chore: consolidate post-stop research artifacts

fix: grandfather pre-cutover stop episodes
docs: document post-stop gate reset results
2026-07-17 20:46:21 +02:00 · 2026-07-17 20:28:42 +02:00 · 2026-07-17 19:58:40 +02:00 · 2026-07-17 19:30:40 +02:00 · 2026-07-17 17:42:13 +02:00 · 2026-07-17 17:37:03 +02:00
35 changed files with 178776 additions and 84 deletions
@@ -43,3 +43,6 @@ combined-ca-bundle.pem
 # Backtest reports in reports/ are tracked: they are the evidence behind the
 # production baseline in the README. The snapshot DBs they run against are not.
 backtest_snapshots/
+# Rebuildable pickle caches are local accelerators, not decision evidence.
+reports/*.pkl
+reports/*.pk1
@@ -2,13 +2,13 @@

 Investing-signal platform for US equities. It runs one strategy, and it is a boring one:

-> **A long-only cross-sectional momentum book.** Buy the top quintile by beta-adjusted 12-1 month momentum, tilt toward higher volatility, hold at most 10 names, cut at 1.5× ATR, then trail at 3× ATR for up to 30 trading days.
+> **A long-only cross-sectional momentum book.** Buy the top quintile by beta-adjusted 12-1 month momentum, tilt toward higher volatility, hold at most 10 names, cut at 1.5× ATR, then trail at 3× ATR for up to 30 trading days. After an initial-stop exit, re-enter only after the gate has failed and subsequently qualified again.

 **Philosophy:** don't predict price — rank it. The edge is *relative* strength across the universe, and the discipline is in the exit: cut losers fast, let winners run until the trail catches them.

 **What is NOT the edge — read this before trusting a number on screen.** The composite score, the 5 dimensions, sentiment, fundamentals, and Structural S/R are **display context**, not validated predictors. The Gate Target Ladder is screening machinery that preserves the production setup population; it is not a claim about true market structure. In particular:

- **The headline "target" is not an exit.** It comes from the internal **Gate Target Ladder** and exists only to compute the R:R and reach-probability used by the activation gate. Human-facing chart S/R is a separate model. The live exit reads neither. Across 320 backtested production trades the exit reasons were **144 initial stop, 98 trailing stop, 78 max hold — and 0 targets.** Honoring the target as a take-profit was tested and *halves CAGR* ([research](docs/research/sr-levels-and-exits.md)).
+- **The headline "target" is not an exit.** It comes from the internal **Gate Target Ladder** and exists only to compute the R:R and reach-probability used by the activation gate. Human-facing chart S/R is a separate model. The live exit reads neither. Across 472 trades in the current daily gate-reset replay, the exit reasons were **229 initial stop, 147 trailing stop, 96 max hold — and 0 targets.** Honoring the target as a take-profit was tested and *halves CAGR* ([research](docs/research/sr-levels-and-exits.md)).
 - **The composite score does not select trades.** Residual momentum does.

 Full experiment log — everything tested, kept, and rejected: **[docs/research/](docs/research/README.md)**.
@@ -36,19 +36,31 @@ flowchart TD
    BOOK -->|yes| OPEN["OPEN — size at 1% account risk"]

    OPEN --> EXIT{"Exit — whichever comes first"}
-    EXIT --> E1["Initial stop hit<br/>entry − 1.5 × ATR → −1R<br/><b>45% of trades</b>"]
+    EXIT --> E1["Initial stop hit<br/>entry − 1.5 × ATR → −1R<br/><b>49% of trades</b>"]
    EXIT --> E2["Trailing stop hit<br/>highest close − 3 × ATR<br/><i>only binds once price is ~1R up</i><br/><b>31% of trades</b>"]
-    EXIT --> E3["Max hold reached<br/>30 trading days<br/><b>24% of trades</b>"]
+    EXIT --> E3["Max hold reached<br/>30 trading days<br/><b>20% of trades</b>"]
    EXIT -.->|"NEVER"| E4["Gate Target Ladder target<br/><b>0% of trades</b>"]

+    E1 --> LOCK["Re-entry locked"]
+    LOCK --> GF{"Later daily scan<br/>fails the gate?"}
+    GF -->|no| LOCK
+    GF -->|yes| GQ{"A subsequent daily scan<br/>qualifies again?"}
+    GQ -->|no| GQ
+    GQ -->|yes| RANK
+
    style M fill:#1e3a5f,color:#fff
    style OPEN fill:#1e4d2b,color:#fff
    style E4 fill:#2a2a2a,color:#888
    style E1 fill:#4a1f1f,color:#fff
    style E2 fill:#1e4d2b,color:#fff
+    style LOCK fill:#4a351f,color:#fff
 ```

-**How to read the exit box.** The initial stop is tight (1.5× ATR) and the trail is wide (3× ATR), so the trail sits *below* the initial stop at entry and only takes over once price has advanced roughly 1R. Cut fast when wrong; give room once right. That asymmetry is what produces the right-tailed return profile the strategy depends on — most trades lose a little (win rate ~37.5%), a few win big (best trade +12.9R), and *that is why there is no take-profit*.
+**How to read the exit box.** The initial stop is tight (1.5× ATR) and the trail is wide (3× ATR), so the trail sits *below* the initial stop at entry and only takes over once price has advanced roughly 1R. Cut fast when wrong; give room once right. That asymmetry is what produces the right-tailed return profile the strategy depends on — most trades lose a little (win rate 36.2%), a few win big (best trade +12.0R), and *that is why there is no take-profit*.
+
+**What happens after an initial stop.** The stop always closes the trade and realizes its costs. The ticker is then locked until a successful daily full-universe scan first observes it outside the production gate and a later scan observes a fresh qualification. A continuously qualified ticker therefore cannot generate an immediate duplicate entry. Other exit reasons do not start this reset. See the [daily post-stop re-entry study](docs/research/post-stop-reentry.md).
+
+**Live timing matters.** The full daily pipeline runs the R:R scan before Outcome Eval. A stop closed by that Outcome Eval—or by an intraday evaluation after the day's full scan—therefore cannot use its stop-day gate state. The earliest failure observation is the next successful full scan, and requalification needs a subsequent full scan. The research `gate_reset` arm evaluated the stop before its same-session gate check; the live boundary is consequently analogous to the study's stricter `strict_gate_reset` arm. This known event-ordering difference is quantified below.

 ## How It Works

@@ -121,7 +133,7 @@ Once a day (default 07:00). Steps run **in dependency order**, each consuming th

 1. **OHLCV** — fetch the latest daily bars for every tracked ticker (Alpaca); new tickers backfill ~5 years.
 2. **Sentiment** — fetch sentiment for the names that matter and are stale (> 5 days): top-pick feeders (residual-momentum leaders with a tradeable long setup), the watchlist, and open paper trades, plus a top-N-by-composite discovery net. Runs *before* the scan so the scan sees fresh sentiment.
-3. **R:R Scan** — persist clean Structural S/R for charts/alerts, recompute the 5-dimension scores, and build long/short setups from a transient Gate Target Ladder (ATR stops and nominal gate targets) for every ticker. Attach each ticker's residual 12‑1 momentum activation percentile plus the promoted 80/20 production rank.
+3. **R:R Scan** — persist clean Structural S/R for charts/alerts, recompute the 5-dimension scores, and build long/short setups from a transient Gate Target Ladder (ATR stops and nominal gate targets) for every ticker. Attach each ticker's residual 12‑1 momentum activation percentile plus the promoted 80/20 production rank. The completed full-universe scan also advances post-stop locks from gate failure to later requalification; failed scans never count as a transition.
 4. **Outcome Eval** — resolve setups that hit target/stop or expired (default 30 trading days) and auto-close paper trades per the exit policy (default: 3x ATR trail with a 30-trading-day max hold).
 5. **Market Regime** — recompute the regime index (breadth/trend).
 6. **Regime Monitor** — separate v2 State/Warning risk thermometer with fixed-basket breadth, VIX, credit, and point-in-time fundamentals; feeds no trades.
@@ -155,6 +167,7 @@ Fundamentals (weekly, early Monday) · Alerts (hourly, Telegram) · Backtest (we
 |---|---|---|
 | **Residual 12-1 cross-sectional momentum** (the activation gate, long-only) | **Production gate — in-sample edge** | Promoted July 2026 after the portfolio variant beat raw 80 on CAGR, Sharpe and drawdown. Raw 12-1 remains a fallback only when benchmark data is unavailable |
 | **3× ATR trailing exit** (+ 1.5× ATR initial stop, 30-day max hold) | **Production exit — best Sharpe of every exit tested** | Beat hold / SMA50 / 20-day-low / technical-40 and both take-profit variants (July 2026) |
+| **Post-stop gate reset** | **Production re-entry policy** | The initial stop always closes; the ticker must later fail the daily gate and subsequently qualify again. At the production capacity of 10: Sharpe 1.67 → 1.77, CAGR 45.2% → 48.3%, DD 24.3% → 21.6% versus immediate re-entry. [Full study](docs/research/post-stop-reentry.md) |
 | **Structural S/R** | **Human-facing context only — not a gate and not an exit** | Clean, capped zones are persisted for charts and alerts. The scanner deliberately does not read them. |
 | **Gate Target Ladder** | **Gate input only — not market structure and not an exit** | Volume-free range grid + pivots preserves the useful legacy screening behavior exactly: 1,086/1,086 qualified setups retained and identical Sharpe 2.03 / CAGR 50.0% / DD 21.4% / 321 trades. The exit never reads its target. [Full write-up](docs/research/sr-levels-and-exits.md#explicit-gate-target-ladder) |
 | Composite score + 5 dimensions | **Display/ranking only** | Sub-scores are hand-built heuristics; none has a measured IC. Note: the "momentum" *dimension* is 5/20-day ROC — NOT the validated 12-1 factor (that lives in `momentum_service`) |
@@ -167,11 +180,28 @@ Fundamentals (weekly, early Monday) · Alerts (hourly, Telegram) · Backtest (we

 Caveats on the momentum result: in-sample, roughly one market regime, costs/slippage approximated at 0.1% per side, and residual momentum still needs SPY benchmark history to compute. The **out-of-sample proof is the forward paper-trade record**: Signals → Track Record compares live qualified expectancy against the backtest.

-### Current production baseline
+### Daily post-stop re-entry decision (2026-07-17)

-Use this as a regression guardrail for future strategy changes, not as a return promise. Backtest run: local production SQLite snapshot, 506 tickers, weekly cadence, 30-trading-day horizon, 2022-06-28 → 2026-07-02, 0.1% per-side costs, price-only SPY benchmark. Numbers below are the 2026-07-11 run (`reports/backtest-20260711-prod-baseline.json`) — measured *after* the primary-target probability floor shipped, which pruned lottery-target setups (1,428 → 1,089 qualified) and lifted Sharpe on all three promotion contenders.
+The production policy is **normal gate reset**, evaluated with daily setup opportunities and live-like full-universe ranking. An initial stop always closes. Re-entry unlocks only after a later successful daily scan observes the ticker failing the gate and a subsequent scan observes it qualifying again. The study replayed 1,011,248 point-in-time candidate observations across 505 tickers from 2022-06-24 through 2026-07-02, with the production GTL gate, 80/20 rank, exit, fees, sizing, and 10-position capacity.

-| Item | Current baseline |
+| Re-entry policy | Total return | CAGR | Max DD | Sharpe | Trades |
+|---|---:|---:|---:|---:|---:|
+| Immediate | 348.4% | 45.2% | -24.3% | 1.67 | 489 |
+| **Gate reset (selected study arm)** | **388.1%** | **48.3%** | **-21.6%** | **1.77** | **472** |
+| Strict gate reset (live timing analogue) | 342.7% | 44.8% | -23.4% | 1.68 | 471 |
+| Fixed five-session cooldown | 250.8% | 36.6% | -22.2% | 1.47 | 473 |
+
+In the disjoint 2025+ book, gate reset also beat immediate re-entry (Sharpe 1.66 vs 1.55; CAGR 41.8% vs 39.3%) and the fixed five-session rule (Sharpe 1.43; CAGR 32.7%). Its lead over both survived costs of 0.2% and 0.3% per side. The result is capacity-specific: cooldown 5 won at capacity 5, while immediate had slightly higher return and Sharpe at capacity 15. Production uses capacity 10, so that is the portfolio for which this decision is valid.
+
+Those promotion numbers belong to the selected normal-reset study arm. Under the live scheduler's stricter first-observation timing, the full-period analogue was Sharpe 1.68 / CAGR 44.8% / DD 23.4%; in the disjoint 2025+ book it was Sharpe 1.38 / CAGR 32.9% / DD 21.0%. The matrix therefore validates the state-machine choice but is not exact scheduler-order parity. Closing this timing gap would require a separately reviewed pipeline-order change, not a documentation reinterpretation.
+
+`gate_reset` and a simple `next_session` block happened to produce the same executed live-universe portfolio in this sample. Their rules are still different: this establishes that same-day re-entry was harmful here, but does not isolate a separate historical return premium from the reset condition. Gate reset was promoted because it represents a genuinely new signal episode and did not sacrifice results in the production book. Full definitions, all nine policy arms, cost/capacity sensitivity, and legacy-rank results are in [docs/research/post-stop-reentry.md](docs/research/post-stop-reentry.md); source report: [`reports/daily_reentry_matrix.json`](reports/daily_reentry_matrix.json).
+
+### Historical weekly production baseline (pre gate-reset)
+
+Use this as the historical ranking/exit regression guardrail, not as a return promise or the current re-entry-policy result. This run predates the post-stop gate reset and uses weekly entry replay, so its portfolio headline is not directly comparable with the daily matrix above. Backtest run: local production SQLite snapshot, 506 tickers, weekly cadence, 30-trading-day horizon, 2022-06-28 → 2026-07-02, 0.1% per-side costs, price-only SPY benchmark. Numbers below are the 2026-07-11 run (`reports/backtest-20260711-prod-baseline.json`) — measured *after* the primary-target probability floor shipped, which pruned lottery-target setups (1,428 → 1,089 qualified) and lifted Sharpe on all three promotion contenders.
+
+| Item | Historical weekly baseline |
 |---|---|
 | Strategy version | `residual_highvol_80_20_atr_trail3_v1` |
 | Production gate | Long-only, residual 12-1 momentum percentile >= 80, headline gate-target R:R >= 2.0 (live `activation_min_rr`; the code default is 1.2), primary-target reach-probability >= 20%, NEUTRAL excluded, confidence floor off (0) |
@@ -218,6 +248,7 @@ A systematic single-variable sweep (offline prod snapshot, production gate/rank/
 | Momentum lookback: 6-1, 3-1, 12-7 (Novy-Marx), composites | **Keep residual 12-1** | 6-1/3-1 rank-IC ≈ 0; 12-7 IC 0.045 / t 1.58 — weaker than residual 12-1 (0.055 / t 1.98) |
 | Selection cutoff {70, 75, 85, 90} × book size {10, 15, 20} | **Keep 80 × 10** | Monotonically worse in both directions from 80; the 10-slot cap never binds (<10 concurrent) |
 | Position sizing: equal-weight, inverse-vol, risk-% sweep | **Keep 1% fixed-fractional** | See the inverse-vol warning below |
+| Post-stop re-entry: immediate, fixed 2–5 sessions, gate resets, confirmation filters | **Keep normal gate reset for the 10-position production book** | Sharpe 1.77 vs 1.67 immediate and 1.47 cooldown 5; rerun before changing portfolio capacity |
 | FIP path-smoothness as an in-book tie-breaker/filter | **Reject** (but see the lead below) | Non-monotonic across FIP quintiles within the qualified set; either half of a median split underperforms the full book — thinning the entry stream costs more compounding than the tilt returns |

 Two findings future sessions must not re-litigate:
@@ -450,6 +481,14 @@ python scripts/run_backtest_snapshot.py backtest_snapshots/prod.sqlite --workers
 .venv\Scripts\python.exe scripts\run_backtest_snapshot.py backtest_snapshots\prod.sqlite --workers 6 --allow-spawn
 ```

+Weekly remains the resource-safe default. Add `--cadence daily` for live-like daily entry opportunities; this performs roughly five times as many setup evaluations. To generate the complete weekly/daily × immediate/gate-reset comparison in one invocation, use:
+
+```bash
+python scripts/run_backtest_cadence_comparison.py backtest_snapshots/prod.sqlite --workers 7
+```
+
+On Windows, add `--allow-spawn`. The comparison runner writes the two full cadence reports plus one compact four-arm report. For the larger nine-policy daily research matrix used in the post-stop decision, see `scripts/run_daily_reentry_matrix.py` and the [research record](docs/research/post-stop-reentry.md).
+
 On an 8-thread machine, `--workers 6` is a good starting point: it leaves a
 couple of threads for Windows, the shell, and browser/UI work while still using
 most of the CPU.
@@ -490,6 +529,7 @@ matching decision. Every change still goes through the factor harness first (see
 | `gate_ablation` | Net expectancy with each floor removed | Drop a floor only if removing it doesn't hurt net expectancy |
 | `time_exit_sweep` | Net avg R / net R-per-day by hold length | Whether a fixed time exit beats the promoted ATR trail |
 | `portfolio_monitor`, `portfolio_sim`, `strategy_variants` | CAGR, Sharpe, max drawdown, per-year returns | Promote a strategy only if it beats the current baseline on CAGR/Sharpe/DD |
+| `production_cadence_comparison` | Immediate vs production gate reset at the selected weekly or daily cadence | Isolates the re-entry rule while keeping gate, rank, exit, fees, sizing, and capacity fixed |
 | `signal_eval` | Mean IC, t-stat, IC>0 %, `reliable` | Iron rule: wire a new factor in only if \|IC\| ≳ 0.03 with a consistent sign and `reliable: true` |
 | `holdout` (opt-in) | Train vs test books, split by entry date | **The only honest OOS read.** Set `BACKTEST_HOLDOUT_SPLIT=YYYY-MM-DD` |
 | `recommendation`, `research_recommendation` | The report's own headline read | A starting point, not a substitute for the sections above |
@@ -0,0 +1,53 @@
+"""add persistent post-stop gate-reset observation
+
+Revision ID: 022
+Revises: 021
+Create Date: 2026-07-17 00:00:00.000000
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+revision: str = "022"
+down_revision: Union[str, None] = "021"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "paper_trades",
+        sa.Column("reentry_gate_failed_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.add_column(
+        "paper_trades",
+        sa.Column(
+            "reentry_gate_requalified_at",
+            sa.DateTime(timezone=True),
+            nullable=True,
+        ),
+    )
+    # The policy starts at this deployment. Historical NULL values mean the
+    # scanner never recorded reset observations, not that those old episodes
+    # are still active. Mark both transitions complete so only stops created
+    # after the migration can open a re-entry lock.
+    op.execute(
+        sa.text(
+            """
+            UPDATE paper_trades
+            SET reentry_gate_failed_at = closed_at,
+                reentry_gate_requalified_at = closed_at
+            WHERE status = 'closed'
+              AND close_reason = 'stop'
+              AND closed_at IS NOT NULL
+            """
+        )
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("paper_trades", "reentry_gate_requalified_at")
+    op.drop_column("paper_trades", "reentry_gate_failed_at")
@@ -36,3 +36,13 @@ class PaperTrade(Base):
    closed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
    # How the trade was closed: "time" | "trailing" | "stop" | "target" | "manual".
    close_reason: Mapped[str | None] = mapped_column(String(10), nullable=True)
+    # A trade stopped at its initial stop starts a re-entry gate-reset episode.
+    # The daily full-universe scanner records both state transitions: the first
+    # failed gate observation and a later fresh qualification. Re-entry remains
+    # non-actionable until both timestamps exist.
+    reentry_gate_failed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    reentry_gate_requalified_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
@@ -387,6 +387,7 @@ async def trigger_job(
        db,
        job_name,
        target_model=body.target_model if body is not None else None,
+        cadence=body.cadence if body is not None else None,
    )
    return APIEnvelope(status="success", data=result)

@@ -36,6 +36,7 @@ async def list_trade_setups(
        recommended_action=recommended_action,
        live_recommendation=True,
        exclude_open_trade_tickers=True,
+        exclude_reentry_gate_locked_tickers=True,
    )

    data = []
@@ -98,6 +99,7 @@ async def get_ticker_trade_setups(
        db,
        symbol=symbol,
        live_recommendation=True,
+        include_reentry_gate_lock=True,
    )
    data = []
    for row in rows:
@@ -37,8 +37,10 @@ from app.services import fundamental_service, ingestion_service, sentiment_servi
 from app.services.alert_service import dispatch_alerts
 from app.services.backtest_service import (
    BACKTEST_TARGET_MODELS,
+    DEFAULT_BACKTEST_CADENCE,
    PRODUCTION_GTL_TARGET_MODEL,
    run_and_store as run_backtest_and_store,
+    validate_backtest_cadence,
    validate_backtest_target_model,
 )
 from app.services.benchmark_service import refresh_benchmark_prices
@@ -112,6 +114,7 @@ def _idle_runtime() -> dict[str, object]:

 _job_runtime: dict[str, dict[str, object]] = {name: _idle_runtime() for name in _JOB_NAMES}
 _next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
+_next_backtest_cadence = DEFAULT_BACKTEST_CADENCE


 # ---------------------------------------------------------------------------
@@ -119,23 +122,44 @@ _next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
 # ---------------------------------------------------------------------------


-def queue_backtest_target_model(target_model: str | None) -> str:
-    """Select the model for the next manual backtest run only.
+def queue_backtest_options(
+    target_model: str | None,
+    cadence: str | None,
+) -> tuple[str, str]:
+    """Select model and cadence for the next manual backtest run only.

