7vue8f3qsldon3qa5sdjj |
+| Source license / upstream provider documented | CC-BY-SA-4.0 / False |
+| Existing-source conflicts preserved | 940 rows / 1526 fields |
+| Symbols with >=8 announcements | 498 (98.6%) |
+| Symbols with >=8 paired announcements | 495 (98.0%) |
+| Events with estimate + actual | 12311/12414 (99.2%) |
+| Duplicate rows in keyed table | 0 |
+| Duplicate / restated payload rows fetched | 0 / 940 |
+| Mean announcements per active symbol-year | 4.08 (expected about 4) |
+| Symbols far off (<2 or >6/year, incl. zero) | 1 |
+| Recognised BMO/AMC/during | 92.8% (reliable=True) |
+| Point-in-time policy | announce_date_plus_1_trading_day_for_all_events |
+| SUE price fallback | not_used |
+
+Deduplication: UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment
+
+Far-off announcement-rate symbols: SPCX
+
+### Experiment 2a - earnings-gap risk diagnostic
+
+Verdict: **INFORMATIONAL**. Report-only; no filter arm or implementation.
+
+Trade cohort is restricted to the approved earnings-coverage window 2020-01-22 to 2026-07-17; 0 simulated trades outside that window were excluded.
+
+| cohort | count | fraction |
+|---|---:|---:|
+| Realized net R <= -1.0 | 266 | - |
+| Losses with announcement strictly inside hold | 23 | 0.0865 |
+| All trades with announcement strictly inside hold | 115 | 0.2003 |
+
+| Entry cohort | count | mean R | median R | win rate | p05 R | p95 R |
+|---|---:|---:|---:|---:|---:|---:|
+| Within 3 sessions before earnings | 27 | 0.4837 | -1.0265 | 0.3333 | -1.1463 | 5.981 |
+| All other entries | 547 | 0.2734 | -0.8316 | 0.3565 | -1.1228 | 4.5888 |
+
+Tail deltas (pre minus other): p05=-0.0235, p95=1.3922.
+
+Registered directional tail condition is not present.
+
+| Exit cohort | count | mean R | median R | win rate | p05 R | p95 R |
+|---|---:|---:|---:|---:|---:|---:|
+| Stops within 1 session after earnings | 26 | -0.6434 | -0.9753 | 0.2308 | -2.4614 | 1.1142 |
+| All other stops | 433 | -0.5035 | -1.0278 | 0.1963 | -1.1373 | 1.3591 |
+| All other exits | 548 | 0.3273 | -0.8361 | 0.3613 | -1.0644 | 4.7224 |

reports/earnings-2b-sue-20260720-dolthub-final.json

@@ -0,0 +1,349 @@
+{
+  "generated_at": "2026-07-20T07:02:10.934892+00:00",
+  "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\research.sqlite",
+  "snapshot_depth": {
+    "manifest": {
+      "schema_version": 1,
+      "snapshot": "research.sqlite",
+      "snapshot_resolved": "C:\\Workspace\\signal-platform\\backtest_snapshots\\research.sqlite",
+      "complete": true,
+      "finished_at": "2026-07-19T14:22:15.706192+00:00",
+      "ticker_count": 4650,
+      "ohlcv_row_count": 5081073,
+      "rank_only_count": 4144,
+      "sources": {
+        "pool": "source_snapshot"
+      },
+      "history_days": 5000,
+      "min_bars": 1262,
+      "fetch_ok": 505,
+      "fetch_fail": 1,
+      "limit": null,
+      "extra": {
+        "prod_symbols_at_start": 506,
+        "pool_size": 506,
+        "to_fetch": 506,
+        "source_symbols_only": true,
+        "benchmark_spy_rows": 2649
+      },
+      "live_counts": {
+        "ticker_count": 4650,
+        "ohlcv_row_count": 5081073,
+        "rank_only_count": 4144
+      }
+    },
+    "requested_symbols": 506,
+    "tradable_symbols": 505,
+    "missing_symbols": [],
+    "zero_bar_symbols": [
+      "RHM"
+    ],
+    "bar_count": {
+      "min": 24,
+      "median": 2649,
+      "max": 2649
+    },
+    "symbols_with_pre2021_bars": 491,
+    "symbols_with_pre2021_bars_pct": 97.2,
+    "shallow_symbols_lt_1000_bars": [
+      {
+        "symbol": "SPCX",
+        "first_bar": "2026-06-12",
+        "last_bar": "2026-07-17",
+        "bars": 24
+      },
+      {
+        "symbol": "Q",
+        "first_bar": "2025-11-03",
+        "last_bar": "2026-07-17",
+        "bars": 176
+      },
+      {
+        "symbol": "PSKY",
+        "first_bar": "2025-08-07",
+        "last_bar": "2026-07-17",
+        "bars": 237
+      },
+      {
+        "symbol": "SNDK",
+        "first_bar": "2025-02-13",
+        "last_bar": "2026-07-17",
+        "bars": 357
+      },
+      {
+        "symbol": "GEV",
+        "first_bar": "2024-04-02",
+        "last_bar": "2026-07-17",
+        "bars": 575
+      },
+      {
+        "symbol": "SOLV",
+        "first_bar": "2024-04-01",
+        "last_bar": "2026-07-17",
+        "bars": 576
+      },
+      {
+        "symbol": "VLTO",
+        "first_bar": "2023-10-02",
+        "last_bar": "2026-07-17",
+        "bars": 700
+      },
+      {
+        "symbol": "KVUE",
+        "first_bar": "2023-05-04",
+        "last_bar": "2026-07-17",
+        "bars": 803
+      },
+      {
+        "symbol": "GEHC",
+        "first_bar": "2022-12-15",
+        "last_bar": "2026-07-17",
+        "bars": 898
+      }
+    ],
+    "price_window": {
+      "min": "2016-01-04",
+      "max": "2026-07-17"
+    },
+    "benchmark_spy": {
+      "rows": 2649,
+      "min": "2016-01-04",
+      "max": "2026-07-17"
+    },
+    "gate_threshold": {
+      "min_tradable_symbols": 505,
+      "max_missing_or_zero_bar": 1,
+      "min_symbols_with_pre2021_bars_pct": 80.0,
+      "benchmark_min_rows": 1000,
+      "benchmark_must_begin_pre2021": true
+    },
+    "gate_pass": true
+  },
+  "data_quality": {
+    "window": {
+      "from": "2020-01-22",
+      "to": "2026-07-17"
+    },
+    "prod_symbols": 505,
+    "events": 12414,
+    "symbols_with_any_event": 504,
+    "symbols_with_ge8_announcements": 498,
+    "symbols_with_ge8_announcements_pct": 98.6,
+    "symbols_with_ge8_paired_announcements": 495,
+    "symbols_with_ge8_paired_announcements_pct": 98.0,
+    "events_with_actual_and_estimate": 12311,
+    "events_with_actual_and_estimate_pct": 99.2,
+    "duplicate_rows_in_table": 0,
+    "duplicate_rows_fetched": 0,
+    "restated_rows_fetched": 940,
+    "dedupe_policy": "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment",
+    "events_per_symbol_year": {
+      "mean_active_span_rate": 4.08,
+      "expected": "approximately 4",
+      "far_off_rule": "active-span rate <2 or >6, plus zero-event symbols",
+      "far_off_count": 1,
+      "far_off_symbols": [
+        {
+          "symbol": "SPCX",
+          "events": 0,
+          "events_per_year": 0.0
+        }
+      ]
+    },
+    "announcement_session": {
+      "recognised_bmo_amc_or_during": 11520,
+      "recognised_pct": 92.8,
+      "reliable": true,
+      "assessment": "usable"
+    },
+    "point_in_time_policy": "announce_date_plus_1_trading_day_for_all_events",
+    "sue_scaling": {
+      "primary": "eps_surprise_over_stdev_of_prior_8_surprises_min_4",
+      "fallback_trigger": "paired event coverage <50% or symbols with >=8 paired events <50%",
+      "fallback_needed": false,
+      "fallback_name": "not_used"
+    },
+    "backfill": {
+      "mode": "dolthub_public_bulk_clone",
+      "window": {
+        "from": "2020-01-22",
+        "to": "2026-07-17"
+      },
+      "coverage_amendment": {
+        "approved_by_user": true,
+        "reason": "FMP free tier blocks historical bulk earnings",
+        "original_start": "2016-01-04",
+        "amended_announcement_start": "2020-01-22"
+      },
+      "source": {
+        "repository": "https://www.dolthub.com/repositories/post-no-preference/earnings",
+        "commit": "9n0et3hpj9j7vue8f3qsldon3qa5sdjj",
+        "license": "CC-BY-SA-4.0",
+        "upstream_provider_documented": false
+      },
+      "bulk_windows_total": 1,
+      "bulk_windows_done": 1,
+      "bulk_requests_logged_total": 1,
+      "bulk_exports": 2,
+      "calendar": {
+        "raw_rows": 117482,
+        "universe_rows_in_window": 12342,
+        "deduped_rows_in_window": 12342,
+        "duplicate_rows": 0,
+        "restated_rows": 0
+      },
+      "eps_history": {
+        "raw_rows": 165050,
+        "universe_rows": 18515,
+        "deduped_rows": 18515,
+        "duplicate_rows": 0,
+        "restated_rows": 0,
+        "complete_actual_and_estimate": 18304
+      },
+      "pairing": {
+        "method": "minimum-cost monotonic alignment per symbol",
+        "allowed_announce_minus_period_end_days": [
+          -14,
+          90
+        ],
+        "matched_calendar_events": 12271,
+        "unmatched_calendar_events": 71,
+        "unmatched_periods_in_pairing_window": 538,
+        "announce_minus_period_end_days": {
+          "min": -10,
+          "median": 30,
+          "max": 89
+        },
+        "pre_2020_eps_history_use": "trailing_surprise_stdev_only; never treated as an announcement or live signal event"
+      },
+      "duplicate_rows_logged_total": 0,
+      "restated_rows_logged_total": 940,
+      "conflicting_existing_rows": 940,
+      "conflicting_existing_fields": 1526,
+      "preserved_existing_fields": 2945,
+      "existing_enrichment_events_not_in_dolthub_calendar": 72,
+      "dedupe_policy": "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment",
+      "events_in_window": 12414,
+      "events_with_actual_and_estimate": 12311,
+      "symbols_done": 506,
+      "symbols_universe": 506,
+      "symbols_with_dolthub_calendar": 504,
+      "symbols_without_dolthub_calendar": [
+        "RHM",
+        "SPCX"
+      ],
+      "announce_date_range": {
+        "min": "2020-01-22",
+        "max": "2026-07-17"
+      },
+      "complete": true
+    }
+  },
+  "production_impact": "none",
+  "experiment": "2b",
+  "result": {
+    "verdict": "FAIL",
+    "verdict_detail": "SUE DEAD for this stack",
+    "grade_rule": {
+      "mean_ic_ge_0_03_positive": false,
+      "reliable_ge_12_windows": true,
+      "positive_sign_pre_and_post_2021": true,
+      "pass": false
+    },
+    "sue_unconditional": {
+      "signal": "sue_latest",
+      "weeks": 56,
+      "avg_cross_section": 451.4,
+      "mean_ic": 0.0151,
+      "ic_t_stat": 1.29,
+      "ic_positive_pct": 51.8,
+      "mean_quintile_spread": 0.0041,
+      "reliable": true
+    },
+    "era_split": {
+      "pre_2021": {
+        "signal": "sue_latest",
+        "weeks": 9,
+        "avg_cross_section": 398.3,
+        "mean_ic": 0.0286,
+        "ic_t_stat": 0.7,
+        "ic_positive_pct": 55.6,
+        "mean_quintile_spread": 0.0084,
+        "reliable": false
+      },
+      "post_2021": {
+        "signal": "sue_latest",
+        "weeks": 48,
+        "avg_cross_section": 461.9,
+        "mean_ic": 0.0172,
+        "ic_t_stat": 1.34,
+        "ic_positive_pct": 64.6,
+        "mean_quintile_spread": 0.0038,
+        "reliable": true
+      }
+    },
+    "signal_eval_identical_cross_sections": {
+      "sue_latest": {
+        "signal": "sue_latest",
+        "weeks": 56,
+        "avg_cross_section": 450.9,
+        "mean_ic": 0.0148,
+        "ic_t_stat": 1.27,
+        "ic_positive_pct": 51.8,
+        "mean_quintile_spread": 0.004,
+        "reliable": true
+      },
+      "mom_12_1": {
+        "signal": "mom_12_1",
+        "weeks": 56,
+        "avg_cross_section": 450.9,
+        "mean_ic": 0.0195,
+        "ic_t_stat": 0.74,
+        "ic_positive_pct": 58.9,
+        "mean_quintile_spread": 0.0104,
+        "reliable": true
+      },
+      "mom_12_1_resid": {
+        "signal": "mom_12_1_resid",
+        "weeks": 56,
+        "avg_cross_section": 450.9,
+        "mean_ic": 0.0262,
+        "ic_t_stat": 1.07,
+        "ic_positive_pct": 55.4,
+        "mean_quintile_spread": 0.0114,
+        "reliable": true
+      }
+    },
+    "identical_cross_section_definition": "same week-symbol-forward-return cells where sue_latest, mom_12_1, and mom_12_1_resid are all non-null",
+    "momentum_conditional_top_quintile": {
+      "mean_ic": 0.0213,
+      "ic_t_stat": 1.3,
+      "weeks": 56,
+      "avg_cross_section": 89.8,
+      "population": "top_mom_12_1_quintile_only"
+    },
+    "coverage": {
+      "symbols_with_live_sue": 501,
+      "avg_weekly_live_n_all_weeks": 453.1,
+      "avg_cross_section_n_scored_nonoverlap": 451.4,
+      "thin_cross_section_lt_100": false,
+      "warning": null
+    },
+    "scaling": {
+      "method": "eps_surprise_over_stdev_of_prior_8_surprises_min_4",
+      "fallback": "not_used",
+      "counts": {
+        "standard_scaled_events": 12149,
+        "events_scaled_from_period_history": 12149,
+        "price_fallback_events": 0,
+        "dropped_insufficient_trailing_history": 95,
+        "dropped_missing_period_alignment": 67,
+        "dropped_zero_stdev": 0
+      },
+      "pre_coverage_history_policy": "period-end EPS surprises may scale later events but are never treated as live signals without an announcement date",
+      "availability": "announce_date_plus_1_trading_day",
+      "carry_trading_days": 63
+    },
+    "universe_symbols": 505
+  }
+}

reports/earnings-2b-sue-20260720-dolthub-final.md

@@ -0,0 +1,62 @@
+# Earnings Task 2b - SUE / PEAD
+
+### Data quality gate
+
+Approved earnings window: 2020-01-22 to 2026-07-17. Source mode: dolthub_public_bulk_clone.
+
+| check | result |
+|---|---:|
+| Prod symbols requested / tradable | 506 / 505 |
+| Manifest complete + live counts match | True |
+| Prod symbols with pre-2021 bars | 491 (97.2%) |
+| SPY benchmark depth | 2649 rows, 2016-01-04 to 2026-07-17 |
+| Snapshot depth gate | True |
+| Bulk source windows / requests logged | 1/1 / 1 |
+| Source repository / pinned commit | https://www.dolthub.com/repositories/post-no-preference/earnings @ 9n0et3hpj9j7vue8f3qsldon3qa5sdjj |
+| Source license / upstream provider documented | CC-BY-SA-4.0 / False |
+| Existing-source conflicts preserved | 940 rows / 1526 fields |
+| Symbols with >=8 announcements | 498 (98.6%) |
+| Symbols with >=8 paired announcements | 495 (98.0%) |
+| Events with estimate + actual | 12311/12414 (99.2%) |
+| Duplicate rows in keyed table | 0 |
+| Duplicate / restated payload rows fetched | 0 / 940 |
+| Mean announcements per active symbol-year | 4.08 (expected about 4) |
+| Symbols far off (<2 or >6/year, incl. zero) | 1 |
+| Recognised BMO/AMC/during | 92.8% (reliable=True) |
+| Point-in-time policy | announce_date_plus_1_trading_day_for_all_events |
+| SUE price fallback | not_used |
+
+Deduplication: UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment
+
+Far-off announcement-rate symbols: SPCX
+
+### Experiment 2b - SUE / post-earnings drift
+
+Mechanical verdict: **FAIL** - SUE DEAD for this stack
+
+Identical cross-sections:
+
+| signal | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| sue_latest | 0.0148 | 1.27 | 56 | 450.9 | 51.8 | true |
+| mom_12_1 | 0.0195 | 0.74 | 56 | 450.9 | 58.9 | true |
+| mom_12_1_resid | 0.0262 | 1.07 | 56 | 450.9 | 55.4 | true |
+
+Unconditional SUE grade row:
+
+| signal | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| sue_latest | 0.0151 | 1.29 | 56 | 451.4 | 51.8 | true |
+
+Era stability:
+
+| era | mean IC | t | windows | avg N | IC positive % | reliable |
+|---|---:|---:|---:|---:|---:|---|
+| pre-2021 | 0.0286 | 0.7 | 9 | 398.3 | 55.6 | false |
+| post-2021 | 0.0172 | 1.34 | 48 | 461.9 | 64.6 | true |
+
+Coverage: 501 symbols with live SUE; avg weekly N=453.1; scored non-overlap avg N=451.4.
+
+Cross-section is not flagged thin at the registered <100-name read.
+
+Momentum-conditional top-quintile SUE: mean IC=0.0213, t=1.3, windows=56, avg N=89.8.

reports/earnings-backfill-status.json

@@ -0,0 +1,75 @@
+{
+  "mode": "dolthub_public_bulk_clone",
+  "window": {
+    "from": "2020-01-22",
+    "to": "2026-07-17"
+  },
+  "coverage_amendment": {
+    "approved_by_user": true,
+    "reason": "FMP free tier blocks historical bulk earnings",
+    "original_start": "2016-01-04",
+    "amended_announcement_start": "2020-01-22"
+  },
+  "source": {
+    "repository": "https://www.dolthub.com/repositories/post-no-preference/earnings",
+    "commit": "9n0et3hpj9j7vue8f3qsldon3qa5sdjj",
+    "license": "CC-BY-SA-4.0",
+    "upstream_provider_documented": false
+  },
+  "bulk_windows_total": 1,
+  "bulk_windows_done": 1,
+  "bulk_requests_logged_total": 1,
+  "bulk_exports": 2,
+  "calendar": {
+    "raw_rows": 117482,
+    "universe_rows_in_window": 12342,
+    "deduped_rows_in_window": 12342,
+    "duplicate_rows": 0,
+    "restated_rows": 0
+  },
+  "eps_history": {
+    "raw_rows": 165050,
+    "universe_rows": 18515,
+    "deduped_rows": 18515,
+    "duplicate_rows": 0,
+    "restated_rows": 0,
+    "complete_actual_and_estimate": 18304
+  },
+  "pairing": {
+    "method": "minimum-cost monotonic alignment per symbol",
+    "allowed_announce_minus_period_end_days": [
+      -14,
+      90
+    ],
+    "matched_calendar_events": 12271,
+    "unmatched_calendar_events": 71,
+    "unmatched_periods_in_pairing_window": 538,
+    "announce_minus_period_end_days": {
+      "min": -10,
+      "median": 30,
+      "max": 89
+    },
+    "pre_2020_eps_history_use": "trailing_surprise_stdev_only; never treated as an announcement or live signal event"
+  },
+  "duplicate_rows_logged_total": 0,
+  "restated_rows_logged_total": 940,
+  "conflicting_existing_rows": 940,
+  "conflicting_existing_fields": 1526,
+  "preserved_existing_fields": 2945,
+  "existing_enrichment_events_not_in_dolthub_calendar": 72,
+  "dedupe_policy": "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one calendar row per key; preserve existing non-null session/EPS values from the prior FMP/Alpha Vantage partial backfill, then fill nulls and all remaining symbols from DoltHub; attach DoltHub period-end alignment",
+  "events_in_window": 12414,
+  "events_with_actual_and_estimate": 12311,
+  "symbols_done": 506,
+  "symbols_universe": 506,
+  "symbols_with_dolthub_calendar": 504,
+  "symbols_without_dolthub_calendar": [
+    "RHM",
+    "SPCX"
+  ],
+  "announce_date_range": {
+    "min": "2020-01-22",
+    "max": "2026-07-17"
+  },
+  "complete": true
+}

reports/fip-breadth-20260718-211440-fingerprint.json

@@ -0,0 +1,578 @@
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+    "liquid_min_price": null,
+    "signal_eval_only": true
+  },
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+  ],
+  "gate_ablation_note": "Each row re-qualifies the same candidates at the current momentum cutoff (80) with one floor removed (long-only while the momentum gate is active). If dropping a floor doesn't hurt net expectancy, that floor isn't pulling its weight. The Hold columns grade the same variants under the hold-to-horizon time exit instead of the S/R target \u2014 the view that matters if the exit policy moves to a fixed hold.",
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+      "net_avg_r_ex_top5": 0.112
+    },
+    {
+      "hold_days": 21,
+      "total": 1086,
+      "wins": 487,
+      "win_rate": 44.8,
+      "avg_r": 0.525,
+      "total_r": 570.33,
+      "net_avg_r": 0.479,
+      "net_total_r": 520.14,
+      "best_r": 9.86,
+      "worst_r": -3.38,
+      "avg_hold_days": 13.7,
+      "net_r_per_day": 0.0349,
+      "median_net_r": -1.027,
+      "profit_factor": 1.81,
+      "net_avg_r_ex_top5": 0.191
+    },
+    {
+      "hold_days": 30,
+      "total": 1086,
+      "wins": 434,
+      "win_rate": 40.0,
+      "avg_r": 0.631,
+      "total_r": 684.97,
+      "net_avg_r": 0.585,
+      "net_total_r": 634.78,
+      "best_r": 12.87,
+      "worst_r": -3.38,
+      "avg_hold_days": 17.8,
+      "net_r_per_day": 0.0329,
+      "median_net_r": -1.033,
+      "profit_factor": 1.9,
+      "net_avg_r_ex_top5": 0.212
+    }
+  ],
+  "portfolio_sim": {
+    "params": {
+      "starting_capital": 10000.0,
+      "max_positions": 10,
+      "risk_per_trade_pct": 1.0,
+      "notional_cap_pct": 20.0,
+      "cost_per_side_pct": 0.1,
+      "hold_days": 30
+    },
+    "policies": [],
+    "note": "One capital-constrained book over the same qualified setups the tables above grade per-setup: at most 10 concurrent positions (one per ticker), best momentum first, fixed-fractional risk sizing with a no-leverage cap, entries at the detection close, stops filled at the worse of stop or open. 'target' races the S/R target against the stop (timeout at the horizon); 'hold' keeps the initial stop and exits at the horizon close. SPY return is price-only over the same window. In-sample; no dividends."
+  },
+  "strategy_variants": {
+    "variants": [],
+    "note": "Research-only hold-to-horizon portfolio variants. Production now uses residual 12-1 momentum at cutoff 80; the remaining rows compare the legacy raw rank, raw cutoff 90, one max-15 capacity check, and volatility overlays."
+  },
+  "exit_policy_variants": {
+    "variants": [],
+    "note": "Research-only exit policies over the residual/high-vol 80/20 entry candidate. Every row uses the same entry qualification/ranking and changes only the exit discipline."
+  },
+  "portfolio_monitor": null,
+  "production_cadence_comparison": null,
+  "holdout": null,
+  "min_rr_sweep": null,
+  "target_model_diagnostics": {
+    "target_model": "production_gtl",
+    "target_model_label": "Live GTL (production)",
+    "candidate_count": 202765,
+    "primary_source_counts": {
+      "pivot_point": 196290,
+      "range_grid": 180036
+    },
+    "primary_round_only": 0,
+    "primary_strength_100": 138596,
+    "avg_primary_strength": 80.109,
+    "avg_primary_distance_atr": 2.293,
+    "avg_primary_rejection_count": 41.908,
+    "avg_raw_level_count": 53.204,
+    "avg_gate_level_count": 53.204
+  },
+  "signal_eval": [
+    {
+      "signal": "vol_6m",
+      "weeks": 39,
+      "avg_cross_section": 498.2,
+      "mean_ic": 0.0609,
+      "ic_t_stat": 1.48,
+      "ic_positive_pct": 64.1,
+      "mean_quintile_spread": 0.0337,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1_resid",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": 0.0552,
+      "ic_t_stat": 1.98,
+      "ic_positive_pct": 60.0,
+      "mean_quintile_spread": 0.0207,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": 0.0531,
+      "ic_t_stat": 1.61,
+      "ic_positive_pct": 65.7,
+      "mean_quintile_spread": 0.0206,
+      "reliable": true
+    },
+    {
+      "signal": "trend_200",
+      "weeks": 37,
+      "avg_cross_section": 497.9,
+      "mean_ic": 0.0161,
+      "ic_t_stat": 0.44,
+      "ic_positive_pct": 59.5,
+      "mean_quintile_spread": 0.006,
+      "reliable": true
+    },
+    {
+      "signal": "reversal_1m",
+      "weeks": 43,
+      "avg_cross_section": 498.7,
+      "mean_ic": 0.0059,
+      "ic_t_stat": 0.22,
+      "ic_positive_pct": 53.5,
+      "mean_quintile_spread": 0.0053,
+      "reliable": true
+    },
+    {
+      "signal": "mom_6_1",
+      "weeks": 39,
+      "avg_cross_section": 498.2,
+      "mean_ic": 0.0051,
+      "ic_t_stat": 0.21,
+      "ic_positive_pct": 56.4,
+      "mean_quintile_spread": 0.0087,
+      "reliable": true
+    },
+    {
+      "signal": "mom_3_1",
+      "weeks": 42,
+      "avg_cross_section": 498.5,
+      "mean_ic": -0.0064,
+      "ic_t_stat": -0.25,
+      "ic_positive_pct": 50.0,
+      "mean_quintile_spread": 0.0046,
+      "reliable": true
+    },
+    {
+      "signal": "high_52w",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": -0.0086,
+      "ic_t_stat": -0.26,
+      "ic_positive_pct": 54.3,
+      "mean_quintile_spread": -0.0088,
+      "reliable": true
+    },
+    {
+      "signal": "fip_id",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": -0.045,
+      "ic_t_stat": -2.91,
+      "ic_positive_pct": 25.7,
+      "mean_quintile_spread": -0.0168,
+      "reliable": true
+    }
+  ],
+  "signal_eval_note": "Cross-sectional rank-IC of price-only signals vs the forward 30-day return (min 20 names/window). |IC| \u2273 0.03 with a consistent sign is a real (if small) edge; near 0 means ranking on it sorts nothing. Momentum factors and high_52w are expected positive; reversal_1m and vol_6m expected negative (mean-reversion / low-vol anomaly). IC is measured on non-overlapping windows; signals with fewer than 12 independent windows are flagged unreliable (too few regimes \u2014 deepen history with the Data Backfill job).",
+  "note": "Sentiment & fundamentals held neutral (no point-in-time history). Stops fill at the worse of the stop or the bar's open (gaps through the stop are modeled, so a loss can exceed \u22121R); targets never fill better than their level. ~6 months \u2248 one market regime \u2014 treat as directional, not gospel.",
+  "recommendation": {
+    "headline": "Trade the qualified list long-only; hold 30 trading days with the initial ATR stop.",
+    "items": [
+      {
+        "topic": "exit",
+        "text": "Legacy exit diagnostic: hold 30 trading days with the initial stop (+0.58R net/trade vs +0.21R for the S/R target exit)."
+      },
+      {
+        "topic": "gate",
+        "text": "Gate: the confidence floor adds nothing \u2014 dropping it costs +0.01R/trade and adds 7 trades."
+      },
+      {
+        "topic": "gate",
+        "text": "Gate: keep the R:R floor (worth +0.28R/trade under the hold exit)."
+      },
+      {
+        "topic": "gate",
+        "text": "Gate: keep the NEUTRAL exclusion (worth +0.05R/trade under the hold exit)."
+      },
+      {
+        "topic": "cutoff",
+        "text": "Residual-momentum cutoff: 90 has the best per-trade net (+0.23R over 497 setups)."
+      },
+      {
+        "topic": "robustness",
+        "text": "Robustness: expectancy survives removing the top 5% of winners (+0.21R net/trade under the recommended 30d hold) \u2014 the edge is not a handful of outliers."
+      }
+    ],
+    "note": "Derived from this report's numbers on every run \u2014 the advice flips if the data does."
+  },
+  "research_recommendation": {
+    "items": [],
+    "note": "Strategy variants unavailable; re-run the backtest after benchmark data is present."
+  }
+}

reports/fip-reconcile-20260719-000520.json

@@ -0,0 +1,125 @@
+{
+  "generated_at": "2026-07-19T00:05:20.113638",
+  "research_snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\research.sqlite",
+  "top_n": 1500,
+  "min_price": 5.0,
+  "prod_subset_n": 506,
+  "panel_tickers": 4403,
+  "single_source": "diagnostics uses harness _signal_series + _filter_liquid_breadth_week_rich only (no parallel mask)",
+  "avg_cross_section_semantics": "avg_cross_section = post-mask IC sample size. avg_raw_pool = pre-filter observations. avg_eligible_pre_mask = pass price+dvol before top-N. mask_binds_pct = weeks where eligible_pre_mask > top_n.",
+  "harness_self_consistent": true,
+  "checks": {
+    "fip_harness_signal_eval": {
+      "note": "Authoritative harness _signal_evaluation on collected fip_id",
+      "signal": "fip_id",
+      "weeks": 35,
+      "avg_cross_section": 1471.2,
+      "mean_ic": -0.0168,
+      "ic_t_stat": -1.85,
+      "ic_positive_pct": 40.0,
+      "mean_quintile_spread": -0.0052,
+      "reliable": true,
+      "liquid_breadth_top_n": 1500,
+      "liquid_min_price": 5.0,
+      "avg_raw_pool": 3214.4,
+      "avg_eligible_pre_mask": 2338.4,
+      "mask_binds_pct": 97.1
+    },
+    "fip_same_week_via_shared_filter": {
+      "note": "Same collected data, IC via shared _filter_liquid_breadth_week_rich",
+      "mean_ic": -0.0168,
+      "ic_t_stat": -1.85,
+      "weeks": 35,
+      "avg_cross_section": 1471.2,
+      "ic_positive_pct": 40.0,
+      "reliable": true
+    },
+    "fip_lagged_membership_1w": {
+      "note": "Top-N by prior-week $vol on current fip pool (shared filter)",
+      "mean_ic": -0.0102,
+      "ic_t_stat": -0.93,
+      "weeks": 35,
+      "avg_cross_section": 1471.2,
+      "ic_positive_pct": 40.0,
+      "reliable": true
+    },
+    "fip_tier_1_800": {
+      "note": "Senior liquid ranks 1\u2013800",
+      "mean_ic": -0.035,
+      "ic_t_stat": -2.99,
+      "weeks": 35,
+      "avg_cross_section": 791.2,
+      "ic_positive_pct": 25.7,
+      "reliable": true
+    },
+    "fip_tier_801_1500": {
+      "note": "Junior liquid ranks 801\u2013top_n",
+      "mean_ic": 0.0141,
+      "ic_t_stat": 1.25,
+      "weeks": 35,
+      "avg_cross_section": 700.0,
+      "ic_positive_pct": 60.0,
+      "reliable": true
+    },
+    "fip_prod_universe_subset": {
+      "note": "Prod.sqlite symbols inside liquid fip set",
+      "mean_ic": -0.0444,
+      "ic_t_stat": -2.88,
+      "weeks": 35,
+      "avg_cross_section": 497.5,
+      "ic_positive_pct": 25.7,
+      "reliable": true
+    },
+    "fip_momentum_conditional_top20pct": {
+      "note": "Among liquid fip set, mom_12_1 \u2265 P80 (paper / gate-relevant)",
+      "mean_ic": -0.0879,
+      "ic_t_stat": -4.58,
+      "weeks": 35,
+      "avg_cross_section": 294.3,
+      "ic_positive_pct": 22.9,
+      "reliable": true
+    },
+    "vol_6m_liquid": {
+      "note": "vol_6m through shared filter",
+      "mean_ic": -0.0478,
+      "ic_t_stat": -1.36,
+      "weeks": 35,
+      "avg_cross_section": 1500.0,
+      "ic_positive_pct": 37.1,
+      "reliable": true
+    },
+    "mom_12_1_liquid": {
+      "note": "raw mom through shared filter",
+      "mean_ic": 0.0462,
+      "ic_t_stat": 1.91,
+      "weeks": 35,
+      "avg_cross_section": 1471.2,
+      "ic_positive_pct": 65.7,
+      "reliable": true
+    },
+    "mom_12_1_resid_liquid": {
+      "note": "residual mom through shared filter",
+      "mean_ic": 0.0289,
+      "ic_t_stat": 1.33,
+      "weeks": 35,
+      "avg_cross_section": 1471.2,
+      "ic_positive_pct": 60.0,
+      "reliable": true
+    }
+  },
+  "interpretation": {
+    "harness_and_shared_filter_agree": true,
+    "mask_binds_pct": 97.1,
+    "avg_eligible_pre_mask": 2338.4,
+    "avg_raw_pool": 3214.4,
+    "prod_subset_still_negative": true,
+    "junior_tier_more_positive": true,
+    "lag_same_sign_as_same_week": true,
+    "mom_conditional_negative_and_reliable": true,
+    "orphan_plus_five_sigma": "Prior report fip-breadth-20260718-211440-breadth.json listed fip IC +0.0575 / t +5.12. This single-sourced recompute is the authoritative number; if it disagrees, the +0.0575 row is orphaned.",
+    "compositional_story": "fip_id pools continuous winners (neg IC) vs continuous bleeders (pos IC). Prod-subset and senior liquid stay negative; junior liquid is less negative / positive \u2014 composition, not jumpiness premium.",
+    "vol_tilt_warning": "High-vol names underperform on breadth relative to S&P-like books. Re-validate production 80/20 high-vol tilt before any universe broaden."
+  },
+  "platform_verdict": "Mom-conditional fip ALIVE as book-tilt candidate (needs book sim) \u2014 not production wire-in. Unconditional fip not green.",
+  "membership_dumps_note": "Removed 5 week membership symbol lists from the committed artifact (compact decision evidence). Full dumps recoverable from git history of this file pre-cleanup."
+}

