Daily-trend veto on M15 + lot-size / already-open lines (campaign 2026-09-30), and the 2026-10-01 whole-codebase bug review

Two bodies of work that touch the same files, so one commit.

Direction and setup-size campaign (2026-09-30 → 10-01, owner's RISK-FIRST rule,
written before the runs):
- SIGNAL: M15 daily-trend veto (factory override 7, xau_strategy.daily_trend_veto
  "both"). A finished setup against a clear daily trend (closed D1 bars, Sunday
  stubs dropped; close vs its 50-bar EMA AND vs the close 60 kept days earlier)
  is removed as the last step of the core. One definition
  (signal_aggregator.daily_trend_series) for live, the bar replay and the twin's
  host side; D1 is a required frame of the M15 scan; mirrored in
  cuda_signal_kernels.py K2. W3 824 / +205.12R / PF 1.73 / max DD 12.4R vs
  1105 / +195.79R / 1.49 / 20.9R; 2019 → 2026 1473 / +223.33R / 1.42 / 41.5R vs
  2091 / +192.53R / 1.24 / 64.8R; unseen holdout −4.62R vs −6.63R.
- FAILED, not shipped: the veto on M5 (W3 drawdown 10.1R → 15.2R) and on H1
  (−9.7R), buy-only and sell-only on M15, the wide-stop cap
  (xau_strategy.wide_stop_cap_atr stays 0.0, a research knob).
- New M15 pins: W2 dfc647ee…, OOS e87b6de1…, W3 ad3b0dbd…. M5 / H1 / H4 pins
  unchanged. --set xau_strategy.daily_trend_veto=off reproduces the previous M15
  pins byte for byte. Twin and reference script MD5-equal on every window;
  recorder and feature-export parity re-proved; base-rate table rebuilt.
- DISPLAY (books byte-identical): exposure.py — the lot size for
  trade_size.risk_per_trade (new config.json block, off by default, private to
  the owner) and the count of alerted same-direction plans still open
  (signals.alerted_at, read-only open_alerted_setups); the daily trend in words
  on Telegram, the CLI and the dashboard; signals.daily_trend logged.
- providers/oanda: a date window with no bars is an empty page, not an error
  (a D1 pad starting on a Saturday could not be fetched).
- Reports only, nothing shipped: seven context screens (no state nominated) and
  eight outside data sources (none adds a forecast to gold's own daily trend).
- Independent verification (report section 5.13): every figure reproduced on
  both scripts, scoring identical to the previous commit on all four
  timeframes, no look-ahead. Caveats recorded there: this is a risk filter, not
  a more accurate direction call; nine tenths of the 2019 → 2026 gain is 2022;
  the neighbour clause is thin (a simple 100-day average misses the R line by
  1.2R); "never adds a trade" is an observation, not a guarantee.

Bug review of the whole codebase (2026-10-01). Signal path clean. 17 defects
elsewhere, plus 4 found by an independent review of the fixes; all fixed, each
with a test; no trade book changed:
- telegram_bot: one _deliver_alert for the candle scan and the level break. The
  row is stamped alerted_at BEFORE the send, on a one-worker alert-db pool, and
  un-stamped when no chat received it or the task is cancelled mid-stamp, so
  two alerts at one candle close count each other and no SQLite write runs on
  the event loop. The 5-bar cooldown counts bars of the scan's own frame
  (== gpu_backtest._apply_cooldown; clock time released the 22:00 UTC bar after
  the settlement pause) and is restored from signals.db at start-up. The
  lot-size line goes to the owner's own chat only (a group the owner ran
  /autoscan in used to receive it). /status gains a news-model line and escapes
  failure details.
- signal_log: mark_alerted returns the stamp it wrote; unmark_alerted;
  last_alerted_bars (read-only).
- advisory_outcome: candle continuity is judged bar by bar inside the walk; a
  hole after a trade's exit no longer voids it. The stored-candle replay
  resolves 108 of 111 logged setups instead of 99, all equal to the live
  resolver's R.
- providers/oanda: an empty date window is accepted only when it is 5 days or
  shorter and no malformed candle was skipped; a longer one is retried, then
  refused, instead of being cached as history with a hole.
- news_engine: blank RSS titles skipped; one unreadable headline no longer
  costs the local model its whole batch (checked on the real bundle); model
  health recorded; no DeepSeek audit call in a process that does not archive;
  event-direction cache keyed per release; peek_week_events never fetches.
- llm_client: chat_json never raises and never logs the API key (requests
  quotes the Authorization value in InvalidHeader; error bodies are redacted).
- signal_aggregator: the scan's news block is guarded; the replay path
  (use_news=False) assigns exactly the values it did before.
- agent_panel: PANEL_PROMPT_VERSION v2.2 — same prompt text, corrected input
  (event timing over the cached calendar week, headlines newest first).
  scripts/panel_replay.py replays the logged payload per persona;
  scripts/label_scan_outcomes.py counts with an ISO cutoff.
- test_all.py: no ALTER TABLE … DROP COLUMN (the live box has SQLite 3.34);
  tests that write Parquet print a notice and return without pyarrow.