-    Scheduled runs and subsequent manual runs return to the production GTL.
+    Scheduled and subsequent manual runs return to production GTL at the
+    resource-safe weekly cadence.
    """
-    global _next_backtest_target_model
-    selected = validate_backtest_target_model(
+    global _next_backtest_target_model, _next_backtest_cadence
+    selected_model = validate_backtest_target_model(
        target_model or PRODUCTION_GTL_TARGET_MODEL
    )
-    _next_backtest_target_model = selected
+    selected_cadence = validate_backtest_cadence(
+        cadence or DEFAULT_BACKTEST_CADENCE
+    )
+    _next_backtest_target_model = selected_model
+    _next_backtest_cadence = selected_cadence
+    return selected_model, selected_cadence
+
+
+def queue_backtest_target_model(target_model: str | None) -> str:
+    """Compatibility wrapper for callers selecting only the target model."""
+    selected, _ = queue_backtest_options(target_model, DEFAULT_BACKTEST_CADENCE)
+    return selected
+
+
+def _consume_backtest_options() -> tuple[str, str]:
+    global _next_backtest_target_model, _next_backtest_cadence
+    selected = (_next_backtest_target_model, _next_backtest_cadence)
+    _next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
+    _next_backtest_cadence = DEFAULT_BACKTEST_CADENCE
    return selected


 def _consume_backtest_target_model() -> str:
-    global _next_backtest_target_model
-    selected = _next_backtest_target_model
-    _next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
+    """Compatibility wrapper consuming all queued one-run options."""
+    selected, _ = _consume_backtest_options()
    return selected


@@ -1028,12 +1052,13 @@ async def compute_regime_monitor() -> None:
 async def run_backtest_job() -> None:
    """Replay the price-derived engine over history and cache the report."""
    job_name = "backtest"
-    target_model = _consume_backtest_target_model()
+    target_model, cadence = _consume_backtest_options()
    _log_event(
        logging.INFO,
        "job_start",
        job=job_name,
        target_model=target_model,
+        cadence=cadence,
    )
    _runtime_start(job_name)

@@ -1051,6 +1076,7 @@ async def run_backtest_job() -> None:
                db,
                _on_progress,
                target_model=target_model,
+                cadence=cadence,
            )

        _runtime_finish(
@@ -1058,6 +1084,7 @@ async def run_backtest_job() -> None:
            processed=report.get("tickers", 0), total=report.get("tickers", 0),
            message=(
                f"{BACKTEST_TARGET_MODELS[target_model]}: "
+                f"{cadence} cadence, "
                f"{report.get('candidates', 0)} setups, "
                f"{report.get('qualified', 0)} qualified"
            ),
@@ -46,6 +46,7 @@ class JobToggle(BaseModel):
 class JobTriggerRequest(BaseModel):
    """Optional parameters for a one-time manual job run."""
    target_model: Literal["production_gtl", "structural_sr"] | None = None
+    cadence: Literal["weekly", "daily"] | None = None


 class RecommendationConfigUpdate(BaseModel):
@@ -59,5 +59,6 @@ class TradeSetupResponse(BaseModel):
    momentum_percentile: float | None = None
    strategy_rank: float | None = None
    volatility_percentile: float | None = None
+    reentry_gate_reset_required: bool = False
    context_as_of: TradeSetupContextAsOfResponse | None = None
    recommendation_summary: RecommendationSummaryResponse | None = None
@@ -607,6 +607,7 @@ async def trigger_job(
    job_name: str,
    *,
    target_model: str | None = None,
+    cadence: str | None = None,
 ) -> dict[str, str]:
    """Trigger a manual job run via the scheduler.

@@ -616,6 +617,8 @@ async def trigger_job(
        raise ValidationError(f"Unknown job: {job_name}. Valid jobs: {', '.join(sorted(VALID_JOB_NAMES))}")
    if target_model is not None and job_name != "backtest":
        raise ValidationError("target_model is supported only for the backtest job")
+    if cadence is not None and job_name != "backtest":
+        raise ValidationError("cadence is supported only for the backtest job")

    from app.scheduler import get_job_runtime_snapshot, scheduler

@@ -643,9 +646,9 @@ async def trigger_job(
        return {"job": job_name, "status": "not_found", "message": f"Job '{job_name}' is not registered in the scheduler"}

    if job_name == "backtest":
-        from app.scheduler import queue_backtest_target_model
+        from app.scheduler import queue_backtest_options

-        target_model = queue_backtest_target_model(target_model)
+        target_model, cadence = queue_backtest_options(target_model, cadence)

    job.modify(next_run_time=None)  # Reset, then trigger immediately
    from datetime import datetime, timezone
@@ -654,6 +657,8 @@ async def trigger_job(
    result = {"job": job_name, "status": "triggered", "message": f"Job '{job_name}' triggered for immediate execution"}
    if target_model is not None:
        result["target_model"] = target_model
+    if cadence is not None:
+        result["cadence"] = cadence
    return result


@@ -282,6 +282,7 @@ async def _qualified_setups(db: AsyncSession) -> list[dict]:
        db,
        live_recommendation=True,
        exclude_open_trade_tickers=True,
+        exclude_reentry_gate_locked_tickers=True,
    )
    config = await get_activation_config(db)
    return [s for s in setups if setup_qualifies(SimpleNamespace(**s), config)]
@@ -1,7 +1,7 @@
 """Historical backtest (Phase 1): replay the price-derived engine over stored
 OHLCV and measure how the CURRENT config would have performed.

-For each ticker we step through history (weekly), and at each as-of date D we
+For each ticker we step through history at the selected entry cadence, and at each as-of date D we
 rebuild the setup using only bars ≤ D (no lookahead), then walk the actual bars
 after D to record the realized outcome. The report contains:

@@ -99,7 +99,17 @@ logger = logging.getLogger(__name__)

 KEY_REPORT = "backtest_report"

-STEP_DAYS = 5          # weekly cadence (≈ 5 trading days)
+WEEKLY_BACKTEST_CADENCE = "weekly"
+DAILY_BACKTEST_CADENCE = "daily"
+DEFAULT_BACKTEST_CADENCE = WEEKLY_BACKTEST_CADENCE
+PRODUCTION_REENTRY_POLICY = "gate_reset"
+BACKTEST_CADENCE_SESSIONS = {
+    WEEKLY_BACKTEST_CADENCE: 5,
+    DAILY_BACKTEST_CADENCE: 1,
+}
+# Compatibility alias for research scripts built around the original weekly
+# replay. New code should select a cadence and call ``backtest_step_sessions``.
+STEP_DAYS = BACKTEST_CADENCE_SESSIONS[WEEKLY_BACKTEST_CADENCE]
 MIN_LOOKBACK = 60      # bars needed before D for indicators (EMA cross needs 51)
 HORIZON = 30           # trading days to resolve an outcome (matches the evaluator)
 ATR_MULTIPLIER = 1.5
@@ -156,6 +166,30 @@ def validate_backtest_target_model(value: str) -> str:
    return normalized


+def validate_backtest_cadence(value: str) -> str:
+    """Validate the supported entry-replay cadences."""
+    normalized = value.strip().lower()
+    if normalized not in BACKTEST_CADENCE_SESSIONS:
+        allowed = ", ".join(BACKTEST_CADENCE_SESSIONS)
+        raise ValueError(
+            f"Unknown backtest cadence {value!r}; expected one of {allowed}"
+        )
+    return normalized
+
+
+def backtest_step_sessions(cadence: str) -> int:
+    return BACKTEST_CADENCE_SESSIONS[validate_backtest_cadence(cadence)]
+
+
+def _ranking_period(as_of: date, cadence: str) -> tuple:
+    """Cross-section key for activation ranks at the selected entry cadence."""
+    cadence = validate_backtest_cadence(cadence)
+    if cadence == DAILY_BACKTEST_CADENCE:
+        return ("date", as_of.toordinal())
+    iso = as_of.isocalendar()
+    return ("week", iso[0], iso[1])
+
+
 # ---------------------------------------------------------------------------
 # RESEARCH / DIAGNOSTIC FALLBACKS (retired experiments)
 #
@@ -455,14 +489,17 @@ def _replay_ticker(
    activation: dict,
    benchmark_closes: dict[date, float] | None = None,
    target_model: str = PRODUCTION_GTL_TARGET_MODEL,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> list[dict]:
-    """Walk one ticker's history weekly, building setups and their realized outcomes."""
+    """Walk one ticker at the selected cadence and resolve each setup outcome."""
+    cadence = validate_backtest_cadence(cadence)
+    step_sessions = backtest_step_sessions(cadence)
    candidates: list[dict] = []
    n = len(records)
    if n < MIN_LOOKBACK + HORIZON:
        return candidates

-    for i in range(MIN_LOOKBACK - 1, n - HORIZON, STEP_DAYS):
+    for i in range(MIN_LOOKBACK - 1, n - HORIZON, step_sessions):
        window = records[: i + 1]
        forward = records[i + 1 :]
        forward_bars = [Bar(date=r.date, high=r.high, low=r.low) for r in forward]
@@ -511,6 +548,7 @@ def _replay_ticker(
                "symbol": symbol,
                "date": records[i].date.isoformat(),
                "iso_week": (iso[0], iso[1]),
+                "ranking_period": _ranking_period(records[i].date, cadence),
                "direction": s["direction"],
                "entry": s["entry"],
                "stop": s["stop"],
@@ -967,6 +1005,7 @@ def _replay_and_signals(
    activation: dict,
    benchmark_closes: dict[date, float] | None = None,
    target_model: str = PRODUCTION_GTL_TARGET_MODEL,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> 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),
@@ -986,11 +1025,117 @@ def _replay_and_signals(
            activation,
            benchmark_closes,
            target_model,
+            cadence,
        ),
        _signal_series(bars, benchmark_closes),
    )


+def _replay_candidates_for_period(
+    symbol: str,
+    columns: tuple,
+    config: dict,
+    activation: dict,
+    benchmark_closes: dict[date, float] | None,
+    start_date: date,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
+    include_short_candidates: bool = False,
+    include_universe_rank_observations: bool = False,
+) -> list[dict]:
+    """Slim picklable replay used by local event studies.
+
+    Unlike the full report worker it skips factor-series construction and only
+    evaluates setup dates on or after ``start_date``. Long-only remains the
+    compatibility default. Set ``include_short_candidates`` when the caller
+    needs the legacy full-backtest candidate-ranking universe; shorts can then
+    contribute to those historical percentiles while the portfolio simulator
+    still trades only qualified longs. ``include_universe_rank_observations``
+    additionally marks exactly one row per ticker/session for a live-like rank
+    across tickers rather than across directional setup candidates. If no setup
+    exists on that session, a non-tradeable rank-only row is emitted.
+    """
+    date_ords, opens, highs, lows, closes, volumes = columns
+    bars = [
+        SimpleNamespace(
+            date=date.fromordinal(o), open=op, high=hi, low=lo, close=cl, volume=vo
+        )
+        for o, op, hi, lo, cl, vo in zip(
+            date_ords, opens, highs, lows, closes, volumes
+        )
+    ]
+    cadence = validate_backtest_cadence(cadence)
+    candidates: list[dict] = []
+    for i in range(
+        MIN_LOOKBACK - 1,
+        len(bars) - HORIZON,
+        backtest_step_sessions(cadence),
+    ):
+        if bars[i].date < start_date:
+            continue
+        window = bars[: i + 1]
+        window_closes = [float(r.close) for r in window]
+        window_dates = [r.date for r in window]
+        residual_momentum = _residual_momentum_12_1(
+            window_dates,
+            window_closes,
+            len(window) - 1,
+            benchmark_closes,
+        )
+        vol_6m = _realized_vol_6m(window_closes, len(window) - 1)
+        iso = bars[i].date.isocalendar()
+        raw_momentum = (
+            window_closes[-22] / window_closes[-253] - 1.0
+            if len(window_closes) >= 253 and window_closes[-253] > 0
+            else None
+        )
+        setups = [
+            setup
+            for setup in _window_setups(window, config, activation)
+            if include_short_candidates or setup["direction"] == "long"
+        ]
+        observation_emitted = False
+        for setup in setups:
+            candidate = {
+                "symbol": symbol,
+                "date": bars[i].date.isoformat(),
+                "iso_week": (iso[0], iso[1]),
+                "ranking_period": _ranking_period(bars[i].date, cadence),
+                "direction": setup["direction"],
+                "entry": setup["entry"],
+                "stop": setup["stop"],
+                "target": setup["target"],
+                "rr": setup["rr"],
+                "confidence": setup["confidence"],
+                "primary_prob": setup["primary_prob"],
+                "best_prob": setup["best_prob"],
+                "momentum": setup["momentum"],
+                "residual_momentum": residual_momentum,
+                "vol_6m": vol_6m,
+                "meets_core": setup["meets_core"],
+                "action": setup["action"],
+                "risk_level": setup["risk_level"],
+            }
+            if include_universe_rank_observations and not observation_emitted:
+                candidate["_universe_rank_observation"] = True
+                observation_emitted = True
+            candidates.append(candidate)
+        if include_universe_rank_observations and not observation_emitted:
+            candidates.append({
+                "symbol": symbol,
+                "date": bars[i].date.isoformat(),
+                "iso_week": (iso[0], iso[1]),
+                "ranking_period": _ranking_period(bars[i].date, cadence),
+                "direction": "rank_only",
+                "momentum": raw_momentum,
+                "residual_momentum": residual_momentum,
+                "vol_6m": vol_6m,
+                "meets_core": False,
+                "_universe_rank_observation": True,
+                "_rank_only": True,
+            })
+    return candidates
+
+
 def _backtest_worker_count() -> int:
    """How many worker processes to replay tickers across. Capped to cpu_count-1
    so a core stays free for the web server; 1 means sequential."""
@@ -1057,14 +1202,17 @@ def _assign_signal_percentiles(
    value_key: str,
    percentile_key: str,
 ) -> None:
-    """Per ISO week, rank candidates by ``value_key`` and attach a 0-100
+    """Per replay period, rank candidates by ``value_key`` and attach a 0-100
    percentile under ``percentile_key`` (100 = strongest). Missing values get
    None and therefore cannot clear a gate based on that signal."""
-    by_week: dict = defaultdict(list)
+    by_period: dict = defaultdict(list)
    for c in candidates:
        if c.get(value_key) is not None:
-            by_week[c["iso_week"]].append(c)
-    for group in by_week.values():
+            # Hand-built/research candidates predating the cadence flag retain
+            # the weekly key as a compatibility fallback.
+            period = c.get("ranking_period") or c["iso_week"]
+            by_period[period].append(c)
+    for group in by_period.values():
        ordered = sorted(group, key=lambda c: c[value_key])
        n = len(ordered)
        for rank, c in enumerate(ordered):
@@ -1074,9 +1222,9 @@ def _assign_signal_percentiles(


 def _assign_momentum_percentiles(candidates: list[dict]) -> None:
-    """Per ISO week, rank candidates by their ticker's 12-1 momentum and attach a
+    """Per replay period, rank candidates by 12-1 momentum and attach a
    0-100 ``momentum_percentile`` (100 = highest momentum in the universe that
-    week). Candidates whose momentum is unknown (insufficient lookback) get None
+    period). Candidates whose momentum is unknown (insufficient lookback) get None
    and therefore can't clear a momentum gate. Mutates ``candidates``."""
    _assign_signal_percentiles(candidates, "momentum", "momentum_percentile")

@@ -1089,7 +1237,7 @@ def _assign_residual_momentum_percentiles(candidates: list[dict]) -> None:


 def _assign_low_volatility_percentiles(candidates: list[dict]) -> None:
-    """Per ISO week, attach volatility ranks where 100 = lowest 6-month vol."""
+    """Per replay period, attach volatility ranks where 100 = lowest 6-month vol."""
    _assign_signal_percentiles(candidates, "vol_6m", VOL_PERCENTILE_KEY)
    for c in candidates:
        raw = c.get(VOL_PERCENTILE_KEY)
@@ -1281,6 +1429,72 @@ LIVE_EXIT_MODE_TO_SIM = {
 }