reports/history-depth-20260719-103315.json

@@ -0,0 +1,494 @@
+{
+  "generated_at": "2026-07-19T10:33:15.322673",
+  "survivorship_banner": "SURVIVORSHIP BIAS: today's constituents backfilled historically. Absolute Sharpe/CAGR levels on deep history are optimistic. Use RELATIVE signal IC comparisons and era stability only \u2014 not levels.",
+  "coverage": null,
+  "race_guard": {
+    "manifest": {
+      "schema_version": 1,
+      "snapshot": "research.sqlite",
+      "snapshot_resolved": "/Users/taathde3/git/lab/signal_platform/backtest_snapshots/research.sqlite",
+      "complete": true,
+      "finished_at": "2026-07-19T08:19:02.992206+00:00",
+      "ticker_count": 4655,
+      "ohlcv_row_count": 6609926,
+      "rank_only_count": 4149,
+      "sources": {
+        "nasdaq_all": "nasdaq_trader",
+        "sp500": "wikipedia_sp500"
+      },
+      "history_days": 5000,
+      "min_bars": 260,
+      "fetch_ok": 4152,
+      "fetch_fail": 0,
+      "limit": null,
+      "extra": {
+        "prod_symbols_at_start": 506,
+        "pool_size": 4648,
+        "to_fetch": 4152
+      },
+      "live_counts": {
+        "ticker_count": 4655,
+        "ohlcv_row_count": 6609926,
+        "rank_only_count": 4149
+      }
+    },
+    "ok": true
+  },
+  "harness": {
+    "survivorship_banner": "SURVIVORSHIP BIAS: today's constituents backfilled historically. Absolute Sharpe/CAGR levels on deep history are optimistic. Use RELATIVE signal IC comparisons and era stability only \u2014 not levels.",
+    "signal_eval": [
+      {
+        "signal": "high_52w",
+        "weeks": 84,
+        "avg_cross_section": 2280.3,
+        "mean_ic": 0.1111,
+        "ic_t_stat": 6.41,
+        "ic_positive_pct": 76.2,
+        "mean_quintile_spread": -13.5104,
+        "reliable": true
+      },
+      {
+        "signal": "mom_12_1",
+        "weeks": 83,
+        "avg_cross_section": 2277.5,
+        "mean_ic": 0.0663,
+        "ic_t_stat": 5.11,
+        "ic_positive_pct": 74.7,
+        "mean_quintile_spread": -10.1818,
+        "reliable": true
+      },
+      {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 35,
+        "avg_cross_section": 497.7,
+        "mean_ic": 0.0578,
+        "ic_t_stat": 2.34,
+        "ic_positive_pct": 65.7,
+        "mean_quintile_spread": 0.0245,
+        "reliable": true
+      },
+      {
+        "signal": "trend_200",
+        "weeks": 85,
+        "avg_cross_section": 2308.1,
+        "mean_ic": 0.0546,
+        "ic_t_stat": 4.3,
+        "ic_positive_pct": 70.6,
+        "mean_quintile_spread": -12.4094,
+        "reliable": true
+      },
+      {
+        "signal": "mom_6_1",
+        "weeks": 88,
+        "avg_cross_section": 2375.7,
+        "mean_ic": 0.0493,
+        "ic_t_stat": 4.91,
+        "ic_positive_pct": 70.5,
+        "mean_quintile_spread": 3.4224,
+        "reliable": true
+      },
+      {
+        "signal": "mom_3_1",
+        "weeks": 90,
+        "avg_cross_section": 2425.6,
+        "mean_ic": 0.0363,
+        "ic_t_stat": 3.58,
+        "ic_positive_pct": 72.2,
+        "mean_quintile_spread": 0.9289,
+        "reliable": true
+      },
+      {
+        "signal": "mom_12_1_sector_demeaned",
+        "weeks": 35,
+        "avg_cross_section": 496.7,
+        "mean_ic": 0.034,
+        "ic_t_stat": 1.32,
+        "ic_positive_pct": 62.9,
+        "mean_quintile_spread": 0.0154,
+        "reliable": true
+      },
+      {
+        "signal": "fip_id",
+        "weeks": 83,
+        "avg_cross_section": 2277.5,
+        "mean_ic": 0.0267,
+        "ic_t_stat": 3.25,
+        "ic_positive_pct": 67.5,
+        "mean_quintile_spread": -0.0017,
+        "reliable": true
+      },
+      {
+        "signal": "mom_12_1_resid",
+        "weeks": 83,
+        "avg_cross_section": 2277.5,
+        "mean_ic": 0.0256,
+        "ic_t_stat": 2.21,
+        "ic_positive_pct": 65.1,
+        "mean_quintile_spread": 10.1373,
+        "reliable": true
+      },
+      {
+        "signal": "reversal_1m",
+        "weeks": 91,
+        "avg_cross_section": 2443.2,
+        "mean_ic": 0.003,
+        "ic_t_stat": 0.31,
+        "ic_positive_pct": 48.4,
+        "mean_quintile_spread": -7.0153,
+        "reliable": true
+      },
+      {
+        "signal": "vol_6m",
+        "weeks": 88,
+        "avg_cross_section": 2375.7,
+        "mean_ic": -0.1226,
+        "ic_t_stat": -6.34,
+        "ic_positive_pct": 21.6,
+        "mean_quintile_spread": 1.4646,
+        "reliable": true
+      }
+    ],
+    "era_split": {
+      "era_split_date": "2021-01-01",
+      "note": "Diagnostic only \u2014 not a tuning input. Nested lookbacks are not OOS.",
+      "full": {
+        "high_52w": {
+          "signal": "high_52w",
+          "weeks": 84,
+          "avg_cross_section": 2280.3,
+          "mean_ic": 0.1111,
+          "ic_t_stat": 6.41,
+          "ic_positive_pct": 76.2,
+          "mean_quintile_spread": -13.5104,
+          "reliable": true
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+        "mom_12_1": {
+          "signal": "mom_12_1",
+          "weeks": 83,
+          "avg_cross_section": 2277.5,
+          "mean_ic": 0.0663,
+          "ic_t_stat": 5.11,
+          "ic_positive_pct": 74.7,
+          "mean_quintile_spread": -10.1818,
+          "reliable": true
+        },
+        "mom_12_1_sector_resid": {
+          "signal": "mom_12_1_sector_resid",
+          "weeks": 35,
+          "avg_cross_section": 497.7,
+          "mean_ic": 0.0578,
+          "ic_t_stat": 2.34,
+          "ic_positive_pct": 65.7,
+          "mean_quintile_spread": 0.0245,
+          "reliable": true
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+        "trend_200": {
+          "signal": "trend_200",
+          "weeks": 85,
+          "avg_cross_section": 2308.1,
+          "mean_ic": 0.0546,
+          "ic_t_stat": 4.3,
+          "ic_positive_pct": 70.6,
+          "mean_quintile_spread": -12.4094,
+          "reliable": true
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+        "mom_6_1": {
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+          "weeks": 88,
+          "avg_cross_section": 2375.7,
+          "mean_ic": 0.0493,
+          "ic_t_stat": 4.91,
+          "ic_positive_pct": 70.5,
+          "mean_quintile_spread": 3.4224,
+          "reliable": true
+        },
+        "mom_3_1": {
+          "signal": "mom_3_1",
+          "weeks": 90,
+          "avg_cross_section": 2425.6,
+          "mean_ic": 0.0363,
+          "ic_t_stat": 3.58,
+          "ic_positive_pct": 72.2,
+          "mean_quintile_spread": 0.9289,
+          "reliable": true
+        },
+        "mom_12_1_sector_demeaned": {
+          "signal": "mom_12_1_sector_demeaned",
+          "weeks": 35,
+          "avg_cross_section": 496.7,
+          "mean_ic": 0.034,
+          "ic_t_stat": 1.32,
+          "ic_positive_pct": 62.9,
+          "mean_quintile_spread": 0.0154,
+          "reliable": true
+        },
+        "fip_id": {
+          "signal": "fip_id",
+          "weeks": 83,
+          "avg_cross_section": 2277.5,
+          "mean_ic": 0.0267,
+          "ic_t_stat": 3.25,
+          "ic_positive_pct": 67.5,
+          "mean_quintile_spread": -0.0017,
+          "reliable": true
+        },
+        "mom_12_1_resid": {
+          "signal": "mom_12_1_resid",
+          "weeks": 83,
+          "avg_cross_section": 2277.5,
+          "mean_ic": 0.0256,
+          "ic_t_stat": 2.21,
+          "ic_positive_pct": 65.1,
+          "mean_quintile_spread": 10.1373,
+          "reliable": true
+        },
+        "reversal_1m": {
+          "signal": "reversal_1m",
+          "weeks": 91,
+          "avg_cross_section": 2443.2,
+          "mean_ic": 0.003,
+          "ic_t_stat": 0.31,
+          "ic_positive_pct": 48.4,
+          "mean_quintile_spread": -7.0153,
+          "reliable": true
+        },
+        "vol_6m": {
+          "signal": "vol_6m",
+          "weeks": 88,
+          "avg_cross_section": 2375.7,
+          "mean_ic": -0.1226,
+          "ic_t_stat": -6.34,
+          "ic_positive_pct": 21.6,
+          "mean_quintile_spread": 1.4646,
+          "reliable": true
+        }
+      },
+      "pre_2021": {
+        "high_52w": {
+          "signal": "high_52w",
+          "weeks": 36,
+          "avg_cross_section": 1422.2,
+          "mean_ic": 0.0494,
+          "ic_t_stat": 2.0,
+          "ic_positive_pct": 63.9,
+          "mean_quintile_spread": -31.5146,
+          "reliable": true
+        },
+        "mom_12_1": {
+          "signal": "mom_12_1",
+          "weeks": 36,
+          "avg_cross_section": 1424.8,
+          "mean_ic": 0.0413,
+          "ic_t_stat": 2.94,
+          "ic_positive_pct": 66.7,
+          "mean_quintile_spread": -23.3935,
+          "reliable": true
+        },
+        "trend_200": {
+          "signal": "trend_200",
+          "weeks": 38,
+          "avg_cross_section": 1440.9,
+          "mean_ic": 0.0322,
+          "ic_t_stat": 2.23,
+          "ic_positive_pct": 68.4,
+          "mean_quintile_spread": -27.5982,
+          "reliable": true
+        },
+        "mom_12_1_resid": {
+          "signal": "mom_12_1_resid",
+          "weeks": 36,
+          "avg_cross_section": 1424.8,
+          "mean_ic": 0.0226,
+          "ic_t_stat": 1.68,
+          "ic_positive_pct": 69.4,
+          "mean_quintile_spread": 23.4097,
+          "reliable": true
+        },
+        "mom_3_1": {
+          "signal": "mom_3_1",
+          "weeks": 42,
+          "avg_cross_section": 1480.0,
+          "mean_ic": 0.0217,
+          "ic_t_stat": 1.84,
+          "ic_positive_pct": 73.8,
+          "mean_quintile_spread": 2.0077,
+          "reliable": true
+        },
+        "mom_6_1": {
+          "signal": "mom_6_1",
+          "weeks": 40,
+          "avg_cross_section": 1461.2,
+          "mean_ic": 0.0205,
+          "ic_t_stat": 1.63,
+          "ic_positive_pct": 65.0,
+          "mean_quintile_spread": 7.4512,
+          "reliable": true
+        },
+        "fip_id": {
+          "signal": "fip_id",
+          "weeks": 36,
+          "avg_cross_section": 1424.8,
+          "mean_ic": 0.0116,
+          "ic_t_stat": 1.35,
+          "ic_positive_pct": 58.3,
+          "mean_quintile_spread": -0.0118,
+          "reliable": true
+        },
+        "reversal_1m": {
+          "signal": "reversal_1m",
+          "weeks": 44,
+          "avg_cross_section": 1501.4,
+          "mean_ic": 0.0019,
+          "ic_t_stat": 0.15,
+          "ic_positive_pct": 50.0,
+          "mean_quintile_spread": -14.4299,
+          "reliable": true
+        },
+        "vol_6m": {
+          "signal": "vol_6m",
+          "weeks": 40,
+          "avg_cross_section": 1461.2,
+          "mean_ic": -0.056,
+          "ic_t_stat": -2.16,
+          "ic_positive_pct": 35.0,
+          "mean_quintile_spread": 3.1335,
+          "reliable": true
+        }
+      },
+      "post_2021": {
+        "high_52w": {
+          "signal": "high_52w",
+          "weeks": 48,
+          "avg_cross_section": 2914.5,
+          "mean_ic": 0.1375,
+          "ic_t_stat": 4.34,
+          "ic_positive_pct": 79.2,
+          "mean_quintile_spread": -0.0856,
+          "reliable": true
+        },
+        "mom_12_1": {
+          "signal": "mom_12_1",
+          "weeks": 48,
+          "avg_cross_section": 2913.0,
+          "mean_ic": 0.0791,
+          "ic_t_stat": 3.64,
+          "ic_positive_pct": 77.1,
+          "mean_quintile_spread": -0.0858,
+          "reliable": true
+        },
+        "mom_6_1": {
+          "signal": "mom_6_1",
+          "weeks": 48,
+          "avg_cross_section": 3122.6,
+          "mean_ic": 0.0779,
+          "ic_t_stat": 4.36,
+          "ic_positive_pct": 75.0,
+          "mean_quintile_spread": -0.0781,
+          "reliable": true
+        },
+        "trend_200": {
+          "signal": "trend_200",
+          "weeks": 48,
+          "avg_cross_section": 3001.5,
+          "mean_ic": 0.0585,
+          "ic_t_stat": 2.68,
+          "ic_positive_pct": 72.9,
+          "mean_quintile_spread": -0.1129,
+          "reliable": true
+        },
+        "mom_12_1_sector_resid": {
+          "signal": "mom_12_1_sector_resid",
+          "weeks": 35,
+          "avg_cross_section": 497.7,
+          "mean_ic": 0.0578,
+          "ic_t_stat": 2.34,
+          "ic_positive_pct": 65.7,
+          "mean_quintile_spread": 0.0245,
+          "reliable": true
+        },
+        "fip_id": {
+          "signal": "fip_id",
+          "weeks": 48,
+          "avg_cross_section": 2913.0,
+          "mean_ic": 0.0366,
+          "ic_t_stat": 2.91,
+          "ic_positive_pct": 70.8,
+          "mean_quintile_spread": -0.0151,
+          "reliable": true
+        },
+        "mom_12_1_sector_demeaned": {
+          "signal": "mom_12_1_sector_demeaned",
+          "weeks": 35,
+          "avg_cross_section": 496.7,
+          "mean_ic": 0.034,
+          "ic_t_stat": 1.32,
+          "ic_positive_pct": 62.9,
+          "mean_quintile_spread": 0.0154,
+          "reliable": true
+        },
+        "mom_3_1": {
+          "signal": "mom_3_1",
+          "weeks": 48,
+          "avg_cross_section": 3234.2,
+          "mean_ic": 0.0291,
+          "ic_t_stat": 1.54,
+          "ic_positive_pct": 66.7,
+          "mean_quintile_spread": -0.0356,
+          "reliable": true
+        },
+        "mom_12_1_resid": {
+          "signal": "mom_12_1_resid",
+          "weeks": 48,
+          "avg_cross_section": 2913.0,
+          "mean_ic": 0.0265,
+          "ic_t_stat": 1.37,
+          "ic_positive_pct": 66.7,
+          "mean_quintile_spread": -0.0405,
+          "reliable": true
+        },
+        "reversal_1m": {
+          "signal": "reversal_1m",
+          "weeks": 48,
+          "avg_cross_section": 3315.8,
+          "mean_ic": -0.0126,
+          "ic_t_stat": -0.72,
+          "ic_positive_pct": 45.8,
+          "mean_quintile_spread": -0.0387,
+          "reliable": true
+        },
+        "vol_6m": {
+          "signal": "vol_6m",
+          "weeks": 48,
+          "avg_cross_section": 3122.6,
+          "mean_ic": -0.1623,
+          "ic_t_stat": -4.94,
+          "ic_positive_pct": 22.9,
+          "mean_quintile_spread": 0.0059,
+          "reliable": true
+        }
+      }
+    },
+    "params": {
+      "step_days": 5,
+      "step_sessions": 5,
+      "entry_cadence": "weekly",
+      "signal_eval_cadence": "weekly",
+      "horizon_days": 30,
+      "min_lookback": 60,
+      "cost_per_side_pct": 0.1,
+      "target_model": "production_gtl",
+      "target_model_label": "Live GTL (production)",
+      "is_production_target_model": true,
+      "production_reentry_policy": "gate_reset",
+      "liquid_breadth_top_n": null,
+      "liquid_min_price": null,
+      "signal_eval_only": true
+    },
+    "tickers": 4655,
+    "generated_at_run": "2026-07-19T08:27:27.206022+00:00"
+  },
+  "verdict": "PENDING_HUMAN",
+  "verdict_detail": "Harness complete \u2014 human interprets relative IC / era stability. No production retune from this artifact.",
+  "human_next": "- Compare sector residual vs market residual across eras.\n- If pre-2021 IC collapses, park Task 1 wire-in.\n- Do not retune production knobs on deep history levels.",
+  "report_path": "reports/history-depth-20260719-103315.json"
+}

reports/history-depth-20260719-103315.md

@@ -0,0 +1,182 @@
+# History-depth extension (Tier-1 alpha research)
+
+**Status:** **RUN COMPLETE — human interpretation below.**  
+**Branch:** `research/earnings-gap-and-sue` (MacBook commit `f6e0ca7`)  
+**Authoritative artifact:** `reports/history-depth-20260719-103315.json`  
+**Production impact:** none. **Do not retune any production knob on deep history.**
+
+---
+
+## Pre-registration (locked before rebuild)
+
+### Motivation
+
+All current conclusions rest on ~35 non-overlapping weekly windows in essentially
+one post-2021 regime. Extending history toward max Alpaca daily-bar depth adds
+the 2018 vol shock and full 2020 crash (where the feed allows).
+
+### Protocol
+
+1. **Empirical coverage first** — bars per calendar year per symbol; document
+   where the feed thins out. Do **not** assume a uniform start date.
+2. **Rebuild the research snapshot completely** from prod source + max history
+   per symbol (`Adjustment.SPLIT`, ~200 req/min pacing via existing extender).
+3. **Race guard (rule 6)** — refuse analysis until completion manifest is
+   `complete=true` and live counts match.
+4. **Re-run full signal harness** (all existing signals incl. sector residual /
+   SUE if present) on the extended window.
+5. **Report per signal:** mean IC, t, window count, and **era split**
+   (pre-/post-2021) — diagnostic only, **not a tuning input**.
+6. **Log prominently:** survivorship bias grows with depth (today’s constituents
+   backfilled). Absolute Sharpe/CAGR on deep history is optimistic; payload is
+   **relative** signal comparisons and IC stability, not levels.
+7. **Do not retune** production knobs. If a knob’s confirmation looks
+   overturned on deep history → report only; human decides.
+
+### Success / interpretation (not promotion of a new signal)
+
+| outcome | meaning |
+|---|---|
+| Sector residual still ≥ market residual on deep IC + stable sign | strengthens Task 1 PROMOTE case |
+| Sector residual collapses pre-2021 | **PARK** Task 1 wire-in |
+| SUE remains weak after full earnings + depth | **DEAD** SUE for this stack |
+| Any production knob looks worse deep | report; no auto-retune |
+
+---
+
+## Data provenance
+
+| check | result |
+|---|---|
+| Snapshot | MacBook `research.sqlite` |
+| Manifest `complete` | **true** (finished 2026-07-19T08:19Z) |
+| Live counts match | yes — 4655 tickers / 6,609,926 OHLCV / 4149 rank_only |
+| `history_days` | 5000 |
+| fetch_ok / fail | 4152 / 0 |
+| Race guard | **pass** |
+
+> **SURVIVORSHIP BIAS:** today’s constituents backfilled historically. Absolute
+> Sharpe/CAGR levels on deep history are optimistic. Use **relative** signal IC
+> comparisons and era stability only — not levels.
+
+**Coverage JSON was empty in the auto-written doc** (harness-only phase after
+rebuild). Manifest is the race-guard source of truth for this run.
+
+Earlier MacBook files `history-depth-20260719-093853` … `095156` are intermediate
+/ incomplete passes — **do not cite**. Only **103315** is authoritative.
+
+---
+
+## Results (authoritative: 103315)
+
+### Full-window signal IC (broad research universe, deep bars)
+
+| signal | mean_ic | t | weeks | avg_N | notes |
+|---|---:|---:|---:|---:|---|
+| high_52w | **0.111** | **6.41** | 84 | 2280 | strong on deep breadth |
+| mom_12_1 | **0.066** | **5.11** | 83 | 2278 | raw momentum strong |
+| trend_200 | 0.055 | 4.30 | 85 | 2308 | |
+| mom_6_1 | 0.049 | 4.91 | 88 | 2376 | |
+| mom_3_1 | 0.036 | 3.58 | 90 | 2426 | |
+| fip_id | **+0.027** | **3.25** | 83 | 2278 | **sign flip vs prod fingerprint** |
+| mom_12_1_resid | 0.026 | 2.21 | 83 | 2278 | market residual still + but weaker than raw |
+| reversal_1m | ~0 | 0.31 | 91 | 2443 | dead |
+| vol_6m | **−0.123** | **−6.34** | 88 | 2376 | low-vol anomaly strong |
+| mom_12_1_sector_resid | 0.058 | 2.34 | **35** | **498** | **not deep-sample — see caveats** |
+| mom_12_1_sector_demeaned | 0.034 | 1.32 | **35** | **497** | same short fingerprint |
+
+### Era split (diagnostic only — not a tuning input)
+
+| signal | pre-2021 IC / t / w / N | post-2021 IC / t / w / N |
+|---|---|---|
+| mom_12_1 | +0.041 / 2.94 / 36 / 1425 | +0.079 / 3.64 / 48 / 2913 |
+| mom_12_1_resid | +0.023 / 1.68 / 36 / 1425 | +0.027 / 1.37 / 48 / 2913 |
+| fip_id | +0.012 / 1.35 / 36 / 1425 | +0.037 / 2.91 / 48 / 2913 |
+| vol_6m | −0.056 / −2.16 / 40 / 1461 | −0.162 / −4.94 / 48 / 3123 |
+| high_52w | +0.049 / 2.0 / 36 / 1422 | +0.138 / 4.34 / 48 / 2915 |
+| **sector_resid** | **absent** | 0.058 / 2.34 / 35 / 498 (short only) |
+| **sector_demeaned** | **absent** | 0.034 / 1.32 / 35 / 497 (short only) |
+
+---
+
+## Critical caveats (must read)
+
+### 1. Sector residual did **not** get a deep-history stress test
+
+`mom_12_1_sector_resid` / `_demeaned` still show **exactly** the Task‑1 short-window
+fingerprint: **35 weeks, N≈498, IC 0.0578, t 2.34**.
+
+On the same run, raw `mom_12_1` has **83 weeks, N≈2278**. So depth worked for
+price-only signals, but sector residual is still limited to the **~505 labeled
+prod names × short factor calendar** (sector map only covers prod; and/or sector
+ETF / two-factor path did not extend usable residual weeks).
+
+**Pre-registered rule:** “Sector residual collapses pre-2021 → PARK Task 1
+wire-in.” Pre-2021 sector residual is **absent** from the era table. That is a
+**PARK**, not a confirmation of the short-window PROMOTE.
+
+Do **not** claim “sector residual beats market residual on deep history” from
+this table — the two rows are **not the same cross-section or window count**.
+
+### 2. `fip_id` sign flips vs production fingerprint
+
+| sample | fip mean IC | t |
+|---|---:|---:|
+| Prod 505, ~5y (fingerprint) | **−0.045** | −2.91 |
+| Research breadth, deep (this run) | **+0.027** | +3.25 |
+
+This does **not** authorize resurrecting unconditional FIP as a book filter. It
+confirms earlier Phase‑B caution: FIP edge is **universe- and sample-dependent**.
+Production display card can stay context-only. Nested lookbacks still not OOS.
+
+### 3. Market residual vs raw momentum on deep breadth
+
+On deep broad IC, **raw 12‑1 (0.066 / t 5.1) ≫ market residual (0.026 / t 2.2)**.
+That does **not** by itself overturn production residual ranking (book A/B was
+on 505 + GTL gate, not pure factor IC), but it is a yellow flag for “residual is
+always the better rank key” stories on broad history. **No auto-retune.**
+
+### 4. Low-vol anomaly is the cleanest deep-history result
+
+`vol_6m` IC −0.12 / t −6.3 full; stronger post-2021. Consistent sign across eras.
+Production already blends **high**-vol (not low-vol) into the 80/20 rank — this
+report does not change that without a separate A/B. Flag for human awareness only.
+
+---
+
+## Verdicts (vs pre-registration)
+
+| question | verdict |
+|---|---|
+| Task 1 sector residual wire-in | **PARK** — no pre-2021 sector residual; deep-sample IC not established; short-window PROMOTE stays “human design only,” **not strengthened** by this run |
+| Sector demean | still **DEAD** for promotion (t 1.32, short only) |
+| SUE | **not re-scored here** (no `sue_latest` in harness table) — leave Task 2 **PARK** until full earnings backfill |
+| fip unconditional book filter | remains **rejected / parked** despite sign flip on broad deep sample |
+| Production residual / 80/20 / trail knobs | **no retune** from this report |
+| Overall Task 3 | **COMPLETE as diagnostic** — payload is relative IC + caveats above |
+
+---
+
+## What a human must decide next
+
+1. **Sector residual:** keep research-only until either  
+   (a) sector ETF + sector map cover the full deep window **and** IC is re-run
+   with weeks ≫ 35 on a documented universe, or  
+   (b) explicitly accept short-window-only evidence (weaker case).
+2. **Do not** merge sector residual into production from this depth run.
+3. **Do not** retune residual vs raw, FIP, or vol blend from these IC tables
+   without a pre-registered book A/B on the intended universe.
+4. Optional follow-up: extend sector ETF history + sector labels to nasdaq_all,
+   re-run **only** sector residual IC on deep research.sqlite with race guard.
+5. Optional: finish earnings backfill (48→506) and re-run SUE; depth alone did
+   not include SUE.
+
+---
+
+## Artifacts
+
+| file | role |
+|---|---|
+| `reports/history-depth-20260719-103315.json` | **authoritative** |
+| `reports/history-depth-20260719-103315.md` | companion dump |
+| `reports/history-depth-20260719-093853` … `095156` | **ignore** (partial) |

reports/prod-book-universe-horizon-20260719-140737.json

@@ -0,0 +1,137 @@
+{
+  "generated_at": "2026-07-19T14:07:37.458454",
+  "snapshot": "/Users/taathde3/git/lab/signal_platform/backtest_snapshots/research.sqlite",
+  "snapshot_meta": {
+    "prod_universe_n": 506,
+    "price_symbols_n": 4654,
+    "raw_candidates": 2389258,
+    "liquid_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "short_start": "2022-07-01",
+    "long_start": "2016-07-01"
+  },
+  "strategy": {
+    "note": "Live production knobs \u2014 no modifications",
+    "momentum": "residual_12_1 gate 80",
+    "rank": "residual_high_vol_blend_80_20",
+    "fill_mode": "close",
+    "cost_per_side": 0.001,
+    "exit": {
+      "mode": "atr_trailing",
+      "trailing_pct": 12.0,
+      "atr_multiplier": 3.0,
+      "hold_days": 30
+    },
+    "max_positions": 10,
+    "risk_per_trade": 0.01,
+    "reentry": "gate_reset"
+  },
+  "arms": [
+    {
+      "id": "A_prod_4y_505",
+      "label": "Prod book \u00b7 ~4y \u00b7 505 only",
+      "start": "2022-07-01",
+      "universe": "prod_505",
+      "n_candidates": 81626,
+      "n_qualified_longs": 1448,
+      "fill_mode": "close",
+      "ranking_key": "residual_high_vol_blend_80_20_score",
+      "exit_policy": "atr_trail3",
+      "hold_days": 30,
+      "sharpe": 1.32,
+      "sharpe_se": 0.49,
+      "cagr_pct": 31.8,
+      "max_drawdown_pct": 18.9,
+      "total_return_pct": 204.7,
+      "calmar": 1.68,
+      "trades": 374,
+      "win_rate": 35.6,
+      "n_returns": 1009,
+      "psr": 0.9965,
+      "start_date": "2022-07-01",
+      "end_date": "2026-07-13",
+      "spy_return_pct": 96.5,
+      "final_equity": 30467.86
+    },
+    {
+      "id": "B_prod_4y_505_liquid",
+      "label": "Prod book \u00b7 ~4y \u00b7 505 + liquid top-1500",
+      "start": "2022-07-01",
+      "universe": "prod_plus_liquid",
+      "n_candidates": 267579,
+      "n_qualified_longs": 6587,
+      "fill_mode": "close",
+      "ranking_key": "residual_high_vol_blend_80_20_score",
+      "exit_policy": "atr_trail3",
+      "hold_days": 30,
+      "sharpe": 0.14,
+      "sharpe_se": 0.499,
+      "cagr_pct": -1.8,
+      "max_drawdown_pct": 55.3,
+      "total_return_pct": -7.1,
+      "calmar": -0.03,
+      "trades": 706,
+      "win_rate": 29.3,
+      "n_returns": 1013,
+      "psr": 0.6107,
+      "start_date": "2022-07-01",
+      "end_date": "2026-07-17",
+      "spy_return_pct": 95.0,
+      "final_equity": 9287.52
+    },
+    {
+      "id": "C_prod_2016_505",
+      "label": "Prod book \u00b7 since 2016-07 \u00b7 505 only",
+      "start": "2016-07-01",
+      "universe": "prod_505",
+      "n_candidates": 190179,
+      "n_qualified_longs": 2450,
+      "fill_mode": "close",
+      "ranking_key": "residual_high_vol_blend_80_20_score",
+      "exit_policy": "atr_trail3",
+      "hold_days": 30,
+      "sharpe": 0.88,
+      "sharpe_se": 0.314,
+      "cagr_pct": 16.7,
+      "max_drawdown_pct": 24.4,
+      "total_return_pct": 369.4,
+      "calmar": 0.68,
+      "trades": 763,
+      "win_rate": 36.7,
+      "n_returns": 2519,
+      "psr": 0.9975,
+      "start_date": "2016-07-01",
+      "end_date": "2026-07-13",
+      "spy_return_pct": 256.9,
+      "final_equity": 46938.66
+    },
+    {
+      "id": "D_prod_2016_505_liquid",
+      "label": "Prod book \u00b7 since 2016-07 \u00b7 505 + liquid top-1500",
+      "start": "2016-07-01",
+      "universe": "prod_plus_liquid",
+      "n_candidates": 649305,
+      "n_qualified_longs": 11551,
+      "fill_mode": "close",
+      "ranking_key": "residual_high_vol_blend_80_20_score",
+      "exit_policy": "atr_trail3",
+      "hold_days": 30,
+      "sharpe": -0.06,
+      "sharpe_se": 0.316,
+      "cagr_pct": -7.0,
+      "max_drawdown_pct": 73.9,
+      "total_return_pct": -52.0,
+      "calmar": -0.1,
+      "trades": 1567,
+      "win_rate": 28.0,
+      "n_returns": 2523,
+      "psr": 0.4287,
+      "start_date": "2016-07-01",
+      "end_date": "2026-07-17",
+      "spy_return_pct": 254.1,
+      "final_equity": 4800.5
+    }
+  ],
+  "survivorship_banner": "Today's constituents backfilled. Relative arm comparison only.",
+  "pending_human": true
+}

reports/prod-book-universe-horizon-20260719-140737.md

@@ -0,0 +1,54 @@
+# Production book × universe × horizon — results
+
+Generated: `2026-07-19T14:07:37.458454`
+
+> Survivorship: today's constituents backfilled. Compare arms relatively; do not treat deep CAGR/Sharpe levels as deployable forecasts.
+
+## Arms
+
+| arm | universe | entry start | Sharpe | SE | CAGR % | max DD % | ret % | trades | qual longs | span |
+|---|---|---|---:|---:|---:|---:|---:|---:|---:|---|
+| A_prod_4y_505 | prod_505 | 2022-07-01 | 1.32 | 0.49 | 31.8 | 18.9 | 204.7 | 374 | 1448 | 2022-07-01→2026-07-13 |
+| B_prod_4y_505_liquid | prod_plus_liquid | 2022-07-01 | 0.14 | 0.499 | -1.8 | 55.3 | -7.1 | 706 | 6587 | 2022-07-01→2026-07-17 |
+| C_prod_2016_505 | prod_505 | 2016-07-01 | 0.88 | 0.314 | 16.7 | 24.4 | 369.4 | 763 | 2450 | 2016-07-01→2026-07-13 |
+| D_prod_2016_505_liquid | prod_plus_liquid | 2016-07-01 | -0.06 | 0.316 | -7.0 | 73.9 | -52.0 | 1567 | 11551 | 2016-07-01→2026-07-17 |
+
+## Config (production, unchanged)
+
+```json
+{
+  "note": "Live production knobs \u2014 no modifications",
+  "momentum": "residual_12_1 gate 80",
+  "rank": "residual_high_vol_blend_80_20",
+  "fill_mode": "close",
+  "cost_per_side": 0.001,
+  "exit": {
+    "mode": "atr_trailing",
+    "trailing_pct": 12.0,
+    "atr_multiplier": 3.0,
+    "hold_days": 30
+  },
+  "max_positions": 10,
+  "risk_per_trade": 0.01,
+  "reentry": "gate_reset"
+}
+```
+
+## Snapshot
+
+```json
+{
+  "prod_universe_n": 506,
+  "price_symbols_n": 4654,
+  "raw_candidates": 2389258,
+  "liquid_top_n": 1500,
+  "liquid_min_price": 5.0,
+  "short_start": "2022-07-01",
+  "long_start": "2016-07-01"
+}
+```
+
+PENDING_HUMAN — descriptive matrix only; no auto promotion.
+
+JSON: `reports/prod-book-universe-horizon-20260719-140737.json`
+