Verification after the last code change: suite 632 passed, 0 failed (also with
both Parquet engines blocked and FB2_BACKEND=cpu, the live box's view); the
five reference MD5s reproduce on both backtesters, 0 cache fallbacks; Module 37
green; static Python 3.9 / SQLite 3.34 check clean on every touched file.

Evidence: backtest/research/daily_trend_campaign_2026-09-30.md,
context_screens_2026-10.md, new_inputs_2026-10.md; CLAUDE.md sections 2, 4, 6,
8, 9, 10 and 11; pair_signals/xauusd.py docstring (M15 override 7).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

scripts/panel_replay.py

@@ -275,15 +275,26 @@ def build_report(con, rowid: int) -> Tuple[Optional[SignalReport], Optional[Fund
     return report, context, frames, _loads(payloads_json, None)
 
 
+def stored_personas(stored: Any) -> List[str]:
+    """The requested personas whose exact payload the row logged."""
+    if not isinstance(stored, dict):
+        return []
+    return [p for p in PERSONAS if isinstance(stored.get(p), dict)]
+
+
 def build_jobs(report: SignalReport, context: FundamentalContext,
                frames: Dict[Timeframe, pd.DataFrame], stored: Optional[Dict[str, Any]],
                settings: Dict[str, Any]) -> Tuple[Tuple[str, str, Dict[str, Any]], ...]:
-    """Persona jobs on the CURRENT prompts: stored payloads when the row
-    has them, else rebuilt through the live payload builders."""
+    """Persona jobs on the CURRENT prompts. A persona whose exact payload
+    the row logged replays that payload; every other persona is rebuilt
+    through the live payload builders. Per persona, not all-or-nothing:
+    the live panel stores only the personas it ran (macro alone since
+    2026-09-17), so "all four or none" never used a stored payload."""
     systems = {"macro": ap._MACRO_SYSTEM, "technical": ap._TECH_SYSTEM,
                "risk": ap._RISK_SYSTEM, "contrarian": ap._CONTRARIAN_SYSTEM}
-    if stored and all(p in stored for p in PERSONAS):
-        return tuple((p, systems[p], stored[p]) for p in PERSONAS)
+    kept = {p: stored[p] for p in stored_personas(stored)}
+    if len(kept) == len(PERSONAS):
+        return tuple((p, systems[p], kept[p]) for p in PERSONAS)
     candles: Dict[str, Any] = {}
     analyses = [report.primary_analysis] + list(report.confirmation_analyses)
     counts = [int(settings.get("candle_bars_primary", ap.DEFAULT_CANDLE_BARS_PRIMARY))] + \
@@ -297,7 +308,8 @@ def build_jobs(report: SignalReport, context: FundamentalContext,
         if atr and atr > 0 and df is not None and len(df) >= 2:
             candles[analysis.timeframe] = ap._normalize_candles(df, atr, n)
     ctx = ap._price_context(report, frames, context)
-    return ap._panel_jobs(report, context, candles or None, ctx, PERSONAS)
+    rebuilt = ap._panel_jobs(report, context, candles or None, ctx, PERSONAS)
+    return tuple((p, system, kept.get(p, payload)) for p, system, payload in rebuilt)
 
 
 # ── Scoring ───────────────────────────────────────────────────────────
@@ -445,7 +457,9 @@ def main(argv: Optional[Sequence[str]] = None) -> int:
             "event_row": rep.id, "bar": report.timestamp, "tf": rep.timeframe,
             "direction": report.trade_setup.direction, "grade": rep.quality,
             "strategy": report.trade_setup.strategy,
-            "R": res.advisory_r if res else None, "stored_payloads": bool(stored),
+            # which personas replayed the exact logged payload ("" = all rebuilt)
+            "R": res.advisory_r if res else None,
+            "stored_payloads": ",".join(stored_personas(stored)),
             "base_p": base_p, "prior_move_4h_atr": ctx.get("prior_move_4h_atr"),
             "move_from_start_atr": ctx.get("move_from_24h_start_atr"),
             "big_candle_last3": ctx.get("big_candle_last3"),