+def _make_gate_reset_reentry_fn(
+    candidates: list[dict],
+    prices: dict[str, tuple],
+    *,
+    cadence: str,
+    qualified_fn: Callable[[dict], bool] | None = None,
+    ranking_key: str = PRODUCTION_PERCENTILE_KEY,
+) -> Callable[[str, int, dict, Any], dict | None]:
+    """Build the production post-stop gate-reset callback.
+
+    Missing candidates count as a gate failure only on dates on which that
+    ticker was actually evaluated at the selected replay cadence. This keeps a
+    weekly backtest from treating the four non-evaluation sessions between two
+    weekly observations as false gate exits.
+    """
+    cadence = validate_backtest_cadence(cadence)
+    if qualified_fn is None:
+        def _default_qualified(candidate: dict) -> bool:
+            return bool(candidate.get("qualified"))
+
+        qualified_fn = _default_qualified
+
+    evaluation_ords: dict[str, set[int]] = {}
+    step_sessions = backtest_step_sessions(cadence)
+    for symbol, columns in prices.items():
+        ordinals = columns[0]
+        evaluation_ords[symbol] = {
+            int(ordinals[index])
+            for index in range(MIN_LOOKBACK - 1, len(ordinals) - HORIZON, step_sessions)
+        }
+
+    qualified_by_symbol_date: dict[tuple[str, int], dict] = {}
+    for candidate in candidates:
+        if candidate.get("direction") != "long" or not qualified_fn(candidate):
+            continue
+        key = (
+            str(candidate["symbol"]),
+            date.fromisoformat(str(candidate["date"])).toordinal(),
+        )
+        previous = qualified_by_symbol_date.get(key)
+        if previous is None or float(candidate.get(ranking_key) or 0.0) > float(
+            previous.get(ranking_key) or 0.0
+        ):
+            qualified_by_symbol_date[key] = candidate
+
+    def _gate_reset(
+        symbol: str,
+        asof_ord: int,
+        state: dict,
+        _bar: Any,
+    ) -> dict | None:
+        if asof_ord not in evaluation_ords.get(symbol, set()):
+            return None
+        candidate = qualified_by_symbol_date.get((symbol, asof_ord))
+        if candidate is None:
+            state["gate_went_unqualified"] = True
+            return None
+        if not state.get("gate_went_unqualified"):
+            return None
+        emitted = dict(candidate)
+        emitted["_reentry_reason"] = "gate_failed_then_requalified"
+        return emitted
+
+    return _gate_reset
+
+
 def _simulate_portfolio(
    candidates: list[dict],
    prices: dict[str, tuple],
@@ -1293,9 +1507,18 @@ def _simulate_portfolio(
    max_positions: int = SIM_MAX_POSITIONS,
    risk_per_trade: float = SIM_RISK_PER_TRADE,
    atr_trail_multiplier: float = ATR_TRAIL_MULTIPLIER,
+    cost_per_side: float = COST_PER_SIDE,
+    reentry_cooldown_sessions: int = 0,
+    initial_stop_refresh_fn: (
+        Callable[[str, int, float, dict, Any], float | None] | None
+    ) = None,
+    post_stop_reentry_fn: (
+        Callable[[str, int, dict, Any], dict | None] | None
+    ) = None,
    start_date: date | None = None,
    end_date: date | None = None,
    include_curve: bool = False,
+    include_trades: bool = False,
 ) -> dict | None:
    """Replay the qualified setups as ONE capital-constrained book and report
    portfolio economics from the daily equity curve (return, CAGR, drawdown,
@@ -1309,8 +1532,19 @@ def _simulate_portfolio(
    runs the ATR trail *and* the S/R take-profit together — the trade ends at
    whichever comes first. Stops fill at the worse of stop or open (gaps
    modeled); positions still open at the end are closed at their last mark.
-    Returns None when there is nothing to trade.
+    ``reentry_cooldown_sessions`` blocks a ticker for that many market sessions
+    after an initial-stop loss. Profitable trailing-stop exits do not trigger
+    it. ``initial_stop_refresh_fn`` may supply a lower, point-in-time valid long
+    stop when the active initial stop is touched; the replacement is still
+    checked against the same bar. ``post_stop_reentry_fn`` turns an initial
+    stop-out into a stateful episode and is the only path by which that ticker
+    can re-enter until the callback emits a new candidate. Returns None when
+    there is nothing to trade. ``cost_per_side`` is charged on entry and exit
+    and therefore changes both cash availability and subsequent position sizing.
    """
+    cost_rate = float(cost_per_side)
+    if not 0.0 <= cost_rate < 1.0:
+        raise ValueError("cost_per_side must be between 0 (inclusive) and 1")
    if qualified_fn is None:
        def _default_qualified(c: dict) -> bool:
            return bool(c.get("qualified"))
@@ -1362,6 +1596,14 @@ def _simulate_portfolio(
    curve: list[tuple[int, float]] = []
    trades: list[dict] = []
    skipped_full = 0
+    skipped_cooldown = 0
+    cooldown_until_index: dict[str, int] = {}
+    stop_refresh_attempts = 0
+    stop_refreshes = 0
+    stop_refresh_same_bar_hits = 0
+    post_stop_states: dict[str, dict] = {}
+    post_stop_events = 0
+    reentry_events: list[dict] = []
    technical_cache: dict[tuple[str, int], float | None] = {}
    atr_cache: dict[tuple[str, int], float | None] = {}

@@ -1426,24 +1668,37 @@ def _simulate_portfolio(
            atr_cache[key] = None
        return atr_cache[key]

-    def _close_trade(sym: str, fill: float, reason: str) -> None:
+    def _close_trade(sym: str, fill: float, reason: str) -> dict:
        nonlocal cash
        pos = positions.pop(sym)
        proceeds = pos["shares"] * fill
-        cost = proceeds * COST_PER_SIDE
+        cost = proceeds * cost_rate
        cash += proceeds - cost
        risk = pos["entry"] - pos["initial_stop"]
        trades.append({
+            "symbol": sym,
+            "entry_ord": pos["entry_ord"],
+            "exit_ord": o,
+            "entry": pos["entry"],
+            "initial_stop": pos["initial_stop"],
+            "active_stop": pos["stop"],
+            "fill": fill,
            "pnl": proceeds - pos["shares"] * pos["entry"] - cost - pos["entry_cost"],
            "r": (fill - pos["entry"]) / risk if risk > 0 else 0.0,
            "hold": pos["bars_held"],
            "reason": reason,
+            "stop_refreshes": pos["stop_refreshes"],
+            "is_reentry": pos["is_reentry"],
+            "reentry_wait_sessions": pos["reentry_wait_sessions"],
+            "transaction_cost": pos["entry_cost"] + cost,
        })
+        return pos

    def _marked_equity() -> float:
        return cash + sum(p["shares"] * p["last_close"] for p in positions.values())

-    for o in calendar:
+    cooldown_sessions = max(0, int(reentry_cooldown_sessions))
+    for calendar_index, o in enumerate(calendar):
        # 1) exits on today's bars (stop intraday, target intraday, time at close)
        for sym in list(positions):
            pos = positions[sym]
@@ -1460,8 +1715,43 @@ def _simulate_portfolio(
                    if pos["stop"] > pos["initial_stop"] + 1e-9
                    else "stop"
                )
-                _close_trade(sym, min(pos["stop"], bar.open), reason)
-                continue
+                survived_refresh = False
+                if reason == "stop" and initial_stop_refresh_fn is not None:
+                    stop_refresh_attempts += 1
+                    refreshed_stop = initial_stop_refresh_fn(
+                        sym, o, float(pos["stop"]), pos, bar
+                    )
+                    if (
+                        refreshed_stop is not None
+                        and 0 < float(refreshed_stop) < pos["stop"] - 1e-9
+                    ):
+                        pos["stop"] = float(refreshed_stop)
+                        pos["stop_refreshes"] += 1
+                        stop_refreshes += 1
+                        if bar.low > pos["stop"]:
+                            survived_refresh = True
+                        else:
+                            stop_refresh_same_bar_hits += 1
+                if not survived_refresh:
+                    fill = min(pos["stop"], bar.open)
+                    closed_pos = _close_trade(sym, fill, reason)
+                    if reason == "stop" and cooldown_sessions:
+                        cooldown_until_index[sym] = calendar_index + cooldown_sessions
+                    if reason == "stop" and post_stop_reentry_fn is not None:
+                        post_stop_events += 1
+                        post_stop_states[sym] = {
+                            "stop_ord": o,
+                            "stop_calendar_index": calendar_index,
+                            "stop_day_high": float(bar.high),
+                            "stop_day_low": float(bar.low),
+                            "stop_day_close": float(bar.close),
+                            "exit_fill": float(fill),
+                            "previous_entry": float(closed_pos["entry"]),
+                            "previous_stop": float(closed_pos["initial_stop"]),
+                            "previous_rank": closed_pos["entry_rank"],
+                            "gate_went_unqualified": False,
+                        }
+                    continue
            if exit_policy in ("target", "atr_trail3_target") and pos["target"] and bar.high >= pos["target"]:
                _close_trade(sym, pos["target"], "target")
                continue
@@ -1493,8 +1783,31 @@ def _simulate_portfolio(

        # 2) entries at today's close, best momentum first
        equity = _marked_equity()
+        fixed_todays = list(entries_by_ord.get(o, ()))
+        reentry_todays: list[dict] = []
+        if post_stop_reentry_fn is not None and (
+            end_ord is None or o < end_ord
+        ):
+            fixed_todays = [
+                candidate
+                for candidate in fixed_todays
+                if candidate["symbol"] not in post_stop_states
+            ]
+            for sym, state in list(post_stop_states.items()):
+                bar = _bar(sym, o)
+                if bar is None:
+                    continue
+                state["sessions_since_stop"] = (
+                    calendar_index - state["stop_calendar_index"]
+                )
+                candidate = post_stop_reentry_fn(sym, o, state, bar)
+                if candidate is None:
+                    continue
+                tagged = dict(candidate)
+                tagged["_post_stop_reentry"] = True
+                reentry_todays.append(tagged)
        todays = sorted(
-            entries_by_ord.get(o, ()),
+            fixed_todays + reentry_todays,
            key=lambda c: c.get(ranking_key) or 0.0,
            reverse=True,
        )
@@ -1502,6 +1815,9 @@ def _simulate_portfolio(
            sym = c["symbol"]
            if sym in positions:
                continue
+            if calendar_index < cooldown_until_index.get(sym, -1):
+                skipped_cooldown += 1
+                continue
            if len(positions) >= max_positions:
                skipped_full += 1
                continue
@@ -1512,15 +1828,29 @@ def _simulate_portfolio(
            shares = min(
                (equity * risk_per_trade) / risk_ps,
                (equity * SIM_NOTIONAL_CAP) / entry,
-                max(cash, 0.0) / (entry * (1.0 + COST_PER_SIDE)),
+                max(cash, 0.0) / (entry * (1.0 + cost_rate)),
            )
            if shares * entry < 1.0:  # can't fund a meaningful position
                continue
-            entry_cost = shares * entry * COST_PER_SIDE
+            entry_cost = shares * entry * cost_rate
            cash -= shares * entry + entry_cost
+            is_reentry = bool(c.get("_post_stop_reentry"))
+            reentry_wait_sessions: int | None = None
+            if is_reentry:
+                state = post_stop_states.pop(sym, None)
+                if state is not None:
+                    reentry_wait_sessions = int(state["sessions_since_stop"])
+                    reentry_events.append({
+                        "symbol": sym,
+                        "stop_ord": state["stop_ord"],
+                        "reentry_ord": o,
+                        "wait_sessions": reentry_wait_sessions,
+                        "reason": c.get("_reentry_reason"),
+                    })
            positions[sym] = {
                "shares": shares,
                "entry": entry,
+                "entry_ord": o,
                "initial_stop": stop,
                "stop": stop,
                "target": float(c["target"]) if c.get("target") else None,
@@ -1528,6 +1858,12 @@ def _simulate_portfolio(
                "bars_held": 0,
                "last_close": entry,
                "highest_close": entry,
+                "entry_rank": (
+                    float(c[ranking_key]) if c.get(ranking_key) is not None else None
+                ),
+                "stop_refreshes": 0,
+                "is_reentry": is_reentry,
+                "reentry_wait_sessions": reentry_wait_sessions,
            }
            equity = _marked_equity()

@@ -1633,6 +1969,7 @@ def _simulate_portfolio(

    result = {
        "starting_capital": SIM_STARTING_CAPITAL,
+        "cost_per_side_pct": round(cost_rate * 100.0, 3),
        "final_equity": round(final_equity, 2),
        "total_return_pct": round(total_return_pct, 1),
        "cagr_pct": round(cagr_pct, 1) if cagr_pct is not None else None,
@@ -1659,6 +1996,42 @@ def _simulate_portfolio(
        result["equity_curve"] = curve_payload
    if benchmark_payload is not None:
        result["benchmark_curve"] = benchmark_payload
+    if cooldown_sessions:
+        result["reentry_cooldown_sessions"] = cooldown_sessions
+        result["skipped_cooldown"] = skipped_cooldown
+    if initial_stop_refresh_fn is not None:
+        result["stop_refresh_attempts"] = stop_refresh_attempts
+        result["stop_refreshes"] = stop_refreshes
+        result["stop_refresh_same_bar_hits"] = stop_refresh_same_bar_hits
+    if post_stop_reentry_fn is not None:
+        result["post_stop_events"] = post_stop_events
+        result["post_stop_reentries"] = len(reentry_events)
+        result["post_stop_states_open_at_end"] = len(post_stop_states)
+        result["reentry_events"] = [
+            {
+                **{
+                    key: value
+                    for key, value in event.items()
+                    if key not in {"stop_ord", "reentry_ord"}
+                },
+                "stop_date": date.fromordinal(event["stop_ord"]).isoformat(),
+                "reentry_date": date.fromordinal(event["reentry_ord"]).isoformat(),
+            }
+            for event in reentry_events
+        ]
+    if include_trades:
+        result["trade_details"] = [
+            {
+                **{
+                    key: value
+                    for key, value in trade.items()
+                    if key not in {"entry_ord", "exit_ord"}
+                },
+                "entry_date": date.fromordinal(trade["entry_ord"]).isoformat(),
+                "exit_date": date.fromordinal(trade["exit_ord"]).isoformat(),
+            }
+            for trade in trades
+        ]
    return result


@@ -2018,19 +2391,35 @@ PORTFOLIO_MONITOR_STRATEGIES: tuple[dict, ...] = (
        "exit_policy": "hold",
    },
    {
-        "strategy": PRODUCTION_PORTFOLIO_STRATEGY,
-        "label": "Production: residual/high-vol 80/20 + 3x ATR trail",
+        "strategy": "production_live_immediate",
+        "label": "Live setup + 3x ATR trail (immediate re-entry)",
        "description": (
-            "The live strategy: production activation gate and Admin exit policy "
-            "as currently configured, 80/20 residual/high-vol rank."
+            "Exact live activation, ordering, and Admin exit policy, with only "
+            "the post-stop gate reset disabled as the comparison baseline."
        ),
        "entry_variant": "residual80_highvol_blend80_20_fixed10",
        "exit_policy": "atr_trail3",
+        "reentry_policy": "immediate",
+        "use_live_config": True,
+        "comparison_arm": "live_immediate",
+    },
+    {
+        "strategy": PRODUCTION_PORTFOLIO_STRATEGY,
+        "label": "Production: residual/high-vol 80/20 + 3x ATR trail + gate reset",
+        "description": (
+            "The live strategy: production activation gate and Admin exit policy "
+            "as currently configured, 80/20 residual/high-vol rank, and re-entry "
+            "only after the gate fails and later qualifies again."
+        ),
+        "entry_variant": "residual80_highvol_blend80_20_fixed10",
+        "exit_policy": "atr_trail3",
+        "reentry_policy": PRODUCTION_REENTRY_POLICY,
        # The production row replays what the platform actually does right now:
        # the live qualification flag (runtime Admin activation settings) and the
        # live Admin exit policy, instead of the frozen research-variant gate.
        "use_live_config": True,
        "is_production": True,
+        "comparison_arm": "live_gate_reset",
    },
 )

@@ -2133,6 +2522,7 @@ def _min_rr_sweep(
    threshold: float,
    hold_days: int,
    live_exit_policy: dict | None = None,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> dict:
    """Portfolio economics of the production book at each R:R floor.

@@ -2149,6 +2539,7 @@ def _min_rr_sweep(
    exit_policy = str(strategy["exit_policy"])
    row_hold_days = hold_days
    trail_multiplier = ATR_TRAIL_MULTIPLIER
+    reentry_policy = str(strategy.get("reentry_policy", "immediate"))
    if strategy.get("use_live_config") and live_exit_policy is not None:
        exit_policy = LIVE_EXIT_MODE_TO_SIM.get(
            str(live_exit_policy.get("mode", "atr_trailing")), "atr_trail3"
@@ -2188,6 +2579,19 @@ def _min_rr_sweep(
            max_positions=int(entry_cfg["max_positions"]),
            risk_per_trade=float(entry_cfg["risk_per_trade"]),
            atr_trail_multiplier=trail_multiplier,
+            post_stop_reentry_fn=(
+                _make_gate_reset_reentry_fn(
+                    candidates,
+                    prices,
+                    cadence=cadence,
+                    qualified_fn=qualified_fn,
+                    ranking_key=str(
+                        entry_cfg.get("ranking_key") or entry_cfg["percentile_key"]
+                    ),
+                )
+                if reentry_policy == "gate_reset"
+                else None
+            ),
            start_date=sweep_start,
        )
        if sim is None:
@@ -2212,6 +2616,7 @@ def _min_rr_sweep(
        "live_qualified_setups": live_qualified,
        "reproduces_production_gate": reproduces,
        "exit_policy": exit_policy,
+        "reentry_policy": reentry_policy,
        "entries_from": sweep_start.isoformat() if sweep_start else None,
        "window": "out-of-sample (test)" if sweep_start else "full history (in-sample)",
        "rows": rows,
@@ -2245,6 +2650,7 @@ def _holdout_evaluation(
    hold_days: int,
    split: date,
    live_exit_policy: dict | None = None,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> dict:
    """The production strategy simulated on entries BEFORE the split (train) and
    on entries ON/AFTER it (test), as separate books.
@@ -2267,6 +2673,7 @@ def _holdout_evaluation(
    exit_policy = str(strategy["exit_policy"])
    row_hold_days = hold_days
    trail_multiplier = ATR_TRAIL_MULTIPLIER
+    reentry_policy = str(strategy.get("reentry_policy", "immediate"))
    if strategy.get("use_live_config") and live_exit_policy is not None:
        exit_policy = LIVE_EXIT_MODE_TO_SIM.get(
            str(live_exit_policy.get("mode", "atr_trailing")), "atr_trail3"
@@ -2279,6 +2686,20 @@ def _holdout_evaluation(
        None if strategy.get("use_live_config")
        else lambda c, config=entry_cfg: _qualifies_strategy_variant(c, config)
    )
+    ranking_key = str(
+        entry_cfg.get("ranking_key") or entry_cfg["percentile_key"]
+    )
+    post_stop_reentry_fn = (
+        _make_gate_reset_reentry_fn(
+            candidates,
+            prices,
+            cadence=cadence,
+            qualified_fn=qualified_fn,
+            ranking_key=ranking_key,
+        )
+        if reentry_policy == "gate_reset"
+        else None
+    )