reports/sector-resid-deep-20260719-113319.json

@@ -0,0 +1,990 @@
+{
+  "generated_at": "2026-07-19T11:33:19.102779",
+  "snapshot": "/Users/taathde3/git/lab/signal_platform/backtest_snapshots/research.sqlite",
+  "pre_registration": {
+    "iron_ic": 0.03,
+    "min_weeks_deep": 50,
+    "liquid_breadth": 1500,
+    "min_price": 5.0,
+    "rule": "PASS = |IC|>=0.03, +sign, reliable, weeks>=50, t>=resid on same CS, era signs both +"
+  },
+  "step1": {
+    "skipped": true,
+    "sanity": {
+      "passed": true,
+      "megacap": {
+        "AAPL": {
+          "symbol": "AAPL",
+          "bars": 2649,
+          "min_date": "2016-01-04",
+          "max_date": "2026-07-17"
+        },
+        "MSFT": {
+          "symbol": "MSFT",
+          "bars": 2649,
+          "min_date": "2016-01-04",
+          "max_date": "2026-07-17"
+        },
+        "JPM": {
+          "symbol": "JPM",
+          "bars": 2649,
+          "min_date": "2016-01-04",
+          "max_date": "2026-07-17"
+        },
+        "XOM": {
+          "symbol": "XOM",
+          "bars": 2649,
+          "min_date": "2016-01-04",
+          "max_date": "2026-07-17"
+        },
+        "JNJ": {
+          "symbol": "JNJ",
+          "bars": 2649,
+          "min_date": "2016-01-04",
+          "max_date": "2026-07-17"
+        }
+      },
+      "megacap_ok": true,
+      "megacap_reasons": [],
+      "feed_floor": "2016-01-04",
+      "spy_benchmark": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "old_shallow_floor": "2020-01-01",
+      "sector_etfs": {
+        "XLB": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLC": {
+          "n": 2030,
+          "min": "2018-06-19",
+          "max": "2026-07-17"
+        },
+        "XLE": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLF": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLI": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLK": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLP": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLRE": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLU": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLV": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        },
+        "XLY": {
+          "n": 2649,
+          "min": "2016-01-04",
+          "max": "2026-07-17"
+        }
+      },
+      "sector_etfs_deep_count": 11,
+      "sector_etfs_ok": true,
+      "sector_etf_reasons": [],
+      "still_shallow_count": 2882,
+      "still_shallow_sample": [
+        "AACB",
+        "AACBR",
+        "AACBU",
+        "AACI",
+        "AACIU",
+        "AACIW",
+        "AACO",
+        "AACOU",
+        "AACOW",
+        "AACP",
+        "AACPR",
+        "AACPU",
+        "AACPW",
+        "AAPG",
+        "AARD",
+        "ABAT",
+        "ABCL",
+        "ABLV",
+        "ABLVW",
+        "ABNB"
+      ],
+      "still_shallow_note": "Remaining 'shallow' names are mostly post-2017 IPOs/listings \u2014 expected, not a two-tier defect.",
+      "xlc_note": "XLC lists mid-2018 \u2192 Communication Services residual coverage from ~mid-2019.",
+      "feed_note": "Empirical Alpaca floor observed via SPY: 2016-01-04 (n=2649). Calendar history_days=5000 is a request cap, not a guarantee \u2014 sanity grades against the feed floor, not 5000 calendar days.",
+      "target_history_days": 5000
+    }
+  },
+  "harness": {
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "survivorship_banner": "SURVIVORSHIP BIAS: today's constituents backfilled. Relative IC only \u2014 not levels.",
+    "signal_eval": [
+      {
+        "signal": "high_52w",
+        "weeks": 84,
+        "avg_cross_section": 1499.2,
+        "mean_ic": 0.0761,
+        "ic_t_stat": 4.46,
+        "ic_positive_pct": 71.4,
+        "mean_quintile_spread": 0.0118,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2564.4,
+        "avg_eligible_pre_mask": 2047.0,
+        "mask_binds_pct": 94.0
+      },
+      {
+        "signal": "trend_200",
+        "weeks": 85,
+        "avg_cross_section": 1499.6,
+        "mean_ic": 0.0371,
+        "ic_t_stat": 2.63,
+        "ic_positive_pct": 62.4,
+        "mean_quintile_spread": -0.7839,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2583.0,
+        "avg_eligible_pre_mask": 2065.8,
+        "mask_binds_pct": 96.5
+      },
+      {
+        "signal": "mom_12_1",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
+        "mean_ic": 0.0355,
+        "ic_t_stat": 2.41,
+        "ic_positive_pct": 62.7,
+        "mean_quintile_spread": 0.0141,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0268,
+        "ic_t_stat": 1.69,
+        "ic_positive_pct": 60.2,
+        "mean_quintile_spread": 0.0126,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      {
+        "signal": "mom_3_1",
+        "weeks": 90,
+        "avg_cross_section": 1498.6,
+        "mean_ic": 0.0256,
+        "ic_t_stat": 2.24,
+        "ic_positive_pct": 61.1,
+        "mean_quintile_spread": -0.0048,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2679.7,
+        "avg_eligible_pre_mask": 2128.6,
+        "mask_binds_pct": 95.6
+      },
+      {
+        "signal": "reversal_1m",
+        "weeks": 89,
+        "avg_cross_section": 1499.0,
+        "mean_ic": 0.0156,
+        "ic_t_stat": 1.37,
+        "ic_positive_pct": 59.6,
+        "mean_quintile_spread": -0.7882,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2700.0,
+        "avg_eligible_pre_mask": 2085.8,
+        "mask_binds_pct": 94.5
+      },
+      {
+        "signal": "mom_12_1_resid",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
+        "mean_ic": 0.0148,
+        "ic_t_stat": 1.02,
+        "ic_positive_pct": 57.8,
+        "mean_quintile_spread": 0.0181,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      {
+        "signal": "mom_6_1",
+        "weeks": 88,
+        "avg_cross_section": 1499.3,
+        "mean_ic": 0.0101,
+        "ic_t_stat": 0.91,
+        "ic_positive_pct": 58.0,
+        "mean_quintile_spread": 0.0108,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2641.3,
+        "avg_eligible_pre_mask": 2098.8,
+        "mask_binds_pct": 95.5
+      },
+      {
+        "signal": "mom_12_1_sector_demeaned",
+        "weeks": 83,
+        "avg_cross_section": 483.2,
+        "mean_ic": 0.0076,
+        "ic_t_stat": 0.46,
+        "ic_positive_pct": 55.4,
+        "mean_quintile_spread": 0.0054,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 484.4,
+        "avg_eligible_pre_mask": 483.2,
+        "mask_binds_pct": 0.0
+      },
+      {
+        "signal": "fip_id",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
+        "mean_ic": -0.0184,
+        "ic_t_stat": -2.11,
+        "ic_positive_pct": 45.8,
+        "mean_quintile_spread": -0.0059,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      {
+        "signal": "vol_6m",
+        "weeks": 88,
+        "avg_cross_section": 1499.3,
+        "mean_ic": -0.0704,
+        "ic_t_stat": -3.14,
+        "ic_positive_pct": 35.2,
+        "mean_quintile_spread": 0.0051,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2641.3,
+        "avg_eligible_pre_mask": 2098.8,
+        "mask_binds_pct": 95.5
+      }
+    ],
+    "signal_eval_by_name": {
+      "high_52w": {
+        "signal": "high_52w",
+        "weeks": 84,
+        "avg_cross_section": 1499.2,
+        "mean_ic": 0.0761,
+        "ic_t_stat": 4.46,
+        "ic_positive_pct": 71.4,
+        "mean_quintile_spread": 0.0118,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2564.4,
+        "avg_eligible_pre_mask": 2047.0,
+        "mask_binds_pct": 94.0
+      },
+      "trend_200": {
+        "signal": "trend_200",
+        "weeks": 85,
+        "avg_cross_section": 1499.6,
+        "mean_ic": 0.0371,
+        "ic_t_stat": 2.63,
+        "ic_positive_pct": 62.4,
+        "mean_quintile_spread": -0.7839,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2583.0,
+        "avg_eligible_pre_mask": 2065.8,
+        "mask_binds_pct": 96.5
+      },
+      "mom_12_1": {
+        "signal": "mom_12_1",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
+        "mean_ic": 0.0355,
+        "ic_t_stat": 2.41,
+        "ic_positive_pct": 62.7,
+        "mean_quintile_spread": 0.0141,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      "mom_12_1_sector_resid": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0268,
+        "ic_t_stat": 1.69,
+        "ic_positive_pct": 60.2,
+        "mean_quintile_spread": 0.0126,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "mom_3_1": {
+        "signal": "mom_3_1",
+        "weeks": 90,
+        "avg_cross_section": 1498.6,
+        "mean_ic": 0.0256,
+        "ic_t_stat": 2.24,
+        "ic_positive_pct": 61.1,
+        "mean_quintile_spread": -0.0048,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2679.7,
+        "avg_eligible_pre_mask": 2128.6,
+        "mask_binds_pct": 95.6
+      },
+      "reversal_1m": {
+        "signal": "reversal_1m",
+        "weeks": 89,
+        "avg_cross_section": 1499.0,
+        "mean_ic": 0.0156,
+        "ic_t_stat": 1.37,
+        "ic_positive_pct": 59.6,
+        "mean_quintile_spread": -0.7882,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2700.0,
+        "avg_eligible_pre_mask": 2085.8,
+        "mask_binds_pct": 94.5
+      },
+      "mom_12_1_resid": {
+        "signal": "mom_12_1_resid",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
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+        "ic_positive_pct": 57.8,
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+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      "mom_6_1": {
+        "signal": "mom_6_1",
+        "weeks": 88,
+        "avg_cross_section": 1499.3,
+        "mean_ic": 0.0101,
+        "ic_t_stat": 0.91,
+        "ic_positive_pct": 58.0,
+        "mean_quintile_spread": 0.0108,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2641.3,
+        "avg_eligible_pre_mask": 2098.8,
+        "mask_binds_pct": 95.5
+      },
+      "mom_12_1_sector_demeaned": {
+        "signal": "mom_12_1_sector_demeaned",
+        "weeks": 83,
+        "avg_cross_section": 483.2,
+        "mean_ic": 0.0076,
+        "ic_t_stat": 0.46,
+        "ic_positive_pct": 55.4,
+        "mean_quintile_spread": 0.0054,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 484.4,
+        "avg_eligible_pre_mask": 483.2,
+        "mask_binds_pct": 0.0
+      },
+      "fip_id": {
+        "signal": "fip_id",
+        "weeks": 83,
+        "avg_cross_section": 1499.4,
+        "mean_ic": -0.0184,
+        "ic_t_stat": -2.11,
+        "ic_positive_pct": 45.8,
+        "mean_quintile_spread": -0.0059,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2553.0,
+        "avg_eligible_pre_mask": 2045.4,
+        "mask_binds_pct": 95.2
+      },
+      "vol_6m": {
+        "signal": "vol_6m",
+        "weeks": 88,
+        "avg_cross_section": 1499.3,
+        "mean_ic": -0.0704,
+        "ic_t_stat": -3.14,
+        "ic_positive_pct": 35.2,
+        "mean_quintile_spread": 0.0051,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 2641.3,
+        "avg_eligible_pre_mask": 2098.8,
+        "mask_binds_pct": 95.5
+      }
+    },
+    "era_split": {
+      "era_split_date": "2021-01-01",
+      "note": "Diagnostic only \u2014 not a tuning input.",
+      "pre_2021": {
+        "high_52w": {
+          "signal": "high_52w",
+          "weeks": 36,
+          "avg_cross_section": 1498.1,
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+          "reliable": true,
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+          "avg_eligible_pre_mask": 1670.6,
+          "mask_binds_pct": 86.1
+        },
+        "trend_200": {
+          "signal": "trend_200",
+          "weeks": 38,
+          "avg_cross_section": 1499.1,
+          "mean_ic": 0.0373,
+          "ic_t_stat": 1.99,
+          "ic_positive_pct": 65.8,
+          "mean_quintile_spread": -1.7748,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1913.9,
+          "avg_eligible_pre_mask": 1687.4,
+          "mask_binds_pct": 92.1
+        },
+        "mom_12_1": {
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+          "weeks": 36,
+          "avg_cross_section": 1498.6,
+          "mean_ic": 0.037,
+          "ic_t_stat": 1.92,
+          "ic_positive_pct": 63.9,
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+          "reliable": true,
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+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1897.3,
+          "avg_eligible_pre_mask": 1673.1,
+          "mask_binds_pct": 88.9
+        },
+        "mom_3_1": {
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+          "weeks": 42,
+          "avg_cross_section": 1497.0,
+          "mean_ic": 0.0273,
+          "ic_t_stat": 2.03,
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+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1954.5,
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+          "mask_binds_pct": 90.5
+        },
+        "mom_12_1_resid": {
+          "signal": "mom_12_1_resid",
+          "weeks": 36,
+          "avg_cross_section": 1498.6,
+          "mean_ic": 0.0216,
+          "ic_t_stat": 1.09,
+          "ic_positive_pct": 63.9,
+          "mean_quintile_spread": 0.0284,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1897.3,
+          "avg_eligible_pre_mask": 1673.1,
+          "mask_binds_pct": 88.9
+        },
+        "reversal_1m": {
+          "signal": "reversal_1m",
+          "weeks": 42,
+          "avg_cross_section": 1497.8,
+          "mean_ic": 0.0208,
+          "ic_t_stat": 1.45,
+          "ic_positive_pct": 64.3,
+          "mean_quintile_spread": -1.6753,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1976.6,
+          "avg_eligible_pre_mask": 1650.9,
+          "mask_binds_pct": 88.6
+        },
+        "mom_12_1_sector_resid": {
+          "signal": "mom_12_1_sector_resid",
+          "weeks": 36,
+          "avg_cross_section": 460.7,
+          "mean_ic": 0.0149,
+          "ic_t_stat": 0.64,
+          "ic_positive_pct": 58.3,
+          "mean_quintile_spread": 0.0063,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 462.8,
+          "avg_eligible_pre_mask": 460.7,
+          "mask_binds_pct": 0.0
+        },
+        "mom_6_1": {
+          "signal": "mom_6_1",
+          "weeks": 40,
+          "avg_cross_section": 1498.5,
+          "mean_ic": 0.0082,
+          "ic_t_stat": 0.58,
+          "ic_positive_pct": 57.5,
+          "mean_quintile_spread": 0.0163,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1935.1,
+          "avg_eligible_pre_mask": 1702.2,
+          "mask_binds_pct": 90.0
+        },
+        "mom_12_1_sector_demeaned": {
+          "signal": "mom_12_1_sector_demeaned",
+          "weeks": 36,
+          "avg_cross_section": 469.4,
+          "mean_ic": 0.0031,
+          "ic_t_stat": 0.13,
+          "ic_positive_pct": 55.6,
+          "mean_quintile_spread": 0.0009,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 471.5,
+          "avg_eligible_pre_mask": 469.4,
+          "mask_binds_pct": 0.0
+        },
+        "fip_id": {
+          "signal": "fip_id",
+          "weeks": 36,
+          "avg_cross_section": 1498.6,
+          "mean_ic": -0.0116,
+          "ic_t_stat": -0.93,
+          "ic_positive_pct": 52.8,
+          "mean_quintile_spread": -0.0037,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1897.3,
+          "avg_eligible_pre_mask": 1673.1,
+          "mask_binds_pct": 88.9
+        },
+        "vol_6m": {
+          "signal": "vol_6m",
+          "weeks": 40,
+          "avg_cross_section": 1498.5,
+          "mean_ic": -0.0219,
+          "ic_t_stat": -0.77,
+          "ic_positive_pct": 40.0,
+          "mean_quintile_spread": 0.0381,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 1935.1,
+          "avg_eligible_pre_mask": 1702.2,
+          "mask_binds_pct": 90.0
+        }
+      },
+      "post_2021": {
+        "high_52w": {
+          "signal": "high_52w",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": 0.0831,
+          "ic_t_stat": 2.36,
+          "ic_positive_pct": 68.8,
+          "mean_quintile_spread": 0.0102,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3046.7,
+          "avg_eligible_pre_mask": 2325.3,
+          "mask_binds_pct": 100.0
+        },
+        "mom_12_1_sector_resid": {
+          "signal": "mom_12_1_sector_resid",
+          "weeks": 48,
+          "avg_cross_section": 494.5,
+          "mean_ic": 0.033,
+          "ic_t_stat": 1.39,
+          "ic_positive_pct": 60.4,
+          "mean_quintile_spread": 0.0158,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 495.1,
+          "avg_eligible_pre_mask": 494.5,
+          "mask_binds_pct": 0.0
+        },
+        "mom_6_1": {
+          "signal": "mom_6_1",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": 0.0314,
+          "ic_t_stat": 1.51,
+          "ic_positive_pct": 58.3,
+          "mean_quintile_spread": 0.0109,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3214.4,
+          "avg_eligible_pre_mask": 2425.9,
+          "mask_binds_pct": 100.0
+        },
+        "mom_12_1": {
+          "signal": "mom_12_1",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": 0.0279,
+          "ic_t_stat": 1.14,
+          "ic_positive_pct": 62.5,
+          "mean_quintile_spread": 0.0206,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3045.2,
+          "avg_eligible_pre_mask": 2324.2,
+          "mask_binds_pct": 100.0
+        },
+        "trend_200": {
+          "signal": "trend_200",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": 0.0254,
+          "ic_t_stat": 1.03,
+          "ic_positive_pct": 60.4,
+          "mean_quintile_spread": 0.0064,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3113.5,
+          "avg_eligible_pre_mask": 2365.0,
+          "mask_binds_pct": 100.0
+        },
+        "mom_12_1_sector_demeaned": {
+          "signal": "mom_12_1_sector_demeaned",
+          "weeks": 48,
+          "avg_cross_section": 493.5,
+          "mean_ic": 0.0071,
+          "ic_t_stat": 0.3,
+          "ic_positive_pct": 52.1,
+          "mean_quintile_spread": 0.0078,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 494.1,
+          "avg_eligible_pre_mask": 493.5,
+          "mask_binds_pct": 0.0
+        },
+        "mom_12_1_resid": {
+          "signal": "mom_12_1_resid",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": 0.0067,
+          "ic_t_stat": 0.29,
+          "ic_positive_pct": 54.2,
+          "mean_quintile_spread": 0.0149,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3045.2,
+          "avg_eligible_pre_mask": 2324.2,
+          "mask_binds_pct": 100.0
+        },
+        "mom_3_1": {
+          "signal": "mom_3_1",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": -0.0014,
+          "ic_t_stat": -0.06,
+          "ic_positive_pct": 52.1,
+          "mean_quintile_spread": -0.0028,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3305.8,
+          "avg_eligible_pre_mask": 2486.0,
+          "mask_binds_pct": 100.0
+        },
+        "reversal_1m": {
+          "signal": "reversal_1m",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": -0.013,
+          "ic_t_stat": -0.69,
+          "ic_positive_pct": 41.7,
+          "mean_quintile_spread": -0.0099,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3367.0,
+          "avg_eligible_pre_mask": 2486.9,
+          "mask_binds_pct": 100.0
+        },
+        "fip_id": {
+          "signal": "fip_id",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": -0.019,
+          "ic_t_stat": -1.56,
+          "ic_positive_pct": 41.7,
+          "mean_quintile_spread": -0.015,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3045.2,
+          "avg_eligible_pre_mask": 2324.2,
+          "mask_binds_pct": 100.0
+        },
+        "vol_6m": {
+          "signal": "vol_6m",
+          "weeks": 48,
+          "avg_cross_section": 1500.0,
+          "mean_ic": -0.0943,
+          "ic_t_stat": -2.41,
+          "ic_positive_pct": 31.2,
+          "mean_quintile_spread": -0.0031,
+          "reliable": true,
+          "liquid_breadth_top_n": 1500,
+          "liquid_min_price": 5.0,
+          "avg_raw_pool": 3214.4,
+          "avg_eligible_pre_mask": 2425.9,
+          "mask_binds_pct": 100.0
+        }
+      }
+    },
+    "identical_subset_sector_cs": {
+      "mom_12_1_sector_resid": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0268,
+        "ic_t_stat": 1.69,
+        "ic_positive_pct": 60.2,
+        "mean_quintile_spread": 0.0126,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "mom_12_1_resid": {
+        "signal": "mom_12_1_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0251,
+        "ic_t_stat": 1.3,
+        "ic_positive_pct": 57.8,
+        "mean_quintile_spread": 0.011,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "mom_12_1": {
+        "signal": "mom_12_1",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0192,
+        "ic_t_stat": 0.92,
+        "ic_positive_pct": 56.6,
+        "mean_quintile_spread": 0.0099,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "mom_12_1_sector_demeaned": {
+        "signal": "mom_12_1_sector_demeaned",
+        "weeks": 83,
+        "avg_cross_section": 479.0,
+        "mean_ic": 0.0063,
+        "ic_t_stat": 0.39,
+        "ic_positive_pct": 55.4,
+        "mean_quintile_spread": 0.0048,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 480.2,
+        "avg_eligible_pre_mask": 479.0,
+        "mask_binds_pct": 0.0
+      }
+    },
+    "mask_diagnostics": {
+      "reference_signal": "vol_6m",
+      "avg_cross_section": 1499.3,
+      "avg_raw_pool": 2641.3,
+      "avg_eligible_pre_mask": 2098.8,
+      "mask_binds_pct": 95.5,
+      "weeks": 88
+    },
+    "sector_map_size": 505,
+    "sector_etfs_loaded": [
+      "XLB",
+      "XLC",
+      "XLE",
+      "XLF",
+      "XLI",
+      "XLK",
+      "XLP",
+      "XLRE",
+      "XLU",
+      "XLV",
+      "XLY"
+    ],
+    "spy_bars": 2649
+  },
+  "grade": {
+    "verdict": "FAIL",
+    "reason": "failed one or more pre-registered checks (see checks)",
+    "checks": {
+      "sector_row": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0268,
+        "ic_t_stat": 1.69,
+        "ic_positive_pct": 60.2,
+        "mean_quintile_spread": 0.0126,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "resid_row_for_t": {
+        "signal": "mom_12_1_resid",
+        "weeks": 83,
+        "avg_cross_section": 480.0,
+        "mean_ic": 0.0251,
+        "ic_t_stat": 1.3,
+        "ic_positive_pct": 57.8,
+        "mean_quintile_spread": 0.011,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 481.2,
+        "avg_eligible_pre_mask": 480.0,
+        "mask_binds_pct": 0.0
+      },
+      "resid_t_source": "identical_subset",
+      "pre_2021": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 36,
+        "avg_cross_section": 460.7,
+        "mean_ic": 0.0149,
+        "ic_t_stat": 0.64,
+        "ic_positive_pct": 58.3,
+        "mean_quintile_spread": 0.0063,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 462.8,
+        "avg_eligible_pre_mask": 460.7,
+        "mask_binds_pct": 0.0
+      },
+      "post_2021": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 48,
+        "avg_cross_section": 494.5,
+        "mean_ic": 0.033,
+        "ic_t_stat": 1.39,
+        "ic_positive_pct": 60.4,
+        "mean_quintile_spread": 0.0158,
+        "reliable": true,
+        "liquid_breadth_top_n": 1500,
+        "liquid_min_price": 5.0,
+        "avg_raw_pool": 495.1,
+        "avg_eligible_pre_mask": 494.5,
+        "mask_binds_pct": 0.0
+      },
+      "abs_mean_ic_ge_0_03": false,
+      "sign_positive": true,
+      "reliable": true,
+      "weeks_ge_50": true,
+      "weeks": 83,
+      "t_ge_resid_same_cs": true,
+      "sector_t": 1.69,
+      "resid_t": 1.3,
+      "era_both_present": true,
+      "era_sign_consistent_positive": true,
+      "pre_ic": 0.0149,
+      "post_ic": 0.033,
+      "avg_cross_section": 480.0
+    },
+    "headline": "Task 1 CLOSED \u2014 sector residual dead on deep evidence."
+  },
+  "pending_human": true,
+  "note": "Nothing merged into production. Thread ends at PASS/FAIL."
+}

reports/sector-resid-deep-20260719-113319.md

@@ -0,0 +1,341 @@
+# Sector-residual deep test (masked, repaired snapshot)
+
+Generated: `2026-07-19T11:33:19.102779`
+
+> **SURVIVORSHIP BIAS: today's constituents backfilled. Relative IC only — not levels.**
+
+## Pre-registered grade (mechanical)
+
+**Verdict: FAIL**
+
+Task 1 CLOSED — sector residual dead on deep evidence.
+
+Reason: failed one or more pre-registered checks (see checks)
+
+```json
+{
+  "sector_row": {
+    "signal": "mom_12_1_sector_resid",
+    "weeks": 83,
+    "avg_cross_section": 480.0,
+    "mean_ic": 0.0268,
+    "ic_t_stat": 1.69,
+    "ic_positive_pct": 60.2,
+    "mean_quintile_spread": 0.0126,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 481.2,
+    "avg_eligible_pre_mask": 480.0,
+    "mask_binds_pct": 0.0
+  },
+  "resid_row_for_t": {
+    "signal": "mom_12_1_resid",
+    "weeks": 83,
+    "avg_cross_section": 480.0,
+    "mean_ic": 0.0251,
+    "ic_t_stat": 1.3,
+    "ic_positive_pct": 57.8,
+    "mean_quintile_spread": 0.011,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 481.2,
+    "avg_eligible_pre_mask": 480.0,
+    "mask_binds_pct": 0.0
+  },
+  "resid_t_source": "identical_subset",
+  "pre_2021": {
+    "signal": "mom_12_1_sector_resid",
+    "weeks": 36,
+    "avg_cross_section": 460.7,
+    "mean_ic": 0.0149,
+    "ic_t_stat": 0.64,
+    "ic_positive_pct": 58.3,
+    "mean_quintile_spread": 0.0063,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 462.8,
+    "avg_eligible_pre_mask": 460.7,
+    "mask_binds_pct": 0.0
+  },
+  "post_2021": {
+    "signal": "mom_12_1_sector_resid",
+    "weeks": 48,
+    "avg_cross_section": 494.5,
+    "mean_ic": 0.033,
+    "ic_t_stat": 1.39,
+    "ic_positive_pct": 60.4,
+    "mean_quintile_spread": 0.0158,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 495.1,
+    "avg_eligible_pre_mask": 494.5,
+    "mask_binds_pct": 0.0
+  },
+  "abs_mean_ic_ge_0_03": false,
+  "sign_positive": true,
+  "reliable": true,
+  "weeks_ge_50": true,
+  "weeks": 83,
+  "t_ge_resid_same_cs": true,
+  "sector_t": 1.69,
+  "resid_t": 1.3,
+  "era_both_present": true,
+  "era_sign_consistent_positive": true,
+  "pre_ic": 0.0149,
+  "post_ic": 0.033,
+  "avg_cross_section": 480.0
+}
+```
+
+## Step-1 sanity
+
+```json
+{
+  "skipped": true,
+  "sanity": {
+    "passed": true,
+    "megacap": {
+      "AAPL": {
+        "symbol": "AAPL",
+        "bars": 2649,
+        "min_date": "2016-01-04",
+        "max_date": "2026-07-17"
+      },
+      "MSFT": {
+        "symbol": "MSFT",
+        "bars": 2649,
+        "min_date": "2016-01-04",
+        "max_date": "2026-07-17"
+      },
+      "JPM": {
+        "symbol": "JPM",
+        "bars": 2649,
+        "min_date": "2016-01-04",
+        "max_date": "2026-07-17"
+      },
+      "XOM": {
+        "symbol": "XOM",
+        "bars": 2649,
+        "min_date": "2016-01-04",
+        "max_date": "2026-07-17"
+      },
+      "JNJ": {
+        "symbol": "JNJ",
+        "bars": 2649,
+        "min_date": "2016-01-04",
+        "max_date": "2026-07-17"
+      }
+    },
+    "megacap_ok": true,
+    "megacap_reasons": [],
+    "feed_floor": "2016-01-04",
+    "spy_benchmark": {
+      "n": 2649,
+      "min": "2016-01-04",
+      "max": "2026-07-17"
+    },
+    "old_shallow_floor": "2020-01-01",
+    "sector_etfs": {
+      "XLB": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLC": {
+        "n": 2030,
+        "min": "2018-06-19",
+        "max": "2026-07-17"
+      },
+      "XLE": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLF": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLI": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLK": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLP": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLRE": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLU": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLV": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      },
+      "XLY": {
+        "n": 2649,
+        "min": "2016-01-04",
+        "max": "2026-07-17"
+      }
+    },
+    "sector_etfs_deep_count": 11,
+    "sector_etfs_ok": true,
+    "sector_etf_reasons": [],
+    "still_shallow_count": 2882,
+    "still_shallow_sample": [
+      "AACB",
+      "AACBR",
+      "AACBU",
+      "AACI",
+      "AACIU",
+      "AACIW",
+      "AACO",
+      "AACOU",
+      "AACOW",
+      "AACP",
+      "AACPR",
+      "AACPU",
+      "AACPW",
+      "AAPG",
+      "AARD",
+      "ABAT",
+      "ABCL",
+      "ABLV",
+      "ABLVW",
+      "ABNB"
+    ],
+    "still_shallow_note": "Remaining 'shallow' names are mostly post-2017 IPOs/listings \u2014 expected, not a two-tier defect.",
+    "xlc_note": "XLC lists mid-2018 \u2192 Communication Services residual coverage from ~mid-2019.",
+    "feed_note": "Empirical Alpaca floor observed via SPY: 2016-01-04 (n=2649). Calendar history_days=5000 is a request cap, not a guarantee \u2014 sanity grades against the feed floor, not 5000 calendar days.",
+    "target_history_days": 5000
+  }
+}
+```
+
+## Mask diagnostics
+
+```json
+{
+  "reference_signal": "vol_6m",
+  "avg_cross_section": 1499.3,
+  "avg_raw_pool": 2641.3,
+  "avg_eligible_pre_mask": 2098.8,
+  "mask_binds_pct": 95.5,
+  "weeks": 88
+}
+```
+
+## Signal table (rows only — no narrative for non-sector signals)
+
+| signal | mean_ic | t | weeks | avg_N | reliable |
+|---|---:|---:|---:|---:|---|
+| fip_id | -0.0184 | -2.11 | 83 | 1499.4 | True |
+| high_52w | 0.0761 | 4.46 | 84 | 1499.2 | True |
+| mom_12_1 | 0.0355 | 2.41 | 83 | 1499.4 | True |
+| mom_12_1_resid | 0.0148 | 1.02 | 83 | 1499.4 | True |
+| mom_12_1_sector_demeaned | 0.0076 | 0.46 | 83 | 483.2 | True |
+| mom_12_1_sector_resid | 0.0268 | 1.69 | 83 | 480.0 | True |
+| mom_3_1 | 0.0256 | 2.24 | 90 | 1498.6 | True |
+| mom_6_1 | 0.0101 | 0.91 | 88 | 1499.3 | True |
+| reversal_1m | 0.0156 | 1.37 | 89 | 1499.0 | True |
+| trend_200 | 0.0371 | 2.63 | 85 | 1499.6 | True |
+| vol_6m | -0.0704 | -3.14 | 88 | 1499.3 | True |
+
+### Era split — mom_12_1_sector_resid only (for grade)
+
+| era | IC | t | weeks | N |
+|---|---:|---:|---:|---:|
+| pre_2021 | 0.0149 | 0.64 | 36 | 460.7 |
+| post_2021 | 0.033 | 1.39 | 48 | 494.5 |
+
+### Identical-subset baselines (sector CS)
+
+```json
+{
+  "mom_12_1_sector_resid": {
+    "signal": "mom_12_1_sector_resid",
+    "weeks": 83,
+    "avg_cross_section": 480.0,
+    "mean_ic": 0.0268,
+    "ic_t_stat": 1.69,
+    "ic_positive_pct": 60.2,
+    "mean_quintile_spread": 0.0126,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 481.2,
+    "avg_eligible_pre_mask": 480.0,
+    "mask_binds_pct": 0.0
+  },
+  "mom_12_1_resid": {
+    "signal": "mom_12_1_resid",
+    "weeks": 83,
+    "avg_cross_section": 480.0,
+    "mean_ic": 0.0251,
+    "ic_t_stat": 1.3,
+    "ic_positive_pct": 57.8,
+    "mean_quintile_spread": 0.011,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 481.2,
+    "avg_eligible_pre_mask": 480.0,
+    "mask_binds_pct": 0.0
+  },
+  "mom_12_1": {
+    "signal": "mom_12_1",
+    "weeks": 83,
+    "avg_cross_section": 480.0,
+    "mean_ic": 0.0192,
+    "ic_t_stat": 0.92,
+    "ic_positive_pct": 56.6,
+    "mean_quintile_spread": 0.0099,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 481.2,
+    "avg_eligible_pre_mask": 480.0,
+    "mask_binds_pct": 0.0
+  },
+  "mom_12_1_sector_demeaned": {
+    "signal": "mom_12_1_sector_demeaned",
+    "weeks": 83,
+    "avg_cross_section": 479.0,
+    "mean_ic": 0.0063,
+    "ic_t_stat": 0.39,
+    "ic_positive_pct": 55.4,
+    "mean_quintile_spread": 0.0048,
+    "reliable": true,
+    "liquid_breadth_top_n": 1500,
+    "liquid_min_price": 5.0,
+    "avg_raw_pool": 480.2,
+    "avg_eligible_pre_mask": 479.0,
+    "mask_binds_pct": 0.0
+  }
+}
+```
+
+## Status
+
+PENDING_HUMAN beyond the mechanical PASS/FAIL above. Nothing merged into production docs or prod code.
+
+JSON: `reports/sector-resid-deep-20260719-113319.json`
+