    rows: list[dict] = []
    for window, start, end in (
@@ -2292,10 +2713,11 @@ def _holdout_evaluation(
            exit_policy,
            row_hold_days,
            qualified_fn=qualified_fn,
-            ranking_key=str(entry_cfg.get("ranking_key") or entry_cfg["percentile_key"]),
+            ranking_key=ranking_key,
            max_positions=int(entry_cfg["max_positions"]),
            risk_per_trade=float(entry_cfg["risk_per_trade"]),
            atr_trail_multiplier=trail_multiplier,
+            post_stop_reentry_fn=post_stop_reentry_fn,
            start_date=start,
            end_date=end,
            include_curve=True,
@@ -2307,6 +2729,7 @@ def _holdout_evaluation(
    return {
        "split_date": split.isoformat(),
        "strategy": strategy["strategy"],
+        "reentry_policy": reentry_policy,
        "rows": rows,
        "note": (
            "Train = entries before the split; test = entries on/after it. The two "
@@ -2322,6 +2745,7 @@ def _portfolio_monitor(
    _spy_closes: dict[date, float] | None,
    hold_days: int,
    live_exit_policy: dict | None = None,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> dict:
    latest_ord = max((max(cols[0]) for cols in prices.values() if cols[0]), default=None)
    rows: list[dict] = []
@@ -2339,6 +2763,7 @@ def _portfolio_monitor(
        # policy. The overlay opts into this deliberately so only ordering
        # changes relative to the production row.
        use_live = bool(strategy.get("use_live_config"))
+        reentry_policy = str(strategy.get("reentry_policy", "immediate"))
        exit_policy = str(strategy["exit_policy"])
        row_hold_days = hold_days
        trail_multiplier = ATR_TRAIL_MULTIPLIER
@@ -2354,6 +2779,17 @@ def _portfolio_monitor(
            None if use_live
            else lambda c, config=entry_cfg: _qualifies_strategy_variant(c, config)
        )
+        post_stop_reentry_fn = (
+            _make_gate_reset_reentry_fn(
+                candidates,
+                prices,
+                cadence=cadence,
+                qualified_fn=qualified_fn,
+                ranking_key=ranking_key,
+            )
+            if reentry_policy == "gate_reset"
+            else None
+        )
        for lookback in PORTFOLIO_MONITOR_LOOKBACKS:
            start = _lookback_start(latest_ord, lookback["days"])
            sim = _simulate_portfolio(
@@ -2367,6 +2803,7 @@ def _portfolio_monitor(
                max_positions=int(entry_cfg["max_positions"]),
                risk_per_trade=float(entry_cfg["risk_per_trade"]),
                atr_trail_multiplier=trail_multiplier,
+                post_stop_reentry_fn=post_stop_reentry_fn,
                start_date=start,
                include_curve=True,
            )
@@ -2377,10 +2814,12 @@ def _portfolio_monitor(
                "label": strategy["label"],
                "description": strategy["description"],
                "is_production": bool(strategy.get("is_production")),
+                "comparison_arm": strategy.get("comparison_arm"),
                "entry_variant": strategy["entry_variant"],
                "ranking_key": ranking_key,
                "exit_policy": exit_policy,
                "live_exit_mode": live_exit_mode,
+                "reentry_policy": reentry_policy,
                "lookback": lookback["lookback"],
                "lookback_label": lookback["label"],
                **sim,
@@ -2393,6 +2832,8 @@ def _portfolio_monitor(
                "label": s["label"],
                "description": s["description"],
                "is_production": bool(s.get("is_production")),
+                "comparison_arm": s.get("comparison_arm"),
+                "reentry_policy": str(s.get("reentry_policy", "immediate")),
            }
            for s in strategies
        ],
@@ -2405,7 +2846,48 @@ def _portfolio_monitor(
            "Portfolio monitor runs supported named strategies across cached lookbacks. "
            "The structural overlay appears only in its explicit research arm and changes "
            "ordering, not production qualification. Local snapshot backtests remain the "
-            "research surface for broad variant sweeps."
+            "research surface for broad variant sweeps. The production row applies the "
+            "same post-initial-stop gate-reset rule as the live setup list."
+        ),
+    }
+
+
+def _production_cadence_comparison(
+    monitor: dict | None,
+    cadence: str,
+) -> dict | None:
+    """Compact full-history live/immediate vs live/gate-reset comparison."""
+    if not monitor:
+        return None
+    arms: list[dict] = []
+    for row in monitor.get("runs") or []:
+        comparison_arm = row.get("comparison_arm")
+        if not comparison_arm or row.get("lookback") != "all":
+            continue
+        compact = {
+            key: value
+            for key, value in row.items()
+            if key not in {"equity_curve", "benchmark_curve"}
+        }
+        arm_name = (
+            "prod_live_setup"
+            if comparison_arm == "live_immediate"
+            else "gate_reset"
+        )
+        compact["arm"] = f"{arm_name}_{cadence}"
+        compact["entry_cadence"] = cadence
+        arms.append(compact)
+    if not arms:
+        return None
+    arms.sort(key=lambda row: row.get("reentry_policy") != "immediate")
+    return {
+        "entry_cadence": cadence,
+        "lookback": "all",
+        "arms": arms,
+        "note": (
+            "Both arms use the exact same live gate, ordering, Admin exit policy, "
+            "fees, and candidate cadence. Only the post-stop gate-reset rule "
+            "changes."
        ),
    }

@@ -2618,7 +3100,8 @@ def _build_recommendation(report: dict) -> dict:
    if production_row is not None:
        headline = (
            "Production baseline: residual/high-vol 80/20 entry rank with a "
-            "3x ATR trailing exit and 30-trading-day max hold."
+            "3x ATR trailing exit, 30-trading-day max hold, and re-entry only "
+            "after the gate fails and later qualifies again."
        )
        if (
            production_row.get("cagr_pct") is not None
@@ -2787,9 +3270,11 @@ async def run_backtest(
    progress_cb: Callable[[int, int, str], None] | None = None,
    *,
    target_model: str = PRODUCTION_GTL_TARGET_MODEL,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> dict:
    """Replay every ticker and aggregate the Phase-1 reports for the current config."""
    target_model = validate_backtest_target_model(target_model)
+    cadence = validate_backtest_cadence(cadence)
    config = await get_recommendation_config(db)
    activation = await get_activation_config(db)

@@ -2798,7 +3283,9 @@ async def run_backtest(
    total = len(tickers)

    candidates: list[dict] = []
-    # collected[signal_name][iso_week] -> list of (signal_value, forward_return)
+    # Signal IC remains a weekly, non-overlapping diagnostic regardless of the
+    # entry cadence. Production activation ranks are assigned from candidates
+    # at their own weekly or exact-date ``ranking_period`` below.
    collected: dict = defaultdict(lambda: defaultdict(list))

    # Residual momentum needs a point-in-time benchmark return stream. Best-effort:
@@ -2855,6 +3342,7 @@ async def run_backtest(
                            pool, _replay_and_signals, ticker.symbol, columns, config, activation,
                            benchmark_closes,
                            target_model,
+                            cadence,
                        ))
                for result in await asyncio.gather(*futures, return_exceptions=True):
                    if isinstance(result, Exception):
@@ -2877,6 +3365,7 @@ async def run_backtest(
                        _replay_and_signals, ticker.symbol, columns, config, activation,
                        benchmark_closes,
                        target_model,
+                        cadence,
                    ))
            except Exception:
                logger.exception("Backtest replay failed for %s", ticker.symbol)
@@ -2966,17 +3455,19 @@ async def run_backtest(
        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,
                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,
+                hold_horizon, live_exit_policy=live_exit_policy, cadence=cadence,
            )
    except Exception:
        logger.exception("Portfolio simulation failed")
@@ -2987,13 +3478,19 @@ async def run_backtest(
        "candidates": len(candidates),
        "qualified": len(qualified),
        "params": {
-            "step_days": STEP_DAYS,
+            # Keep step_days for old report consumers; the value counts stored
+            # market sessions rather than calendar days.
+            "step_days": backtest_step_sessions(cadence),
+            "step_sessions": backtest_step_sessions(cadence),
+            "entry_cadence": cadence,
+            "signal_eval_cadence": WEEKLY_BACKTEST_CADENCE,
            "horizon_days": HORIZON,
            "min_lookback": MIN_LOOKBACK,
            "cost_per_side_pct": round(COST_PER_SIDE * 100, 3),
            "target_model": target_model,
            "target_model_label": BACKTEST_TARGET_MODELS[target_model],
            "is_production_target_model": target_model == PRODUCTION_GTL_TARGET_MODEL,
+            "production_reentry_policy": PRODUCTION_REENTRY_POLICY,
        },
        "activation": activation,
        "overall_qualified": _bucket_stats(qualified),
@@ -3055,6 +3552,11 @@ async def run_backtest(
            ),
        },
        "portfolio_monitor": portfolio_monitor_report,
+        "production_cadence_comparison": (
+            _production_cadence_comparison(portfolio_monitor_report, cadence)
+            if target_model == PRODUCTION_GTL_TARGET_MODEL
+            else None
+        ),
        "holdout": holdout_report,
        "min_rr_sweep": min_rr_sweep_report,
        "target_model_diagnostics": _target_model_diagnostics(
@@ -3090,9 +3592,15 @@ async def run_and_store(
    progress_cb: Callable[[int, int, str], None] | None = None,
    *,
    target_model: str = PRODUCTION_GTL_TARGET_MODEL,
+    cadence: str = DEFAULT_BACKTEST_CADENCE,
 ) -> dict:
    """Run the backtest and cache the report in a SystemSetting. Job entrypoint."""
-    report = await run_backtest(db, progress_cb, target_model=target_model)
+    report = await run_backtest(
+        db,
+        progress_cb,
+        target_model=target_model,
+        cadence=cadence,
+    )
    await update_setting(db, KEY_REPORT, json.dumps(report))
    return report

@@ -20,6 +20,7 @@ from app.services.outcome_service import (
    Bar,
    evaluate_setup_against_bars,
 )
+from app.services.trade_policy import get_reentry_gate_locks

 # Exit policy for OPEN paper trades (auto-close). Production defaults to the
 # July 2026 promoted strategy: initial stop + 3x ATR trailing stop, with a max
@@ -318,6 +319,10 @@ async def create_trade(
        raise ValidationError("shares and entry_price must be positive")

    ticker = await _get_ticker(db, symbol)
+    if ticker.id in await get_reentry_gate_locks(db):
+        raise ValidationError(
+            f"{ticker.symbol} requires a post-stop gate reset before re-entry"
+        )
    trade = PaperTrade(
        user_id=user_id,
        ticker_id=ticker.id,
@@ -28,7 +28,12 @@ from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
 from app.services.indicator_service import _extract_ohlcv, compute_atr
 from app.services.price_service import query_ohlcv
+from app.services.qualification import setup_qualifies
 from app.services.sr_service import detect_gate_target_ladder
+from app.services.trade_policy import (
+    get_reentry_gate_locks,
+    observe_reentry_gate_transitions,
+)
 from app.services.recommendation_service import (
    _risk_level_from_conflicts,
    build_recommendation_snapshot,
@@ -699,12 +704,24 @@ async def scan_all_tickers(
    ``progress_callback(processed, total, current_symbol)`` is invoked as each
    ticker is scanned so callers (e.g. the scheduler) can surface live progress.
    """
-    # Plain strings, not Ticker instances: the rollbacks below expire any ORM
-    # objects held across them, and touching an expired attribute afterwards
+    # Plain ids/strings, not Ticker instances: the rollbacks below expire any
+    # ORM objects held across them, and touching an expired attribute afterwards
    # triggers sync lazy-loading, which raises on an AsyncSession.
-    result = await db.execute(select(Ticker.symbol).order_by(Ticker.symbol))
-    symbols = list(result.scalars().all())
-    total = len(symbols)
+    result = await db.execute(select(Ticker.id, Ticker.symbol).order_by(Ticker.symbol))
+    ticker_rows = [(int(ticker_id), symbol) for ticker_id, symbol in result.all()]
+    total = len(ticker_rows)
+
+    # Gate-reset observations must use the same runtime activation settings as
+    # the live setup list. If the config cannot be loaded, scan normally but do
+    # not mutate reset state from an evaluation whose rules are unknown.
+    activation: dict | None = None
+    try:
+        from app.services.admin_service import get_activation_config
+
+        activation = await get_activation_config(db)
+    except Exception:
+        await db.rollback()
+        logger.exception("Activation config load for re-entry gate reset failed")

    # Rank the universe up front so each new setup carries both the residual
    # activation gate percentile and the promoted production ordering score.
@@ -720,7 +737,10 @@ async def scan_all_tickers(
        ranks = {}

    all_setups: list[TradeSetup] = []
-    for index, symbol in enumerate(symbols):
+    evaluated_ticker_ids: set[int] = set()
+    qualified_ticker_ids: set[int] = set()
+    gate_observation_started_at = datetime.now(timezone.utc)
+    for index, (ticker_id, symbol) in enumerate(ticker_rows):
        if progress_callback is not None:
            progress_callback(index, total, symbol)
        # Refresh scores first so the scheduled scan works off current data.
@@ -753,10 +773,33 @@ async def scan_all_tickers(
                primary_min_rr=PRIMARY_TARGET_MIN_RR,
            )
            all_setups.extend(setups)
+            if activation is not None:
+                try:
+                    if any(setup_qualifies(setup, activation) for setup in setups):
+                        qualified_ticker_ids.add(ticker_id)
+                    evaluated_ticker_ids.add(ticker_id)
+                except Exception:
+                    logger.exception(
+                        "Gate-reset qualification observation failed for %s", symbol
+                    )
        except Exception:
            await db.rollback()
            logger.exception("Error scanning ticker %s", symbol)

+    if activation is not None:
+        transitioned_ticker_ids = await observe_reentry_gate_transitions(
+            db,
+            evaluated_ticker_ids=evaluated_ticker_ids,
+            qualified_ticker_ids=qualified_ticker_ids,
+            observed_at=gate_observation_started_at,
+        )
+        await db.commit()
+        if transitioned_ticker_ids:
+            logger.info(
+                "Updated post-stop gate-reset state for %d ticker(s)",
+                len(transitioned_ticker_ids),
+            )
+
    if progress_callback is not None and total:
        progress_callback(total, total, "")

@@ -771,6 +814,8 @@ async def get_trade_setups(
    symbol: str | None = None,
    live_recommendation: bool = False,
    exclude_open_trade_tickers: bool = False,
+    exclude_reentry_gate_locked_tickers: bool = False,
+    include_reentry_gate_lock: bool = False,
 ) -> list[dict]:
    """Get latest stored trade setups, optionally filtered.

@@ -794,15 +839,23 @@ async def get_trade_setups(
        stmt = stmt.where(TradeSetup.confidence_score >= min_confidence)
    if recommended_action is not None and not live_recommendation:
        stmt = stmt.where(TradeSetup.recommended_action == recommended_action)
+    excluded_ticker_ids: set[int] = set()
+    reentry_gate_locks: dict[int, datetime] = {}
    if exclude_open_trade_tickers:
        open_trade_result = await db.execute(
            select(PaperTrade.ticker_id)
            .where(PaperTrade.status == "open")
            .distinct()
        )
-        open_ticker_ids = {ticker_id for ticker_id, in open_trade_result.all()}
-        if open_ticker_ids:
-            stmt = stmt.where(~TradeSetup.ticker_id.in_(open_ticker_ids))
+        excluded_ticker_ids.update(
+            ticker_id for ticker_id, in open_trade_result.all()
+        )
+    if exclude_reentry_gate_locked_tickers or include_reentry_gate_lock:
+        reentry_gate_locks = await get_reentry_gate_locks(db)
+    if exclude_reentry_gate_locked_tickers:
+        excluded_ticker_ids.update(reentry_gate_locks)
+    if excluded_ticker_ids:
+        stmt = stmt.where(~TradeSetup.ticker_id.in_(excluded_ticker_ids))

    stmt = stmt.order_by(TradeSetup.detected_at.desc(), TradeSetup.id.desc())

@@ -855,6 +908,15 @@ async def get_trade_setups(
            ),
            reverse=True,
        )
+    if include_reentry_gate_lock:
+        ticker_by_setup_id = {
+            setup.id: setup.ticker_id for setup, _ in latest_rows
+        }
+        for row in rows_out:
+            ticker_id = ticker_by_setup_id.get(row["id"])
+            row["reentry_gate_reset_required"] = (
+                ticker_id in reentry_gate_locks if ticker_id is not None else False
+            )
    return rows_out


@@ -0,0 +1,101 @@
+"""Shared live trading-policy state and availability checks."""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from datetime import datetime, timezone
+
+from sqlalchemy import func, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.paper_trade import PaperTrade
+
+
+async def _latest_initial_stop_trades(
+    db: AsyncSession,
+    *,
+    closed_before: datetime | None = None,
+) -> dict[int, PaperTrade]:
+    """Return a ticker's latest closed trade only when it was an initial stop."""
+    ranked_stmt = (
+        select(
+            PaperTrade.id.label("trade_id"),
+            func.row_number()
+            .over(
+                partition_by=PaperTrade.ticker_id,
+                order_by=(PaperTrade.closed_at.desc(), PaperTrade.id.desc()),
+            )
+            .label("recency"),
+        )
+        .where(
+            PaperTrade.status == "closed",
+            PaperTrade.closed_at.is_not(None),
+        )
+    )
+    if closed_before is not None:
+        ranked_stmt = ranked_stmt.where(PaperTrade.closed_at <= closed_before)
+    ranked = ranked_stmt.subquery()
+    stmt = (
+        select(PaperTrade)
+        .join(ranked, ranked.c.trade_id == PaperTrade.id)
+        .where(
+            ranked.c.recency == 1,
+            PaperTrade.close_reason == "stop",
+        )
+    )
+    result = await db.execute(stmt)
+    return {trade.ticker_id: trade for trade in result.scalars()}
+
+
+async def get_reentry_gate_locks(db: AsyncSession) -> dict[int, datetime]:
+    """Return tickers still waiting for a post-stop gate failure.
+
+    A later qualified setup is actionable only after the daily scanner has
+    observed an unqualified evaluation after the latest initial-stop exit and
+    then a fresh qualification. The returned timestamp is the stop time and is
+    useful for diagnostics; callers normally only need the keys.
+    """
+    latest = await _latest_initial_stop_trades(db)
+    return {
+        ticker_id: trade.closed_at
+        for ticker_id, trade in latest.items()
+        if trade.reentry_gate_requalified_at is None and trade.closed_at is not None
+    }
+
+
+async def observe_reentry_gate_transitions(
+    db: AsyncSession,
+    *,
+    evaluated_ticker_ids: Iterable[int],
+    qualified_ticker_ids: Iterable[int],
+    observed_at: datetime | None = None,
+) -> set[int]:
+    """Persist gate-failure and later requalification observations.
+
+    Only tickers whose scan completed successfully belong in
+    ``evaluated_ticker_ids``. This prevents a scanner exception from being
+    mistaken for a real gate exit. The caller owns the transaction; this helper
+    flushes so the new state is immediately visible in that transaction.
+    """
+    evaluated = {int(ticker_id) for ticker_id in evaluated_ticker_ids}
+    if not evaluated:
+        return set()
+    qualified = {int(ticker_id) for ticker_id in qualified_ticker_ids}
+    timestamp = observed_at or datetime.now(timezone.utc)
+    latest = await _latest_initial_stop_trades(db, closed_before=timestamp)
+    updated: set[int] = set()
+    for ticker_id in evaluated:
+        trade = latest.get(ticker_id)
+        if trade is None or trade.reentry_gate_requalified_at is not None:
+            continue
+        if trade.reentry_gate_failed_at is None:
+            if ticker_id not in qualified:
+                trade.reentry_gate_failed_at = timestamp
+                updated.add(ticker_id)
+        elif ticker_id in qualified:
+            trade.reentry_gate_requalified_at = timestamp
+            updated.add(ticker_id)
+
+    if updated:
+        await db.flush()
+    return updated
@@ -8,7 +8,9 @@ was run and the data said no.** Detail lives in the linked docs and in
 **The one-line summary of the whole platform:** it is a **long-only
 cross-sectional momentum book** — buy the top quintile by beta-adjusted 12-1
 momentum, tilt toward higher volatility, hold ≤ 10 names, cut at 1.5× ATR, then
-trail at 3× ATR for up to 30 trading days. Everything else in the app (composite
+trail at 3× ATR for up to 30 trading days. After an initial stop, require the
+daily production gate to fail and subsequently qualify again before re-entry.
+Everything else in the app (composite
 score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is
 **display or screening**, not edge.

@@ -22,6 +24,7 @@ score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is
 | 80/20 residual-momentum / 6m-volatility rank | Ranking tilt | Buys ~2pp CAGR over momentum-only; costs ~6pp drawdown |
 | 1.5× ATR initial stop | Real exit | Cuts losers fast |
 | 3× ATR trailing stop, 30-day max hold | Real exit | Best Sharpe of every exit tested |
+| Post-stop normal gate reset | Re-entry policy | Stop always closes; a later gate failure and subsequent fresh qualification define the next signal episode. The selected study arm reached Sharpe 1.77 / CAGR 48.3% at capacity 10; live scan-before-outcome timing is stricter (Sharpe 1.68 / CAGR 44.8% analogue). [Full study](post-stop-reentry.md) |
 | Max 10 concurrent positions, 1% risk per trade | Sizing | Cap never binds in practice |
 | Structural S/R | Human-facing product context | Clean, capped zones for charts and alerts; not read by the scanner |
 | Gate Target Ladder | Screening machinery | Volume-free transient proposals preserve the production candidate set exactly; never an exit |
@@ -63,6 +66,7 @@ invites overfitting.
 | Primary-target R:R selector | **Keep 1.5** — target choice is intentionally independent of the later 2.0 activation floor |
 | Exit policy (hold / SMA50 / 20-day low / technical-40 / ATR trail) | **Keep 3× ATR trail** — best Sharpe (2.04) |
 | **Activation R:R floor `min_rr`** (swept 2026-07-12) | **Keep 2.0** — best in-sample *and* out-of-sample. But it is a **spike, not a plateau** — see below |
+| Post-stop re-entry (nine daily policy arms) | **Keep normal gate reset at production capacity 10** — Sharpe 1.77 vs 1.67 immediate and 1.47 fixed cooldown 5. The result changes with book capacity; see [post-stop-reentry.md](post-stop-reentry.md) |

 ### The `min_rr` sweep (2026-07-12)

@@ -142,6 +146,10 @@ it is internal screening machinery whose broad historical-price-traffic behavior
 was preserved explicitly and volume-free, with exact full-period parity. It is
 still neither market structure nor an exit. The one component that *does* have
 measured predictive edge is the momentum gate, and every knob on it has been
-swept and confirmed.
+swept and confirmed. After an initial-stop exit, that same gate now also defines
+when a new episode may begin: one later failed observation followed by a fresh
+qualification. The [daily re-entry matrix](post-stop-reentry.md) supports this
+for the current 10-position book, but not as a universal rule for other
+portfolio capacities.

 The next real evidence is **forward**, not backward: the live paper-trade record.
@@ -0,0 +1,196 @@
+# Post-stop re-entry: daily policy study and production decision
+
+## Decision
+
+Use a **normal gate reset** after an initial-stop exit:
+
+1. The initial stop always closes the trade. It is never cancelled because the
+   ticker still passes the gate.
+2. Re-entry remains locked until a later full-universe daily scan observes the
+   ticker **failing** the production activation gate.
+3. The lock remains in place until a subsequent daily scan observes a **fresh
+   qualification**.
+4. Only then may the ticker return to the actionable setup list or be opened
+   through `create_trade`.
+
+Trailing-stop, time, target, and manual exits do not start this state machine.
+Scanner errors do not count as a gate failure. The two transitions are persisted
+on the latest initial-stop `PaperTrade`, so neither a service call nor a restart
+can bypass the rule.
+
+Migration 022 applies the policy prospectively. Existing initial-stop rows are
+grandfathered by marking both reset timestamps complete at their historical
+`closed_at`; otherwise their new NULL columns would be mistaken for active
+locks despite no scanner observations having existed. At runtime, only the
+actual latest closed trade per ticker can start a lock, and only when that exit
+was an initial stop. A newer trailing, time, target, or manual exit therefore
+cannot revive an older stop episode.
+
+This replaces the previously proposed fixed five-session lockdown. The normal
+reset counts an unqualified stop-day close when that close is observed after the
+stop. The stricter experiment, which required a failed close on a later session,
+was not promoted as the research policy.
+
+### Live scheduling boundary
+
+The live daily pipeline runs the R:R scan **before** Outcome Eval. A trade that
+is closed at its initial stop by that Outcome Eval—or by an intraday evaluation
+after the full scan—was therefore still open when the day's gate observation
+ran. Its stop-day state cannot establish the failure. The earliest possible
+failure is the next successful full scan, and a fresh qualification requires a
+subsequent full scan.
+
+The study simulator closes positions before checking same-session re-entry
+state, so its normal `gate_reset` arm can count the stop-day close. At this first
+transition boundary, current live ordering is instead analogous to
+`strict_gate_reset`. The distinction is material: the strict full-period row
+recorded Sharpe 1.68, CAGR 44.8%, and 23.4% drawdown; its disjoint 2025+ row
+recorded Sharpe 1.38, CAGR 32.9%, and 21.0% drawdown. The selected normal-reset
+result (Sharpe 1.77) is therefore policy-study evidence, not exact live
+scheduler-order parity. Changing that ordering would be a separate production
+decision.
+
+## Experiment design
+
+Source: [`reports/daily_reentry_matrix.json`](../../reports/daily_reentry_matrix.json),
+generated 2026-07-17.
+
+| Input | Value |
+|---|---|
+| Snapshot | Production SQLite snapshot through 2026-07-02 |
+| Period used by the all/5y rows | 2022-06-24 to 2026-07-02 |
+| Tickers | 505 |
+| Point-in-time candidate observations | 1,011,248 (492,850 long; 518,398 short) |
+| Live-universe rank observations | 584,393 |
+| Qualified candidates under `live_universe` ranking | 5,189 |
+| Entry cadence | Daily |
+| Selection and ordering | Production GTL gate; residual/high-vol 80/20 rank; long-only after ranking |
+| Exit | 1.5× ATR initial stop; 3× ATR trailing stop; 30-session maximum hold |
+| Portfolio | 10 positions; 1% risk per trade; $10,000 initial capital |
+| Trading cost | 0.1% per side in the primary matrix; 0.1–0.3% robustness sweep |
+| Holdout split | 2025-01-01 |
+
+The expensive daily candidate replay was performed once. Every policy arm then
+used the same candidates, prices, costs, position sizing, capacity, and exit
+logic. `live_universe` ranks all eligible tickers once per session like the live
+scanner. `backtest_legacy` retains the older candidate-only rank approximation as
+a sensitivity check.
+
+### Policies tested
+
+| Arm | Rule after an initial stop |
+|---|---|
+| `immediate` | No memory; a same-day close re-entry is possible |
+| `next_session` | Block only the stop session |
+| `cooldown_2/3/5` | Re-entry allowed at wait-session N |
+| `gate_reset` | Require a failed gate observation, then a later qualification; the stop-day close may establish the failure |
+| `strict_gate_reset` | Ignore the stop-day failure; require a later failed close and then requalification |
+| `gate_reset_improved` | Gate reset plus a higher new stop and non-weaker production rank |
+| `two_session_confirmation` | Require two consecutive qualified post-stop closes |
+
+## Primary result: production-like `live_universe` ranking
+
+The available history is shorter than five years, so the report's `5y` and
+`all` rows cover the same period.
+
+| Policy | Total return | CAGR | Max DD | Sharpe | Trades | Win rate | Post-stop re-entries |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| Immediate | 348.4% | 45.2% | 24.3% | 1.67 | 489 | 35.6% | 155 |
+| Next session | 388.1% | 48.3% | 21.6% | 1.77 | 472 | 36.2% | 142 |
+| Cooldown 2 | 343.5% | 44.8% | 23.4% | 1.68 | 472 | 35.8% | 146 |
+| Cooldown 3 | 293.4% | 40.6% | 22.7% | 1.56 | 474 | 35.9% | 146 |
+| Cooldown 5 | 250.8% | 36.6% | 22.2% | 1.47 | 473 | 35.9% | 145 |
+| **Gate reset** | **388.1%** | **48.3%** | **21.6%** | **1.77** | **472** | **36.2%** | **142** |
+| Strict gate reset | 342.7% | 44.8% | 23.4% | 1.68 | 471 | 35.9% | 144 |
+| Gate reset + improved setup | 267.4% | 38.2% | 24.9% | 1.60 | 422 | 36.3% | 69 |
+| Two-session confirmation | 296.1% | 40.8% | **17.6%** | 1.65 | 441 | **37.9%** | 96 |
+
+At the production capacity, normal gate reset improved all four portfolio
+objectives relative to immediate re-entry: higher total return, CAGR, and
+Sharpe, with lower drawdown. The fixed five-session rule reduced churn but gave
+up too many profitable re-entry opportunities.
+
+`gate_reset` and `next_session` produced exactly the same executed portfolio in
+the `live_universe` runs. Their rules are not equivalent. In this sample, the
+portfolio-level candidate path happened to converge to the same trades. This is
+evidence that blocking same-day re-entry helped; it does **not** isolate an
+independent return premium for the reset condition itself.
+
+## Disjoint 2025+ test window
+
+These are separate books with entries on or after 2025-01-01. They are a useful
+temporal sensitivity check, but not forward evidence: the policy was still
+selected after the historical data existed.
+
+| Policy | Total return | CAGR | Max DD | Sharpe | Trades |
+|---|---:|---:|---:|---:|---:|
+| Immediate | 64.1% | 39.3% | 19.6% | 1.55 | 181 |
+| Next session | 68.5% | 41.8% | 19.2% | 1.66 | 183 |
+| **Gate reset** | **68.5%** | **41.8%** | **19.2%** | **1.66** | **183** |
+| Cooldown 5 | 52.7% | 32.7% | 19.8% | 1.43 | 175 |
+| Strict gate reset | 52.9% | 32.9% | 21.0% | 1.38 | 181 |
+| Two-session confirmation | 35.4% | 22.5% | **18.1%** | 1.02 | 183 |
+
+The gate-reset result did not depend solely on the earlier training period: it
+also beat immediate and the fixed five-session rule in the disjoint test book.
+
+## Cost and capacity sensitivity
+
+At the production capacity of 10, gate reset remained ahead of both immediate
+and cooldown 5 as costs increased.
+
+| Cost per side | Policy | Total return | CAGR | Max DD | Sharpe |
+|---:|---|---:|---:|---:|---:|
+| 0.1% | Immediate | 348.4% | 45.2% | 24.3% | 1.67 |
+| 0.1% | **Gate reset** | **388.1%** | **48.3%** | **21.6%** | **1.77** |
+| 0.1% | Cooldown 5 | 250.8% | 36.6% | 22.2% | 1.47 |
+| 0.2% | Immediate | 296.3% | 40.8% | 25.2% | 1.54 |
+| 0.2% | **Gate reset** | **333.0%** | **44.0%** | **22.9%** | **1.64** |
+| 0.2% | Cooldown 5 | 209.9% | 32.5% | 23.4% | 1.33 |
+| 0.3% | Immediate | 249.8% | 36.5% | 26.0% | 1.41 |
+| 0.3% | **Gate reset** | **284.0%** | **39.7%** | **24.2%** | **1.51** |
+| 0.3% | Cooldown 5 | 164.0% | 27.3% | 24.7% | 1.16 |
+
+The capacity sweep is a real limitation, not a footnote:
+
+| Capacity at 0.1% cost | Immediate Sharpe / CAGR / DD | Gate-reset Sharpe / CAGR / DD | Cooldown-5 Sharpe / CAGR / DD |
+|---:|---|---|---|
+| 5 | 1.33 / 31.9% / 16.8% | 1.37 / 32.8% / 17.5% | **1.46 / 35.8% / 18.3%** |
+| **10 (production)** | 1.67 / 45.2% / 24.3% | **1.77 / 48.3% / 21.6%** | 1.47 / 36.6% / 22.2% |
+| 15 | **1.66 / 44.8% / 24.3%** | 1.63 / 43.0% / **21.6%** | 1.33 / 32.8% / 22.2% |
+
+The promotion is therefore specific to the actual 10-position production book.
+At capacity 5, cooldown 5 ranked best; at capacity 15, immediate had slightly
+higher return and Sharpe while gate reset retained the shallower drawdown. Do
+not generalize the chosen rule to a differently sized portfolio without rerunning
+the matrix.
+
+## Legacy-rank sensitivity
+
+The older candidate-only ranking approximation also favored normal gate reset
+over immediate and cooldown 5, although `next_session` was slightly stronger.
+
+| Policy | Total return | CAGR | Max DD | Sharpe | Trades |
+|---|---:|---:|---:|---:|---:|
+| Immediate | 357.4% | 45.9% | 17.9% | 1.73 | 480 |
+| Next session | **421.5%** | **50.8%** | 18.5% | **1.86** | 466 |
+| **Gate reset** | 408.3% | 49.8% | 18.3% | 1.84 | 464 |
+| Cooldown 5 | 332.3% | 43.9% | 19.6% | 1.71 | 459 |
+| Strict gate reset | 351.3% | 45.5% | 20.4% | 1.72 | 457 |
+
+## Why gate reset was promoted
+
+- It is tied to a new signal episode instead of an arbitrary elapsed time.
+- At the production capacity, it beat immediate and five-session cooldown on
+  return, CAGR, drawdown, and Sharpe.
+- The advantage survived costs of 0.2% and 0.3% per side and the disjoint 2025+
+  test book.
+- It avoids cancelling a valid stop: the loss and transaction costs are always
+  realized before any later trade.
+- It avoids the extra filters that weakened strict reset, improved-setup reset,
+  and two-close confirmation.
+
+The correct interpretation is deliberately modest: **normal gate reset is the
+best production rule among the tested policies for the current 10-position
+book.** It is not proof that gate reset is a universal source of alpha. Forward
+paper-trade monitoring is still the only genuinely new evidence.
@@ -201,9 +201,11 @@ export interface TriggerJobResponse {
  status: 'triggered' | 'busy' | 'blocked' | 'not_found';
  message: string;
  target_model?: BacktestTargetModel;
+  cadence?: BacktestCadence;
 }

 export type BacktestTargetModel = 'production_gtl' | 'structural_sr';
+export type BacktestCadence = 'weekly' | 'daily';

 export function listJobs() {
  return apiClient.get<JobStatus[]>('admin/jobs').then((r) => r.data);
@@ -219,7 +221,10 @@ export function toggleJob(jobName: string, enabled: boolean) {
    .then((r) => r.data);
 }

-export function triggerJob(jobName: string, options?: { target_model?: BacktestTargetModel }) {
+export function triggerJob(
+  jobName: string,
+  options?: { target_model?: BacktestTargetModel; cadence?: BacktestCadence },
+) {
  return apiClient
    .post<TriggerJobResponse>(`admin/jobs/${jobName}/trigger`, options)
    .then((r) => r.data);
@@ -2,7 +2,7 @@ import { useMemo, useState } from 'react';
 import { useMutation, useQueryClient } from '@tanstack/react-query';
 import { useBacktestReport } from '../../hooks/useMarketRegime';
 import { triggerJob } from '../../api/admin';
-import type { BacktestTargetModel } from '../../api/admin';
+import type { BacktestCadence, BacktestTargetModel } from '../../api/admin';
 import { Button } from '../ui/Button';
 import { Callout } from '../ui/Callout';
 import { Disclosure } from '../ui/Disclosure';
@@ -145,6 +145,7 @@ export function BacktestPanel() {
  const [selectedStrategy, setSelectedStrategy] = useState('');
  const [selectedLookback, setSelectedLookback] = useState('');
  const [targetModel, setTargetModel] = useState<BacktestTargetModel>('production_gtl');
+  const [cadence, setCadence] = useState<BacktestCadence>('weekly');

  const monitor = report?.portfolio_monitor ?? null;
  const activeStrategy =
@@ -161,11 +162,11 @@ export function BacktestPanel() {
  );

  const run = useMutation({
-    mutationFn: () => triggerJob('backtest', { target_model: targetModel }),
+    mutationFn: () => triggerJob('backtest', { target_model: targetModel, cadence }),
    onSuccess: (res) => {
      if (res.status === 'triggered') {
        const label = targetModel === 'production_gtl' ? 'Live GTL' : 'Structural S/R comparison';
-        toast.addToast('success', `${label} backtest started — results appear when it finishes.`);
+        toast.addToast('success', `${label} ${cadence} backtest started — results appear when it finishes.`);
        setTimeout(() => queryClient.invalidateQueries({ queryKey: ['backtest-report'] }), 8000);
      } else {
        toast.addToast('info', res.message || 'Could not start backtest');
@@ -180,7 +181,7 @@ export function BacktestPanel() {
        <div className="flex flex-wrap items-start justify-between gap-3">
          <Disclosure summary="How this is measured">
            <p className="max-w-2xl text-xs text-gray-400">
-              The backtest replays the current config weekly through history — at each point the setup is
+              The backtest replays the current config at the selected cadence — at each point the setup is
              rebuilt using only data up to that day (no lookahead) and the following ~30 trading days decide
              its outcome — then simulates one capital-constrained book against the S&P 500. Sentiment and
              fundamentals are held neutral (no point-in-time history). ~6 months is roughly one market regime,
@@ -238,6 +239,56 @@ export function BacktestPanel() {
                </span>
              </label>
            </fieldset>
+            <fieldset className="grid w-full grid-cols-2 gap-2 sm:w-[34rem]">
+              <legend className="mb-1 text-[11px] font-medium uppercase tracking-wider text-gray-500">
+                Entry cadence
+              </legend>
+              <label
+                className={`cursor-pointer rounded-lg border px-3 py-2 transition-colors focus-within:ring-2 focus-within:ring-blue-400/60 ${
+                  cadence === 'weekly'
+                    ? 'border-blue-400/60 bg-blue-500/10'
+                    : 'border-white/10 bg-white/[0.03] hover:border-white/20'
+                }`}
+              >
+                <input
+                  className="sr-only"
+                  type="radio"
+                  name="backtest-cadence"
+                  value="weekly"
+                  checked={cadence === 'weekly'}
+                  onChange={() => setCadence('weekly')}
+                />
+                <span className="flex items-center justify-between gap-2 text-sm font-medium text-gray-100">
+                  Weekly
+                  <span className="rounded-full border border-blue-400/40 bg-blue-400/10 px-2 py-0.5 text-[9px] font-semibold uppercase tracking-widest text-blue-300">
+                    Default
+                  </span>
+                </span>
+                <span className="mt-1 block text-[11px] leading-4 text-gray-500">
+                  Resource-safe server run at five-session intervals.
+                </span>
+              </label>
+              <label
+                className={`cursor-pointer rounded-lg border px-3 py-2 transition-colors focus-within:ring-2 focus-within:ring-amber-400/60 ${
+                  cadence === 'daily'
+                    ? 'border-amber-400/50 bg-amber-500/10'
+                    : 'border-white/10 bg-white/[0.03] hover:border-white/20'
+                }`}
+              >
+                <input
+                  className="sr-only"
+                  type="radio"
+                  name="backtest-cadence"
+                  value="daily"
+                  checked={cadence === 'daily'}
+                  onChange={() => setCadence('daily')}
+                />
+                <span className="text-sm font-medium text-gray-200">Daily</span>
+                <span className="mt-1 block text-[11px] leading-4 text-amber-300/80">
+                  Research run: roughly 5× the replay work; prefer the offline snapshot runner.
+                </span>
+              </label>
+            </fieldset>
            <Button onClick={() => run.mutate()} loading={run.isPending} className="shrink-0">
              {run.isPending ? 'Starting…' : report ? 'Re-run backtest' : 'Run backtest'}
            </Button>
@@ -257,7 +308,8 @@ export function BacktestPanel() {
          <>
            <p className="text-[11px] text-gray-500">
              Ran {timeAgo(report.generated_at)} · {report.tickers} tickers · {report.candidates} setups
-              ({report.qualified} qualified) · weekly cadence, {report.params.horizon_days}-day horizon
+              ({report.qualified} qualified) · {report.params.entry_cadence ?? 'weekly'} cadence,
+              {' '}{report.params.horizon_days}-day horizon
              {report.params.cost_per_side_pct != null && (
                <> · net of {report.params.cost_per_side_pct}%/side costs</>
              )}
@@ -321,6 +373,9 @@ export function BacktestPanel() {
                <p className="text-[11px] text-gray-500">
                  Avg hold {fmtDays(monitorRun.avg_hold_days)} · Best {fmtR(monitorRun.best_trade_r)} / Worst{' '}
                  {fmtR(monitorRun.worst_trade_r)} · Avg P&amp;L per trade {fmtMoney(monitorRun.avg_trade_pnl)}
+                  {monitorRun.reentry_policy === 'gate_reset' ? (
+                    <> · Re-entry after gate failure and fresh qualification</>
+                  ) : null}
                </p>