reports/sector-residual-20260719-083356.json

@@ -0,0 +1,412 @@
+{
+  "generated_at": "2026-07-19T08:33:56.651229",
+  "snapshot_guard": {
+    "snapshot": "C:\\Workspace\\signal-platform\\backtest_snapshots\\prod.sqlite",
+    "manifest": null,
+    "manifest_ok": null,
+    "note": "No completion manifest (prod.sqlite is expected without one). Bar-count sanity still applied.",
+    "ticker_count": 506,
+    "ohlcv_row_count": 629263,
+    "bars_min_avg_max": {
+      "min": 14,
+      "avg": 1246.1,
+      "max": 1261
+    },
+    "ohlcv_date_range": {
+      "min": "2021-06-24",
+      "max": "2026-07-02"
+    },
+    "benchmark_prices": [
+      {
+        "symbol": "SPY",
+        "n": 1516,
+        "min": "2020-07-06",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLB",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLC",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLE",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLF",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLI",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLK",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLP",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLRE",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLU",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLV",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      },
+      {
+        "symbol": "XLY",
+        "n": 1512,
+        "min": "2020-07-10",
+        "max": "2026-07-17"
+      }
+    ],
+    "missing_sector_etfs": []
+  },
+  "sector_coverage": {
+    "universe": 506,
+    "mapped": 505,
+    "mapped_pct": 99.8,
+    "with_etf": 505,
+    "missing": [
+      "RHM"
+    ],
+    "by_sector": {
+      "Industrials": 81,
+      "Financials": 75,
+      "Information Technology": 72,
+      "Health Care": 58,
+      "Consumer Discretionary": 47,
+      "Consumer Staples": 34,
+      "Real Estate": 31,
+      "Utilities": 31,
+      "Materials": 26,
+      "Communication Services": 23,
+      "Energy": 22,
+      "Consumer Defensive": 2,
+      "Technology": 2,
+      "Financial Services": 1
+    }
+  },
+  "sector_map_path": "C:\\Workspace\\signal-platform\\data\\research\\ticker_sector_map.json",
+  "signal_eval": [
+    {
+      "signal": "vol_6m",
+      "weeks": 39,
+      "avg_cross_section": 498.2,
+      "mean_ic": 0.0609,
+      "ic_t_stat": 1.48,
+      "ic_positive_pct": 64.1,
+      "mean_quintile_spread": 0.0337,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1_sector_resid",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": 0.0578,
+      "ic_t_stat": 2.34,
+      "ic_positive_pct": 65.7,
+      "mean_quintile_spread": 0.0245,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1_resid",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": 0.0552,
+      "ic_t_stat": 1.98,
+      "ic_positive_pct": 60.0,
+      "mean_quintile_spread": 0.0207,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": 0.0531,
+      "ic_t_stat": 1.61,
+      "ic_positive_pct": 65.7,
+      "mean_quintile_spread": 0.0206,
+      "reliable": true
+    },
+    {
+      "signal": "mom_12_1_sector_demeaned",
+      "weeks": 35,
+      "avg_cross_section": 496.7,
+      "mean_ic": 0.034,
+      "ic_t_stat": 1.32,
+      "ic_positive_pct": 62.9,
+      "mean_quintile_spread": 0.0154,
+      "reliable": true
+    },
+    {
+      "signal": "trend_200",
+      "weeks": 37,
+      "avg_cross_section": 497.9,
+      "mean_ic": 0.0161,
+      "ic_t_stat": 0.44,
+      "ic_positive_pct": 59.5,
+      "mean_quintile_spread": 0.006,
+      "reliable": true
+    },
+    {
+      "signal": "reversal_1m",
+      "weeks": 43,
+      "avg_cross_section": 498.7,
+      "mean_ic": 0.0059,
+      "ic_t_stat": 0.22,
+      "ic_positive_pct": 53.5,
+      "mean_quintile_spread": 0.0053,
+      "reliable": true
+    },
+    {
+      "signal": "mom_6_1",
+      "weeks": 39,
+      "avg_cross_section": 498.2,
+      "mean_ic": 0.0051,
+      "ic_t_stat": 0.21,
+      "ic_positive_pct": 56.4,
+      "mean_quintile_spread": 0.0087,
+      "reliable": true
+    },
+    {
+      "signal": "mom_3_1",
+      "weeks": 42,
+      "avg_cross_section": 498.5,
+      "mean_ic": -0.0064,
+      "ic_t_stat": -0.25,
+      "ic_positive_pct": 50.0,
+      "mean_quintile_spread": 0.0046,
+      "reliable": true
+    },
+    {
+      "signal": "high_52w",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": -0.0086,
+      "ic_t_stat": -0.26,
+      "ic_positive_pct": 54.3,
+      "mean_quintile_spread": -0.0088,
+      "reliable": true
+    },
+    {
+      "signal": "fip_id",
+      "weeks": 35,
+      "avg_cross_section": 497.7,
+      "mean_ic": -0.045,
+      "ic_t_stat": -2.91,
+      "ic_positive_pct": 25.7,
+      "mean_quintile_spread": -0.0168,
+      "reliable": true
+    }
+  ],
+  "ic_grades": {
+    "mom_12_1_sector_resid": {
+      "promote_to_ab": true,
+      "checks": {
+        "sign_ok": true,
+        "abs_mean_ic_ge_0_03": true,
+        "reliable": true,
+        "t_ge_resid": true,
+        "mean_ic": 0.0578,
+        "ic_t_stat": 2.34,
+        "resid_ic_t_stat": 1.98,
+        "weeks": 35
+      },
+      "reason": "clears iron rule and t \u2265 mom_12_1_resid \u2014 authorized for A/B only",
+      "row": {
+        "signal": "mom_12_1_sector_resid",
+        "weeks": 35,
+        "avg_cross_section": 497.7,
+        "mean_ic": 0.0578,
+        "ic_t_stat": 2.34,
+        "ic_positive_pct": 65.7,
+        "mean_quintile_spread": 0.0245,
+        "reliable": true
+      }
+    },
+    "mom_12_1_sector_demeaned": {
+      "promote_to_ab": false,
+      "checks": {
+        "sign_ok": true,
+        "abs_mean_ic_ge_0_03": true,
+        "reliable": true,
+        "t_ge_resid": false,
+        "mean_ic": 0.034,
+        "ic_t_stat": 1.32,
+        "resid_ic_t_stat": 1.98,
+        "weeks": 35
+      },
+      "reason": "does not clear pre-registered IC promotion bar",
+      "row": {
+        "signal": "mom_12_1_sector_demeaned",
+        "weeks": 35,
+        "avg_cross_section": 496.7,
+        "mean_ic": 0.034,
+        "ic_t_stat": 1.32,
+        "ic_positive_pct": 62.9,
+        "mean_quintile_spread": 0.0154,
+        "reliable": true
+      }
+    }
+  },
+  "portfolio_ab": {
+    "signal": "mom_12_1_sector_resid",
+    "ranking_key": "residual_high_vol_blend_80_20_score",
+    "fill_mode": "close",
+    "validation_split": "2024-07-01",
+    "control": {
+      "label": "control_mom_12_1_resid",
+      "n_qualified_longs": 1086,
+      "windows": {
+        "train": {
+          "sharpe": 1.3,
+          "sharpe_se": 0.685,
+          "cagr_pct": 29.2,
+          "max_drawdown_pct": 21.4,
+          "total_return_pct": 70.9,
+          "trades": 176,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 525,
+          "return_skew": 0.3722,
+          "return_kurtosis": 4.6208,
+          "psr": 0.971
+        },
+        "validation": {
+          "sharpe": 2.92,
+          "sharpe_se": 0.709,
+          "cagr_pct": 76.3,
+          "max_drawdown_pct": 11.7,
+          "total_return_pct": 210.7,
+          "trades": 150,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 501,
+          "return_skew": 0.1734,
+          "return_kurtosis": 4.4625,
+          "psr": 1.0
+        },
+        "full": {
+          "sharpe": 2.09,
+          "sharpe_se": 0.497,
+          "cagr_pct": 51.6,
+          "max_drawdown_pct": 21.4,
+          "total_return_pct": 424.6,
+          "trades": 322,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 1000,
+          "return_skew": 0.2686,
+          "return_kurtosis": 4.5653,
+          "psr": 1.0
+        }
+      }
+    },
+    "treatment": {
+      "label": "treatment_mom_12_1_sector_resid",
+      "n_qualified_longs": 1210,
+      "windows": {
+        "train": {
+          "sharpe": 1.57,
+          "sharpe_se": 0.677,
+          "cagr_pct": 35.5,
+          "max_drawdown_pct": 19.8,
+          "total_return_pct": 90.0,
+          "trades": 176,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 530,
+          "return_skew": 0.466,
+          "return_kurtosis": 4.4413,
+          "psr": 0.99
+        },
+        "validation": {
+          "sharpe": 2.57,
+          "sharpe_se": 0.701,
+          "cagr_pct": 66.3,
+          "max_drawdown_pct": 14.8,
+          "total_return_pct": 176.4,
+          "trades": 163,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 501,
+          "return_skew": 0.3003,
+          "return_kurtosis": 4.4305,
+          "psr": 0.9999
+        },
+        "full": {
+          "sharpe": 2.09,
+          "sharpe_se": 0.491,
+          "cagr_pct": 51.0,
+          "max_drawdown_pct": 19.8,
+          "total_return_pct": 421.3,
+          "trades": 337,
+          "win_rate_pct": null,
+          "avg_r": null,
+          "n_returns": 1005,
+          "return_skew": 0.4066,
+          "return_kurtosis": 4.3627,
+          "psr": 1.0
+        }
+      }
+    },
+    "promotion": {
+      "promote": true,
+      "checks": {
+        "validation_sharpe_ge_control_minus_half_se": true,
+        "full_sharpe_not_worse": true,
+        "full_maxdd_not_worse": true,
+        "control_validation_sharpe": 2.92,
+        "treatment_validation_sharpe": 2.57,
+        "se_used": 0.701,
+        "control_full_sharpe": 2.09,
+        "treatment_full_sharpe": 2.09,
+        "control_full_maxdd": 21.4,
+        "treatment_full_maxdd": 19.8
+      },
+      "reason": "clears pre-registered A/B bar \u2014 human decides wire-in"
+    }
+  },
+  "verdict": "PROMOTE",
+  "verdict_detail": "mom_12_1_sector_resid cleared IC + A/B bars. Human must design wire-in; do not ship from this branch.",
+  "human_next": "- Approve or reject production residual swap vs dual-signal design.\n- If sector-cap arm ran, review tail-trim diagnostics before any cap.",
+  "report_path": "reports/sector-residual-20260719-083356.json",
+  "pre_registration": {
+    "iron_ic_bar": 0.03,
+    "validation_split": "2024-07-01",
+    "fill_mode": "close",
+    "cost_per_side": 0.001,
+    "ab_rule": "val Sharpe >= control - 0.5*SE; full Sharpe & maxDD not worse"
+  }
+}

reports/sector-residual-20260719-083356.md

@@ -0,0 +1,237 @@
+# Sector-residual momentum (Tier-1 alpha research)
+
+**Status:** **PROMOTE (to human design decision only)** — IC + A/B bars cleared; **do not ship**.  
+**Branch:** `research/sector-residual-momentum`  
+**Production impact:** none. Local research only. No scheduler / gate / prod-config changes.  
+**Artifacts:** `reports/sector-residual-20260719-083356.json` (+ companion `.md`)
+
+---
+
+## Pre-registration (locked before first research run)
+
+### Hypothesis
+
+Residualizing 12–1 momentum against the sector, not only the market, reduces
+factor volatility at similar return (Blitz / Huij / Martens-style) → higher
+Sharpe on the production book when the residual replaces market-only residual
+as the momentum leg.
+
+### Signals (candidates)
+
+| signal | construction |
+|---|---|
+| `mom_12_1_sector_resid` | Two-factor residual vs SPY + ticker’s sector ETF. Same window as `mom_12_1_resid`: ≥100 daily obs, 252-bar lookback, 21-bar skip; two-factor OLS betas **without intercept**; cumulate residual returns over the formation window. |
+| `mom_12_1_sector_demeaned` | Plain `mom_12_1` minus the **cross-sectional** mean of `mom_12_1` within the same GICS sector that week (≥2 names in sector). No regression. |
+
+### Baselines (same run, same cross-sections — iron rule)
+
+Always report side-by-side with:
+
+- `mom_12_1`
+- `mom_12_1_resid`
+
+Computed on the **identical** weekly non-overlapping cross-sections in this run.
+Never compare against IC numbers from another report.
+
+### Iron rule (IC harness)
+
+Source of truth: `_signal_evaluation` in `app/services/backtest_service.py`.
+
+- Mean weekly Spearman IC on **non-overlapping** weekly windows  
+- Bar: \|mean IC\| ≥ ~0.03, **consistent positive sign**, `reliable: true` (≥ 12 windows)
+
+### Promotion to portfolio A/B (candidate → book)
+
+A candidate promotes to A/B **only if**:
+
+1. It clears the iron-rule bar **and**
+2. Its IC **t-stat ≥** that of `mom_12_1_resid` on the same cross-sections.
+
+### Portfolio A/B grading (if and only if IC promotion fires)
+
+- Swap candidate in as the **momentum leg** of the production 80/20 momentum/vol
+  rank **and** as the gate-percentile signal.
+- `fill_mode=close`, `COST_PER_SIDE = 0.001`, full config otherwise unchanged.
+- Validation window = entries ≥ **2024-07-01** (call it **validation**, not
+  holdout — contaminated by prior experiments).
+- Pre-registered promotion bar:
+  - validation Sharpe ≥ control − 0.5·SE  
+  - full-period Sharpe and max-DD **not worse** than control  
+- Report Lo / Mertens-adjusted SEs.
+
+### Optional sector-cap sub-experiment
+
+Only if labels are in **and** A/B ran: max **3** positions per sector in the
+10-slot book. Same A/B grading. **Tail-trim presumption of guilt** (rule 4):
+report entry counts and both tails of the R distribution. Rising win rate with
+falling Sharpe/CAGR = red flag → do not promote.
+
+**This run:** sector-cap arm **not executed** (optional; A/B unconstrained book
+only). Can be a human-approved follow-up.
+
+### Verdict labels
+
+| label | meaning |
+|---|---|
+| **PROMOTE** | Clears pre-registered bar; human decides next (wire design separate) |
+| **PARK** | Inconclusive / weak; keep machinery, no book change |
+| **DEAD** | Failed iron rule or worse than residual baseline with clear sign |
+
+### Explicit non-goals
+
+- No production deploy from this doc  
+- Do not resurrect: take-profit exits, EV gate, regime entry-blocking,
+  inverse-vol sizing, gap-caps, unconditional FIP filter
+
+---
+
+## Data provenance
+
+### Snapshot race guard
+
+| check | result |
+|---|---|
+| Snapshot path | `backtest_snapshots/prod.sqlite` |
+| Manifest | none (expected for prod snapshot); bar-count sanity applied |
+| Tickers / OHLCV | **506** / **629,263** |
+| Bars min / avg / max | 14 / 1246.1 / 1261 |
+| OHLCV range | 2021-06-24 → 2026-07-02 |
+| Partial-build red flags | none (avg bars healthy) |
+
+Integrity fingerprint on same run: `fip_id` mean IC **−0.045** / t **−2.91**
+(35 weeks, N≈498) — matches the established prod fingerprint.
+
+### Sector labels
+
+| source | count |
+|---|---:|
+| Public S&P 500 GICS CSV | 496 newly filled |
+| FMP profile requests | 10 (all missing after CSV) |
+| Mapped / universe | **505 / 506 (99.8%)** |
+| With mappable ETF | 505 |
+| Still missing | **RHM** only |
+
+Persist path: `data/research/ticker_sector_map.json`.
+
+FMP aliases (`Technology`, `Consumer Defensive`, `Financial Services`) map to
+SPDRs via the alias table in `app/services/sector_map.py`.
+
+### Sector ETFs in `benchmark_prices` (auxiliary only — not tradable)
+
+| symbol | bars | min date | max date |
+|---|---:|---|---|
+| SPY | 1516 | 2020-07-06 | 2026-07-17 |
+| XLB…XLY (11) | 1512 each | 2020-07-10 | 2026-07-17 |
+
+Fetched via Alpaca `Adjustment.SPLIT` into **`benchmark_prices`** (same table as
+SPY) so they never enter the ticker universe or candidate replay.
+
+---
+
+## Results
+
+Generated: `2026-07-19T08:33:56`
+
+### IC harness (identical cross-sections, production 506-name universe)
+
+| signal | mean_ic | ic_t_stat | weeks | avg_N | reliable | ic+_pct | quintile spread |
+|---|---:|---:|---:|---:|---|---:|---:|
+| **mom_12_1_sector_resid** | **0.0578** | **2.34** | 35 | 497.7 | true | 65.7 | 0.0245 |
+| mom_12_1_resid | 0.0552 | 1.98 | 35 | 497.7 | true | 60.0 | 0.0207 |
+| mom_12_1 | 0.0531 | 1.61 | 35 | 497.7 | true | 65.7 | 0.0206 |
+| mom_12_1_sector_demeaned | 0.0340 | 1.32 | 35 | 496.7 | true | 62.9 | 0.0154 |
+
+### IC promotion grades
+
+| candidate | iron rule | t ≥ resid | promote_to_ab |
+|---|---|---|---|
+| `mom_12_1_sector_resid` | pass (IC 0.058, +sign, reliable) | **yes** (2.34 ≥ 1.98) | **yes** |
+| `mom_12_1_sector_demeaned` | pass (IC 0.034, +sign, reliable) | **no** (1.32 < 1.98) | **no** |
+
+### Portfolio A/B — `mom_12_1_sector_resid` as residual leg
+
+Config: production 80/20 residual/high-vol rank + gate percentile, `fill_mode=close`,
+cost 10 bps/side, ATR trail / gate-reset re-entry as live. Validation split
+2024-07-01.
+
+| window | arm | Sharpe | Sharpe SE (Mertens) | CAGR % | max DD % | trades | n_days |
+|---|---|---:|---:|---:|---:|---:|---:|
+| train | control (resid) | 1.30 | 0.685 | 29.2 | 21.4 | 176 | 525 |
+| train | treatment (sector resid) | **1.57** | 0.677 | **35.5** | **19.8** | 176 | 530 |
+| validation | control | **2.92** | 0.709 | **76.3** | **11.7** | 150 | 501 |
+| validation | treatment | 2.57 | 0.701 | 66.3 | 14.8 | 163 | 501 |
+| full | control | 2.09 | 0.497 | 51.6 | 21.4 | 322 | 1000 |
+| full | treatment | 2.09 | 0.491 | 51.0 | **19.8** | 337 | 1005 |
+
+**Pre-registered A/B checks**
+
+| check | result |
+|---|---|
+| val Sharpe ≥ control − 0.5·SE | **pass** (2.57 ≥ 2.92 − 0.5×0.701 = 2.5695) — **knife-edge** |
+| full Sharpe not worse | **pass** (2.09 = 2.09) |
+| full max DD not worse | **pass** (19.8 < 21.4) |
+
+Qualified long candidates: control 1086 vs treatment 1210 (sector residual
+gates a slightly larger set).
+
+---
+
+## Verdict
+
+| signal | verdict | note |
+|---|---|---|
+| **`mom_12_1_sector_resid`** | **PROMOTE → human wire-in decision** | IC modestly beats market residual; A/B clears pre-reg bar narrowly. **Do not ship from this branch.** |
+| **`mom_12_1_sector_demeaned`** | **DEAD** (for promotion) | Iron-rule IC magnitude ok, but t-stat loses to `mom_12_1_resid`. Cheap variant not competitive. |
+
+### Read carefully (for the human)
+
+1. **IC edge is real but small.** Sector residual IC 0.0578 / t 2.34 vs market
+   residual 0.0552 / t 1.98 on the **same** 35 windows — better consistency
+   (ic+ 65.7% vs 60%) and slightly higher mean, not a different factor class.
+2. **A/B is not a clear Sharpe win.** Full-period Sharpe is flat (2.09).
+   Validation Sharpe is **lower** than control (2.57 vs 2.92) and only clears
+   the pre-registered “within 0.5 SE” cushion by ~0.001. Train improves;
+   validation worsens — classic regime-split noise on ~2 years.
+3. **Risk side is friendly.** Full max DD improves (19.8% vs 21.4%); train DD
+   also better. Matches the “lower factor vol” half of the hypothesis more than
+   the “higher Sharpe” half on this window.
+4. **Survivorship / short history.** Same caveats as all current research:
+   today’s constituents, ~35 independent weekly windows, one post-2021 regime
+   dominant. Task 3 (history depth) should re-check IC stability before any
+   wire-in.
+5. **Not shipped.** Machinery lives on the research branch; production residual
+   path is untouched.
+
+---
+
+## What a human must decide next
+
+1. **Accept or reject** replacing `mom_12_1_resid` with `mom_12_1_sector_resid`
+   as the production residual (gate + 80/20 mom leg), **or** keep market residual
+   and treat sector residual as research-only.
+2. If leaning accept: require **Task 3 history-depth** confirmation (IC era split
+   pre/post-2021) before any production PR.
+3. Optional: run **sector-cap ≤3** A/B with full tail diagnostics (not run here).
+4. **Do not** merge this verdict into main strategy docs without review.
+5. Wire-in design (live sector map refresh, ETF series ops, fallback when sector
+   missing) is a **separate** approved engineering step.
+
+---
+
+## Implementation notes (research machinery)
+
+| piece | role |
+|---|---|
+| `app/services/sector_map.py` | GICS→ETF map, symbol normalise, JSON load/save |
+| `app/services/backtest_service.py` | multi-factor residual; `mom_12_1_sector_resid` in `_signal_values`; demean inject |
+| `scripts/build_ticker_sector_map.py` | SP500 CSV + FMP gap fill |
+| `scripts/fetch_sector_etfs_to_snapshot.py` | Alpaca → snapshot `benchmark_prices` |
+| `scripts/run_sector_residual_research.py` | race guard, IC, optional A/B, reports |
+| `data/research/ticker_sector_map.json` | persisted labels (research only) |
+
+---
+
+## Artifacts
+
+- JSON: `reports/sector-residual-20260719-083356.json`
+- MD copy: `reports/sector-residual-20260719-083356.md`

scripts/backfill_earnings_events.py

@@ -0,0 +1,515 @@
+"""Bulk-only historical earnings backfill for a local SQLite snapshot.
+
+The job uses FMP's date-range earnings-calendar endpoint. One request covers all
+symbols in a date window; per-symbol endpoints are intentionally not available
+in this task runner. Successful windows are committed independently so a later
+run resumes after a daily quota boundary without repeating completed windows.
+
+Example:
+  python scripts/backfill_earnings_events.py --snapshot backtest_snapshots/prod.sqlite \
+      --from-date 2012-01-01 --window-days 30 --limit 250
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import math
+import sys
+from datetime import date, datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any
+
+import httpx
+from sqlalchemy import create_engine, text
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from app.ssl_bootstrap import bootstrap_ssl  # noqa: E402
+
+bootstrap_ssl()
+
+FMP_STABLE = "https://financialmodelingprep.com/stable"
+EVENTS_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_events (
+    id INTEGER PRIMARY KEY,
+    symbol TEXT NOT NULL,
+    announce_date TEXT NOT NULL,
+    announce_time TEXT,
+    eps_estimate REAL,
+    eps_actual REAL,
+    revenue_estimate REAL,
+    revenue_actual REAL,
+    source TEXT NOT NULL,
+    fetched_at TEXT NOT NULL,
+    UNIQUE(symbol, announce_date)
+)
+"""
+META_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_backfill_meta (
+    symbol TEXT PRIMARY KEY,
+    status TEXT NOT NULL,
+    n_events INTEGER NOT NULL DEFAULT 0,
+    updated_at TEXT NOT NULL,
+    note TEXT
+)
+"""
+WINDOW_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_backfill_windows (
+    from_date TEXT NOT NULL,
+    to_date TEXT NOT NULL,
+    status TEXT NOT NULL,
+    requests INTEGER NOT NULL DEFAULT 0,
+    rows_raw INTEGER NOT NULL DEFAULT 0,
+    rows_universe INTEGER NOT NULL DEFAULT 0,
+    duplicate_rows INTEGER NOT NULL DEFAULT 0,
+    restated_rows INTEGER NOT NULL DEFAULT 0,
+    updated_at TEXT NOT NULL,
+    note TEXT,
+    PRIMARY KEY(from_date, to_date)
+)
+"""
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--snapshot", default="backtest_snapshots/prod.sqlite")
+    parser.add_argument("--from-date", default="2012-01-01")
+    parser.add_argument("--to-date", default=None)
+    parser.add_argument("--window-days", type=int, default=30)
+    parser.add_argument("--limit", type=int, default=250)
+    parser.add_argument("--sleep", type=float, default=0.35)
+    parser.add_argument(
+        "--refetch-windows",
+        action="store_true",
+        help="Re-fetch date windows already logged as done.",
+    )
+    return parser.parse_args()
+
+
+def _ensure_tables(engine) -> None:
+    with engine.begin() as conn:
+        conn.execute(text(EVENTS_DDL))
+        conn.execute(text(META_DDL))
+        conn.execute(text(WINDOW_DDL))
+
+
+def _number(value: Any) -> float | None:
+    if value is None or value == "":
+        return None
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return None
+    return result if math.isfinite(result) else None
+
+
+def _normalise_session(value: Any) -> str | None:
+    if value is None:
+        return None
+    cleaned = str(value).strip().lower().replace("_", " ").replace("-", " ")
+    aliases = {
+        "bmo": "bmo",
+        "before market open": "bmo",
+        "before open": "bmo",
+        "amc": "amc",
+        "after market close": "amc",
+        "after close": "amc",
+        "during market hours": "during",
+        "dmh": "during",
+    }
+    return aliases.get(cleaned, cleaned or None)
+
+
+def _parse_bulk_item(item: dict) -> dict | None:
+    symbol = str(item.get("symbol") or "").strip().upper().replace(".", "-")
+    raw_date = item.get("date") or item.get("earningsDate")
+    if not symbol or not raw_date:
+        return None
+    return {
+        "symbol": symbol,
+        "announce_date": str(raw_date)[:10],
+        "announce_time": _normalise_session(
+            item.get("time") or item.get("announceTime")
+        ),
+        "eps_estimate": _number(
+            item.get("epsEstimated")
+            if item.get("epsEstimated") is not None
+            else item.get("estimatedEarning")
+        ),
+        "eps_actual": _number(
+            item.get("epsActual")
+            if item.get("epsActual") is not None
+            else item.get("eps")
+        ),
+        "revenue_estimate": _number(item.get("revenueEstimated")),
+        "revenue_actual": _number(item.get("revenueActual")),
+    }
+
+
+def _windows(start: date, end: date, window_days: int) -> list[tuple[date, date]]:
+    if window_days < 1:
+        raise ValueError("window_days must be positive")
+    result: list[tuple[date, date]] = []
+    cursor = start
+    while cursor <= end:
+        window_end = min(end, cursor + timedelta(days=window_days - 1))
+        result.append((cursor, window_end))
+        cursor = window_end + timedelta(days=1)
+    return result
+
+
+def _dedupe_bulk_rows(rows: list[dict]) -> tuple[list[dict], int, int]:
+    """Prefer the most complete duplicate; use the later row as the tie-break."""
+    fields = (
+        "announce_time",
+        "eps_estimate",
+        "eps_actual",
+        "revenue_estimate",
+        "revenue_actual",
+    )
+    chosen: dict[tuple[str, str], dict] = {}
+    duplicate_extras = 0
+    restated = 0
+    for row in rows:
+        key = (str(row["symbol"]), str(row["announce_date"]))
+        previous = chosen.get(key)
+        if previous is None:
+            chosen[key] = row
+            continue
+        duplicate_extras += 1
+        if any(
+            previous.get(field) is not None
+            and row.get(field) is not None
+            and previous.get(field) != row.get(field)
+            for field in fields
+        ):
+            restated += 1
+        previous_score = sum(previous.get(field) is not None for field in fields)
+        new_score = sum(row.get(field) is not None for field in fields)
+        if new_score >= previous_score:
+            chosen[key] = row
+    return list(chosen.values()), duplicate_extras, restated
+
+
+def _upsert_events(conn, rows: list[dict]) -> int:
+    if not rows:
+        return 0
+    fetched_at = datetime.now(timezone.utc).isoformat()
+    statement = text(
+        """
+        INSERT INTO earnings_events (
+            symbol, announce_date, announce_time, eps_estimate, eps_actual,
+            revenue_estimate, revenue_actual, source, fetched_at
+        ) VALUES (
+            :symbol, :announce_date, :announce_time, :eps_estimate, :eps_actual,
+            :revenue_estimate, :revenue_actual, 'fmp_earnings_calendar', :fetched_at
+        )
+        ON CONFLICT(symbol, announce_date) DO UPDATE SET
+            announce_time=COALESCE(excluded.announce_time, earnings_events.announce_time),
+            eps_estimate=COALESCE(excluded.eps_estimate, earnings_events.eps_estimate),
+            eps_actual=COALESCE(excluded.eps_actual, earnings_events.eps_actual),
+            revenue_estimate=COALESCE(excluded.revenue_estimate, earnings_events.revenue_estimate),
+            revenue_actual=COALESCE(excluded.revenue_actual, earnings_events.revenue_actual),
+            source=excluded.source,
+            fetched_at=excluded.fetched_at
+        """
+    )
+    conn.execute(statement, [{**row, "fetched_at": fetched_at} for row in rows])
+    return len(rows)
+
+
+async def _fetch_bulk_window(
+    client: httpx.AsyncClient, api_key: str, start: date, end: date
+) -> tuple[list[dict], int, str | None]:
+    response = await client.get(
+        f"{FMP_STABLE}/earnings-calendar",
+        params={"from": start.isoformat(), "to": end.isoformat(), "apikey": api_key},
+    )
+    if response.status_code in (402, 403):
+        return [], response.status_code, "bulk_endpoint_unavailable"
+    if response.status_code == 429:
+        return [], response.status_code, "daily_limit_reached"
+    response.raise_for_status()
+    payload = response.json()
+    if not isinstance(payload, list):
+        return [], response.status_code, f"unexpected_payload:{type(payload).__name__}"
+    rows = []
+    for item in payload:
+        if isinstance(item, dict):
+            parsed = _parse_bulk_item(item)
+            if parsed:
+                rows.append(parsed)
+    return rows, response.status_code, None
+
+
+def _write_window_status(
+    engine,
+    *,
+    start: date,
+    end: date,
+    status: str,
+    raw_n: int = 0,
+    universe_n: int = 0,
+    duplicate_n: int = 0,
+    restated_n: int = 0,
+    note: str | None = None,
+) -> None:
+    with engine.begin() as conn:
+        conn.execute(
+            text(
+                """
+                INSERT INTO earnings_backfill_windows(
+                    from_date, to_date, status, requests, rows_raw, rows_universe,
+                    duplicate_rows, restated_rows, updated_at, note
+                ) VALUES (:a, :b, :status, 1, :raw, :uni, :dup, :rest, :now, :note)
+                ON CONFLICT(from_date, to_date) DO UPDATE SET
+                    status=excluded.status,
+                    requests=earnings_backfill_windows.requests + 1,
+                    rows_raw=excluded.rows_raw,
+                    rows_universe=excluded.rows_universe,
+                    duplicate_rows=excluded.duplicate_rows,
+                    restated_rows=excluded.restated_rows,
+                    updated_at=excluded.updated_at,
+                    note=excluded.note
+                """
+            ),
+            {
+                "a": start.isoformat(),
+                "b": end.isoformat(),
+                "status": status,
+                "raw": raw_n,
+                "uni": universe_n,
+                "dup": duplicate_n,
+                "rest": restated_n,
+                "now": datetime.now(timezone.utc).isoformat(),
+                "note": note,
+            },
+        )
+
+
+async def _main() -> None:
+    args = _parse_args()
+    snapshot = Path(args.snapshot)
+    if not snapshot.exists():
+        raise SystemExit(f"Snapshot not found: {snapshot}")
+
+    from app.config import settings
+
+    if not settings.fmp_api_key:
+        raise SystemExit("FMP_API_KEY required")
+    start = date.fromisoformat(args.from_date)
+    end = date.fromisoformat(args.to_date) if args.to_date else date.today()
+    if start > end:
+        raise SystemExit("--from-date must not be after --to-date")
+
+    engine = create_engine(f"sqlite:///{snapshot.resolve().as_posix()}", future=True)
+    _ensure_tables(engine)
+    all_windows = _windows(start, end, int(args.window_days))
+    with engine.connect() as conn:
+        symbols = [
+            str(row[0]).upper().replace(".", "-")
+            for row in conn.execute(text("SELECT symbol FROM tickers ORDER BY symbol"))
+        ]
+        completed = {
+            (str(row[0]), str(row[1]))
+            for row in conn.execute(
+                text(
+                    "SELECT from_date, to_date FROM earnings_backfill_windows "
+                    "WHERE status='done'"
+                )
+            )
+        }
+    pending = [
+        window
+        for window in all_windows
+        if args.refetch_windows
+        or (window[0].isoformat(), window[1].isoformat()) not in completed
+    ]
+    universe = set(symbols)
+    print(f"Snapshot: {snapshot}")
+    print(f"Universe: {len(symbols)} symbols")
+    print(f"Window: {start} -> {end}")
+    print(
+        f"Bulk windows: {len(all_windows)} total; "
+        f"{len(all_windows) - len(pending)} done; {len(pending)} pending"
+    )
+    print("Provider: FMP bulk earnings-calendar only")
+
+    requests_this_run = 0
+    rows_upserted = 0
+    duplicate_rows = 0
+    restated_rows = 0
+    stop_note: str | None = None
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        for index, (window_start, window_end) in enumerate(pending, 1):
+            if requests_this_run >= int(args.limit):
+                stop_note = "request_budget_exhausted"
+                break
+            try:
+                raw_rows, status_code, error = await _fetch_bulk_window(
+                    client, settings.fmp_api_key, window_start, window_end
+                )
+            except Exception as exc:
+                raw_rows, status_code = [], 0
+                error = f"request_error:{type(exc).__name__}:{exc}"
+            requests_this_run += 1
+            if error:
+                _write_window_status(
+                    engine,
+                    start=window_start,
+                    end=window_end,
+                    status="error",
+                    note=f"http={status_code} {error}"[:300],
+                )
+                stop_note = error
+                print(
+                    f"STOP {window_start}..{window_end}: {error} "
+                    f"(http={status_code}, request={requests_this_run})"
+                )
+                break
+
+            in_universe = [row for row in raw_rows if row["symbol"] in universe]
+            deduped, duplicate_n, restated_n = _dedupe_bulk_rows(in_universe)
+            with engine.begin() as conn:
+                rows_upserted += _upsert_events(conn, deduped)
+            _write_window_status(
+                engine,
+                start=window_start,
+                end=window_end,
+                status="done",
+                raw_n=len(raw_rows),
+                universe_n=len(deduped),
+                duplicate_n=duplicate_n,
+                restated_n=restated_n,
+                note="bulk",
+            )
+            duplicate_rows += duplicate_n
+            restated_rows += restated_n
+            if index == 1 or index % 10 == 0 or index == len(pending):
+                print(
+                    f"progress windows={index}/{len(pending)} "
+                    f"requests={requests_this_run}/{args.limit} "
+                    f"last={window_start}..{window_end} rows={len(deduped)}"
+                )
+            if args.sleep > 0:
+                await asyncio.sleep(float(args.sleep))
+
+    with engine.begin() as conn:
+        windows_done = int(
+            conn.execute(
+                text(
+                    "SELECT COUNT(*) FROM earnings_backfill_windows "
+                    "WHERE status='done' AND from_date >= :a AND to_date <= :b"
+                ),
+                {"a": start.isoformat(), "b": end.isoformat()},
+            ).scalar_one()
+        )
+        complete = windows_done >= len(all_windows)
+        if complete:
+            now = datetime.now(timezone.utc).isoformat()
+            for symbol in symbols:
+                count = int(
+                    conn.execute(
+                        text(
+                            "SELECT COUNT(*) FROM earnings_events "
+                            "WHERE symbol=:symbol AND announce_date BETWEEN :a AND :b"
+                        ),
+                        {"symbol": symbol, "a": start.isoformat(), "b": end.isoformat()},
+                    ).scalar_one()
+                )
+                conn.execute(
+                    text(
+                        """
+                        INSERT INTO earnings_backfill_meta(symbol, status, n_events, updated_at, note)
+                        VALUES (:symbol, 'done', :count, :now, 'bulk_complete')
+                        ON CONFLICT(symbol) DO UPDATE SET
+                            status='done', n_events=excluded.n_events,
+                            updated_at=excluded.updated_at, note=excluded.note
+                        """
+                    ),
+                    {"symbol": symbol, "count": count, "now": now},
+                )
+        params = {"a": start.isoformat(), "b": end.isoformat()}
+        total_events = int(
+            conn.execute(
+                text(
+                    "SELECT COUNT(*) FROM earnings_events "
+                    "WHERE symbol IN (SELECT symbol FROM tickers) "
+                    "AND announce_date BETWEEN :a AND :b"
+                ),
+                params,
+            ).scalar_one()
+        )
+        paired_events = int(
+            conn.execute(
+                text(
+                    "SELECT COUNT(*) FROM earnings_events "
+                    "WHERE symbol IN (SELECT symbol FROM tickers) "
+                    "AND announce_date BETWEEN :a AND :b "
+                    "AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL"
+                ),
+                params,
+            ).scalar_one()
+        )
+        date_range = conn.execute(
+            text(
+                "SELECT MIN(announce_date), MAX(announce_date) FROM earnings_events "
+                "WHERE symbol IN (SELECT symbol FROM tickers) "
+                "AND announce_date BETWEEN :a AND :b"
+            ),
+            params,
+        ).fetchone()
+        done_symbols = int(
+            conn.execute(
+                text("SELECT COUNT(*) FROM earnings_backfill_meta WHERE status='done'")
+            ).scalar_one()
+        )
+        totals = conn.execute(
+            text(
+                "SELECT COALESCE(SUM(requests),0), COALESCE(SUM(duplicate_rows),0), "
+                "COALESCE(SUM(restated_rows),0) FROM earnings_backfill_windows "
+                "WHERE from_date >= :a AND to_date <= :b"
+            ),
+            params,
+        ).fetchone()
+
+    summary = {
+        "mode": "fmp_bulk_date_range_only",
+        "window": {"from": start.isoformat(), "to": end.isoformat()},
+        "window_days": int(args.window_days),
+        "bulk_windows_total": len(all_windows),
+        "bulk_windows_done": windows_done,
+        "bulk_requests_this_run": requests_this_run,
+        "bulk_requests_logged_total": int(totals[0]),
+        "rows_upserted_this_run": rows_upserted,
+        "duplicate_rows_this_run": duplicate_rows,
+        "restated_rows_this_run": restated_rows,
+        "duplicate_rows_logged_total": int(totals[1]),
+        "restated_rows_logged_total": int(totals[2]),
+        "dedupe_policy": (
+            "UNIQUE(symbol, announce_date); prefer more non-null fields, then "
+            "the provider's later occurrence; non-null bulk fields replace prior "
+            "values while null bulk fields retain existing values"
+        ),
+        "events_in_window": total_events,
+        "events_with_actual_and_estimate": paired_events,
+        "symbols_done": done_symbols,
+        "symbols_universe": len(symbols),
+        "announce_date_range": {"min": date_range[0], "max": date_range[1]},
+        "request_budget": int(args.limit),
+        "stop_note": stop_note,
+        "complete": complete,
+    }
+    output = Path("reports/earnings-backfill-status.json")
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(summary, indent=2))
+    print(f"Wrote {output}")
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

scripts/extend_snapshot_universe.py

@@ -0,0 +1,490 @@
+"""Extend a *copy* of the production backtest snapshot with broad-universe OHLCV.
+
+Research only — never writes to production Postgres.
+
+Pipeline
+--------
+1. Copy ``--source`` snapshot (default ``backtest_snapshots/prod.sqlite``) to
+   ``--output`` (default ``backtest_snapshots/research.sqlite``).
+2. Resolve symbol pool = nasdaq_all ∪ sp500 via ``ticker_universe_service``.
+3. Fetch ~5y daily bars from Alpaca for symbols missing (or short) in the copy.
+4. Insert new tickers + OHLCV; mark them in side table ``research_rank_only``
+   so the harness can feed signal IC without GTL/candidate replay.
+5. Write a **completion manifest** (``<output>.manifest.json``) with ticker /
+   OHLCV / rank_only counts and finished-at. Breadth runners refuse to start
+   without a matching complete manifest — same class of guard as calendar
+   truncation (see 2026-07-18 21:14 race: orphaned +0.0575 on a partial pool).
+
+Resume-friendly: re-running skips symbols that already have ≥ ``--min-bars``.
+A ``--limit`` smoke run writes ``complete: false`` so breadth mode still refuses.
+
+Example
+-------
+  python scripts/extend_snapshot_universe.py \\
+      --source backtest_snapshots/prod.sqlite \\
+      --output backtest_snapshots/research.sqlite \\
+      --force-copy
+
+  # smoke: first 50 missing symbols only
+  python scripts/extend_snapshot_universe.py --limit 50
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import shutil
+import sys
+import time
+from datetime import date, datetime, timedelta, timezone
+from pathlib import Path
+
+from sqlalchemy import create_engine, text
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from app.ssl_bootstrap import bootstrap_ssl  # noqa: E402
+
+bootstrap_ssl()
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument(
+        "--source",
+        default="backtest_snapshots/prod.sqlite",
+        help="Existing prod snapshot to copy (read-only after copy).",
+    )
+    p.add_argument(
+        "--output",
+        default="backtest_snapshots/research.sqlite",
+        help="Research snapshot path (created/updated).",
+    )
+    p.add_argument(
+        "--force-copy",
+        action="store_true",
+        help="Overwrite output by re-copying from source first.",
+    )
+    p.add_argument(
+        "--history-days",
+        type=int,
+        default=1825,
+        help="OHLCV lookback days (~5y). Default 1825.",
+    )
+    p.add_argument(
+        "--min-bars",
+        type=int,
+        default=260,
+        help="Skip re-fetch when a symbol already has this many bars.",
+    )
+    p.add_argument(
+        "--limit",
+        type=int,
+        default=None,
+        help="Max *new* symbols to fetch (smoke tests).",
+    )
+    p.add_argument(
+        "--sleep",
+        type=float,
+        default=0.15,
+        help="Seconds between Alpaca symbol requests (rate-limit cushion).",
+    )
+    p.add_argument(
+        "--max-retries",
+        type=int,
+        default=5,
+        help="Retries per symbol on RateLimitError.",
+    )
+    p.add_argument(
+        "--source-symbols-only",
+        action="store_true",
+        help=(
+            "Refresh only symbols present in --source. Useful for repairing "
+            "per-symbol depth without re-fetching the broad rank-only pool."
+        ),
+    )
+    p.add_argument("--quiet", action="store_true")
+    return p.parse_args()
+
+
+def _ensure_rank_only_table(engine) -> None:
+    """DDL in its own connection/transaction (don't share with ORM Session)."""
+    with engine.begin() as conn:
+        conn.execute(
+            text(
+                """
+                CREATE TABLE IF NOT EXISTS research_rank_only (
+                    ticker_id INTEGER PRIMARY KEY,
+                    symbol TEXT NOT NULL UNIQUE
+                )
+                """
+            )
+        )
+
+
+async def _resolve_pool() -> tuple[list[str], dict[str, str]]:
+    """Return sorted unique symbols and source labels.
+
+    Offline-safe: does **not** use production Postgres or SystemSetting cache
+    (those require a schema). Public sources first, then FMP, then seeds.
+    """
+    from app.services.ticker_universe_service import (
+        _SEED_UNIVERSES,
+        _fetch_universe_symbols_from_fmp,
+        _fetch_universe_symbols_from_public,
+        _normalise_symbols,
+    )
+
+    sources: dict[str, str] = {}
+    symbols: set[str] = set()
+
+    for universe in ("nasdaq_all", "sp500"):
+        cleaned: list[str] = []
+        src = "none"
+
+        public_symbols, public_failures, public_source = (
+            await _fetch_universe_symbols_from_public(universe)
+        )
+        cleaned = _normalise_symbols(public_symbols)
+        if cleaned:
+            src = public_source or "public"
+        else:
+            if public_failures:
+                print(
+                    f"  WARNING: public fetch {universe}: "
+                    f"{'; '.join(public_failures[:3])}"
+                )
+            try:
+                fmp_symbols = await _fetch_universe_symbols_from_fmp(universe)
+                cleaned = _normalise_symbols(fmp_symbols)
+                if cleaned:
+                    src = "fmp"
+            except Exception as exc:
+                print(f"  WARNING: FMP fetch {universe}: {exc}")
+
+        if not cleaned:
+            cleaned = _normalise_symbols(_SEED_UNIVERSES.get(universe, []))
+            if cleaned:
+                src = "seed"
+                print(
+                    f"  WARNING: {universe} fell back to seed list "
+                    f"({len(cleaned)} symbols) — not full universe"
+                )
+
+        if not cleaned:
+            print(f"  WARNING: universe {universe} returned no symbols")
+            continue
+
+        sources[universe] = src
+        symbols.update(cleaned)
+        print(f"  {universe}: {len(cleaned)} symbols (source={src})")
+
+    return sorted(symbols), sources
+
+
+async def _fetch_symbol_bars(
+    provider,
+    symbol: str,
+    start: date,
+    end: date,
+    *,
+    max_retries: int,
+    sleep_s: float,
+) -> list:
+    from app.exceptions import ProviderError, RateLimitError
+
+    for attempt in range(max_retries):
+        try:
+            bars = await provider.fetch_ohlcv(symbol, start, end)
+            if sleep_s > 0:
+                await asyncio.sleep(sleep_s)
+            return bars
+        except RateLimitError:
+            wait = min(60.0, 2.0 ** attempt)
+            print(f"  rate limited on {symbol}; sleep {wait:.0f}s")
+            await asyncio.sleep(wait)
+        except ProviderError as exc:
+            if attempt + 1 >= max_retries:
+                raise
+            await asyncio.sleep(1.0)
+            _ = exc
+    return []
+
+
+async def _main() -> None:
+    # ROOT is already on sys.path; keep the helper import path-local.
+    from research_snapshot_manifest import (  # type: ignore[import-not-found]
+        clear_manifest,
+        write_completion_manifest,
+    )
+
+    args = _parse_args()
+    source = Path(args.source)
+    output = Path(args.output)
+    if not source.exists():
+        raise SystemExit(f"Source snapshot not found: {source}")
+
+    source_engine = create_engine(
+        f"sqlite:///{source.resolve().as_posix()}", future=True
+    )
+    with source_engine.connect() as conn:
+        source_symbols = {
+            str(row[0]) for row in conn.execute(text("SELECT symbol FROM tickers"))
+        }
+    source_engine.dispose()
+
+    # Any rebuild/update invalidates prior completion until we finish cleanly.
+    clear_manifest(output)
+
+    if args.force_copy or not output.exists():
+        output.parent.mkdir(parents=True, exist_ok=True)
+        if output.exists():
+            output.unlink()
+        print(f"Copying {source} → {output}")
+        shutil.copy2(source, output)
+    else:
+        print(f"Updating existing research snapshot: {output}")
+
+    from app.config import settings
+    from app.providers.alpaca import AlpacaOHLCVProvider
+
+    if not settings.alpaca_api_key or not settings.alpaca_api_secret:
+        raise SystemExit("ALPACA_API_KEY / ALPACA_API_SECRET required in .env")
+
+    provider = AlpacaOHLCVProvider(settings.alpaca_api_key, settings.alpaca_api_secret)
+    end = date.today()
+    start = end - timedelta(days=int(args.history_days))
+
+    print("Resolving universe pool (nasdaq_all ∪ sp500)…")
+    if args.source_symbols_only:
+        pool = sorted(source_symbols)
+        sources = {"pool": "source_snapshot"}
+        print("  source snapshot: symbol pool selected")
+    else:
+        pool, sources = await _resolve_pool()
+    print(f"Pool size: {len(pool)} (sources={sources})")
+
+    # Sync sqlite via raw SQL — one short transaction per symbol so a failed
+    # write never leaves the session in "transaction is inactive".
+    engine = create_engine(
+        f"sqlite:///{output.resolve().as_posix()}",
+        future=True,
+    )
+    _ensure_rank_only_table(engine)
+
+    with engine.connect() as conn:
+        existing_rows = conn.execute(
+            text("SELECT id, symbol FROM tickers")
+        ).fetchall()
+        existing_ids = {str(sym): int(tid) for tid, sym in existing_rows}
+        prod_symbols = set(source_symbols)
+
+        bar_counts: dict[str, int] = {}
+        for sym, tid in existing_ids.items():
+            n = conn.execute(
+                text("SELECT COUNT(*) FROM ohlcv_records WHERE ticker_id = :tid"),
+                {"tid": tid},
+            ).scalar_one()
+            bar_counts[sym] = int(n)
+
+        to_fetch: list[str] = []
+        for sym in pool:
+            if sym in existing_ids and bar_counts.get(sym, 0) >= args.min_bars:
+                continue
+            to_fetch.append(sym)
+
+        if args.limit is not None:
+            to_fetch = to_fetch[: max(0, int(args.limit))]
+
+        print(f"Symbols to fetch/extend: {len(to_fetch)}")
+        ok = 0
+        fail = 0
+        t0 = time.monotonic()
+
+        insert_ohlcv = text(
+            """
+            INSERT INTO ohlcv_records
+                (ticker_id, date, open, high, low, close, volume, created_at)
+            VALUES
+                (:ticker_id, :date, :open, :high, :low, :close, :volume, :created_at)
+            """
+        )
+
+        for index, sym in enumerate(to_fetch, 1):
+            try:
+                bars = await _fetch_symbol_bars(
+                    provider,
+                    sym,
+                    start,
+                    end,
+                    max_retries=args.max_retries,
+                    sleep_s=args.sleep,
+                )
+            except Exception as exc:
+                fail += 1
+                if not args.quiet:
+                    print(f"  [{index}/{len(to_fetch)}] {sym} FAIL {exc}")
+                continue
+
+            if not bars:
+                fail += 1
+                if not args.quiet:
+                    print(f"  [{index}/{len(to_fetch)}] {sym} empty")
+                continue
+
+            try:
+                with engine.begin() as write:
+                    ticker_id = existing_ids.get(sym)
+                    is_new = ticker_id is None
+                    if is_new:
+                        write.execute(
+                            text(
+                                "INSERT INTO tickers (symbol, name, created_at) "
+                                "VALUES (:sym, NULL, :created)"
+                            ),
+                            {
+                                "sym": sym,
+                                "created": datetime.now(timezone.utc).isoformat(),
+                            },
+                        )
+                        ticker_id = int(
+                            write.execute(
+                                text("SELECT id FROM tickers WHERE symbol = :sym"),
+                                {"sym": sym},
+                            ).scalar_one()
+                        )
+                        existing_ids[sym] = ticker_id
+
+                    write.execute(
+                        text(
+                            "DELETE FROM ohlcv_records WHERE ticker_id = :tid "
+                            "AND date >= :start AND date <= :end"
+                        ),
+                        {
+                            "tid": ticker_id,
+                            "start": start.isoformat(),
+                            "end": end.isoformat(),
+                        },
+                    )
+                    now = datetime.now(timezone.utc).replace(tzinfo=None)
+                    write.execute(
+                        insert_ohlcv,
+                        [
+                            {
+                                "ticker_id": ticker_id,
+                                "date": b.date.isoformat(),
+                                "open": float(b.open),
+                                "high": float(b.high),
+                                "low": float(b.low),
+                                "close": float(b.close),
+                                "volume": int(b.volume),
+                                "created_at": now.isoformat(),
+                            }
+                            for b in bars
+                        ],
+                    )
+                    if is_new and sym not in prod_symbols:
+                        write.execute(
+                            text(
+                                "INSERT OR REPLACE INTO research_rank_only "
+                                "(ticker_id, symbol) VALUES (:tid, :sym)"
+                            ),
+                            {"tid": ticker_id, "sym": sym},
+                        )
+            except Exception as exc:
+                fail += 1
+                if not args.quiet:
+                    print(f"  [{index}/{len(to_fetch)}] {sym} WRITE FAIL {exc}")
+                continue
+
+            ok += 1
+            if not args.quiet and (index % 25 == 0 or index == len(to_fetch)):
+                elapsed = time.monotonic() - t0
+                print(
+                    f"  progress {index}/{len(to_fetch)} ok={ok} fail={fail} "
+                    f"elapsed={elapsed/60:.1f}m last={sym} bars={len(bars)}"
+                )
+
+        benchmark_rows = 0
+        try:
+            benchmark_bars = await _fetch_symbol_bars(
+                provider,
+                "SPY",
+                start,
+                end,
+                max_retries=args.max_retries,
+                sleep_s=args.sleep,
+            )
+            with engine.begin() as write:
+                write.execute(
+                    text(
+                        "DELETE FROM benchmark_prices WHERE symbol='SPY' "
+                        "AND date >= :start AND date <= :end"
+                    ),
+                    {"start": start.isoformat(), "end": end.isoformat()},
+                )
+                if benchmark_bars:
+                    write.execute(
+                        text(
+                            "INSERT INTO benchmark_prices(symbol, date, close) "
+                            "VALUES ('SPY', :date, :close)"
+                        ),
+                        [
+                            {"date": bar.date.isoformat(), "close": float(bar.close)}
+                            for bar in benchmark_bars
+                        ],
+                    )
+                    benchmark_rows = len(benchmark_bars)
+        except Exception as exc:
+            print(f"  benchmark SPY refresh FAIL {exc}")
+
+        rank_only_n = conn.execute(
+            text("SELECT COUNT(*) FROM research_rank_only")
+        ).scalar_one()
+        ticker_n = conn.execute(text("SELECT COUNT(*) FROM tickers")).scalar_one()
+        ohlcv_n = conn.execute(
+            text("SELECT COUNT(*) FROM ohlcv_records")
+        ).scalar_one()
+
+    # Full planned work only when --limit is unset. Smoke runs stay incomplete
+    # so breadth mode cannot mythologize a 50-symbol toy pool.
+    is_complete = args.limit is None
+    manifest_path = write_completion_manifest(
+        output,
+        complete=is_complete,
+        sources=sources,
+        history_days=int(args.history_days),
+        min_bars=int(args.min_bars),
+        fetch_ok=ok,
+        fetch_fail=fail,
+        limit=args.limit,
+        extra={
+            "prod_symbols_at_start": len(prod_symbols),
+            "pool_size": len(pool),
+            "to_fetch": len(to_fetch),
+            "source_symbols_only": bool(args.source_symbols_only),
+            "benchmark_spy_rows": benchmark_rows,
+        },
+    )
+
+    print("Done.")
+    print(f"  output: {output}")
+    print(f"  tickers: {ticker_n}")
+    print(f"  ohlcv rows: {ohlcv_n}")
+    print(f"  research_rank_only: {rank_only_n}")
+    print(f"  fetched ok/fail: {ok}/{fail}")
+    print(
+        f"  completion manifest: {manifest_path} "
+        f"(complete={is_complete})"
+    )
+    if not is_complete:
+        print(
+            "  NOTE: --limit set → complete=false; breadth runners will refuse "
+            "this snapshot until a full extend finishes."
+        )
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

scripts/import_dolthub_earnings.py

@@ -0,0 +1,657 @@
+"""Import the public post-no-preference/earnings DoltHub database.
+
+The earnings calendar and EPS history are separate tables in the source. This
+importer aligns them monotonically per symbol, keeps every calendar event for
+the defensive gap study, and stores the longer EPS history separately for SUE
+scaling. EPS history without an announcement date is never exposed as a live
+signal event.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import sqlite3
+from collections import defaultdict
+from datetime import date, datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any
+
+
+EVENTS_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_events (
+    id INTEGER PRIMARY KEY,
+    symbol TEXT NOT NULL,
+    announce_date TEXT NOT NULL,
+    announce_time TEXT,
+    eps_estimate REAL,
+    eps_actual REAL,
+    revenue_estimate REAL,
+    revenue_actual REAL,
+    source TEXT NOT NULL,
+    fetched_at TEXT NOT NULL,
+    period_end_date TEXT,
+    UNIQUE(symbol, announce_date)
+)
+"""
+META_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_backfill_meta (
+    symbol TEXT PRIMARY KEY,
+    status TEXT NOT NULL,
+    n_events INTEGER NOT NULL DEFAULT 0,
+    updated_at TEXT NOT NULL,
+    note TEXT
+)
+"""
+SURPRISE_HISTORY_DDL = """
+CREATE TABLE IF NOT EXISTS earnings_surprise_history (
+    symbol TEXT NOT NULL,
+    period_end_date TEXT NOT NULL,
+    eps_estimate REAL,
+    eps_actual REAL,
+    source TEXT NOT NULL,
+    fetched_at TEXT NOT NULL,
+    PRIMARY KEY(symbol, period_end_date)
+)
+"""
+
+SKIP_EVENT_COST = 45.0
+SKIP_PERIOD_COST = 45.0
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--snapshot", default="backtest_snapshots/prod.sqlite")
+    parser.add_argument("--calendar-csv", required=True)
+    parser.add_argument("--history-csv", required=True)
+    parser.add_argument("--from-date", default="2020-01-22")
+    parser.add_argument("--to-date", required=True)
+    parser.add_argument("--source-commit", required=True)
+    parser.add_argument(
+        "--source-url",
+        default="https://www.dolthub.com/repositories/post-no-preference/earnings",
+    )
+    parser.add_argument("--max-period-lag-days", type=int, default=90)
+    parser.add_argument("--max-period-lead-days", type=int, default=14)
+    parser.add_argument(
+        "--status-output", default="reports/earnings-backfill-status.json"
+    )
+    return parser.parse_args()
+
+
+def _normalise_symbol(value: Any) -> str:
+    return str(value or "").strip().upper().replace(".", "-")
+
+
+def _normalise_session(value: Any) -> str | None:
+    cleaned = str(value or "").strip().lower().replace("_", " ").replace("-", " ")
+    aliases = {
+        "before market open": "bmo",
+        "before open": "bmo",
+        "bmo": "bmo",
+        "after market close": "amc",
+        "after close": "amc",
+        "amc": "amc",
+        "during market hours": "during",
+        "dmh": "during",
+    }
+    return aliases.get(cleaned, cleaned or None)
+
+
+def _number(value: Any) -> float | None:
+    if value is None or str(value).strip() == "":
+        return None
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return None
+    return result if math.isfinite(result) else None
+
+
+def _read_calendar(
+    path: Path,
+    universe: set[str],
+    start: date,
+    end: date,
+) -> tuple[dict[str, list[dict[str, Any]]], dict[str, int]]:
+    by_key: dict[tuple[str, date], dict[str, Any]] = {}
+    raw_rows = 0
+    universe_rows = 0
+    duplicate_rows = 0
+    restated_rows = 0
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for raw in csv.DictReader(handle):
+            raw_rows += 1
+            symbol = _normalise_symbol(raw.get("act_symbol"))
+            raw_date = str(raw.get("date") or "")[:10]
+            if symbol not in universe or not raw_date:
+                continue
+            event_date = date.fromisoformat(raw_date)
+            if not start <= event_date <= end:
+                continue
+            universe_rows += 1
+            row = {
+                "symbol": symbol,
+                "announce_date": event_date,
+                "announce_time": _normalise_session(raw.get("when")),
+            }
+            key = (symbol, event_date)
+            previous = by_key.get(key)
+            if previous is not None:
+                duplicate_rows += 1
+                if (
+                    previous.get("announce_time") is not None
+                    and row.get("announce_time") is not None
+                    and previous["announce_time"] != row["announce_time"]
+                ):
+                    restated_rows += 1
+                if row.get("announce_time") is not None:
+                    by_key[key] = row
+            else:
+                by_key[key] = row
+    grouped: dict[str, list[dict[str, Any]]] = defaultdict(list)
+    for row in by_key.values():
+        grouped[row["symbol"]].append(row)
+    for rows in grouped.values():
+        rows.sort(key=lambda item: item["announce_date"])
+    return grouped, {
+        "raw_rows": raw_rows,
+        "universe_rows_in_window": universe_rows,
+        "deduped_rows_in_window": len(by_key),
+        "duplicate_rows": duplicate_rows,
+        "restated_rows": restated_rows,
+    }
+
+
+def _read_history(
+    path: Path, universe: set[str]
+) -> tuple[dict[str, list[dict[str, Any]]], dict[str, int]]:
+    by_key: dict[tuple[str, date], dict[str, Any]] = {}
+    raw_rows = 0
+    universe_rows = 0
+    duplicate_rows = 0
+    restated_rows = 0
+    fields = ("eps_actual", "eps_estimate")
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for raw in csv.DictReader(handle):
+            raw_rows += 1
+            symbol = _normalise_symbol(raw.get("act_symbol"))
+            raw_date = str(raw.get("period_end_date") or "")[:10]
+            if symbol not in universe or not raw_date:
+                continue
+            universe_rows += 1
+            period_end = date.fromisoformat(raw_date)
+            row = {
+                "symbol": symbol,
+                "period_end_date": period_end,
+                "eps_actual": _number(raw.get("reported")),
+                "eps_estimate": _number(raw.get("estimate")),
+            }
+            key = (symbol, period_end)
+            previous = by_key.get(key)
+            if previous is not None:
+                duplicate_rows += 1
+                if any(
+                    previous.get(field) is not None
+                    and row.get(field) is not None
+                    and previous[field] != row[field]
+                    for field in fields
+                ):
+                    restated_rows += 1
+                previous_score = sum(previous.get(field) is not None for field in fields)
+                row_score = sum(row.get(field) is not None for field in fields)
+                if row_score >= previous_score:
+                    by_key[key] = row
+            else:
+                by_key[key] = row
+    grouped: dict[str, list[dict[str, Any]]] = defaultdict(list)
+    for row in by_key.values():
+        grouped[row["symbol"]].append(row)
+    for rows in grouped.values():
+        rows.sort(key=lambda item: item["period_end_date"])
+    return grouped, {
+        "raw_rows": raw_rows,
+        "universe_rows": universe_rows,
+        "deduped_rows": len(by_key),
+        "duplicate_rows": duplicate_rows,
+        "restated_rows": restated_rows,
+    }
+
+
+def _match_cost(event: dict[str, Any], period: dict[str, Any]) -> float:
+    delta = (event["announce_date"] - period["period_end_date"]).days
+    missing_session_penalty = 3.0 if event.get("announce_time") is None else 0.0
+    return float(abs(delta - 30)) + missing_session_penalty
+
+
+def _align_symbol(
+    events: list[dict[str, Any]],
+    periods: list[dict[str, Any]],
+    *,
+    max_lag_days: int,
+    max_lead_days: int,
+) -> tuple[list[tuple[int, int]], list[int], list[int]]:
+    """Return a minimum-cost monotonic calendar-to-period alignment."""
+    n_events = len(events)
+    n_periods = len(periods)
+    scores = [[0.0] * (n_periods + 1) for _ in range(n_events + 1)]
+    choices = [[""] * (n_periods + 1) for _ in range(n_events + 1)]
+    for event_index in range(n_events - 1, -1, -1):
+        scores[event_index][n_periods] = (
+            scores[event_index + 1][n_periods] + SKIP_EVENT_COST
+        )
+        choices[event_index][n_periods] = "event"
+    for period_index in range(n_periods - 1, -1, -1):
+        scores[n_events][period_index] = (
+            scores[n_events][period_index + 1] + SKIP_PERIOD_COST
+        )
+        choices[n_events][period_index] = "period"
+
+    for event_index in range(n_events - 1, -1, -1):
+        for period_index in range(n_periods - 1, -1, -1):
+            options = [
+                (
+                    scores[event_index + 1][period_index] + SKIP_EVENT_COST,
+                    2,
+                    "event",
+                ),
+                (
+                    scores[event_index][period_index + 1] + SKIP_PERIOD_COST,
+                    1,
+                    "period",
+                ),
+            ]
+            delta = (
+                events[event_index]["announce_date"]
+                - periods[period_index]["period_end_date"]
+            ).days
+            if -max_lead_days <= delta <= max_lag_days:
+                options.append(
+                    (
+                        scores[event_index + 1][period_index + 1]
+                        + _match_cost(events[event_index], periods[period_index]),
+                        0,
+                        "match",
+                    )
+                )
+            score, _, choice = min(options)
+            scores[event_index][period_index] = score
+            choices[event_index][period_index] = choice
+
+    matches: list[tuple[int, int]] = []
+    unmatched_events: list[int] = []
+    unmatched_periods: list[int] = []
+    event_index = 0
+    period_index = 0
+    while event_index < n_events or period_index < n_periods:
+        if event_index >= n_events:
+            unmatched_periods.extend(range(period_index, n_periods))
+            break
+        if period_index >= n_periods:
+            unmatched_events.extend(range(event_index, n_events))
+            break
+        choice = choices[event_index][period_index]
+        if choice == "match":
+            matches.append((event_index, period_index))
+            event_index += 1
+            period_index += 1
+        elif choice == "period":
+            unmatched_periods.append(period_index)
+            period_index += 1
+        else:
+            unmatched_events.append(event_index)
+            event_index += 1
+    return matches, unmatched_events, unmatched_periods
+
+
+def _ensure_schema(connection: sqlite3.Connection) -> None:
+    connection.execute(EVENTS_DDL)
+    columns = {
+        str(row[1])
+        for row in connection.execute("PRAGMA table_info(earnings_events)")
+    }
+    if "period_end_date" not in columns:
+        connection.execute("ALTER TABLE earnings_events ADD COLUMN period_end_date TEXT")
+    connection.execute(META_DDL)
+    connection.execute(SURPRISE_HISTORY_DDL)
+
+
+def _main() -> None:
+    args = _parse_args()
+    snapshot = Path(args.snapshot)
+    calendar_csv = Path(args.calendar_csv)
+    history_csv = Path(args.history_csv)
+    for path in (snapshot, calendar_csv, history_csv):
+        if not path.exists():
+            raise SystemExit(f"Missing input: {path}")
+    start = date.fromisoformat(args.from_date)
+    end = date.fromisoformat(args.to_date)
+    if start > end:
+        raise SystemExit("--from-date must not be after --to-date")
+
+    connection = sqlite3.connect(snapshot)
+    try:
+        universe = {
+            _normalise_symbol(row[0])
+            for row in connection.execute("SELECT symbol FROM tickers")
+        }
+    finally:
+        connection.close()
+    calendar, calendar_stats = _read_calendar(calendar_csv, universe, start, end)
+    history, history_stats = _read_history(history_csv, universe)
+
+    aligned_events: list[dict[str, Any]] = []
+    pairing_deltas: list[int] = []
+    unmatched_calendar = 0
+    unmatched_periods_in_pairing_window = 0
+    matched = 0
+    for symbol in sorted(universe):
+        events = calendar.get(symbol, [])
+        lower = start - timedelta(days=int(args.max_period_lag_days))
+        upper = end + timedelta(days=int(args.max_period_lead_days))
+        periods = [
+            row
+            for row in history.get(symbol, [])
+            if lower <= row["period_end_date"] <= upper
+        ]
+        matches, unmatched_events, unmatched_periods = _align_symbol(
+            events,
+            periods,
+            max_lag_days=int(args.max_period_lag_days),
+            max_lead_days=int(args.max_period_lead_days),
+        )
+        matched_by_event = {event_index: period_index for event_index, period_index in matches}
+        matched += len(matches)
+        unmatched_calendar += len(unmatched_events)
+        unmatched_periods_in_pairing_window += len(unmatched_periods)
+        for event_index, event in enumerate(events):
+            row = dict(event)
+            period_index = matched_by_event.get(event_index)
+            if period_index is None:
+                row.update(
+                    {
+                        "period_end_date": None,
+                        "eps_actual": None,
+                        "eps_estimate": None,
+                    }
+                )
+            else:
+                period = periods[period_index]
+                row.update(
+                    {
+                        "period_end_date": period["period_end_date"],
+                        "eps_actual": period["eps_actual"],
+                        "eps_estimate": period["eps_estimate"],
+                    }
+                )
+                pairing_deltas.append(
+                    (event["announce_date"] - period["period_end_date"]).days
+                )
+            aligned_events.append(row)
+
+    now = datetime.now(timezone.utc).isoformat()
+    source = f"dolthub_post_no_preference@{args.source_commit}"
+    conflicting_existing_rows = 0
+    conflicting_existing_fields = 0
+    preserved_existing_fields = 0
+    incoming_keys = {
+        (row["symbol"], row["announce_date"].isoformat()) for row in aligned_events
+    }
+    connection = sqlite3.connect(snapshot)
+    try:
+        _ensure_schema(connection)
+        existing = {
+            (str(row[0]), str(row[1])): row
+            for row in connection.execute(
+                """
+                SELECT symbol, announce_date, announce_time, eps_estimate,
+                       eps_actual, period_end_date, source
+                FROM earnings_events
+                WHERE announce_date BETWEEN ? AND ?
+                """,
+                (start.isoformat(), end.isoformat()),
+            )
+        }
+        upsert = """
+            INSERT INTO earnings_events(
+                symbol, announce_date, announce_time, eps_estimate, eps_actual,
+                revenue_estimate, revenue_actual, source, fetched_at, period_end_date
+            ) VALUES (?, ?, ?, ?, ?, NULL, NULL, ?, ?, ?)
+            ON CONFLICT(symbol, announce_date) DO UPDATE SET
+                announce_time=COALESCE(earnings_events.announce_time, excluded.announce_time),
+                eps_estimate=COALESCE(earnings_events.eps_estimate, excluded.eps_estimate),
+                eps_actual=COALESCE(earnings_events.eps_actual, excluded.eps_actual),
+                period_end_date=COALESCE(excluded.period_end_date, earnings_events.period_end_date),
+                source=excluded.source,
+                fetched_at=excluded.fetched_at
+        """
+        for row in aligned_events:
+            key = (row["symbol"], row["announce_date"].isoformat())
+            old = existing.get(key)
+            retained = 0
+            conflicts = 0
+            if old is not None:
+                old_values = {
+                    "announce_time": old[2],
+                    "eps_estimate": old[3],
+                    "eps_actual": old[4],
+                    "period_end_date": old[5],
+                }
+                new_values = {
+                    "announce_time": row.get("announce_time"),
+                    "eps_estimate": row.get("eps_estimate"),
+                    "eps_actual": row.get("eps_actual"),
+                    "period_end_date": (
+                        row["period_end_date"].isoformat()
+                        if row.get("period_end_date")
+                        else None
+                    ),
+                }
+                for field, new_value in new_values.items():
+                    old_value = old_values[field]
+                    if field != "period_end_date" and old_value is not None:
+                        retained += 1
+                    if new_value is not None and old_value is not None:
+                        if field in {"eps_estimate", "eps_actual"}:
+                            differs = not math.isclose(
+                                float(new_value), float(old_value), rel_tol=0.0, abs_tol=1e-9
+                            )
+                        else:
+                            differs = str(new_value) != str(old_value)
+                        conflicts += int(differs)
+                conflicting_existing_rows += int(conflicts > 0)
+                conflicting_existing_fields += conflicts
+                preserved_existing_fields += retained
+            row_source = source
+            if retained and old is not None:
+                row_source = f"{old[6]}+calendar:{source}"
+            connection.execute(
+                upsert,
+                (
+                    row["symbol"],
+                    row["announce_date"].isoformat(),
+                    row.get("announce_time"),
+                    row.get("eps_estimate"),
+                    row.get("eps_actual"),
+                    row_source,
+                    now,
+                    (
+                        row["period_end_date"].isoformat()
+                        if row.get("period_end_date")
+                        else None
+                    ),
+                ),
+            )
+
+        history_upsert = """
+            INSERT INTO earnings_surprise_history(
+                symbol, period_end_date, eps_estimate, eps_actual, source, fetched_at
+            ) VALUES (?, ?, ?, ?, ?, ?)
+            ON CONFLICT(symbol, period_end_date) DO UPDATE SET
+                eps_estimate=COALESCE(excluded.eps_estimate, earnings_surprise_history.eps_estimate),
+                eps_actual=COALESCE(excluded.eps_actual, earnings_surprise_history.eps_actual),
+                source=excluded.source,
+                fetched_at=excluded.fetched_at
+        """
+        for symbol, rows in history.items():
+            connection.executemany(
+                history_upsert,
+                [
+                    (
+                        symbol,
+                        row["period_end_date"].isoformat(),
+                        row.get("eps_estimate"),
+                        row.get("eps_actual"),
+                        source,
+                        now,
+                    )
+                    for row in rows
+                ],
+            )
+
+        for symbol in sorted(universe):
+            count = int(
+                connection.execute(
+                    """
+                    SELECT COUNT(*) FROM earnings_events
+                    WHERE symbol=? AND announce_date BETWEEN ? AND ?
+                    """,
+                    (symbol, start.isoformat(), end.isoformat()),
+                ).fetchone()[0]
+            )
+            connection.execute(
+                """
+                INSERT INTO earnings_backfill_meta(symbol, status, n_events, updated_at, note)
+                VALUES (?, 'done', ?, ?, 'dolthub_bulk_complete')
+                ON CONFLICT(symbol) DO UPDATE SET
+                    status='done', n_events=excluded.n_events,
+                    updated_at=excluded.updated_at, note=excluded.note
+                """,
+                (symbol, count, now),
+            )
+        connection.commit()
+
+        params = (start.isoformat(), end.isoformat())
+        total_events = int(
+            connection.execute(
+                """
+                SELECT COUNT(*) FROM earnings_events
+                WHERE symbol IN (SELECT symbol FROM tickers)
+                  AND announce_date BETWEEN ? AND ?
+                """,
+                params,
+            ).fetchone()[0]
+        )
+        paired_events = int(
+            connection.execute(
+                """
+                SELECT COUNT(*) FROM earnings_events
+                WHERE symbol IN (SELECT symbol FROM tickers)
+                  AND announce_date BETWEEN ? AND ?
+                  AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL
+                """,
+                params,
+            ).fetchone()[0]
+        )
+        date_range = connection.execute(
+            """
+            SELECT MIN(announce_date), MAX(announce_date) FROM earnings_events
+            WHERE symbol IN (SELECT symbol FROM tickers)
+              AND announce_date BETWEEN ? AND ?
+            """,
+            params,
+        ).fetchone()
+        source_symbols = set(calendar)
+        history_complete = int(
+            connection.execute(
+                """
+                SELECT COUNT(*) FROM earnings_surprise_history
+                WHERE symbol IN (SELECT symbol FROM tickers)
+                  AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL
+                """
+            ).fetchone()[0]
+        )
+    finally:
+        connection.close()
+
+    deltas = sorted(pairing_deltas)
+    summary = {
+        "mode": "dolthub_public_bulk_clone",
+        "window": {"from": start.isoformat(), "to": end.isoformat()},
+        "coverage_amendment": {
+            "approved_by_user": True,
+            "reason": "FMP free tier blocks historical bulk earnings",
+            "original_start": "2016-01-04",
+            "amended_announcement_start": start.isoformat(),
+        },
+        "source": {
+            "repository": args.source_url,
+            "commit": args.source_commit,
+            "license": "CC-BY-SA-4.0",
+            "upstream_provider_documented": False,
+        },
+        "bulk_windows_total": 1,
+        "bulk_windows_done": 1,
+        "bulk_requests_logged_total": 1,
+        "bulk_exports": 2,
+        "calendar": calendar_stats,
+        "eps_history": {**history_stats, "complete_actual_and_estimate": history_complete},
+        "pairing": {
+            "method": "minimum-cost monotonic alignment per symbol",
+            "allowed_announce_minus_period_end_days": [
+                -int(args.max_period_lead_days),
+                int(args.max_period_lag_days),
+            ],
+            "matched_calendar_events": matched,
+            "unmatched_calendar_events": unmatched_calendar,
+            "unmatched_periods_in_pairing_window": unmatched_periods_in_pairing_window,
+            "announce_minus_period_end_days": {
+                "min": min(deltas) if deltas else None,
+                "median": deltas[len(deltas) // 2] if deltas else None,
+                "max": max(deltas) if deltas else None,
+            },
+            "pre_2020_eps_history_use": (
+                "trailing_surprise_stdev_only; never treated as an announcement "
+                "or live signal event"
+            ),
+        },
+        "duplicate_rows_logged_total": (
+            calendar_stats["duplicate_rows"] + history_stats["duplicate_rows"]
+        ),
+        "restated_rows_logged_total": (
+            calendar_stats["restated_rows"]
+            + history_stats["restated_rows"]
+            + conflicting_existing_rows
+        ),
+        "conflicting_existing_rows": conflicting_existing_rows,
+        "conflicting_existing_fields": conflicting_existing_fields,
+        "preserved_existing_fields": preserved_existing_fields,
+        "existing_enrichment_events_not_in_dolthub_calendar": max(
+            0, total_events - len(incoming_keys)
+        ),
+        "dedupe_policy": (
+            "UNIQUE(symbol, announce_date); normalise dot/dash symbols; retain one "
+            "calendar row per key; preserve existing non-null session/EPS values from "
+            "the prior FMP/Alpha Vantage partial backfill, then fill nulls and all "
+            "remaining symbols from DoltHub; attach DoltHub period-end alignment"
+        ),
+        "events_in_window": total_events,
+        "events_with_actual_and_estimate": paired_events,
+        "symbols_done": len(universe),
+        "symbols_universe": len(universe),
+        "symbols_with_dolthub_calendar": len(source_symbols),
+        "symbols_without_dolthub_calendar": sorted(universe - source_symbols),
+        "announce_date_range": {"min": date_range[0], "max": date_range[1]},
+        "complete": True,
+    }
+    output = Path(args.status_output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(summary, indent=2))
+    print(f"Wrote {output}")
+
+
+if __name__ == "__main__":
+    _main()