                {monitorRun.yearly_returns && monitorRun.yearly_returns.length > 0 && (
@@ -66,14 +66,18 @@ function entryDrift(setup: TradeSetup, currentPrice?: number) {
  return { pct, r, status };
 }

-/**
- * The only state with no tradeable setup left: price has gone through the stop.
- * Returns null when there's no live price.
- */
+type NotActionableState =
+  | { kind: 'gate-reset' }
+  | { kind: 'invalidated' }
+  | null;
+
 function notActionableState(setup: TradeSetup, currentPrice?: number) {
+  if (setup.reentry_gate_reset_required) {
+    return { kind: 'gate-reset' } satisfies NotActionableState;
+  }
  if (currentPrice == null) return null;
  if (entryDrift(setup, currentPrice)?.status !== 'invalidated') return null;
-  return { invalidated: true };
+  return { kind: 'invalidated' } satisfies NotActionableState;
 }

 function riskClass(risk: TradeSetup['risk_level']) {
@@ -218,9 +222,6 @@ function SetupCard({ setup, action, currentPrice, risk, regime, exitPolicy, sele
  const exitPlan = deriveExitPlan(setup, exitPolicy);
  const honorsTarget = exitPlan?.honorsTarget ?? false;

-  // Only price through the stop leaves no tradeable setup.
-  const notActionable = notActionableState(setup, currentPrice) != null;
-
  const createTrade = useCreatePaperTrade();
  const [taking, setTaking] = useState(false);
  const [takeShares, setTakeShares] = useState<number>(sizing?.shares ?? 0);
@@ -268,7 +269,24 @@ function SetupCard({ setup, action, currentPrice, risk, regime, exitPolicy, sele
    );
  };

-  if (notActionable) {
+  const inactiveState = notActionableState(setup, currentPrice);
+  if (inactiveState?.kind === 'gate-reset') {
+    return (
+      <div data-direction={setup.direction} className="rounded-xl border border-amber-400/20 bg-amber-400/[0.04] p-4">
+        <div className="flex flex-wrap items-center gap-2">
+          <DirTag direction={setup.direction} />
+          <span className="num text-[10px] uppercase tracking-[0.16em] text-amber-300">awaiting gate reset</span>
+          <span className="num ml-auto text-xs text-gray-500">re-entry paused</span>
+        </div>
+        <p className="mt-2 text-[11.5px] leading-relaxed text-gray-400">
+          This setup remains visible for context but cannot be marked as taken. The ticker must first fail
+          the production gate; only a later fresh qualification can become actionable again.
+        </p>
+      </div>
+    );
+  }
+
+  if (inactiveState?.kind === 'invalidated') {
    const dir = setup.direction.toUpperCase();
    return (
      <div data-direction={setup.direction} className="rounded-xl border border-white/[0.07] p-4">
@@ -617,7 +635,21 @@ export function RecommendationPanel({ symbol, longSetup, shortSetup, currentPric
        <div className="min-w-0">
          {preferredInactive ? (
            <span className="text-sm font-semibold text-gray-400">
-              No current setup <span className="font-normal text-gray-500">(last {preferredDirection} bias {recommendationActionLabel(action).toLowerCase()} — invalidated at the stop)</span>
+              {preferredInactive.kind === 'gate-reset' ? (
+                <>
+                  Re-entry paused{' '}
+                  <span className="font-normal text-gray-500">
+                    (waiting for the gate to fail before a fresh qualification)
+                  </span>
+                </>
+              ) : (
+                <>
+                  No current setup{' '}
+                  <span className="font-normal text-gray-500">
+                    (last {preferredDirection} bias {recommendationActionLabel(action).toLowerCase()} — invalidated at the stop)
+                  </span>
+                </>
+              )}
            </span>
          ) : (() => {
            const reasoning = summary?.reasoning ?? '';
@@ -43,6 +43,7 @@ export function liveRiskReward(setup: TradeSetup, currentPrice: number): number
 * app/services/qualification.py — keep the two in sync.
 */
 export function qualifiesSetup(setup: TradeSetup, config: ActivationConfig): boolean {
+  if (setup.reentry_gate_reset_required) return false;
  if (setup.rr_ratio < config.min_rr) return false;
  // Live R:R from current price — drops setups whose price has already run
  // toward target (reward consumed) or through the stop.
@@ -79,6 +80,9 @@ export function qualifiesSetup(setup: TradeSetup, config: ActivationConfig): boo
 * qualifiesSetup rule-for-rule (keep the order in sync).
 */
 export function disqualifyReason(setup: TradeSetup, config: ActivationConfig): string | null {
+  if (setup.reentry_gate_reset_required) {
+    return 'post-stop gate reset required';
+  }
  if (setup.rr_ratio < config.min_rr) {
    return `R:R ${setup.rr_ratio.toFixed(1)} below gate ${config.min_rr.toFixed(1)}`;
  }
@@ -144,6 +144,7 @@ export interface TradeSetup {
  momentum_percentile?: number | null;
  strategy_rank?: number | null;
  volatility_percentile?: number | null;
+  reentry_gate_reset_required?: boolean;
  context_as_of?: TradeSetupContextAsOf | null;
  recommendation_summary?: RecommendationSummary;
 }
@@ -355,15 +356,24 @@ export interface BacktestPortfolioMonitorRun extends BacktestPortfolioPolicy {
  label: string;
  description: string;
  is_production: boolean;
+  comparison_arm?: 'live_immediate' | 'live_gate_reset' | null;
  entry_variant: string;
  exit_policy: string;
+  reentry_policy?: 'immediate' | 'gate_reset';
  lookback: string;
  lookback_label: string;
 }

 export interface BacktestPortfolioMonitor {
  production_strategy: string;
-  strategies: { strategy: string; label: string; description: string; is_production: boolean }[];
+  strategies: {
+    strategy: string;
+    label: string;
+    description: string;
+    is_production: boolean;
+    comparison_arm?: 'live_immediate' | 'live_gate_reset' | null;
+    reentry_policy?: 'immediate' | 'gate_reset';
+  }[];
  lookbacks: { lookback: string; label: string }[];
  runs: BacktestPortfolioMonitorRun[];
  note?: string;
@@ -396,12 +406,16 @@ export interface BacktestReport {
  qualified: number;
  params: {
    step_days: number;
+    step_sessions?: number;
+    entry_cadence?: 'weekly' | 'daily';
+    signal_eval_cadence?: 'weekly';
    horizon_days: number;
    min_lookback: number;
    cost_per_side_pct?: number;
    target_model?: 'production_gtl' | 'structural_sr';
    target_model_label?: string;
    is_production_target_model?: boolean;
+    production_reentry_policy?: 'gate_reset';
  };
  overall_qualified: BacktestBucket;
  overall_all: BacktestBucket;
@@ -25,3 +25,19 @@ in Git history if a forensic reconstruction is ever necessary.

 The initial untracked `backtest-20260712-sr-detector-rewrite.json` is local-only
 and is intentionally not part of the repository.
+
+The 2026-07-17 post-stop re-entry decision is preserved in
+`daily_reentry_matrix.json`. It is the canonical source for the nine-policy
+daily replay, production-like full-universe ranking, the disjoint 2025+ book,
+and the cost/capacity sensitivity matrix. The interpretation and production
+decision live in
+[`docs/research/post-stop-reentry.md`](../docs/research/post-stop-reentry.md).
+
+The earlier `post-stop-reentry-20260717.json`,
+`post-stop-cooldown-sweep-20260717.json`, and
+`gate-protected-stop-20260717.json` reports were removed as superseded
+intermediate experiments. They used weekly/hybrid entry cadence or tested the
+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.
@@ -0,0 +1,180 @@
+"""Run the four production cadence/re-entry arms on one offline snapshot.
+
+The command executes the complete backtest once weekly and once daily. Each
+backtest contains two otherwise identical live-policy portfolio arms: immediate
+post-stop re-entry and the production gate-reset rule. It writes both full
+reports plus one compact four-arm comparison report.
+"""
+
+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))
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "snapshot",
+        help="SQLite snapshot created by scripts/create_backtest_snapshot.py.",
+    )
+    parser.add_argument(
+        "--out-dir",
+        default="reports",
+        help="Directory for the weekly, daily, and comparison JSON reports.",
+    )
+    parser.add_argument(
+        "--prefix",
+        default=None,
+        help="Output prefix. Defaults to backtest-cadence-<timestamp>.",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=None,
+        help="Override worker count; on a powerful offline PC use CPU count minus one.",
+    )
+    parser.add_argument(
+        "--allow-spawn",
+        action="store_true",
+        help="Enable multiprocessing spawn for the offline Windows run.",
+    )
+    parser.add_argument("--quiet", action="store_true", help="Hide ticker progress.")
+    return parser.parse_args()
+
+
+def _sqlite_url(path: Path) -> str:
+    return f"sqlite+aiosqlite:///{path.resolve().as_posix()}"
+
+
+def _write_json(path: Path, payload: dict) -> None:
+    path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+
+
+def _comparison_arms(report: dict) -> list[dict]:
+    comparison = report.get("production_cadence_comparison") or {}
+    arms = list(comparison.get("arms") or [])
+    if len(arms) != 2:
+        cadence = (report.get("params") or {}).get("entry_cadence", "unknown")
+        raise RuntimeError(
+            f"Expected two live comparison arms for {cadence}; found {len(arms)}"
+        )
+    return arms
+
+
+def _print_arm(row: dict) -> None:
+    print(
+        f"  {row['arm']}: Sharpe {row.get('sharpe')}, "
+        f"CAGR {row.get('cagr_pct')}%, DD {row.get('max_drawdown_pct')}%, "
+        f"trades {row.get('trades')}, post-stop re-entries "
+        f"{row.get('post_stop_reentries', 0)}"
+    )
+
+
+async def _main() -> None:
+    args = _parse_args()
+    snapshot = Path(args.snapshot)
+    if not snapshot.exists():
+        raise SystemExit(f"Snapshot not found: {snapshot}")
+
+    os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1"
+    if args.allow_spawn:
+        os.environ["BACKTEST_ALLOW_SPAWN"] = "1"
+
+    from app.config import settings
+    from app.services.backtest_service import run_backtest
+
+    if args.workers is not None:
+        settings.backtest_workers = args.workers
+
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    prefix = args.prefix or f"backtest-cadence-{datetime.now():%Y%m%d-%H%M%S}"
+
+    engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
+    Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
+    reports: dict[str, dict] = {}
+    try:
+        async with Session() as db:
+            for cadence in ("weekly", "daily"):
+                last_progress: tuple[int, int] | None = None
+
+                def progress(done: int, total: int, symbol: str) -> None:
+                    nonlocal last_progress
+                    if args.quiet or last_progress == (done, total):
+                        return
+                    last_progress = (done, total)
+                    label = f" {symbol}" if symbol else ""
+                    print(
+                        f"{cadence} progress: {done}/{total}{label}",
+                        end="\r",
+                    )
+
+                reports[cadence] = await run_backtest(
+                    db,
+                    progress_cb=progress,
+                    target_model="production_gtl",
+                    cadence=cadence,
+                )
+                if not args.quiet:
+                    print("")
+                _write_json(out_dir / f"{prefix}-{cadence}.json", reports[cadence])
+    finally:
+        await engine.dispose()
+
+    arms = [
+        *_comparison_arms(reports["weekly"]),
+        *_comparison_arms(reports["daily"]),
+    ]
+    expected = {
+        "prod_live_setup_weekly",
+        "prod_live_setup_daily",
+        "gate_reset_weekly",
+        "gate_reset_daily",
+    }
+    if {row.get("arm") for row in arms} != expected:
+        raise RuntimeError("The generated cadence report does not contain all four arms")
+    arm_order = {
+        "prod_live_setup_weekly": 0,
+        "prod_live_setup_daily": 1,
+        "gate_reset_weekly": 2,
+        "gate_reset_daily": 3,
+    }
+    arms.sort(key=lambda row: arm_order[str(row["arm"])])
+
+    comparison = {
+        "generated_at": datetime.now().astimezone().isoformat(),
+        "snapshot": str(snapshot.resolve()),
+        "target_model": "production_gtl",
+        "arms": arms,
+        "full_reports": {
+            cadence: str((out_dir / f"{prefix}-{cadence}.json").resolve())
+            for cadence in ("weekly", "daily")
+        },
+        "note": (
+            "All four arms use the same snapshot, activation settings, target model, "
+            "live Admin exit policy, fees, sizing, and portfolio constraints. Within "
+            "each cadence pair, only the post-stop gate-reset rule differs."
+        ),
+    }
+    comparison_path = out_dir / f"{prefix}-comparison.json"
+    _write_json(comparison_path, comparison)
+
+    print(f"Comparison written: {comparison_path}")
+    for row in arms:
+        _print_arm(row)
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())
@@ -32,7 +32,10 @@ def _parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--out",
        default=None,
-        help="JSON report path. Defaults to reports/backtest-<timestamp>.json.",
+        help=(
+            "JSON report path. Defaults to "
+            "reports/backtest-<cadence>-<timestamp>.json."
+        ),
    )
    parser.add_argument(
        "--workers",
@@ -55,6 +58,15 @@ def _parse_args() -> argparse.Namespace:
            "structural_sr is a comparison-only chart-S/R model."
        ),
    )
+    parser.add_argument(
+        "--cadence",
+        choices=("weekly", "daily"),
+        default="weekly",
+        help=(
+            "Entry replay cadence. Weekly is the resource-safe production default; "
+            "daily performs roughly five times as many setup evaluations."
+        ),
+    )
    parser.add_argument(
        "--holdout-split",
        default=None,
@@ -63,9 +75,9 @@ def _parse_args() -> argparse.Namespace:
    return parser.parse_args()


-def _default_output_path() -> Path:
+def _default_output_path(cadence: str) -> Path:
    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-    return Path("reports") / f"backtest-{stamp}.json"
+    return Path("reports") / f"backtest-{cadence}-{stamp}.json"


 def _pct(value: Any) -> str:
@@ -93,6 +105,7 @@ def _print_summary(report: dict) -> None:

    print("")
    print("Backtest summary")
+    print(f"  entry cadence: {(report.get('params') or {}).get('entry_cadence', 'weekly')}")
    print(f"  candidates: {report.get('candidates')}")
    print(f"  qualified: {report.get('qualified')}")
    print(f"  all setups net avg R: {_r(all_setups.get('net_avg_r'))}")
@@ -183,7 +196,7 @@ async def _main() -> None:
    if args.workers is not None:
        settings.backtest_workers = args.workers