scripts/research_snapshot_manifest.py

@@ -0,0 +1,172 @@
+"""Completion manifest for research.sqlite — cheap race guard.
+
+The 2026-07-18 21:14 breadth run fired while ``extend_snapshot_universe`` was
+still (or had just been) building the snapshot. Harness and shared-filter
+recomputes agree on *complete* data, so the orphaned +0.0575 was incomplete
+universe, not a code path bug.
+
+Same class of protection as calendar-truncation assertions in the research
+matrix: refuse to read results from a half-built artifact.
+
+Layout
+------
+Sidecar path: ``<snapshot>.manifest.json`` next to the sqlite file
+(e.g. ``backtest_snapshots/research.sqlite.manifest.json``).
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy import create_engine, text
+
+MANIFEST_SCHEMA_VERSION = 1
+
+
+def manifest_path_for(snapshot: Path) -> Path:
+    """Sidecar path for a research snapshot."""
+    return Path(str(snapshot) + ".manifest.json")
+
+
+def _count_snapshot(snapshot: Path) -> dict[str, int]:
+    engine = create_engine(
+        f"sqlite:///{snapshot.resolve().as_posix()}",
+        future=True,
+    )
+    try:
+        with engine.connect() as conn:
+            ticker_n = int(conn.execute(text("SELECT COUNT(*) FROM tickers")).scalar_one())
+            ohlcv_n = int(
+                conn.execute(text("SELECT COUNT(*) FROM ohlcv_records")).scalar_one()
+            )
+            try:
+                rank_only_n = int(
+                    conn.execute(text("SELECT COUNT(*) FROM research_rank_only")).scalar_one()
+                )
+            except Exception:
+                rank_only_n = 0
+    finally:
+        engine.dispose()
+    return {
+        "ticker_count": ticker_n,
+        "ohlcv_row_count": ohlcv_n,
+        "rank_only_count": rank_only_n,
+    }
+
+
+def write_completion_manifest(
+    snapshot: Path,
+    *,
+    complete: bool,
+    sources: dict[str, str] | None = None,
+    history_days: int | None = None,
+    min_bars: int | None = None,
+    fetch_ok: int | None = None,
+    fetch_fail: int | None = None,
+    limit: int | None = None,
+    extra: dict[str, Any] | None = None,
+) -> Path:
+    """Write (or overwrite) the sidecar completion manifest for *snapshot*."""
+    snapshot = Path(snapshot)
+    counts = _count_snapshot(snapshot) if snapshot.exists() else {
+        "ticker_count": 0,
+        "ohlcv_row_count": 0,
+        "rank_only_count": 0,
+    }
+    payload: dict[str, Any] = {
+        "schema_version": MANIFEST_SCHEMA_VERSION,
+        "snapshot": snapshot.name,
+        "snapshot_resolved": str(snapshot.resolve()) if snapshot.exists() else str(snapshot),
+        "complete": bool(complete),
+        "finished_at": datetime.now(timezone.utc).isoformat(),
+        **counts,
+        "sources": sources or {},
+        "history_days": history_days,
+        "min_bars": min_bars,
+        "fetch_ok": fetch_ok,
+        "fetch_fail": fetch_fail,
+        "limit": limit,
+    }
+    if extra:
+        payload["extra"] = extra
+    path = manifest_path_for(snapshot)
+    path.write_text(json.dumps(payload, indent=2, default=str) + "\n", encoding="utf-8")
+    return path
+
+
+def clear_manifest(snapshot: Path) -> None:
+    """Remove any existing completion manifest (start of a rebuild)."""
+    path = manifest_path_for(Path(snapshot))
+    if path.exists():
+        path.unlink()
+
+
+def load_manifest(snapshot: Path) -> dict[str, Any] | None:
+    path = manifest_path_for(Path(snapshot))
+    if not path.exists():
+        return None
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def assert_research_snapshot_complete(snapshot: Path) -> dict[str, Any]:
+    """Refuse breadth-mode work unless the extender finished cleanly.
+
+    Raises ``SystemExit`` with a clear message on any failure (missing
+    manifest, incomplete flag, or live counts that no longer match the
+    recorded totals — e.g. a mid-run overwrite of the sqlite file).
+    """
+    snapshot = Path(snapshot)
+    if not snapshot.exists():
+        raise SystemExit(
+            f"Research snapshot missing: {snapshot}\n"
+            "Build it with: python scripts/extend_snapshot_universe.py"
+        )
+
+    path = manifest_path_for(snapshot)
+    if not path.exists():
+        raise SystemExit(
+            f"Research snapshot completion manifest missing: {path}\n"
+            "Refusing breadth run — this is the guard that would have caught "
+            "the 2026-07-18 21:14 race against a half-built research.sqlite.\n"
+            "Re-run extend_snapshot_universe.py to completion (no --limit), "
+            "or for a trusted existing full snapshot:\n"
+            "  python -c \"from pathlib import Path; "
+            "from scripts.research_snapshot_manifest import write_completion_manifest; "
+            f"write_completion_manifest(Path(r'{snapshot}'), complete=True)\""
+        )
+
+    try:
+        manifest = json.loads(path.read_text(encoding="utf-8"))
+    except json.JSONDecodeError as exc:
+        raise SystemExit(f"Corrupt research snapshot manifest {path}: {exc}") from exc
+
+    if not manifest.get("complete"):
+        raise SystemExit(
+            f"Research snapshot marked incomplete in {path}\n"
+            f"(finished_at={manifest.get('finished_at')}, limit={manifest.get('limit')}).\n"
+            "Re-run extend_snapshot_universe.py without --limit until Done."
+        )
+
+    live = _count_snapshot(snapshot)
+    mismatches: list[str] = []
+    for key in ("ticker_count", "ohlcv_row_count", "rank_only_count"):
+        recorded = manifest.get(key)
+        if recorded is None:
+            mismatches.append(f"{key}: missing in manifest")
+            continue
+        if int(recorded) != int(live[key]):
+            mismatches.append(
+                f"{key}: manifest={recorded} live={live[key]}"
+            )
+    if mismatches:
+        raise SystemExit(
+            "Research snapshot does not match its completion manifest "
+            f"({path}). Likely a partial rewrite or concurrent extend:\n  - "
+            + "\n  - ".join(mismatches)
+            + "\nRe-run extend_snapshot_universe.py to completion."
+        )
+
+    return {**manifest, "live_counts": live}

scripts/run_fip_breadth_diagnostics.py

@@ -0,0 +1,669 @@
+"""fip_id breadth diagnostics — single-sourced through harness mask helpers.
+
+Uses the same collection + ``_filter_liquid_breadth_week_rich`` as
+``run_backtest`` signal_eval. No parallel mask implementation.
+
+Single-sourced liquid-breadth fip diagnostics through harness mask helpers.
+Re-runs unconditional / tier / prod-subset / mom-conditional ICs and context
+signals. Requires a complete research.sqlite completion manifest.
+
+Research branch only. Example:
+
+  .\\.venv\\Scripts\\python.exe scripts\\run_fip_breadth_diagnostics.py ^
+      --research-snapshot backtest_snapshots\\research.sqlite ^
+      --prod-snapshot backtest_snapshots\\prod.sqlite ^
+      --workers 6 --allow-spawn
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import multiprocessing as mp
+import os
+import sys
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from datetime import date, datetime
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy import create_engine, text
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+# Match production signal_eval cadence / reliability bars.
+MIN_CROSS = 20
+MIN_RELIABLE = 12
+MOM_WINNER_PCT = 80.0
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--research-snapshot", default="backtest_snapshots/research.sqlite")
+    p.add_argument("--prod-snapshot", default="backtest_snapshots/prod.sqlite")
+    p.add_argument("--top-n", type=int, default=1500)
+    p.add_argument("--min-price", type=float, default=5.0)
+    p.add_argument("--workers", type=int, default=max(1, (mp.cpu_count() or 4) - 1))
+    p.add_argument("--allow-spawn", action="store_true")
+    p.add_argument(
+        "--dump-weeks",
+        type=int,
+        default=0,
+        help="Weeks of liquid membership symbol lists to embed (default 0 — keep reports compact)",
+    )
+    p.add_argument("--out", default=None)
+    p.add_argument("--quiet", action="store_true")
+    return p.parse_args()
+
+
+def _week_ord(wk: tuple[int, int]) -> int:
+    return int(wk[0]) * 53 + int(wk[1])
+
+
+def _nonoverlap(weeks: list[tuple[int, int]], stride: int) -> list[tuple[int, int]]:
+    from app.services.backtest_service import _nonoverlapping_weeks
+
+    return _nonoverlapping_weeks(weeks, stride)
+
+
+def _ic_from_weekly(
+    week_pairs: dict[tuple[int, int], list[tuple[float, float]]],
+) -> dict[str, Any]:
+    from app.services.backtest_service import HORIZON, _spearman
+
+    stride = max(1, round(HORIZON / 5))
+    usable = [wk for wk, ps in week_pairs.items() if len(ps) >= MIN_CROSS]
+    kept = _nonoverlap(usable, stride)
+    ics: list[float] = []
+    sizes: list[int] = []
+    for wk in kept:
+        ps = week_pairs[wk]
+        if len(ps) < MIN_CROSS:
+            continue
+        ic = _spearman([p[0] for p in ps], [p[1] for p in ps])
+        if ic is not None:
+            ics.append(ic)
+            sizes.append(len(ps))
+    if not ics:
+        return {
+            "mean_ic": None,
+            "ic_t_stat": None,
+            "weeks": 0,
+            "avg_cross_section": None,
+            "ic_positive_pct": None,
+            "reliable": False,
+        }
+    mean_ic = sum(ics) / len(ics)
+    if len(ics) > 1:
+        var = sum((x - mean_ic) ** 2 for x in ics) / (len(ics) - 1)
+        std = math.sqrt(var) if var > 0 else 0.0
+        t_stat = mean_ic / std * math.sqrt(len(ics)) if std > 0 else None
+    else:
+        t_stat = None
+    return {
+        "mean_ic": round(mean_ic, 4),
+        "ic_t_stat": round(t_stat, 2) if t_stat is not None else None,
+        "weeks": len(ics),
+        "avg_cross_section": round(sum(sizes) / len(sizes), 1),
+        "ic_positive_pct": round(sum(1 for x in ics if x > 0) / len(ics) * 100, 1),
+        "reliable": len(ics) >= MIN_RELIABLE,
+    }
+
+
+def _worker(payload: tuple) -> dict:
+    """Return harness-style signal series for one ticker (liquid-mode dicts)."""
+    symbol, ords, opens, highs, lows, closes, volumes, spy = payload
+    from types import SimpleNamespace
+    from app.services.backtest_service import _signal_series
+
+    bars = [
+        SimpleNamespace(
+            date=date.fromordinal(int(o)),
+            open=float(op),
+            high=float(hi),
+            low=float(lo),
+            close=float(cl),
+            volume=float(vo),
+        )
+        for o, op, hi, lo, cl, vo in zip(ords, opens, highs, lows, closes, volumes)
+    ]
+    return _signal_series(bars, spy, symbol=symbol)
+
+
+def _load_spy(conn) -> dict[date, float]:
+    rows = conn.execute(
+        text("SELECT date, close FROM benchmark_prices WHERE symbol='SPY' ORDER BY date")
+    ).fetchall()
+    out: dict[date, float] = {}
+    for d, c in rows:
+        if isinstance(d, str):
+            d = date.fromisoformat(d[:10])
+        out[d] = float(c)
+    return out
+
+
+def _load_job(conn, symbol: str, spy: dict) -> tuple | None:
+    tid = conn.execute(
+        text("SELECT id FROM tickers WHERE symbol=:s"), {"s": symbol}
+    ).scalar()
+    if tid is None:
+        return None
+    rows = conn.execute(
+        text(
+            "SELECT date, open, high, low, close, volume FROM ohlcv_records "
+            "WHERE ticker_id=:t ORDER BY date"
+        ),
+        {"t": tid},
+    ).fetchall()
+    if len(rows) < 90:
+        return None
+    ords, opens, highs, lows, closes, vols = [], [], [], [], [], []
+    for d, o, h, l, c, v in rows:
+        if isinstance(d, str):
+            d = date.fromisoformat(d[:10])
+        ords.append(d.toordinal())
+        opens.append(float(o))
+        highs.append(float(h))
+        lows.append(float(l))
+        closes.append(float(c))
+        vols.append(float(v or 0))
+    return (symbol, ords, opens, highs, lows, closes, vols, spy)
+
+
+def main() -> None:
+    args = _parse_args()
+    research = Path(args.research_snapshot)
+    prod = Path(args.prod_snapshot)
+
+    # Refuse half-built research.sqlite (2026-07-18 21:14 race).
+    scripts_dir = Path(__file__).resolve().parent
+    if str(scripts_dir) not in sys.path:
+        sys.path.insert(0, str(scripts_dir))
+    from research_snapshot_manifest import (  # type: ignore[import-not-found]
+        assert_research_snapshot_complete,
+    )
+
+    manifest = assert_research_snapshot_complete(research)
+    if not args.quiet:
+        print(
+            f"Manifest ok: tickers={manifest.get('ticker_count')} "
+            f"ohlcv={manifest.get('ohlcv_row_count')} "
+            f"finished_at={manifest.get('finished_at')}",
+            flush=True,
+        )
+
+    # Force harness liquid-mode collection (same env as breadth run).
+    os.environ["BACKTEST_LIQUID_BREADTH"] = str(int(args.top_n))
+    os.environ["BACKTEST_LIQUID_MIN_PRICE"] = str(float(args.min_price))
+    if args.allow_spawn:
+        os.environ["BACKTEST_ALLOW_SPAWN"] = "1"
+
+    from app.services.backtest_service import (
+        HORIZON,
+        _filter_liquid_breadth_week_rich,
+        _liquid_breadth_week_stats,
+        _signal_evaluation,
+    )
+
+    eng = create_engine(f"sqlite:///{research.resolve().as_posix()}")
+    prod_symbols: set[str] = set()
+    if prod.exists():
+        peng = create_engine(f"sqlite:///{prod.resolve().as_posix()}")
+        with peng.connect() as c:
+            prod_symbols = {
+                str(r[0]) for r in c.execute(text("SELECT symbol FROM tickers"))
+            }
+        peng.dispose()
+
+    with eng.connect() as conn:
+        spy = _load_spy(conn)
+        symbols = [
+            str(r[0])
+            for r in conn.execute(text("SELECT symbol FROM tickers ORDER BY symbol"))
+        ]
+        jobs = []
+        for i, sym in enumerate(symbols, 1):
+            job = _load_job(conn, sym, spy)
+            if job is not None:
+                jobs.append(job)
+            if not args.quiet and i % 500 == 0:
+                print(f"  queued {i}/{len(symbols)}", flush=True)
+
+    if not args.quiet:
+        print(f"Collecting harness signal series for {len(jobs)} tickers…", flush=True)
+
+    collected: dict = defaultdict(lambda: defaultdict(list))
+    workers = max(1, int(args.workers))
+
+    def _merge(series: dict) -> None:
+        for name, weeks in series.items():
+            for wk, recs in weeks.items():
+                # week keys may arrive as lists after JSON; normalize to tuple
+                key = tuple(wk) if not isinstance(wk, tuple) else wk
+                collected[name][key].extend(recs)
+
+    if workers == 1:
+        for j, job in enumerate(jobs, 1):
+            _merge(_worker(job))
+            if not args.quiet and j % 200 == 0:
+                print(f"  series {j}/{len(jobs)}", flush=True)
+    else:
+        ctx = mp.get_context("spawn") if args.allow_spawn or sys.platform == "win32" else None
+        with ProcessPoolExecutor(max_workers=workers, mp_context=ctx) as pool:
+            futs = [pool.submit(_worker, job) for job in jobs]
+            for j, fut in enumerate(as_completed(futs), 1):
+                try:
+                    _merge(fut.result())
+                except Exception as exc:
+                    if not args.quiet:
+                        print(f"  worker error: {exc}", flush=True)
+                if not args.quiet and j % 200 == 0:
+                    print(f"  series {j}/{len(jobs)}", flush=True)
+
+    # --- Harness signal_eval (authoritative unconditional ICs) ---
+    harness_rows = _signal_evaluation(dict(collected))
+    harness_by_name = {r["signal"]: r for r in harness_rows}
+
+    top_n = int(args.top_n)
+    min_price = float(args.min_price)
+    fip_weeks = collected.get("fip_id") or {}
+    mom_weeks = collected.get("mom_12_1") or {}
+    vol_weeks = collected.get("vol_6m") or {}
+    momr_weeks = collected.get("mom_12_1_resid") or {}
+
+    # Index mom/vol by (week, symbol) for joins
+    def _index(weeks_map: dict) -> dict[tuple, dict]:
+        out: dict[tuple, dict] = {}
+        for wk, recs in weeks_map.items():
+            key_wk = tuple(wk) if not isinstance(wk, tuple) else wk
+            for rec in recs:
+                if not isinstance(rec, dict):
+                    continue
+                sym = rec.get("symbol")
+                if not sym:
+                    continue
+                out[(key_wk, str(sym))] = rec
+        return out
+
+    mom_ix = _index(mom_weeks)
+    vol_ix = _index(vol_weeks)
+    momr_ix = _index(momr_weeks)
+
+    # Per-week membership + extended checks via shared rich filter
+    same_week: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    lag_week: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    tier_hi: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    tier_lo: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    prod_sub: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    mom_cond: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    vol_pairs: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    mom_pairs: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+    momr_pairs: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
+
+    ordered = sorted((tuple(w) for w in fip_weeks.keys()), key=_week_ord)
+    prev: dict[tuple, tuple] = {}
+    for i, wk in enumerate(ordered):
+        if i:
+            prev[wk] = ordered[i - 1]
+
+    # Prior-week dvol for lag: (symbol, week) from fip recs
+    dvol_sw: dict[tuple[str, tuple], float] = {}
+    for wk, recs in fip_weeks.items():
+        key_wk = tuple(wk) if not isinstance(wk, tuple) else wk
+        for rec in recs:
+            if isinstance(rec, dict) and rec.get("symbol") and rec.get("median_dvol_63"):
+                dvol_sw[(str(rec["symbol"]), key_wk)] = float(rec["median_dvol_63"])
+
+    membership_dumps: list[dict] = []
+    dump_count = 0
+    stride = max(1, round(HORIZON / 5))
+    dump_weeks = _nonoverlap(ordered, stride)[: max(0, int(args.dump_weeks))]
+
+    for wk_raw, recs in fip_weeks.items():
+        wk = tuple(wk_raw) if not isinstance(wk_raw, tuple) else wk_raw
+        stats = _liquid_breadth_week_stats(recs, top_n=top_n, min_price=min_price)
+        rich = _filter_liquid_breadth_week_rich(
+            recs, top_n=top_n, min_price=min_price
+        )
+        for rank, row in enumerate(rich, 1):
+            same_week[wk].append((float(row["val"]), float(row["fwd"])))
+            if rank <= 800:
+                tier_hi[wk].append((float(row["val"]), float(row["fwd"])))
+            elif rank <= top_n:
+                tier_lo[wk].append((float(row["val"]), float(row["fwd"])))
+            sym = row.get("symbol")
+            if sym and str(sym) in prod_symbols:
+                prod_sub[wk].append((float(row["val"]), float(row["fwd"])))
+            # Join mom for conditional
+            mrec = mom_ix.get((wk, str(sym))) if sym else None
+            if mrec is not None:
+                row["mom_12_1"] = mrec.get("val")
+
+        # Mom-conditional among liquid fip set
+        with_mom = [
+            r for r in rich
+            if r.get("mom_12_1") is not None or mom_ix.get((wk, str(r.get("symbol"))))
+        ]
+        # ensure mom filled
+        for r in with_mom:
+            if r.get("mom_12_1") is None and r.get("symbol"):
+                m = mom_ix.get((wk, str(r["symbol"])))
+                if m is not None:
+                    r["mom_12_1"] = m["val"]
+        with_mom = [r for r in rich if r.get("mom_12_1") is not None]
+        if len(with_mom) >= MIN_CROSS:
+            with_mom.sort(key=lambda r: float(r["mom_12_1"]))
+            cut = int(math.floor(len(with_mom) * (MOM_WINNER_PCT / 100.0)))
+            for r in with_mom[cut:]:
+                mom_cond[wk].append((float(r["val"]), float(r["fwd"])))
+
+        # Context signals via same shared filter on their own pools
+        for r in _filter_liquid_breadth_week_rich(
+            vol_weeks.get(wk_raw) or vol_weeks.get(wk) or [],
+            top_n=top_n,
+            min_price=min_price,
+        ):
+            vol_pairs[wk].append((float(r["val"]), float(r["fwd"])))
+        for r in _filter_liquid_breadth_week_rich(
+            mom_weeks.get(wk_raw) or mom_weeks.get(wk) or [],
+            top_n=top_n,
+            min_price=min_price,
+        ):
+            mom_pairs[wk].append((float(r["val"]), float(r["fwd"])))
+        for r in _filter_liquid_breadth_week_rich(
+            momr_weeks.get(wk_raw) or momr_weeks.get(wk) or [],
+            top_n=top_n,
+            min_price=min_price,
+        ):
+            momr_pairs[wk].append((float(r["val"]), float(r["fwd"])))
+
+        # Lagged membership using prior week dvol on current fip pool
+        pw = prev.get(wk)
+        if pw is not None:
+            lagged_recs = []
+            for rec in recs:
+                if not isinstance(rec, dict) or not rec.get("symbol"):
+                    continue
+                pdv = dvol_sw.get((str(rec["symbol"]), pw))
+                if pdv is None or pdv <= 0:
+                    continue
+                # Clone with lag dvol for ranking
+                lagged_recs.append({
+                    **rec,
+                    "median_dvol_63": pdv,
+                })
+            for r in _filter_liquid_breadth_week_rich(
+                lagged_recs, top_n=top_n, min_price=min_price
+            ):
+                lag_week[wk].append((float(r["val"]), float(r["fwd"])))
+
+        if wk in dump_weeks and dump_count < args.dump_weeks:
+            membership_dumps.append({
+                "week": list(wk),
+                "stats": stats,
+                "symbols": sorted(
+                    str(r["symbol"]) for r in rich if r.get("symbol")
+                ),
+                "n_symbols": len(rich),
+            })
+            dump_count += 1
+
+    # IC rows
+    checks = {
+        "fip_harness_signal_eval": {
+            "note": "Authoritative harness _signal_evaluation on collected fip_id",
+            **(harness_by_name.get("fip_id") or {}),
+        },
+        "fip_same_week_via_shared_filter": {
+            "note": "Same collected data, IC via shared _filter_liquid_breadth_week_rich",
+            **_ic_from_weekly(same_week),
+        },
+        "fip_lagged_membership_1w": {
+            "note": "Top-N by prior-week $vol on current fip pool (shared filter)",
+            **_ic_from_weekly(lag_week),
+        },
+        "fip_tier_1_800": {
+            "note": "Senior liquid ranks 1–800",
+            **_ic_from_weekly(tier_hi),
+        },
+        "fip_tier_801_1500": {
+            "note": "Junior liquid ranks 801–top_n",
+            **_ic_from_weekly(tier_lo),
+        },
+        "fip_prod_universe_subset": {
+            "note": "Prod.sqlite symbols inside liquid fip set",
+            **_ic_from_weekly(prod_sub),
+        },
+        "fip_momentum_conditional_top20pct": {
+            "note": (
+                f"Among liquid fip set, mom_12_1 ≥ P{MOM_WINNER_PCT:.0f} "
+                "(paper / gate-relevant)"
+            ),
+            **_ic_from_weekly(mom_cond),
+        },
+        "vol_6m_liquid": {
+            "note": "vol_6m through shared filter",
+            **_ic_from_weekly(vol_pairs),
+        },
+        "mom_12_1_liquid": {
+            "note": "raw mom through shared filter",
+            **_ic_from_weekly(mom_pairs),
+        },
+        "mom_12_1_resid_liquid": {
+            "note": "residual mom through shared filter",
+            **_ic_from_weekly(momr_pairs),
+        },
+    }
+
+    h = checks["fip_harness_signal_eval"]
+    s = checks["fip_same_week_via_shared_filter"]
+    cond = checks["fip_momentum_conditional_top20pct"]
+    prod = checks["fip_prod_universe_subset"]
+    hi = checks["fip_tier_1_800"]
+    lo = checks["fip_tier_801_1500"]
+    lag = checks["fip_lagged_membership_1w"]
+
+    # Self-consistency: harness eval vs manual IC on same filter must match
+    harness_ic = h.get("mean_ic")
+    shared_ic = s.get("mean_ic")
+    consistent = (
+        harness_ic is not None
+        and shared_ic is not None
+        and abs(float(harness_ic) - float(shared_ic)) < 0.005
+    )
+
+    mom_alive = (
+        cond.get("mean_ic") is not None
+        and float(cond["mean_ic"]) < 0
+        and abs(float(cond["mean_ic"])) >= 0.03
+        and bool(cond.get("reliable"))
+    )
+
+    results = {
+        "generated_at": datetime.now().isoformat(),
+        "research_snapshot": str(research.resolve()),
+        "top_n": top_n,
+        "min_price": min_price,
+        "prod_subset_n": len(prod_symbols),
+        "panel_tickers": len(jobs),
+        "single_source": (
+            "diagnostics uses harness _signal_series + "
+            "_filter_liquid_breadth_week_rich only (no parallel mask)"
+        ),
+        "avg_cross_section_semantics": (
+            "avg_cross_section = post-mask IC sample size. "
+            "avg_raw_pool = pre-filter observations. "
+            "avg_eligible_pre_mask = pass price+dvol before top-N. "
+            "mask_binds_pct = weeks where eligible_pre_mask > top_n."
+        ),
+        "harness_self_consistent": consistent,
+        "checks": checks,
+        "membership_dumps": membership_dumps,
+        "interpretation": {
+            "harness_and_shared_filter_agree": consistent,
+            "mask_binds_pct": h.get("mask_binds_pct"),
+            "avg_eligible_pre_mask": h.get("avg_eligible_pre_mask"),
+            "avg_raw_pool": h.get("avg_raw_pool"),
+            "prod_subset_still_negative": (
+                prod.get("mean_ic") is not None and float(prod["mean_ic"]) < 0
+            ),
+            "junior_tier_more_positive": (
+                lo.get("mean_ic") is not None
+                and hi.get("mean_ic") is not None
+                and float(lo["mean_ic"]) > float(hi["mean_ic"])
+            ),
+            "lag_same_sign_as_same_week": (
+                lag.get("mean_ic") is not None
+                and s.get("mean_ic") is not None
+                and (float(lag["mean_ic"]) < 0) == (float(s["mean_ic"]) < 0)
+            ),
+            "mom_conditional_negative_and_reliable": mom_alive,
+            "orphan_plus_five_sigma": (
+                "Orphaned 21:14 row (+0.0575 / t +5.12) raced a partial "
+                "research.sqlite and was removed from reports/ (Git history only). "
+                "Harness path and shared filter agree on complete data."
+            ),
+            "compositional_story": (
+                "fip_id pools continuous winners (neg IC) vs continuous bleeders "
+                "(pos IC). Prod-subset and senior liquid stay negative; junior "
+                "liquid is less negative / positive — composition, not jumpiness premium."
+            ),
+            "vol_tilt_warning": (
+                "Authoritative liquid vol_6m IC ≈ −0.048 / t ≈ −1.36 — directional "
+                "hypothesis only, not significant. Do not cite the orphaned −0.16 / "
+                "t −6.1. Re-validate production 80/20 high-vol tilt before any "
+                "universe broaden; it is not a settled finding on this pool."
+            ),
+            "breadth_momentum_thesis": (
+                "Residual mom on liquid-1500 is +0.029 / t +1.33 vs fingerprint "
+                "0.055 / t 1.98 on 505 names — more breadth did not strengthen the "
+                "momentum t-stat on this pool. Clean mom edge lives in the large-cap "
+                "universe already traded. A fip tilt presupposes a breadth mom book "
+                "worth tilting; that baseline must be proven first."
+            ),
+        },
+        "platform_verdict": (
+            "Mom-conditional fip ALIVE as book-tilt candidate only — requires a "
+            "pre-registered two-arm breadth book (baseline liquid-1500 mom vs +fip "
+            "tilt) before any gate talk. Unconditional fip not green. Production: none."
+            if mom_alive
+            else (
+                "fip CLOSED for production: mom-conditional does not clear iron rule "
+                "on single-sourced path. Display card is the resting place."
+            )
+        ),
+        "research_snapshot_manifest": {
+            "finished_at": manifest.get("finished_at"),
+            "ticker_count": manifest.get("ticker_count"),
+            "ohlcv_row_count": manifest.get("ohlcv_row_count"),
+            "rank_only_count": manifest.get("rank_only_count"),
+            "complete": manifest.get("complete"),
+        },
+    }
+
+    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    out = Path(args.out) if args.out else Path("reports") / f"fip-reconcile-{stamp}.json"
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(results, indent=2, default=str), encoding="utf-8")
+
+    # Append a machine reconciliation stub next to the JSON only — never clobber
+    # the curated research log at docs/research/fip-breadth-ic.md.
+    _update_md(out.with_suffix(".md"), results, out)
+
+    if not args.quiet:
+        print("=== Harness fip_id (authoritative) ===")
+        print(json.dumps(h, indent=2, default=str))
+        print("=== Shared-filter same-week (must match) ===")
+        print(json.dumps(s, indent=2, default=str))
+        print("=== Mom-conditional ===")
+        print(json.dumps(cond, indent=2, default=str))
+        print("self_consistent:", consistent)
+        print("platform_verdict:", results["platform_verdict"])
+        print(f"Wrote {out}")
+
+
+def _update_md(path: Path, results: dict, artifact: Path) -> None:
+    checks = results["checks"]
+    interp = results["interpretation"]
+    h = checks.get("fip_harness_signal_eval") or {}
+    lines = [
+        "",
+        "---",
+        "",
+        f"## Reconciliation ({results['generated_at'][:10]})",
+        "",
+        "### Problem",
+        "",
+        "Machine stub only — curated narrative lives in `docs/research/fip-breadth-ic.md`.",
+        "",
+        f"- **Single source:** {results.get('single_source')}",
+        f"- Harness vs shared-filter agree: "
+        f"**{interp.get('harness_and_shared_filter_agree')}**",
+        "",
+        "### Authoritative unconditional fip (liquid top-N, post-mask)",
+        "",
+        f"| metric | value |",
+        f"|---|---|",
+        f"| mean_ic | {h.get('mean_ic')} |",
+        f"| ic_t_stat | {h.get('ic_t_stat')} |",
+        f"| weeks | {h.get('weeks')} |",
+        f"| avg_cross_section (post-mask) | {h.get('avg_cross_section')} |",
+        f"| avg_raw_pool | {h.get('avg_raw_pool')} |",
+        f"| avg_eligible_pre_mask | {h.get('avg_eligible_pre_mask')} |",
+        f"| mask_binds_pct | {h.get('mask_binds_pct')} |",
+        f"| reliable | {h.get('reliable')} |",
+        "",
+        "### Checks (single-sourced)",
+        "",
+        "| check | mean_ic | t | weeks | avg N | reliable |",
+        "|---|---:|---:|---:|---:|---|",
+    ]
+    for key in [
+        "fip_harness_signal_eval",
+        "fip_same_week_via_shared_filter",
+        "fip_lagged_membership_1w",
+        "fip_tier_1_800",
+        "fip_tier_801_1500",
+        "fip_prod_universe_subset",
+        "fip_momentum_conditional_top20pct",
+        "vol_6m_liquid",
+        "mom_12_1_liquid",
+        "mom_12_1_resid_liquid",
+    ]:
+        row = checks.get(key) or {}
+        lines.append(
+            f"| {key} | {row.get('mean_ic')} | {row.get('ic_t_stat')} | "
+            f"{row.get('weeks')} | {row.get('avg_cross_section')} | {row.get('reliable')} |"
+        )
+    lines.extend([
+        "",
+        "### Flags",
+        "",
+        f"- Prod subset still negative: **{interp.get('prod_subset_still_negative')}**",
+        f"- Junior tier more positive than senior: **{interp.get('junior_tier_more_positive')}**",
+        f"- Lag same sign as same-week: **{interp.get('lag_same_sign_as_same_week')}**",
+        f"- Mom-conditional negative + reliable: **{interp.get('mom_conditional_negative_and_reliable')}**",
+        "",
+        "### Platform verdict (post-reconciliation)",
+        "",
+        results.get("platform_verdict", ""),
+        "",
+        "### Vol-tilt / breadth-momentum notes",
+        "",
+        interp.get("vol_tilt_warning", ""),
+        "",
+        interp.get("breadth_momentum_thesis", ""),
+        "",
+        f"Artifact: `{artifact.as_posix()}`",
+        "",
+    ])
+    # Always overwrite the machine stub (never the curated research log).
+    path.write_text("\n".join(lines).lstrip() + "\n", encoding="utf-8")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_fip_breadth_research.py

@@ -0,0 +1,315 @@
+"""Phase B: fip_id IC on liquid-breadth cross-section (local research only).
+
+1. Fingerprint check on the unextended prod snapshot (must ≈ IC −0.045 / t −2.9).
+2. Assert research.sqlite has a matching **completion manifest** (race guard).
+3. Run signal_eval on research.sqlite with BACKTEST_LIQUID_BREADTH=1500 PIT mask.
+4. Write a research report under docs/research/ and reports/.
+
+Does not modify production DB, gate, scanner, or schedule.
+
+Example
+-------
+  # After extend_snapshot_universe.py has built research.sqlite:
+  python scripts/run_fip_breadth_research.py \\
+      --prod-snapshot backtest_snapshots/prod.sqlite \\
+      --research-snapshot backtest_snapshots/research.sqlite \\
+      --workers 6 --allow-spawn
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+FINGERPRINT_IC = -0.045
+FINGERPRINT_T = -2.9
+FINGERPRINT_IC_TOL = 0.015
+FINGERPRINT_T_TOL = 0.6
+
+
+def _sqlite_url(path: Path) -> str:
+    return f"sqlite+aiosqlite:///{path.resolve().as_posix()}"
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--prod-snapshot", default="backtest_snapshots/prod.sqlite")
+    p.add_argument("--research-snapshot", default="backtest_snapshots/research.sqlite")
+    p.add_argument("--workers", type=int, default=6)
+    p.add_argument("--allow-spawn", action="store_true")
+    p.add_argument("--skip-fingerprint", action="store_true")
+    p.add_argument("--skip-research", action="store_true")
+    p.add_argument("--liquid-breadth", type=int, default=1500)
+    p.add_argument("--min-price", type=float, default=5.0)
+    p.add_argument(
+        "--out",
+        default=None,
+        help="JSON report path (default reports/fip-breadth-YYYYMMDD.json)",
+    )
+    p.add_argument("--quiet", action="store_true")
+    return p.parse_args()
+
+
+def _find_fip(signal_eval: list[dict]) -> dict | None:
+    for row in signal_eval or []:
+        if row.get("signal") == "fip_id":
+            return row
+    return None
+
+
+def _verdict(row: dict | None) -> dict:
+    if row is None:
+        return {
+            "green": False,
+            "reason": "fip_id missing from signal_eval",
+        }
+    mean_ic = row.get("mean_ic")
+    t_stat = row.get("ic_t_stat")
+    reliable = bool(row.get("reliable"))
+    if mean_ic is None or t_stat is None:
+        return {"green": False, "reason": "missing mean_ic or ic_t_stat", "row": row}
+    sign_ok = mean_ic < 0
+    mag_ok = abs(float(mean_ic)) >= 0.03
+    green = sign_ok and mag_ok and reliable
+    return {
+        "green": green,
+        "reason": (
+            "iron rule cleared — follow-up proposal only, not production wire-in"
+            if green
+            else "iron rule not met on liquid-breadth cross-section"
+        ),
+        "checks": {
+            "mean_ic": mean_ic,
+            "abs_mean_ic_ge_0_03": mag_ok,
+            "sign_negative": sign_ok,
+            "ic_t_stat": t_stat,
+            "reliable": reliable,
+            "weeks": row.get("weeks"),
+            "avg_cross_section": row.get("avg_cross_section"),
+        },
+        "row": row,
+    }
+
+
+async def _run_signal_eval(snapshot: Path, *, workers: int, quiet: bool) -> dict:
+    from app.config import settings
+    from app.services.backtest_service import run_backtest
+
+    settings.backtest_workers = workers
+    engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
+    Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
+
+    def progress(done: int, total: int, symbol: str) -> None:
+        if quiet:
+            return
+        print(f"  progress {done}/{total} {symbol}", end="\r")
+
+    try:
+        async with Session() as db:
+            report = await run_backtest(db, progress_cb=progress, cadence="weekly")
+    finally:
+        await engine.dispose()
+    if not quiet:
+        print()
+    return report
+
+
+def _write_md(path: Path, payload: dict) -> None:
+    fp = payload.get("fingerprint") or {}
+    br = payload.get("breadth") or {}
+    v = payload.get("verdict") or {}
+    lines = [
+        "# Broad-universe fip_id IC research (Phase B)",
+        "",
+        f"Generated: {payload.get('generated_at')}",
+        "",
+        "## Scope",
+        "",
+        "- **Research only** — production universe, gate, scanner, schedule unchanged.",
+        "- Price-only signal harness; no sentiment/fundamentals on the broad tier.",
+        "- Point-in-time liquidity mask: top "
+        f"**{payload.get('liquid_breadth_top_n')}** by 63d median $vol, "
+        f"price ≥ **${payload.get('liquid_min_price')}** at as-of.",
+        "",
+        "## Caveats",
+        "",
+        "- **Survivorship bias**: today's constituents backfilled historically "
+        "(worse in small caps).",
+        "- **IEX volume undercount**: relative $vol rank only, not absolute floors.",
+        "- **Pool skew**: nasdaq_all ∪ sp500 tilts tech/biotech; missing pure NYSE mid-caps.",
+        "",
+        "## Fingerprint (505-name prod snapshot)",
+        "",
+        f"- Expected: IC ≈ {FINGERPRINT_IC}, t ≈ {FINGERPRINT_T}",
+        f"- Observed: IC = {fp.get('mean_ic')}, t = {fp.get('ic_t_stat')}, "
+        f"weeks = {fp.get('weeks')}, reliable = {fp.get('reliable')}",
+        f"- Pass: **{fp.get('pass')}**",
+        "",
+        "## Liquid-breadth signal_eval (fip_id)",
+        "",
+    ]
+    row = br.get("row") or br
+    if row:
+        lines.extend([
+            f"| metric | value |",
+            f"|---|---|",
+            f"| mean_ic | {row.get('mean_ic')} |",
+            f"| ic_t_stat | {row.get('ic_t_stat')} |",
+            f"| ic_positive_pct | {row.get('ic_positive_pct')} |",
+            f"| weeks | {row.get('weeks')} |",
+            f"| avg_cross_section | {row.get('avg_cross_section')} |",
+            f"| reliable | {row.get('reliable')} |",
+            f"| mean_quintile_spread | {row.get('mean_quintile_spread')} |",
+            "",
+        ])
+    else:
+        lines.append("_No breadth result (run skipped or failed)._")
+        lines.append("")
+    lines.extend([
+        "## Verdict (iron rule)",
+        "",
+        f"- **Green: {v.get('green')}**",
+        f"- {v.get('reason')}",
+        f"- Checks: `{json.dumps(v.get('checks') or {}, default=str)}`",
+        "",
+        "A green verdict authorizes a **follow-up proposal** only "
+        "(two-tier universe / gate revalidation) — **not** production wire-in.",
+        "",
+        "## Artifacts",
+        "",
+        f"- Fingerprint report: `{payload.get('fingerprint_report_path')}`",
+        f"- Breadth report: `{payload.get('breadth_report_path')}`",
+        "",
+    ])
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+async def _main() -> None:
+    args = _parse_args()
+    prod = Path(args.prod_snapshot)
+    research = Path(args.research_snapshot)
+    if not prod.exists():
+        raise SystemExit(f"Prod snapshot missing: {prod}")
+
+    os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1"
+    if args.allow_spawn:
+        os.environ["BACKTEST_ALLOW_SPAWN"] = "1"
+    os.environ["BACKTEST_SIGNAL_EVAL_ONLY"] = "1"
+
+    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    out_json = Path(args.out) if args.out else Path("reports") / f"fip-breadth-{stamp}.json"
+    out_json.parent.mkdir(parents=True, exist_ok=True)
+    # Never clobber the curated research log (docs/research/fip-breadth-ic.md).
+    # Machine summary goes next to the JSON report only.
+    out_md = out_json.with_suffix(".md")
+
+    payload: dict = {
+        "generated_at": datetime.now().isoformat(),
+        "liquid_breadth_top_n": args.liquid_breadth,
+        "liquid_min_price": args.min_price,
+        "fingerprint": None,
+        "breadth": None,
+        "verdict": None,
+    }
+
+    # --- 1) Fingerprint ---
+    if not args.skip_fingerprint:
+        # Clear liquid breadth for fingerprint
+        os.environ.pop("BACKTEST_LIQUID_BREADTH", None)
+        os.environ.pop("BACKTEST_LIQUID_MIN_PRICE", None)
+        if not args.quiet:
+            print(f"Fingerprint run on {prod}…")
+        fp_report = await _run_signal_eval(prod, workers=args.workers, quiet=args.quiet)
+        fp_path = out_json.with_name(out_json.stem + "-fingerprint.json")
+        fp_path.write_text(json.dumps(fp_report, indent=2, default=str), encoding="utf-8")
+        fip = _find_fip(fp_report.get("signal_eval") or [])
+        if fip is None:
+            raise SystemExit("ABORT: fip_id missing from fingerprint signal_eval")
+        ic_ok = abs(float(fip["mean_ic"]) - FINGERPRINT_IC) <= FINGERPRINT_IC_TOL
+        t_ok = abs(float(fip["ic_t_stat"]) - FINGERPRINT_T) <= FINGERPRINT_T_TOL
+        passed = ic_ok and t_ok and bool(fip.get("reliable"))
+        payload["fingerprint"] = {
+            **fip,
+            "pass": passed,
+            "expected_ic": FINGERPRINT_IC,
+            "expected_t": FINGERPRINT_T,
+        }
+        payload["fingerprint_report_path"] = str(fp_path)
+        if not args.quiet:
+            print(
+                f"Fingerprint fip_id IC={fip.get('mean_ic')} t={fip.get('ic_t_stat')} "
+                f"pass={passed}"
+            )
+        if not passed:
+            out_json.write_text(json.dumps(payload, indent=2, default=str), encoding="utf-8")
+            raise SystemExit(
+                "ABORT: fingerprint mismatch — investigate before trusting breadth runs "
+                f"(got IC={fip.get('mean_ic')} t={fip.get('ic_t_stat')})"
+            )
+
+    # --- 2) Breadth ---
+    if not args.skip_research:
+        # Refuse half-built research.sqlite (2026-07-18 21:14 race).
+        scripts_dir = Path(__file__).resolve().parent
+        if str(scripts_dir) not in sys.path:
+            sys.path.insert(0, str(scripts_dir))
+        from research_snapshot_manifest import (  # type: ignore[import-not-found]
+            assert_research_snapshot_complete,
+        )
+
+        manifest = assert_research_snapshot_complete(research)
+        payload["research_snapshot_manifest"] = {
+            "finished_at": manifest.get("finished_at"),
+            "ticker_count": manifest.get("ticker_count"),
+            "ohlcv_row_count": manifest.get("ohlcv_row_count"),
+            "rank_only_count": manifest.get("rank_only_count"),
+            "complete": manifest.get("complete"),
+        }
+        os.environ["BACKTEST_LIQUID_BREADTH"] = str(int(args.liquid_breadth))
+        os.environ["BACKTEST_LIQUID_MIN_PRICE"] = str(float(args.min_price))
+        if not args.quiet:
+            print(
+                f"Breadth run on {research} "
+                f"(top {args.liquid_breadth}, min_price={args.min_price}; "
+                f"manifest ok tickers={manifest.get('ticker_count')} "
+                f"finished_at={manifest.get('finished_at')})…"
+            )
+        br_report = await _run_signal_eval(
+            research, workers=args.workers, quiet=args.quiet
+        )
+        br_path = out_json.with_name(out_json.stem + "-breadth.json")
+        br_path.write_text(json.dumps(br_report, indent=2, default=str), encoding="utf-8")
+        fip_b = _find_fip(br_report.get("signal_eval") or [])
+        payload["breadth"] = fip_b or {"error": "fip_id missing"}
+        payload["breadth_report_path"] = str(br_path)
+        payload["breadth_tickers"] = br_report.get("tickers")
+        payload["breadth_rank_only_tickers"] = br_report.get("rank_only_tickers")
+        payload["verdict"] = _verdict(fip_b)
+        if not args.quiet:
+            print(
+                f"Breadth fip_id IC={ (fip_b or {}).get('mean_ic') } "
+                f"t={ (fip_b or {}).get('ic_t_stat') } "
+                f"green={payload['verdict'].get('green')}"
+            )
+
+    out_json.write_text(json.dumps(payload, indent=2, default=str), encoding="utf-8")
+    out_md.parent.mkdir(parents=True, exist_ok=True)
+    _write_md(out_md, payload)
+    if not args.quiet:
+        print(f"Wrote {out_json}")
+        print(f"Wrote {out_md}")
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

scripts/run_prod_book_universe_matrix.py

@@ -0,0 +1,706 @@
+#!/usr/bin/env python3
+"""Production book × universe × horizon matrix (research only).
+
+Four pre-registered arms — same live strategy knobs; only entry start date and
+tradable universe change. See docs/research/prod-book-universe-horizon.md.
+
+  A  2022-07-01  prod ~505
+  B  2022-07-01  prod ∪ liquid top-1500
+  C  2016-07-01  prod ~505
+  D  2016-07-01  prod ∪ liquid top-1500
+
+Example (MacBook, deep research.sqlite)
+---------------------------------------
+  python scripts/run_prod_book_universe_matrix.py \\
+      --snapshot backtest_snapshots/research.sqlite \\
+      --workers 8 --allow-spawn \\
+      --candidate-cache reports/.cache/prod-book-univ-cands.pkl
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import os
+import pickle
+import sys
+import time
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor
+from datetime import date, datetime
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy import create_engine, text
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from app.ssl_bootstrap import bootstrap_ssl  # noqa: E402
+
+bootstrap_ssl()
+
+SHORT_START = date(2022, 7, 1)
+LONG_START = date(2016, 7, 1)
+LIQUID_TOP_N = 1500
+LIQUID_MIN_PRICE = 5.0
+CACHE_VERSION = "prod-book-universe-horizon-v1"
+
+ARMS: tuple[dict[str, Any], ...] = (
+    {
+        "id": "A_prod_4y_505",
+        "label": "Prod book · ~4y · 505 only",
+        "start": SHORT_START,
+        "universe": "prod_505",
+    },
+    {
+        "id": "B_prod_4y_505_liquid",
+        "label": "Prod book · ~4y · 505 + liquid top-1500",
+        "start": SHORT_START,
+        "universe": "prod_plus_liquid",
+    },
+    {
+        "id": "C_prod_2016_505",
+        "label": "Prod book · since 2016-07 · 505 only",
+        "start": LONG_START,
+        "universe": "prod_505",
+    },
+    {
+        "id": "D_prod_2016_505_liquid",
+        "label": "Prod book · since 2016-07 · 505 + liquid top-1500",
+        "start": LONG_START,
+        "universe": "prod_plus_liquid",
+    },
+)
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--snapshot", default="backtest_snapshots/research.sqlite")
+    p.add_argument("--workers", type=int, default=8)
+    p.add_argument("--allow-spawn", action="store_true")
+    p.add_argument("--quiet", action="store_true")
+    p.add_argument(
+        "--candidate-cache",
+        default="reports/.cache/prod-book-universe-cands.pkl",
+        help="Pickle cache for full GTL candidate pass (expensive).",
+    )
+    p.add_argument(
+        "--rebuild-cache",
+        action="store_true",
+        help="Ignore existing candidate cache.",
+    )
+    p.add_argument("--out", default=None)
+    p.add_argument(
+        "--skip-race-guard",
+        action="store_true",
+        help="Allow run without completion manifest (not recommended).",
+    )
+    return p.parse_args()
+
+
+def _sqlite_url(path: Path) -> str:
+    return f"sqlite+aiosqlite:///{path.resolve().as_posix()}"
+
+
+def _load_prod_and_all_symbols(snapshot: Path) -> tuple[set[str], list[str]]:
+    engine = create_engine(
+        f"sqlite:///{snapshot.resolve().as_posix()}",
+        future=True,
+    )
+    try:
+        with engine.connect() as conn:
+            all_syms = [
+                str(r[0]).upper()
+                for r in conn.execute(text("SELECT symbol FROM tickers ORDER BY 1"))
+            ]
+            try:
+                rank_only = {
+                    str(r[0]).upper()
+                    for r in conn.execute(text("SELECT symbol FROM research_rank_only"))
+                }
+            except Exception:
+                rank_only = set()
+    finally:
+        engine.dispose()
+    prod = {s for s in all_syms if s not in rank_only}
+    return prod, all_syms
+
+
+def _median(xs: list[float]) -> float | None:
+    if len(xs) < 20:
+        return None
+    s = sorted(xs)
+    mid = len(s) // 2
+    if len(s) % 2:
+        return s[mid]
+    return 0.5 * (s[mid - 1] + s[mid])
+
+
+def _build_liquid_membership(
+    prices: dict[str, tuple],
+    *,
+    top_n: int,
+    min_price: float,
+) -> dict[date, set[str]]:
+    """For each calendar date present in any series, top-N by 63d median $vol."""
+    # Collect per-symbol (date -> (close, dvol63))
+    per_sym: dict[str, dict[date, tuple[float, float | None]]] = {}
+    all_dates: set[date] = set()
+    for sym, cols in prices.items():
+        ords, _o, _h, _l, closes, vols = cols
+        dates = [date.fromordinal(int(o)) for o in ords]
+        n = len(dates)
+        series: dict[date, tuple[float, float | None]] = {}
+        for i in range(n):
+            d = dates[i]
+            c = float(closes[i])
+            dvol = None
+            if i + 1 >= 63:
+                dvs = []
+                for k in range(i - 62, i + 1):
+                    ck = float(closes[k])
+                    vk = float(vols[k] or 0)
+                    if ck > 0 and vk >= 0:
+                        dvs.append(ck * vk)
+                dvol = _median(dvs)
+            series[d] = (c, dvol)
+            all_dates.add(d)
+        per_sym[sym] = series
+
+    membership: dict[date, set[str]] = {}
+    for d in sorted(all_dates):
+        eligible: list[tuple[float, str]] = []
+        for sym, series in per_sym.items():
+            row = series.get(d)
+            if row is None:
+                continue
+            c, dvol = row
+            if c < min_price or dvol is None or dvol <= 0:
+                continue
+            eligible.append((-dvol, sym))  # highest dvol first
+        eligible.sort()
+        membership[d] = {sym for _, sym in eligible[:top_n]}
+    return membership
+
+
+def _worker_replay(
+    symbol: str,
+    columns: tuple,
+    config: dict,
+    activation: dict,
+    spy: dict,
+    cadence: str,
+) -> list[dict]:
+    """Picklable full GTL+signals candidate replay (no signal-only)."""
+    from app.services import backtest_service as bt
+
+    cands, _series = bt._replay_and_signals(
+        symbol,
+        columns,
+        config,
+        activation,
+        spy,
+        bt.PRODUCTION_GTL_TARGET_MODEL,
+        cadence,
+        False,  # always full replay for book matrix
+        None,
+        None,
+    )
+    return cands
+
+
+async def _load_or_build_candidates(
+    snapshot: Path,
+    *,
+    cache_path: Path | None,
+    rebuild: bool,
+    workers: int,
+    quiet: bool,
+) -> tuple[list[dict], dict[str, tuple], dict, set[str], dict]:
+    from app.config import settings
+    from app.services import backtest_service as bt
+    from app.services.admin_service import get_activation_config
+    from app.services.recommendation_service import get_recommendation_config
+    from app.services.paper_trade_service import get_exit_policy
+    from app.services.benchmark_service import load_benchmark_closes
+    from app.models.ticker import Ticker
+    from sqlalchemy import select
+
+    os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1"
+    settings.backtest_workers = max(1, workers)
+
+    prod_set, all_syms = _load_prod_and_all_symbols(snapshot)
+    print(f"Symbols: all={len(all_syms)} prod_505={len(prod_set)}")
+
+    cache_key = {
+        "version": CACHE_VERSION,
+        "snapshot": str(snapshot.resolve()),
+        "prod_n": len(prod_set),
+        "all_n": len(all_syms),
+    }
+    if cache_path and cache_path.exists() and not rebuild:
+        with cache_path.open("rb") as fh:
+            blob = pickle.load(fh)
+        if blob.get("key") == cache_key and blob.get("candidates"):
+            print(f"Loaded candidate cache: {cache_path} ({len(blob['candidates'])} rows)")
+            return (
+                blob["candidates"],
+                blob["prices"],
+                blob["spy"],
+                set(blob["prod_set"]),
+                blob["exit_config"],
+            )
+        print("Cache key mismatch — rebuilding candidates")
+
+    engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
+    Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
+
+    candidates: list[dict] = []
+    prices: dict[str, tuple] = {}
+    try:
+        async with Session() as db:
+            config = await get_recommendation_config(db)
+            activation = await get_activation_config(db)
+            exit_config = await get_exit_policy(db)
+            spy = await load_benchmark_closes(db, "SPY")
+            tickers = list(
+                (await db.execute(select(Ticker).order_by(Ticker.symbol))).scalars()
+            )
+
+            # Fetch all price columns first (I/O).
+            for idx, t in enumerate(tickers):
+                if not quiet and idx % 100 == 0:
+                    print(f"  fetch prices {idx}/{len(tickers)}", end="\r", flush=True)
+                cols = await bt._fetch_columns(db, t.symbol)
+                if cols is not None:
+                    prices[t.symbol.upper()] = cols
+            if not quiet:
+                print()
+
+            # Parallel GTL replay for every symbol with prices.
+            syms = sorted(prices)
+            print(f"GTL replay on {len(syms)} symbols (workers={workers})…")
+            t0 = time.monotonic()
+            if workers <= 1:
+                for i, sym in enumerate(syms):
+                    if not quiet and i % 50 == 0:
+                        print(f"  replay {i}/{len(syms)}", end="\r", flush=True)
+                    candidates.extend(
+                        _worker_replay(
+                            sym, prices[sym], config, activation, spy, "weekly"
+                        )
+                    )
+            else:
+                # Process pool: pass column batches.
+                import multiprocessing as mp
+
+                ctx = mp.get_context("spawn")
+                chunk = max(1, workers * 2)
+                with ProcessPoolExecutor(max_workers=workers, mp_context=ctx) as pool:
+                    for start in range(0, len(syms), chunk):
+                        batch = syms[start : start + chunk]
+                        futs = [
+                            pool.submit(
+                                _worker_replay,
+                                sym,
+                                prices[sym],
+                                config,
+                                activation,
+                                spy,
+                                "weekly",
+                            )
+                            for sym in batch
+                        ]
+                        for fut in futs:
+                            try:
+                                candidates.extend(fut.result())
+                            except Exception as exc:
+                                print(f"  worker error: {exc}")
+                        if not quiet:
+                            print(
+                                f"  replay {min(start+chunk, len(syms))}/{len(syms)} "
+                                f"cands={len(candidates)} "
+                                f"elapsed={(time.monotonic()-t0)/60:.1f}m",
+                                end="\r",
+                                flush=True,
+                            )
+            if not quiet:
+                print()
+    finally:
+        await engine.dispose()
+
+    print(f"Total raw candidates: {len(candidates)}")
+    if cache_path:
+        cache_path.parent.mkdir(parents=True, exist_ok=True)
+        with cache_path.open("wb") as fh:
+            pickle.dump(
+                {
+                    "key": cache_key,
+                    "candidates": candidates,
+                    "prices": prices,
+                    "spy": spy,
+                    "prod_set": sorted(prod_set),
+                    "exit_config": exit_config,
+                },
+                fh,
+                protocol=pickle.HIGHEST_PROTOCOL,
+            )
+        print(f"Wrote cache {cache_path}")
+
+    return candidates, prices, spy, prod_set, exit_config
+
+
+def _candidate_eligible(
+    cand: dict,
+    *,
+    prod_set: set[str],
+    universe: str,
+    liquid_by_date: dict[date, set[str]],
+) -> bool:
+    if cand.get("direction") != "long":
+        return False
+    sym = str(cand.get("symbol") or "").upper()
+    if not sym:
+        return False
+    if universe == "prod_505":
+        return sym in prod_set
+    # prod_plus_liquid
+    if sym in prod_set:
+        return True
+    try:
+        d = date.fromisoformat(str(cand["date"])[:10])
+    except Exception:
+        return False
+    return sym in (liquid_by_date.get(d) or set())
+
+
+def _run_arm(
+    arm: dict[str, Any],
+    *,
+    all_candidates: list[dict],
+    prices: dict[str, tuple],
+    spy: dict,
+    prod_set: set[str],
+    liquid_by_date: dict[date, set[str]],
+    exit_config: dict,
+) -> dict[str, Any]:
+    from app.services import backtest_service as bt
+
+    start: date = arm["start"]
+    universe: str = arm["universe"]
+
+    filtered: list[dict] = []
+    for c in all_candidates:
+        try:
+            d = date.fromisoformat(str(c["date"])[:10])
+        except Exception:
+            continue
+        if d < start:
+            continue
+        if not _candidate_eligible(
+            c, prod_set=prod_set, universe=universe, liquid_by_date=liquid_by_date
+        ):
+            continue
+        filtered.append(dict(c))
+
+    # Re-rank inside this arm's universe (production percentile logic).
+    bt._assign_momentum_percentiles(filtered)
+    bt._assign_residual_momentum_percentiles(filtered)
+    bt._assign_low_volatility_percentiles(filtered)
+    bt._assign_activation_momentum_percentiles(filtered)
+    bt._assign_residual_high_vol_blend(filtered)
+    for c in filtered:
+        c["qualified"] = bt._momentum_qualifies(c, 80.0)
+
+    longs = [
+        c for c in filtered if c.get("qualified") and c.get("direction") == "long"
+    ]
+
+    strategy = next(s for s in bt.PORTFOLIO_MONITOR_STRATEGIES if s.get("is_production"))
+    entry_cfg = bt._entry_variant_config(str(strategy["entry_variant"]))
+    assert entry_cfg is not None
+    ranking_key = str(
+        entry_cfg.get("ranking_key") or entry_cfg["percentile_key"]
+    )
+    exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get(
+        str(exit_config.get("mode", "atr_trailing")), "atr_trail3"
+    )
+    hold_days = int(exit_config.get("hold_days", 30))
+    trail = float(exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER))
+    risk = float(entry_cfg["risk_per_trade"])
+    max_pos = int(entry_cfg["max_positions"])
+
+    reentry = bt._make_gate_reset_reentry_fn(
+        longs, prices, cadence="weekly", ranking_key=ranking_key
+    )
+    sim = bt._simulate_portfolio(
+        longs,
+        prices,
+        spy,
+        exit_policy,
+        hold_days,
+        ranking_key=ranking_key,
+        max_positions=max_pos,
+        risk_per_trade=risk,
+        atr_trail_multiplier=trail,
+        post_stop_reentry_fn=reentry,
+        start_date=start,
+        end_date=None,
+        fill_mode=bt.FILL_MODE_CLOSE,
+        include_trades=False,
+    )
+    if sim is None:
+        return {
+            "id": arm["id"],
+            "label": arm["label"],
+            "start": start.isoformat(),
+            "universe": universe,
+            "n_candidates": len(filtered),
+            "n_qualified_longs": 0,
+            "error": "no_trades",
+        }
+
+    keep = {
+        k: sim.get(k)
+        for k in (
+            "sharpe",
+            "sharpe_se",
+            "cagr_pct",
+            "max_drawdown_pct",
+            "total_return_pct",
+            "calmar",
+            "trades",
+            "win_rate",
+            "n_returns",
+            "psr",
+            "start_date",
+            "end_date",
+            "spy_return_pct",
+            "final_equity",
+        )
+    }
+    return {
+        "id": arm["id"],
+        "label": arm["label"],
+        "start": start.isoformat(),
+        "universe": universe,
+        "n_candidates": len(filtered),
+        "n_qualified_longs": len(longs),
+        "fill_mode": "close",
+        "ranking_key": ranking_key,
+        "exit_policy": exit_policy,
+        "hold_days": hold_days,
+        **keep,
+    }
+
+
+def _write_outputs(payload: dict, out_json: Path, doc_path: Path) -> None:
+    out_json.parent.mkdir(parents=True, exist_ok=True)
+    out_json.write_text(
+        json.dumps(payload, indent=2, default=str) + "\n", encoding="utf-8"
+    )
+
+    lines = [
+        "# Production book × universe × horizon — results",
+        "",
+        f"Generated: `{payload.get('generated_at')}`",
+        "",
+        "> Survivorship: today's constituents backfilled. Compare arms relatively; "
+        "do not treat deep CAGR/Sharpe levels as deployable forecasts.",
+        "",
+        "## Arms",
+        "",
+        "| arm | universe | entry start | Sharpe | SE | CAGR % | max DD % | ret % | trades | qual longs | span |",
+        "|---|---|---|---:|---:|---:|---:|---:|---:|---:|---|",
+    ]
+    for row in payload.get("arms") or []:
+        if row.get("error"):
+            lines.append(
+                f"| {row.get('id')} | {row.get('universe')} | {row.get('start')} | "
+                f"ERR | | | | | | {row.get('n_qualified_longs')} | {row.get('error')} |"
+            )
+            continue
+        lines.append(
+            f"| {row.get('id')} | {row.get('universe')} | {row.get('start')} | "
+            f"{row.get('sharpe')} | {row.get('sharpe_se')} | {row.get('cagr_pct')} | "
+            f"{row.get('max_drawdown_pct')} | {row.get('total_return_pct')} | "
+            f"{row.get('trades')} | {row.get('n_qualified_longs')} | "
+            f"{row.get('start_date')}→{row.get('end_date')} |"
+        )
+    lines.extend([
+        "",
+        "## Config (production, unchanged)",
+        "",
+        f"```json\n{json.dumps(payload.get('strategy') or {}, indent=2)}\n```",
+        "",
+        "## Snapshot",
+        "",
+        f"```json\n{json.dumps(payload.get('snapshot_meta') or {}, indent=2, default=str)}\n```",
+        "",
+        "PENDING_HUMAN — descriptive matrix only; no auto promotion.",
+        "",
+        f"JSON: `{out_json.as_posix()}`",
+        "",
+    ])
+    out_json.with_suffix(".md").write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+    # Fill results section of the research doc.
+    if doc_path.exists():
+        text = doc_path.read_text(encoding="utf-8")
+        marker = "## Results"
+        idx = text.find(marker)
+        header = text[:idx] if idx >= 0 else text
+        # Drop old results/verdict tail
+        for m in ("## Results", "## Verdict"):
+            pass
+        body = [
+            header.rstrip(),
+            "",
+            "## Results",
+            "",
+            f"Generated: `{payload.get('generated_at')}`",
+            "",
+            "| arm | universe | entry start | Sharpe | SE | CAGR % | max DD % | trades |",
+            "|---|---|---|---:|---:|---:|---:|---:|",
+        ]
+        for row in payload.get("arms") or []:
+            body.append(
+                f"| {row.get('id')} | {row.get('universe')} | {row.get('start')} | "
+                f"{row.get('sharpe', '')} | {row.get('sharpe_se', '')} | "
+                f"{row.get('cagr_pct', '')} | {row.get('max_drawdown_pct', '')} | "
+                f"{row.get('trades', '')} |"
+            )
+        body.extend([
+            "",
+            f"Full report: `{out_json.as_posix()}`",
+            "",
+            "## Verdict",
+            "",
+            "**PENDING_HUMAN** — descriptive only; production knobs unchanged.",
+            "",
+        ])
+        doc_path.write_text("\n".join(body) + "\n", encoding="utf-8")
+
+
+async def _main() -> None:
+    args = _parse_args()
+    snapshot = Path(args.snapshot)
+    if not snapshot.exists():
+        raise SystemExit(f"Missing snapshot: {snapshot}")
+    if args.allow_spawn:
+        os.environ["BACKTEST_ALLOW_SPAWN"] = "1"
+
+    if not args.skip_race_guard:
+        try:
+            from scripts.research_snapshot_manifest import (
+                assert_research_snapshot_complete,
+            )
+
+            manifest = assert_research_snapshot_complete(snapshot)
+            print(
+                f"Race guard OK: tickers={manifest.get('ticker_count')} "
+                f"ohlcv={manifest.get('ohlcv_row_count')}"
+            )
+        except SystemExit as exc:
+            # Prod-only snapshot without manifest: allow with warning if ~505.
+            engine = create_engine(
+                f"sqlite:///{snapshot.resolve().as_posix()}",
+                future=True,
+            )
+            try:
+                with engine.connect() as conn:
+                    n = int(conn.execute(text("SELECT COUNT(*) FROM tickers")).scalar_one())
+            finally:
+                engine.dispose()
+            if n < 400:
+                raise
+            print(f"WARNING: no research manifest ({exc}); proceeding n_tickers={n}")
+
+    cache = Path(args.candidate_cache) if args.candidate_cache else None
+    candidates, prices, spy, prod_set, exit_config = await _load_or_build_candidates(
+        snapshot,
+        cache_path=cache,
+        rebuild=args.rebuild_cache,
+        workers=args.workers,
+        quiet=args.quiet,
+    )
+
+    print("Building PIT liquid membership (top-1500, price≥5)…")
+    t0 = time.monotonic()
+    liquid_by_date = _build_liquid_membership(
+        prices, top_n=LIQUID_TOP_N, min_price=LIQUID_MIN_PRICE
+    )
+    print(
+        f"  liquid dates={len(liquid_by_date)} "
+        f"elapsed={(time.monotonic()-t0)/60:.1f}m"
+    )
+
+    arms_out = []
+    for arm in ARMS:
+        print(f"Running arm {arm['id']}…")
+        row = _run_arm(
+            arm,
+            all_candidates=candidates,
+            prices=prices,
+            spy=spy,
+            prod_set=prod_set,
+            liquid_by_date=liquid_by_date,
+            exit_config=exit_config,
+        )
+        arms_out.append(row)
+        print(
+            f"  Sharpe={row.get('sharpe')} CAGR={row.get('cagr_pct')} "
+            f"DD={row.get('max_drawdown_pct')} trades={row.get('trades')} "
+            f"qual={row.get('n_qualified_longs')}"
+        )
+
+    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    out = (
+        Path(args.out)
+        if args.out
+        else Path("reports") / f"prod-book-universe-horizon-{stamp}.json"
+    )
+    payload = {
+        "generated_at": datetime.now().isoformat(),
+        "snapshot": str(snapshot.resolve()),
+        "snapshot_meta": {
+            "prod_universe_n": len(prod_set),
+            "price_symbols_n": len(prices),
+            "raw_candidates": len(candidates),
+            "liquid_top_n": LIQUID_TOP_N,
+            "liquid_min_price": LIQUID_MIN_PRICE,
+            "short_start": SHORT_START.isoformat(),
+            "long_start": LONG_START.isoformat(),
+        },
+        "strategy": {
+            "note": "Live production knobs — no modifications",
+            "momentum": "residual_12_1 gate 80",
+            "rank": "residual_high_vol_blend_80_20",
+            "fill_mode": "close",
+            "cost_per_side": 0.001,
+            "exit": exit_config,
+            "max_positions": 10,
+            "risk_per_trade": 0.01,
+            "reentry": "gate_reset",
+        },
+        "arms": arms_out,
+        "survivorship_banner": (
+            "Today's constituents backfilled. Relative arm comparison only."
+        ),
+        "pending_human": True,
+    }
+    _write_outputs(
+        payload,
+        out,
+        Path("docs/research/prod-book-universe-horizon.md"),
+    )
+    print(f"Wrote {out}")
+    print(f"Wrote {out.with_suffix('.md')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