-    output = Path(args.out) if args.out else _default_output_path()
+    output = Path(args.out) if args.out else _default_output_path(args.cadence)
    output.parent.mkdir(parents=True, exist_ok=True)

    engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
@@ -208,6 +221,7 @@ async def _main() -> None:
                db,
                progress_cb=progress,
                target_model=args.target_model,
+                cadence=args.cadence,
            )
    finally:
        await engine.dispose()
@@ -0,0 +1,861 @@
+"""Run the full daily post-stop re-entry study from one candidate replay.
+
+The expensive point-in-time setup replay happens once. The result is ranked in
+both the existing backtest candidate universe and a live-like one-row-per-ticker
+universe. Every policy, lookback, transaction-cost, capacity, and holdout arm is
+then evaluated under both ranking modes.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import copy
+import json
+import multiprocessing
+import os
+import pickle
+import sys
+from collections import Counter
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from datetime import date, datetime
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy import select
+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))
+
+POLICY_NAMES = (
+    "immediate",
+    "next_session",
+    "cooldown_2",
+    "cooldown_3",
+    "cooldown_5",
+    "gate_reset",
+    "strict_gate_reset",
+    "gate_reset_improved",
+    "two_session_confirmation",
+)
+RANKING_MODES = ("backtest_legacy", "live_universe")
+CACHE_VERSION = "daily-reentry-matrix-v3-dual-ranking"
+
+
+def _sqlite_url(path: Path) -> str:
+    return f"sqlite+aiosqlite:///{path.resolve().as_posix()}"
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("snapshot", help="SQLite backtest snapshot.")
+    parser.add_argument(
+        "--start-date",
+        default=None,
+        help="Optional earliest replay/simulation date (YYYY-MM-DD).",
+    )
+    parser.add_argument("--workers", type=int, default=6)
+    parser.add_argument("--out", default=None)
+    parser.add_argument(
+        "--candidate-cache",
+        default=None,
+        help=(
+            "Optional pickle cache. It stores both ranked, long-only qualified "
+            "candidate sets, not the much larger raw replay."
+        ),
+    )
+    parser.add_argument(
+        "--policies",
+        nargs="+",
+        choices=POLICY_NAMES,
+        default=list(POLICY_NAMES),
+    )
+    parser.add_argument(
+        "--ranking-modes",
+        nargs="+",
+        choices=RANKING_MODES,
+        default=list(RANKING_MODES),
+        help=(
+            "backtest_legacy reproduces the existing directional-candidate "
+            "ranking; live_universe ranks each ticker once per session."
+        ),
+    )
+    parser.add_argument("--base-cost-per-side-pct", type=float, default=0.1)
+    parser.add_argument("--base-capacity", type=int, default=10)
+    parser.add_argument(
+        "--costs-per-side-pct",
+        type=float,
+        nargs="+",
+        default=[0.1, 0.2, 0.3],
+    )
+    parser.add_argument(
+        "--capacities", type=int, nargs="+", default=[5, 10, 15]
+    )
+    parser.add_argument(
+        "--holdout-split",
+        default="2025-01-01",
+        help="Train/test split date (YYYY-MM-DD), or 'none' to disable.",
+    )
+    parser.add_argument("--quiet", action="store_true")
+    return parser.parse_args()
+
+
+def _default_output_path() -> Path:
+    stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+    return Path("reports") / f"daily-reentry-matrix-{stamp}.json"
+
+
+def _period_percentiles(
+    observations: list[dict], value_key: str
+) -> dict[tuple[str, str], float]:
+    """Production-style percentiles, one deterministic symbol row per period."""
+    by_period: dict[tuple, list[dict]] = {}
+    seen: set[tuple[str, str]] = set()
+    for row in observations:
+        identity = (str(row["symbol"]), str(row["date"]))
+        if identity in seen:
+            raise ValueError(f"Duplicate universe rank observation: {identity}")
+        seen.add(identity)
+        if row.get(value_key) is None:
+            continue
+        period = tuple(row["ranking_period"])
+        by_period.setdefault(period, []).append(row)
+
+    result: dict[tuple[str, str], float] = {}
+    for group in by_period.values():
+        ordered = sorted(
+            group,
+            key=lambda row: (float(row[value_key]), str(row["symbol"])),
+        )
+        denominator = len(ordered) - 1
+        for rank, row in enumerate(ordered):
+            result[(str(row["symbol"]), str(row["date"]))] = round(
+                rank / denominator * 100.0 if denominator > 0 else 100.0,
+                2,
+            )
+    return result
+
+
+def _live_universe_rank_map(
+    observations: list[dict],
+    benchmark_closes: dict[date, float],
+    momentum_weight: float,
+) -> dict[tuple[str, str], dict[str, float | None]]:
+    """Historical equivalent of ``compute_activation_ranks``.
+
+    Every ticker contributes at most once per session. Residual momentum starts
+    only once 252 benchmark closes were point-in-time available; earlier dates
+    use the same raw-momentum fallback as production.
+    """
+    identities = [(str(row["symbol"]), str(row["date"])) for row in observations]
+    if len(identities) != len(set(identities)):
+        raise ValueError("Universe ranking requires one observation per ticker/date")
+
+    raw_pct = _period_percentiles(observations, "momentum")
+    residual_pct = _period_percentiles(observations, "residual_momentum")
+    vol_pct = _period_percentiles(observations, "vol_6m")
+    benchmark_ords = sorted(value.toordinal() for value in benchmark_closes)
+    residual_start_ord = benchmark_ords[251] if len(benchmark_ords) >= 252 else None
+
+    ranks: dict[tuple[str, str], dict[str, float | None]] = {}
+    for row in observations:
+        identity = (str(row["symbol"]), str(row["date"]))
+        asof_ord = date.fromisoformat(identity[1]).toordinal()
+        momentum_pct = (
+            residual_pct.get(identity)
+            if residual_start_ord is not None and asof_ord >= residual_start_ord
+            else raw_pct.get(identity)
+        )
+        volatility_pct = vol_pct.get(identity)
+        strategy_rank = (
+            round(
+                momentum_pct * momentum_weight
+                + volatility_pct * (1.0 - momentum_weight),
+                2,
+            )
+            if momentum_pct is not None and volatility_pct is not None
+            else momentum_pct
+        )
+        ranks[identity] = {
+            "momentum_percentile": momentum_pct,
+            "volatility_percentile": volatility_pct,
+            "strategy_rank": strategy_rank,
+        }
+    return ranks
+
+
+class PrecomputedDailyEngine:
+    """Exact date/symbol lookup over the already-ranked production gate."""
+
+    def __init__(self, qualified_candidates: list[dict]) -> None:
+        self.by_key = {
+            (row["symbol"], date.fromisoformat(row["date"]).toordinal()): row
+            for row in qualified_candidates
+        }
+
+    def candidate(self, symbol: str, asof_ord: int) -> dict | None:
+        row = self.by_key.get((symbol, asof_ord))
+        return dict(row) if row is not None else None
+
+
+class ReentryPolicy:
+    """Stateful policy evaluated after every initial-stop exit."""
+
+    def __init__(
+        self,
+        name: str,
+        engine: PrecomputedDailyEngine,
+        ranking_key: str,
+    ) -> None:
+        if name not in POLICY_NAMES:
+            raise ValueError(f"Unknown re-entry policy: {name}")
+        self.name = name
+        self.engine = engine
+        self.ranking_key = ranking_key
+        self.checks = 0
+        self.gate_passes = 0
+        self.emitted = Counter()
+
+    def __call__(
+        self,
+        symbol: str,
+        asof_ord: int,
+        state: dict,
+        _bar: Any,
+    ) -> dict | None:
+        self.checks += 1
+        sessions = int(state["sessions_since_stop"])
+        candidate = self.engine.candidate(symbol, asof_ord)
+        if candidate is None:
+            # The strict reset deliberately ignores the stop-day gate state:
+            # it requires a later completed session to go unqualified before
+            # any requalification can trigger a new entry.
+            if self.name != "strict_gate_reset" or sessions >= 1:
+                state["gate_went_unqualified"] = True
+            state["qualified_streak"] = 0
+            return None
+        self.gate_passes += 1
+
+        # Two-session confirmation means two complete post-stop closes. The
+        # stop day's close (sessions=0) deliberately does not count.
+        if self.name == "two_session_confirmation" and sessions == 0:
+            state["qualified_streak"] = 0
+            return None
+        state["qualified_streak"] = int(state.get("qualified_streak", 0)) + 1
+
+        reason: str | None = None
+        if self.name == "immediate":
+            reason = "gate_still_or_again_qualified"
+        elif self.name == "next_session":
+            if sessions >= 1:
+                reason = "stop_day_block_complete"
+        elif self.name.startswith("cooldown_"):
+            cooldown_sessions = int(self.name.removeprefix("cooldown_"))
+            if sessions >= cooldown_sessions:
+                reason = f"{cooldown_sessions}_session_cooldown_complete"
+        elif self.name == "gate_reset":
+            if state["gate_went_unqualified"]:
+                reason = "gate_failed_then_requalified"
+        elif self.name == "strict_gate_reset":
+            if state["gate_went_unqualified"]:
+                reason = "post_stop_gate_failed_then_requalified"
+        elif self.name == "gate_reset_improved":
+            previous_rank = state.get("previous_rank")
+            current_rank = candidate.get(self.ranking_key)
+            rank_not_weaker = (
+                current_rank is not None
+                and (
+                    previous_rank is None
+                    or float(current_rank) >= float(previous_rank)
+                )
+            )
+            if (
+                state["gate_went_unqualified"]
+                and float(candidate["stop"]) > float(state["previous_stop"])
+                and rank_not_weaker
+            ):
+                reason = "gate_reset_with_improved_stop_and_rank"
+        elif self.name == "two_session_confirmation":
+            if state["qualified_streak"] >= 2:
+                reason = "two_qualified_post_stop_closes"
+
+        if reason is None:
+            return None
+        emitted = dict(candidate)
+        emitted["_reentry_reason"] = reason
+        self.emitted[reason] += 1
+        return emitted
+
+    def summary(self) -> dict:
+        return {
+            "daily_checks": self.checks,
+            "qualified_checks": self.gate_passes,
+            "emitted_candidates_by_reason": dict(self.emitted),
+        }
+
+
+def _trade_summary(trades: list[dict]) -> dict:
+    reentries = [trade for trade in trades if trade.get("is_reentry")]
+    waits = [
+        int(trade["reentry_wait_sessions"])
+        for trade in reentries
+        if trade.get("reentry_wait_sessions") is not None
+    ]
+    return {
+        "transaction_cost": round(
+            sum(float(trade["transaction_cost"]) for trade in trades), 2
+        ),
+        "reentry_trades": len(reentries),
+        "same_day_reentries": sum(wait == 0 for wait in waits),
+        "next_session_reentries": sum(wait == 1 for wait in waits),
+        "reentries_within_5_sessions": sum(wait <= 5 for wait in waits),
+        "avg_reentry_wait_sessions": (
+            round(sum(waits) / len(waits), 1) if waits else None
+        ),
+        "reentry_win_rate": (
+            round(
+                sum(float(trade["pnl"]) > 0 for trade in reentries)
+                / len(reentries)
+                * 100.0,
+                1,
+            )
+            if reentries
+            else None
+        ),
+        "reentry_total_pnl": round(
+            sum(float(trade["pnl"]) for trade in reentries), 2
+        ),
+    }
+
+
+def _parse_optional_date(value: str | None, option: str) -> date | None:
+    if value is None or value.strip().lower() == "none":
+        return None
+    try:
+        return date.fromisoformat(value)
+    except ValueError as exc:
+        raise SystemExit(f"{option} must use YYYY-MM-DD or 'none'") from exc
+
+
+def _max_date(left: date | None, right: date | None) -> date | None:
+    if left is None:
+        return right
+    if right is None:
+        return left
+    return max(left, right)
+
+
+async def _main() -> None:
+    args = _parse_args()
+    snapshot = Path(args.snapshot)
+    if not snapshot.exists():
+        raise SystemExit(f"Snapshot not found: {snapshot}")
+    requested_start = _parse_optional_date(args.start_date, "--start-date")
+    holdout_split = _parse_optional_date(args.holdout_split, "--holdout-split")
+    if args.workers < 1:
+        raise SystemExit("--workers must be positive")
+    if args.base_capacity < 1 or any(value < 1 for value in args.capacities):
+        raise SystemExit("capacities must be positive")
+    all_costs = sorted(
+        set([args.base_cost_per_side_pct, *args.costs_per_side_pct])
+    )
+    if any(value < 0 or value >= 100 for value in all_costs):
+        raise SystemExit("cost percentages must be in [0, 100)")
+    all_capacities = sorted(set([args.base_capacity, *args.capacities]))
+    policies = tuple(dict.fromkeys(args.policies))
+    ranking_modes = tuple(dict.fromkeys(args.ranking_modes))
+
+    os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1"
+    os.environ["BACKTEST_ALLOW_SPAWN"] = "1"
+
+    from app.models.ticker import Ticker
+    from app.services import backtest_service as bt
+    from app.services.admin_service import get_activation_config
+    from app.services.paper_trade_service import get_exit_policy
+    from app.services.recommendation_service import get_recommendation_config
+
+    db_engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True)
+    Session = async_sessionmaker(
+        db_engine, class_=AsyncSession, expire_on_commit=False
+    )
+    try:
+        async with Session() as db:
+            recommendation_config = await get_recommendation_config(db)
+            activation = await get_activation_config(db)
+            exit_config = await get_exit_policy(db)
+            benchmark_closes = await bt._load_benchmark_closes_for_backtest(
+                db, days=None, refresh=False
+            )
+            ticker_result = await db.execute(select(Ticker).order_by(Ticker.symbol))
+            symbols = [ticker.symbol for ticker in ticker_result.scalars().all()]
+            prices: dict[str, tuple] = {}
+            for index, symbol in enumerate(symbols, 1):
+                columns = await bt._fetch_columns(db, symbol)
+                if columns is not None:
+                    prices[symbol] = columns
+                if not args.quiet and index % 50 == 0:
+                    print(f"loaded prices: {index}/{len(symbols)}", flush=True)
+    finally:
+        await db_engine.dispose()
+
+    replay_start = requested_start or date(1900, 1, 1)
+    snapshot_stat = snapshot.stat()
+    cache_key = {
+        "version": CACHE_VERSION,
+        "snapshot": str(snapshot.resolve()),
+        "snapshot_size": snapshot_stat.st_size,
+        "snapshot_mtime_ns": snapshot_stat.st_mtime_ns,
+        "start_date": replay_start.isoformat(),
+        "cadence": "daily",
+        "target_model": "production_gtl",
+    }
+    cache_path = Path(args.candidate_cache) if args.candidate_cache else None
+    qualified_candidates_by_mode: dict[str, list[dict]] | None = None
+    entry_candidate_count = 0
+    entry_candidates_by_direction: dict[str, int] = {}
+    universe_rank_observations = 0
+    last_eligible_replay_date: date | None = None
+    if cache_path is not None and cache_path.exists():
+        with cache_path.open("rb") as handle:
+            cached = pickle.load(handle)  # noqa: S301 - trusted local cache
+        if cached.get("key") == cache_key:
+            qualified_candidates_by_mode = {
+                mode: list(rows)
+                for mode, rows in cached["qualified_candidates_by_mode"].items()
+            }
+            entry_candidate_count = int(cached["entry_candidate_count"])
+            entry_candidates_by_direction = dict(
+                cached["entry_candidates_by_direction"]
+            )
+            universe_rank_observations = int(cached["universe_rank_observations"])
+            last_eligible_replay_date = date.fromisoformat(
+                cached["last_eligible_replay_date"]
+            )
+            if not args.quiet:
+                print(f"loaded qualified candidate cache: {cache_path}", flush=True)
+        elif not args.quiet:
+            print(f"candidate cache mismatch; rebuilding: {cache_path}", flush=True)
+
+    if qualified_candidates_by_mode is None:
+        replay_rows: list[dict] = []
+        workers = max(1, min(int(args.workers), multiprocessing.cpu_count() - 1))
+        context = bt._mp_context() or multiprocessing.get_context("spawn")
+        with ProcessPoolExecutor(max_workers=workers, mp_context=context) as pool:
+            futures = {
+                pool.submit(
+                    bt._replay_candidates_for_period,
+                    symbol,
+                    columns,
+                    recommendation_config,
+                    activation,
+                    benchmark_closes,
+                    replay_start,
+                    "daily",
+                    True,
+                    True,
+                ): symbol
+                for symbol, columns in prices.items()
+            }
+            for index, future in enumerate(as_completed(futures), 1):
+                replay_rows.extend(future.result())
+                if not args.quiet and index % 25 == 0:
+                    print(f"daily replay: {index}/{len(futures)} tickers", flush=True)
+
+        setup_candidates = [
+            row for row in replay_rows if not row.get("_rank_only")
+        ]
+        rank_observations = [
+            row for row in replay_rows if row.get("_universe_rank_observation")
+        ]
+        entry_candidate_count = len(setup_candidates)
+        entry_candidates_by_direction = dict(
+            Counter(row["direction"] for row in setup_candidates)
+        )
+        universe_rank_observations = len(rank_observations)
+        last_eligible_replay_date = max(
+            date.fromisoformat(row["date"]) for row in rank_observations
+        )
+
+        # Existing research-backtest semantics: rank every directional setup
+        # candidate, then apply the long-only production gate.
+        bt._assign_momentum_percentiles(setup_candidates)
+        bt._assign_residual_momentum_percentiles(setup_candidates)
+        bt._assign_low_volatility_percentiles(setup_candidates)
+        bt._assign_activation_momentum_percentiles(setup_candidates)
+        bt._assign_residual_high_vol_blend(setup_candidates)
+        threshold = float(activation.get("min_momentum_percentile", 80.0))
+        for candidate in setup_candidates:
+            candidate["qualified"] = bt._momentum_qualifies(candidate, threshold)
+        legacy_qualified = [
+            {
+                key: value
+                for key, value in candidate.items()
+                if not key.startswith("_universe_")
+            }
+            for candidate in setup_candidates
+            if candidate["qualified"] and candidate.get("direction") == "long"
+        ]
+
+        # Live semantics: rank each ticker once per session, independent of
+        # whether it has a setup, and attach that ticker rank only to longs.
+        live_ranks = _live_universe_rank_map(
+            rank_observations,
+            benchmark_closes,
+            bt.STRATEGY_RANK_MOMENTUM_WEIGHT,
+        )
+        live_qualified: list[dict] = []
+        for setup in setup_candidates:
+            if setup.get("direction") != "long":
+                continue
+            candidate = {
+                key: value
+                for key, value in setup.items()
+                if not key.startswith("_universe_")
+            }
+            rank = live_ranks[(str(setup["symbol"]), str(setup["date"]))]
+            candidate[bt.PRODUCTION_PERCENTILE_KEY] = rank["momentum_percentile"]
+            candidate[bt.VOL_PERCENTILE_KEY] = rank["volatility_percentile"]
+            candidate[bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY] = rank["strategy_rank"]
+            candidate["qualified"] = bt._momentum_qualifies(candidate, threshold)
+            if candidate["qualified"]:
+                live_qualified.append(candidate)
+
+        qualified_candidates_by_mode = {
+            "backtest_legacy": legacy_qualified,
+            "live_universe": live_qualified,
+        }
+        del replay_rows, setup_candidates, rank_observations, live_ranks
+        if cache_path is not None:
+            cache_path.parent.mkdir(parents=True, exist_ok=True)
+            with cache_path.open("wb") as handle:
+                pickle.dump(
+                    {
+                        "key": cache_key,
+                        "entry_candidate_count": entry_candidate_count,
+                        "entry_candidates_by_direction": (
+                            entry_candidates_by_direction
+                        ),
+                        "universe_rank_observations": universe_rank_observations,
+                        "last_eligible_replay_date": (
+                            last_eligible_replay_date.isoformat()
+                        ),
+                        "qualified_candidates_by_mode": (
+                            qualified_candidates_by_mode
+                        ),
+                    },
+                    handle,
+                    protocol=pickle.HIGHEST_PROTOCOL,
+                )
+            if not args.quiet:
+                print(f"wrote qualified candidate cache: {cache_path}", flush=True)
+
+    if last_eligible_replay_date is None or qualified_candidates_by_mode is None:
+        raise RuntimeError("Daily replay produced no ranking observations")
+    for mode in ranking_modes:
+        if not qualified_candidates_by_mode.get(mode):
+            raise RuntimeError(f"Daily replay produced no qualified candidates for {mode}")
+
+    strategy = next(
+        row for row in bt.PORTFOLIO_MONITOR_STRATEGIES if row.get("is_production")
+    )
+    entry_config = bt._entry_variant_config(str(strategy["entry_variant"]))
+    if entry_config is None:
+        raise RuntimeError("Production entry configuration missing")
+    ranking_key = str(
+        entry_config.get("ranking_key") or entry_config["percentile_key"]
+    )
+    threshold = float(activation.get("min_momentum_percentile", 80.0))
+    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", max(bt.TIME_EXIT_DAYS)))
+    trail_multiplier = float(
+        exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER)
+    )
+    if ranking_key != bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY:
+        raise RuntimeError(
+            "Daily matrix expects the production 80/20 strategy ranking key"
+        )
+    simulation_prices = prices
+    last_candidate_date = last_eligible_replay_date
+    latest_ord = max(max(columns[0]) for columns in simulation_prices.values())
+    latest_date = date.fromordinal(latest_ord)
+    if holdout_split is not None and not (
+        (requested_start or date.min) < holdout_split <= latest_date
+    ):
+        raise SystemExit(
+            f"--holdout-split must be after the start and no later than {latest_date}"
+        )
+
+    total_completed_sims = 0
+
+    def run_ranking_mode(mode: str, qualified_candidates: list[dict]) -> dict:
+        nonlocal total_completed_sims
+        daily_engine = PrecomputedDailyEngine(qualified_candidates)
+        run_cache: dict[tuple, dict] = {}
+        completed_sims = 0
+
+        def run_policy(
+            policy_name: str,
+            *,
+            start_date: date | None,
+            end_date: date | None,
+            cost_pct: float,
+            capacity: int,
+        ) -> dict:
+            nonlocal completed_sims, total_completed_sims
+            key = (policy_name, start_date, end_date, float(cost_pct), int(capacity))
+            if key in run_cache:
+                return copy.deepcopy(run_cache[key])
+            policy = ReentryPolicy(policy_name, daily_engine, ranking_key)
+            sim = bt._simulate_portfolio(
+                qualified_candidates,
+                simulation_prices,
+                benchmark_closes,
+                exit_policy,
+                hold_days,
+                ranking_key=ranking_key,
+                max_positions=capacity,
+                risk_per_trade=float(entry_config["risk_per_trade"]),
+                atr_trail_multiplier=trail_multiplier,
+                cost_per_side=cost_pct / 100.0,
+                start_date=start_date,
+                end_date=end_date,
+                post_stop_reentry_fn=policy,
+                include_trades=True,
+            )
+            if sim is None:
+                raise RuntimeError(f"Policy {policy_name} produced no trades in {mode}")
+            trades = list(sim.pop("trade_details"))
+            events = list(sim.pop("reentry_events", []))
+            row = {
+                **sim,
+                "turnover": _trade_summary(trades),
+                "policy": policy.summary(),
+                "reentry_events": events,
+            }
+            run_cache[key] = row
+            completed_sims += 1
+            total_completed_sims += 1
+            if not args.quiet:
+                print(
+                    f"portfolio simulations: {total_completed_sims} "
+                    f"({mode}, {policy_name}, cost={cost_pct}%, capacity={capacity})",
+                    flush=True,
+                )
+            return copy.deepcopy(row)
+
+        primary: list[dict] = []
+        for lookback in bt.PORTFOLIO_MONITOR_LOOKBACKS:
+            lookback_start = bt._lookback_start(latest_ord, lookback["days"])
+            sim_start = _max_date(requested_start, lookback_start)
+            for policy_name in policies:
+                row = run_policy(
+                    policy_name,
+                    start_date=sim_start,
+                    end_date=None,
+                    cost_pct=args.base_cost_per_side_pct,
+                    capacity=args.base_capacity,
+                )
+                if lookback["lookback"] != "all":
+                    row.pop("reentry_events", None)
+                primary.append({
+                    "arm": policy_name,
+                    "lookback": lookback["lookback"],
+                    "lookback_label": lookback["label"],
+                    "capacity": args.base_capacity,
+                    **row,
+                })
+
+        immediate_baseline_parity: dict
+        if "immediate" in policies:
+            direct_daily_baseline = bt._simulate_portfolio(
+                qualified_candidates,
+                simulation_prices,
+                benchmark_closes,
+                exit_policy,
+                hold_days,
+                ranking_key=ranking_key,
+                max_positions=args.base_capacity,
+                risk_per_trade=float(entry_config["risk_per_trade"]),
+                atr_trail_multiplier=trail_multiplier,
+                cost_per_side=args.base_cost_per_side_pct / 100.0,
+                start_date=requested_start,
+            )
+            if direct_daily_baseline is None:
+                raise RuntimeError(
+                    f"Direct daily no-lockdown baseline produced no trades in {mode}"
+                )
+            immediate_all = next(
+                row
+                for row in primary
+                if row["lookback"] == "all" and row["arm"] == "immediate"
+            )
+            parity_fields = tuple(sorted(direct_daily_baseline))
+            parity_differences = {
+                field: {
+                    "direct_daily_baseline": direct_daily_baseline.get(field),
+                    "immediate_callback": immediate_all.get(field),
+                }
+                for field in parity_fields
+                if direct_daily_baseline.get(field) != immediate_all.get(field)
+            }
+            if parity_differences:
+                raise RuntimeError(
+                    f"Immediate callback diverges in {mode}: {parity_differences}"
+                )
+            immediate_baseline_parity = {
+                "passed": True,
+                "compared_fields": list(parity_fields),
+                "direct_daily_baseline": direct_daily_baseline,
+            }
+        else:
+            immediate_baseline_parity = {
+                "passed": None,
+                "skipped": "immediate policy was not selected",
+            }
+
+        robustness: list[dict] = []
+        for cost_pct in all_costs:
+            for capacity in all_capacities:
+                for policy_name in policies:
+                    row = run_policy(
+                        policy_name,
+                        start_date=requested_start,
+                        end_date=None,
+                        cost_pct=cost_pct,
+                        capacity=capacity,
+                    )
+                    row.pop("reentry_events", None)
+                    robustness.append({
+                        "arm": policy_name,
+                        "lookback": "all",
+                        "cost_per_side_pct_requested": cost_pct,
+                        "capacity": capacity,
+                        **row,
+                    })
+
+        holdout: list[dict] = []
+        if holdout_split is not None:
+            for segment, segment_start, segment_end in (
+                ("train", requested_start, holdout_split),
+                ("test", _max_date(requested_start, holdout_split), None),
+            ):
+                for policy_name in policies:
+                    row = run_policy(
+                        policy_name,
+                        start_date=segment_start,
+                        end_date=segment_end,
+                        cost_pct=args.base_cost_per_side_pct,
+                        capacity=args.base_capacity,
+                    )
+                    row.pop("reentry_events", None)
+                    holdout.append({
+                        "arm": policy_name,
+                        "segment": segment,
+                        "split_date": holdout_split.isoformat(),
+                        "capacity": args.base_capacity,
+                        **row,
+                    })
+
+        return {
+            "description": (
+                "Existing historical backtest approximation: directional setup "
+                "candidates form the rank cross-section; shorts never qualify."
+                if mode == "backtest_legacy"
+                else "Live-like historical rank: every ticker contributes once per "
+                "session before the long-only setup gate is applied."
+            ),
+            "qualified_candidates": len(qualified_candidates),
+            "tickers_qualified": len({row["symbol"] for row in qualified_candidates}),
+            "primary_lookback_matrix": primary,
+            "immediate_baseline_parity": immediate_baseline_parity,
+            "cost_capacity_robustness": robustness,
+            "holdout": holdout,
+            "portfolio_simulations_executed": completed_sims,
+        }
+
+    ranking_results = {
+        mode: run_ranking_mode(mode, qualified_candidates_by_mode[mode])
+        for mode in ranking_modes
+    }
+
+    output = Path(args.out) if args.out else _default_output_path()
+    report = {
+        "generated_at": datetime.now().astimezone().isoformat(),
+        "snapshot": str(snapshot.resolve()),
+        "period_start_requested": (
+            requested_start.isoformat() if requested_start else None
+        ),
+        "last_eligible_candidate_date": last_candidate_date.isoformat(),
+        "portfolio_asof_date": latest_date.isoformat(),
+        "tickers_loaded": len(prices),
+        "entry_candidates": entry_candidate_count,
+        "entry_candidates_by_direction": entry_candidates_by_direction,
+        "universe_rank_observations": universe_rank_observations,
+        "qualified_candidates_by_ranking_mode": {
+            mode: len(qualified_candidates_by_mode[mode]) for mode in ranking_modes
+        },
+        "params": {
+            "entry_cadence": "daily",
+            "target_model": "production_gtl",
+            "ranking_modes": list(ranking_modes),
+            "policies": list(policies),
+            "base_cost_per_side_pct": args.base_cost_per_side_pct,
+            "base_capacity": args.base_capacity,
+            "robustness_costs_per_side_pct": all_costs,
+            "robustness_capacities": all_capacities,
+            "holdout_split": (
+                holdout_split.isoformat() if holdout_split else None
+            ),
+            "setup_stop_atr_multiplier": bt.ATR_MULTIPLIER,
+            "exit_policy": exit_policy,
+            "exit_atr_multiplier": trail_multiplier,
+            "hold_days": hold_days,
+            "risk_per_trade": float(entry_config["risk_per_trade"]),
+            "momentum_percentile_floor": threshold,
+            "ranking_key": ranking_key,
+        },
+        "ranking_results": ranking_results,
+        "portfolio_simulations_executed": total_completed_sims,
+        "validation_simulations_executed": (
+            len(ranking_modes) if "immediate" in policies else 0
+        ),
+        "note": (
+            "The expensive point-in-time daily setup replay is executed once. "
+            "backtest_legacy preserves the existing candidate-rank approximation; "
+            "live_universe ranks every ticker once per session like production. "
+            "Both modes remain strictly long-only after ranking and then run the "
+            "same policy, lookback, cost, capacity, and holdout matrix. Each "
+            "immediate callback must match its direct no-lockdown simulation exactly. "
+            "Immediate is the daily no-lockdown baseline; next_session blocks only "
+            "the stop day; cooldown_N permits re-entry at wait_sessions=N; gate_reset "
+            "counts the stop-day gate state, while strict_gate_reset requires an "
+            "unqualified close on a later completed session before requalification; "
+            "gate_reset_improved additionally requires a higher stop and a non-weaker "
+            "production rank; two_session_confirmation requires two consecutive "
+            "qualified post-stop closes and excludes the stop day's close. Transaction "
+            "costs alter cash and position sizing, not just reported P&L."
+        ),
+    }
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
+
+    print(f"Report written: {output}")
+    for mode, mode_result in ranking_results.items():
+        print(f"Ranking mode: {mode}")
+        for row in mode_result["primary_lookback_matrix"]:
+            if row["lookback"] == "all":
+                print(
+                    f"  {row['arm']}: Sharpe {row['sharpe']}, "
+                    f"CAGR {row['cagr_pct']}%, DD {row['max_drawdown_pct']}%, "
+                    f"trades {row['trades']}, "
+                    f"reentries {row['turnover']['reentry_trades']}, "
+                    f"fees ${row['turnover']['transaction_cost']}"
+                )
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())
@@ -543,6 +543,7 @@ class TestSimulatePortfolio:
        sim = bt._simulate_portfolio([cand], prices, None, "hold", 3)
        assert sim is not None
        assert sim["trades"] == 1
+        assert sim["cost_per_side_pct"] == pytest.approx(0.1)
        # 20 shares (1% risk / $5 stop distance), exit at the day-3 close 106:
        # pnl = 2120 − 2000 − 2.00 entry cost − 2.12 exit cost = 115.88
        assert sim["final_equity"] == pytest.approx(10_115.88, abs=0.01)
@@ -556,6 +557,29 @@ class TestSimulatePortfolio:
            {"year": 2025, "return_pct": pytest.approx(1.2, abs=0.05)}
        ]