scripts/run_tier1_macbook.sh

@@ -0,0 +1,141 @@
+#!/usr/bin/env bash
+# Research helpers for a high-CPU MacBook (local only).
+#
+# Kept after Tier-1 cleanup:
+#   --ssl-check          diagnose corporate CA / proxy
+#   --earnings-only      resume FMP earnings backfill + 2a/2b (parked)
+#   --prod-book-matrix   re-run 505 vs liquid universe × horizon book matrix
+#
+# Prerequisites: git checkout research branch, .env, deep research.sqlite for
+# book matrix, combined-ca-bundle.pem or certifi when on corp network.
+#
+#   chmod +x scripts/run_tier1_macbook.sh
+#   ./scripts/run_tier1_macbook.sh --ssl-check
+#   ./scripts/run_tier1_macbook.sh --prod-book-matrix
+
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$ROOT"
+
+RESEARCH_SNAP="${RESEARCH_SNAP:-backtest_snapshots/research.sqlite}"
+PROD_SNAP="${PROD_SNAP:-backtest_snapshots/prod.sqlite}"
+WORKERS="${WORKERS:-8}"
+FMP_LIMIT="${FMP_LIMIT:-250}"
+FMP_SLEEP="${FMP_SLEEP:-0.35}"
+PYTHON="${PYTHON:-python3}"
+USE_CORP_PROXY="${USE_CORP_PROXY:-0}"
+PHASE=""
+
+usage() {
+  sed -n '2,16p' "$0" | sed 's/^# \?//'
+  exit "${1:-0}"
+}
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --ssl-check) PHASE=ssl; shift ;;
+    --earnings-only) PHASE=earnings; shift ;;
+    --prod-book-matrix) PHASE=prod_book; shift ;;
+    --corp-proxy) USE_CORP_PROXY=1; shift ;;
+    --workers) WORKERS="$2"; shift 2 ;;
+    --python) PYTHON="$2"; shift 2 ;;
+    -h|--help) usage 0 ;;
+    *) echo "Unknown flag: $1" >&2; usage 1 ;;
+  esac
+done
+
+if [[ -z "$PHASE" ]]; then
+  echo "Pick a phase: --ssl-check | --earnings-only | --prod-book-matrix" >&2
+  usage 1
+fi
+
+if [[ -x .venv/bin/python ]]; then
+  PYTHON=".venv/bin/python"
+elif command -v "$PYTHON" >/dev/null 2>&1; then
+  :
+else
+  echo "ERROR: no Python found" >&2
+  exit 1
+fi
+
+log() { printf '\n==> %s\n' "$*"; }
+die() { echo "ERROR: $*" >&2; exit 1; }
+need_file() { [[ -f "$1" ]] || die "missing $1"; }
+
+setup_ssl() {
+  export USE_CORP_PROXY
+  if [[ -z "${SSL_CERT_FILE:-}" ]]; then
+    if [[ -f "$ROOT/combined-ca-bundle.pem" ]]; then
+      export SSL_CERT_FILE="$ROOT/combined-ca-bundle.pem"
+    elif [[ -f "$HOME/combined-ca-bundle.pem" ]]; then
+      export SSL_CERT_FILE="$HOME/combined-ca-bundle.pem"
+    fi
+  fi
+  if [[ -n "${SSL_CERT_FILE:-}" && -f "$SSL_CERT_FILE" ]]; then
+    export REQUESTS_CA_BUNDLE="$SSL_CERT_FILE" CURL_CA_BUNDLE="$SSL_CERT_FILE"
+    log "SSL CA bundle: $SSL_CERT_FILE"
+  else
+    local certifi_path
+    certifi_path="$("$PYTHON" -c 'import certifi; print(certifi.where())' 2>/dev/null || true)"
+    if [[ -n "$certifi_path" && -f "$certifi_path" ]]; then
+      export SSL_CERT_FILE="$certifi_path" REQUESTS_CA_BUNDLE="$certifi_path" CURL_CA_BUNDLE="$certifi_path"
+      log "SSL CA bundle (certifi): $SSL_CERT_FILE"
+    else
+      log "WARNING: no CA bundle found — SSL may fail on corp networks"
+    fi
+  fi
+  if [[ "$USE_CORP_PROXY" == "1" ]]; then
+    export HTTP_PROXY="${HTTP_PROXY:-http://aproxy.corproot.net:8080}"
+    export HTTPS_PROXY="${HTTPS_PROXY:-http://aproxy.corproot.net:8080}"
+    export NO_PROXY="${NO_PROXY:-corproot.net,sharedtcs.net,127.0.0.1,localhost}"
+    export http_proxy="$HTTP_PROXY" https_proxy="$HTTPS_PROXY" no_proxy="$NO_PROXY"
+    log "Corp proxy enabled: $HTTPS_PROXY"
+  fi
+  export PYTHONPATH="${ROOT}${PYTHONPATH:+:$PYTHONPATH}"
+}
+
+ssl_check() {
+  setup_ssl
+  "$PYTHON" - <<'PY'
+from app.ssl_bootstrap import bootstrap_ssl, ssl_status
+import json, urllib.request
+print(json.dumps(ssl_status(), indent=2))
+print("bootstrap ->", bootstrap_ssl())
+for url in (
+    "https://data.alpaca.markets/v2/stocks/SPY/bars?timeframe=1Day&limit=1",
+    "https://financialmodelingprep.com/stable/profile?symbol=AAPL",
+):
+    try:
+        req = urllib.request.Request(url, headers={"User-Agent": "ssl-check"})
+        with urllib.request.urlopen(req, timeout=20) as resp:
+            print(f"OK  {resp.status} {url[:60]}")
+    except Exception as exc:
+        print(f"FAIL {type(exc).__name__}: {exc}")
+PY
+}
+
+setup_ssl
+case "$PHASE" in
+  ssl) ssl_check ;;
+  earnings)
+    need_file "$PROD_SNAP"
+    need_file "$RESEARCH_SNAP"
+    log "Earnings Task 2 bulk backfill + registered 2a/2b closeout"
+    "$PYTHON" scripts/backfill_earnings_events.py \
+      --snapshot "$PROD_SNAP" --from-date 2016-01-04 --window-days 30 \
+      --limit "$FMP_LIMIT" --sleep "$FMP_SLEEP"
+    "$PYTHON" scripts/run_earnings_research.py \
+      --snapshot "$RESEARCH_SNAP" --universe-snapshot "$PROD_SNAP" \
+      --earnings-snapshot "$PROD_SNAP" --workers "$WORKERS" --allow-spawn
+    ;;
+  prod_book)
+    need_file "$RESEARCH_SNAP"
+    log "Production book universe × horizon matrix"
+    "$PYTHON" scripts/run_prod_book_universe_matrix.py \
+      --snapshot "$RESEARCH_SNAP" --workers "$WORKERS" --allow-spawn \
+      --candidate-cache reports/.cache/prod-book-universe-cands.pkl
+    ;;
+  *) die "unknown phase $PHASE" ;;
+esac
+log "Done."

tests/unit/test_alpaca_provider_window.py

@@ -0,0 +1,88 @@
+"""Alpaca fetch window / feed selection.
+
+Regression cover for the 2026-07-20 outage: the near-close scan silently ran on
+the previous session's close because ``end`` resolved to midnight on end_date,
+which is *before* that day's bar timestamp (04:00Z under EDT). Widening the
+window also has to stay clear of the delayed-data period, which rejects the whole
+request.
+"""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta, timezone
+
+import pytest
+
+from app.providers.alpaca import AlpacaOHLCVProvider
+
+
+class _CapturingClient:
+    """Stands in for StockHistoricalDataClient, recording the request."""
+
+    def __init__(self) -> None:
+        self.request = None
+
+    def get_stock_bars(self, request):
+        self.request = request
+        return {"AAPL": []}
+
+
+def _provider() -> tuple[AlpacaOHLCVProvider, _CapturingClient]:
+    provider = AlpacaOHLCVProvider("key", "secret")
+    client = _CapturingClient()
+    provider._client = client
+    return provider, client
+
+
+def _midnight(day: date) -> datetime:
+    """Naive-UTC midnight — the SDK strips tzinfo from request datetimes."""
+    return datetime.combine(day, datetime.min.time())
+
+
+def _utcnow() -> datetime:
+    return datetime.now(timezone.utc).replace(tzinfo=None)
+
+
+@pytest.mark.asyncio
+async def test_todays_in_progress_bar_is_inside_the_window():
+    """The whole near-close design depends on today's bar being fetchable."""
+    provider, client = _provider()
+    today = date.today()
+
+    await provider.fetch_ohlcv("AAPL", today - timedelta(days=5), today)
+
+    # Daily bars are stamped at session start (04:00Z); a midnight end drops them.
+    assert client.request.end > _midnight(today)
+
+
+@pytest.mark.asyncio
+async def test_window_stays_out_of_the_delayed_data_period():
+    """A window reaching the last ~15 minutes fails the entire request."""
+    provider, client = _provider()
+
+    await provider.fetch_ohlcv("AAPL", date.today() - timedelta(days=5), date.today())
+
+    assert client.request.end <= _utcnow() - timedelta(minutes=15)
+
+
+@pytest.mark.asyncio
+async def test_completed_past_day_is_fully_covered():
+    """Clamping must not swallow the last day of a historical window."""
+    provider, client = _provider()
+    end_date = date.today() - timedelta(days=3)
+
+    await provider.fetch_ohlcv("AAPL", end_date - timedelta(days=5), end_date)
+
+    assert client.request.end > _midnight(end_date)
+
+
+@pytest.mark.asyncio
+async def test_window_collapsing_to_nothing_skips_the_call():
+    """A start inside the delayed period yields no request at all, not an error."""
+    provider, client = _provider()
+    tomorrow = date.today() + timedelta(days=1)
+
+    records = await provider.fetch_ohlcv("AAPL", tomorrow, tomorrow)
+
+    assert records == []
+    assert client.request is None

tests/unit/test_earnings_research.py

@@ -0,0 +1,225 @@
+from datetime import date, timedelta
+
+from scripts.backfill_earnings_events import _dedupe_bulk_rows, _windows
+from scripts.import_dolthub_earnings import _align_symbol
+from scripts.run_earnings_research import (
+    _analyse_2a_trades,
+    _build_sue_series,
+    _mechanical_sue_grade,
+)
+
+
+def test_dolthub_alignment_is_monotonic_across_close_calendar_events() -> None:
+    events = [
+        {"announce_date": date(2020, 3, 17), "announce_time": "bmo"},
+        {"announce_date": date(2020, 4, 30), "announce_time": "bmo"},
+    ]
+    periods = [
+        {"period_end_date": date(2019, 12, 31)},
+        {"period_end_date": date(2020, 3, 31)},
+    ]
+    matches, unmatched_events, unmatched_periods = _align_symbol(
+        events, periods, max_lag_days=90, max_lead_days=14
+    )
+    assert matches == [(0, 0), (1, 1)]
+    assert unmatched_events == []
+    assert unmatched_periods == []
+
+
+def test_dolthub_alignment_allows_fiscal_period_label_after_announcement() -> None:
+    events = [
+        {"announce_date": date(2023, 2, 28), "announce_time": "bmo"},
+        {"announce_date": date(2023, 5, 23), "announce_time": "bmo"},
+    ]
+    periods = [
+        {"period_end_date": date(2023, 2, 28)},
+        {"period_end_date": date(2023, 5, 31)},
+    ]
+    matches, _, _ = _align_symbol(
+        events, periods, max_lag_days=90, max_lead_days=14
+    )
+    assert matches == [(0, 0), (1, 1)]
+
+
+def test_bulk_windows_cover_range_without_overlap() -> None:
+    result = _windows(date(2020, 1, 1), date(2020, 1, 10), 4)
+    assert result == [
+        (date(2020, 1, 1), date(2020, 1, 4)),
+        (date(2020, 1, 5), date(2020, 1, 8)),
+        (date(2020, 1, 9), date(2020, 1, 10)),
+    ]
+
+
+def test_bulk_dedupe_prefers_more_complete_and_counts_restatement() -> None:
+    rows = [
+        {
+            "symbol": "AAPL",
+            "announce_date": "2024-01-01",
+            "announce_time": None,
+            "eps_estimate": 1.0,
+            "eps_actual": 1.1,
+            "revenue_estimate": None,
+            "revenue_actual": None,
+        },
+        {
+            "symbol": "AAPL",
+            "announce_date": "2024-01-01",
+            "announce_time": "amc",
+            "eps_estimate": 1.0,
+            "eps_actual": 1.2,
+            "revenue_estimate": 10.0,
+            "revenue_actual": 11.0,
+        },
+    ]
+    deduped, duplicates, restated = _dedupe_bulk_rows(rows)
+    assert duplicates == 1
+    assert restated == 1
+    assert deduped == [rows[1]]
+
+
+def test_2a_uses_net_r_strict_hold_and_next_session_stop() -> None:
+    calendar = [
+        date(2024, 1, 2),
+        date(2024, 1, 3),
+        date(2024, 1, 4),
+        date(2024, 1, 5),
+        date(2024, 1, 8),
+        date(2024, 1, 9),
+    ]
+    events = [
+        {
+            "symbol": "AAPL",
+            "announce_date": date(2024, 1, 5),
+        }
+    ]
+    trades = [
+        {
+            "symbol": "AAPL",
+            "entry_date": "2024-01-03",
+            "exit_date": "2024-01-08",
+            "entry": 100.0,
+            "initial_stop": 90.0,
+            "fill": 90.0,
+            "r": -1.0,
+            "reason": "stop",
+        },
+        {
+            "symbol": "MSFT",
+            "entry_date": "2024-01-02",
+            "exit_date": "2024-01-09",
+            "entry": 100.0,
+            "initial_stop": 90.0,
+            "fill": 110.0,
+            "r": 1.0,
+            "reason": "time",
+        },
+    ]
+    result = _analyse_2a_trades(
+        trades, events, calendar, cost_per_side=0.001
+    )
+    assert result["q1_loss_concentration"]["losses_count"] == 1
+    assert result["q1_loss_concentration"]["losses_with_announcement_count"] == 1
+    assert (
+        result["q2_entries_within_3_trading_days_before_announcement"][
+            "pre_earnings"
+        ]["count"]
+        == 1
+    )
+    assert (
+        result["q3_stop_exits_within_1_trading_day_after_announcement"][
+            "stops_after_earnings"
+        ]["count"]
+        == 1
+    )
+    assert result["q1_loss_concentration"]["loss_definition"] == (
+        "realized_net_R <= -1.0"
+    )
+
+
+def test_sue_needs_four_prior_surprises_and_starts_next_trading_day() -> None:
+    dates = [date(2024, 1, 1) + timedelta(days=index) for index in range(100)]
+    columns = (
+        [value.toordinal() for value in dates],
+        [100.0] * len(dates),
+        [101.0] * len(dates),
+        [99.0] * len(dates),
+        [100.0] * len(dates),
+        [1_000_000] * len(dates),
+    )
+    event_dates = [date(2024, 1, 2) + timedelta(days=10 * index) for index in range(5)]
+    surprises = [0.1, -0.2, 0.3, -0.1, 0.4]
+    events = {
+        "AAPL": [
+            {
+                "announce_date": event_date,
+                "eps_actual": 1.0 + surprise,
+                "eps_estimate": 1.0,
+            }
+            for event_date, surprise in zip(event_dates, surprises)
+        ]
+    }
+    series, counts = _build_sue_series(
+        events, {"AAPL": columns}, use_price_fallback=False
+    )
+    first_live = event_dates[-1] + timedelta(days=1)
+    assert first_live in series["AAPL"]
+    assert event_dates[-1] not in series["AAPL"]
+    assert counts["standard_scaled_events"] == 1
+    assert counts["price_fallback_events"] == 0
+
+
+def test_sue_uses_period_history_only_for_scaling() -> None:
+    dates = [date(2020, 1, 1) + timedelta(days=index) for index in range(100)]
+    columns = (
+        [value.toordinal() for value in dates],
+        [100.0] * len(dates),
+        [101.0] * len(dates),
+        [99.0] * len(dates),
+        [100.0] * len(dates),
+        [1_000_000] * len(dates),
+    )
+    event_date = date(2020, 2, 3)
+    events = {
+        "AAPL": [
+            {
+                "announce_date": event_date,
+                "period_end_date": date(2019, 12, 31),
+                "eps_actual": 1.4,
+                "eps_estimate": 1.0,
+            }
+        ]
+    }
+    history = {
+        "AAPL": [
+            {
+                "period_end_date": date(2018, 12, 31)
+                + timedelta(days=90 * index),
+                "eps_actual": 1.0 + surprise,
+                "eps_estimate": 1.0,
+            }
+            for index, surprise in enumerate([0.1, -0.2, 0.3, -0.1])
+        ]
+    }
+    series, counts = _build_sue_series(
+        events,
+        {"AAPL": columns},
+        use_price_fallback=False,
+        surprise_history_by_symbol=history,
+    )
+    assert event_date + timedelta(days=1) in series["AAPL"]
+    assert event_date not in series["AAPL"]
+    assert counts["events_scaled_from_period_history"] == 1
+
+
+def test_sue_grade_requires_positive_both_eras() -> None:
+    full = {"mean_ic": 0.03, "reliable": True}
+    passed, stable = _mechanical_sue_grade(
+        full, {"mean_ic": 0.01}, {"mean_ic": 0.02}
+    )
+    assert passed is True
+    assert stable is True
+    failed, stable = _mechanical_sue_grade(
+        full, {"mean_ic": -0.01}, {"mean_ic": 0.02}
+    )
+    assert failed is False
+    assert stable is False

tests/unit/test_research_snapshot_manifest.py

@@ -0,0 +1,133 @@
+"""Completion-manifest guard for research.sqlite breadth runs."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+from sqlalchemy import create_engine, text
+
+ROOT = Path(__file__).resolve().parents[2]
+SCRIPTS = ROOT / "scripts"
+if str(SCRIPTS) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS))
+
+from research_snapshot_manifest import (  # noqa: E402
+    assert_research_snapshot_complete,
+    clear_manifest,
+    load_manifest,
+    manifest_path_for,
+    write_completion_manifest,
+)
+
+
+def _tiny_research_db(path: Path, *, tickers: int = 3, bars_each: int = 5) -> None:
+    engine = create_engine(f"sqlite:///{path.resolve().as_posix()}", future=True)
+    with engine.begin() as conn:
+        conn.execute(
+            text(
+                "CREATE TABLE tickers ("
+                "id INTEGER PRIMARY KEY, symbol TEXT NOT NULL UNIQUE, "
+                "name TEXT, created_at TEXT)"
+            )
+        )
+        conn.execute(
+            text(
+                "CREATE TABLE ohlcv_records ("
+                "id INTEGER PRIMARY KEY, ticker_id INTEGER, date TEXT, "
+                "open REAL, high REAL, low REAL, close REAL, volume INTEGER, "
+                "created_at TEXT)"
+            )
+        )
+        conn.execute(
+            text(
+                "CREATE TABLE research_rank_only ("
+                "ticker_id INTEGER PRIMARY KEY, symbol TEXT NOT NULL UNIQUE)"
+            )
+        )
+        for i in range(tickers):
+            sym = f"T{i}"
+            conn.execute(
+                text(
+                    "INSERT INTO tickers (id, symbol, name, created_at) "
+                    "VALUES (:id, :sym, NULL, '2026-01-01')"
+                ),
+                {"id": i + 1, "sym": sym},
+            )
+            if i > 0:
+                conn.execute(
+                    text(
+                        "INSERT INTO research_rank_only (ticker_id, symbol) "
+                        "VALUES (:id, :sym)"
+                    ),
+                    {"id": i + 1, "sym": sym},
+                )
+            for d in range(bars_each):
+                conn.execute(
+                    text(
+                        "INSERT INTO ohlcv_records "
+                        "(ticker_id, date, open, high, low, close, volume, created_at) "
+                        "VALUES (:tid, :date, 1,1,1,1,100, '2026-01-01')"
+                    ),
+                    {"tid": i + 1, "date": f"2026-01-{d+1:02d}"},
+                )
+    engine.dispose()
+
+
+def test_write_and_assert_complete(tmp_path: Path) -> None:
+    snap = tmp_path / "research.sqlite"
+    _tiny_research_db(snap)
+    path = write_completion_manifest(snap, complete=True, sources={"t": "unit"})
+    assert path == manifest_path_for(snap)
+    assert path.exists()
+
+    m = assert_research_snapshot_complete(snap)
+    assert m["complete"] is True
+    assert m["ticker_count"] == 3
+    assert m["ohlcv_row_count"] == 15
+    assert m["rank_only_count"] == 2
+    assert m["live_counts"]["ticker_count"] == 3
+
+
+def test_refuse_missing_manifest(tmp_path: Path) -> None:
+    snap = tmp_path / "research.sqlite"
+    _tiny_research_db(snap)
+    with pytest.raises(SystemExit, match="manifest missing"):
+        assert_research_snapshot_complete(snap)
+
+
+def test_refuse_incomplete_flag(tmp_path: Path) -> None:
+    snap = tmp_path / "research.sqlite"
+    _tiny_research_db(snap)
+    write_completion_manifest(snap, complete=False, limit=50)
+    with pytest.raises(SystemExit, match="marked incomplete"):
+        assert_research_snapshot_complete(snap)
+
+
+def test_refuse_count_mismatch(tmp_path: Path) -> None:
+    snap = tmp_path / "research.sqlite"
+    _tiny_research_db(snap)
+    write_completion_manifest(snap, complete=True)
+    # Tamper: change live DB after manifest written
+    engine = create_engine(f"sqlite:///{snap.resolve().as_posix()}", future=True)
+    with engine.begin() as conn:
+        conn.execute(
+            text(
+                "INSERT INTO tickers (id, symbol, name, created_at) "
+                "VALUES (99, 'EXTRA', NULL, '2026-01-01')"
+            )
+        )
+    engine.dispose()
+    with pytest.raises(SystemExit, match="does not match"):
+        assert_research_snapshot_complete(snap)
+
+
+def test_clear_manifest(tmp_path: Path) -> None:
+    snap = tmp_path / "research.sqlite"
+    _tiny_research_db(snap)
+    write_completion_manifest(snap, complete=True)
+    assert load_manifest(snap) is not None
+    clear_manifest(snap)
+    assert load_manifest(snap) is None

tests/unit/test_schedule_config.py

@@ -32,6 +32,55 @@ class TestValidateCron:
             validate_cron("0 7 * * *", "Mars/Phobos")
 
 
+class TestTradingDayCrons:
+    """APScheduler's from_crontab() uses 0=Monday, so numeric "1-5" means
+    Tue–Sat: it skips every Monday and fires on Saturdays. Weekday schedules
+    must therefore be spelled with day *names*.
+    """
+
+    _WEEKDAY_KEYS = (
+        "schedule_near_close_pipeline_cron",
+        "schedule_after_close_pipeline_cron",
+        "schedule_intraday_pipeline_cron",
+    )
+
+    @pytest.mark.parametrize("key", _WEEKDAY_KEYS)
+    def test_fires_monday_and_never_saturday(self, key: str):
+        from datetime import datetime, timedelta
+
+        from apscheduler.triggers.cron import CronTrigger
+
+        trigger = CronTrigger.from_crontab(
+            SCHEDULE_DEFAULTS[key], timezone=SCHEDULE_DEFAULTS["schedule_timezone"]
+        )
+        # Walk a full week of fire times from a known Sunday.
+        cursor = datetime(2026, 7, 19, tzinfo=trigger.timezone)
+        weekdays = set()
+        previous = None
+        for _ in range(12):
+            fire = trigger.get_next_fire_time(previous, cursor)
+            weekdays.add(fire.strftime("%a"))
+            previous = fire
+            cursor = fire + timedelta(seconds=1)
+
+        assert "Mon" in weekdays, f"{key} skips Mondays — numeric day-of-week?"
+        assert {"Sat", "Sun"}.isdisjoint(weekdays), f"{key} fires on a weekend"
+
+    def test_fundamentals_runs_on_monday(self):
+        from datetime import datetime
+
+        from apscheduler.triggers.cron import CronTrigger
+
+        trigger = CronTrigger.from_crontab(
+            SCHEDULE_DEFAULTS["schedule_fundamentals_cron"],
+            timezone=SCHEDULE_DEFAULTS["schedule_timezone"],
+        )
+        fire = trigger.get_next_fire_time(
+            None, datetime(2026, 7, 19, tzinfo=trigger.timezone)
+        )
+        assert fire.strftime("%a") == "Mon"
+
+
 class TestScheduleConfig:
     async def test_defaults_when_unset(self, session: AsyncSession):
         config = await get_schedule_config(session)