+    def test_cost_parameter_changes_cash_and_position_path(self):
+        closes = [100.0, 102.0, 104.0, 106.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        cand = _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=130.0)
+
+        free = bt._simulate_portfolio(
+            [cand], prices, None, "hold", 3, cost_per_side=0.0
+        )
+        stressed = bt._simulate_portfolio(
+            [cand], prices, None, "hold", 3, cost_per_side=0.002
+        )
+
+        assert free is not None and stressed is not None
+        assert free["final_equity"] == pytest.approx(10_120.0, abs=0.01)
+        assert stressed["cost_per_side_pct"] == pytest.approx(0.2)
+        assert stressed["final_equity"] == pytest.approx(10_111.76, abs=0.01)
+
+    def test_cost_parameter_rejects_invalid_rate(self):
+        with pytest.raises(ValueError, match="cost_per_side"):
+            bt._simulate_portfolio(
+                [], {}, None, "hold", 3, cost_per_side=-0.001
+            )
+
    def test_target_policy_exits_at_target(self):
        closes = [100.0, 102.0, 104.0, 106.0, 108.0, 110.0]
        prices = {"AAA": _sim_prices(self.ORD, closes)}
@@ -575,6 +599,298 @@ class TestSimulatePortfolio:
        assert sim["trades"] == 1
        assert sim["worst_trade_r"] == pytest.approx(-2.0)  # (90 − 100) / 5

+    def test_initial_stop_cooldown_blocks_immediate_reentry(self):
+        closes = [100.0, 94.0, 96.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        candidates = [
+            _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0),
+            _sim_cand("AAA", self.ORD + 1, entry=94.0, stop=89.0, target=110.0),
+        ]
+
+        baseline = bt._simulate_portfolio(candidates, prices, None, "hold", 30)
+        cooldown = bt._simulate_portfolio(
+            candidates,
+            prices,
+            None,
+            "hold",
+            30,
+            reentry_cooldown_sessions=5,
+        )
+
+        assert baseline is not None and baseline["trades"] == 2
+        assert cooldown is not None and cooldown["trades"] == 1
+        assert cooldown["skipped_cooldown"] == 1
+        assert cooldown["reentry_cooldown_sessions"] == 5
+
+    def test_initial_stop_cooldown_unlocks_exactly_after_session_five(self):
+        closes = [100.0, 94.0, 96.0, 96.0, 96.0, 96.0, 97.0, 98.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        candidates = [
+            _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0),
+            # Four completed sessions since the stop: still locked.
+            _sim_cand("AAA", self.ORD + 5, entry=96.0, stop=90.0, target=115.0),
+            # Five completed sessions since the stop: first permitted re-entry.
+            _sim_cand("AAA", self.ORD + 6, entry=97.0, stop=90.0, target=118.0),
+        ]
+
+        sim = bt._simulate_portfolio(
+            candidates,
+            prices,
+            None,
+            "hold",
+            30,
+            reentry_cooldown_sessions=5,
+            include_trades=True,
+        )
+
+        assert sim is not None
+        assert sim["trades"] == 2
+        assert sim["skipped_cooldown"] == 1
+        assert sim["trade_details"][1]["entry_date"] == date.fromordinal(
+            self.ORD + 6
+        ).isoformat()
+
+    def test_post_stop_reentry_cannot_cross_holdout_end(self):
+        prices = {"AAA": _sim_prices(self.ORD, [100.0, 94.0, 96.0, 98.0])}
+        candidate = _sim_cand(
+            "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0
+        )
+        callback_dates: list[int] = []
+
+        def reenter_after_split(symbol, asof_ord, _state, _bar):
+            callback_dates.append(asof_ord)
+            if asof_ord < self.ORD + 2:
+                return None
+            return _sim_cand(
+                symbol, asof_ord, entry=96.0, stop=90.0, target=115.0
+            )
+
+        sim = bt._simulate_portfolio(
+            [candidate],
+            prices,
+            None,
+            "hold",
+            3,
+            end_date=date.fromordinal(self.ORD + 2),
+            post_stop_reentry_fn=reenter_after_split,
+        )
+
+        assert sim is not None
+        assert sim["trades"] == 1
+        assert callback_dates == [self.ORD + 1]
+
+    def test_gate_reset_waits_for_failed_evaluation_then_requalification(self):
+        closes = [100.0] * 95
+        entry_ord = self.ORD + bt.MIN_LOOKBACK - 1
+        stop_ord = entry_ord + 1
+        reentry_ord = entry_ord + 3
+        closes[bt.MIN_LOOKBACK] = 94.0
+        closes[bt.MIN_LOOKBACK + 1] = 95.0
+        closes[bt.MIN_LOOKBACK + 2] = 96.0
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        candidates = [
+            _sim_cand("AAA", entry_ord, entry=100.0, stop=95.0, target=120.0),
+            # Still qualified on the stop day: this must not unlock re-entry.
+            _sim_cand("AAA", stop_ord, entry=94.0, stop=89.0, target=110.0),
+            # No candidate on the intervening session means the daily gate
+            # failed. A fresh qualification on the next session may re-enter.
+            _sim_cand("AAA", reentry_ord, entry=96.0, stop=90.0, target=115.0),
+        ]
+        gate_reset = bt._make_gate_reset_reentry_fn(
+            candidates,
+            prices,
+            cadence="daily",
+        )
+
+        sim = bt._simulate_portfolio(
+            candidates,
+            prices,
+            None,
+            "hold",
+            30,
+            post_stop_reentry_fn=gate_reset,
+            include_trades=True,
+        )
+
+        assert sim is not None
+        assert sim["post_stop_reentries"] == 1
+        assert sim["trade_details"][1]["entry_date"] == date.fromordinal(
+            reentry_ord
+        ).isoformat()
+        assert sim["reentry_events"][0]["wait_sessions"] == 2
+
+    def test_production_monitor_applies_live_gate_reset(self, monkeypatch):
+        def fake_simulator(*_args, **kwargs):
+            return {
+                "trades": 0,
+                "applied_gate_reset": kwargs.get("post_stop_reentry_fn") is not None,
+            }
+
+        monkeypatch.setattr(bt, "_simulate_portfolio", fake_simulator)
+        market_ord = date(2026, 7, 1).toordinal()
+        prices = {"AAA": ([market_ord], [], [], [], [], [])}
+
+        monitor = bt._portfolio_monitor([], prices, None, 30)
+        production_rows = [
+            row for row in monitor["runs"] if row["is_production"]
+        ]
+        immediate_rows = [
+            row for row in monitor["runs"]
+            if row["comparison_arm"] == "live_immediate"
+        ]
+
+        assert production_rows
+        assert all(
+            row["reentry_policy"] == "gate_reset"
+            and row["applied_gate_reset"] is True
+            for row in production_rows
+        )
+        assert immediate_rows
+        assert all(
+            row["reentry_policy"] == "immediate"
+            and row["applied_gate_reset"] is False
+            for row in immediate_rows
+        )
+
+    def test_production_cadence_comparison_names_exact_two_arms(self):
+        monitor = {
+            "runs": [
+                {
+                    "comparison_arm": "live_immediate",
+                    "lookback": "all",
+                    "reentry_policy": "immediate",
+                    "trades": 10,
+                    "equity_curve": [{"date": "2026-01-01", "value": 1.0}],
+                },
+                {
+                    "comparison_arm": "live_gate_reset",
+                    "lookback": "all",
+                    "reentry_policy": "gate_reset",
+                    "trades": 8,
+                    "benchmark_curve": [{"date": "2026-01-01", "value": 1.0}],
+                },
+            ]
+        }
+
+        comparison = bt._production_cadence_comparison(monitor, "daily")
+
+        assert comparison is not None
+        assert [row["arm"] for row in comparison["arms"]] == [
+            "prod_live_setup_daily",
+            "gate_reset_daily",
+        ]
+        assert all("equity_curve" not in row for row in comparison["arms"])
+        assert all("benchmark_curve" not in row for row in comparison["arms"])
+
+    def test_initial_stop_can_refresh_lower_and_survive_same_bar(self):
+        closes = [100.0, 94.0, 96.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        candidate = _sim_cand(
+            "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0
+        )
+
+        sim = bt._simulate_portfolio(
+            [candidate],
+            prices,
+            None,
+            "hold",
+            2,
+            initial_stop_refresh_fn=lambda *_: 90.0,
+            include_trades=True,
+        )
+
+        assert sim is not None
+        assert sim["stop_refresh_attempts"] == 1
+        assert sim["stop_refreshes"] == 1
+        assert sim["stop_refresh_same_bar_hits"] == 0
+        assert sim["exit_reasons"] == {"time": 1}
+        assert sim["trade_details"][0]["stop_refreshes"] == 1
+
+    def test_refreshed_stop_is_checked_against_same_bar(self):
+        ords = list(range(self.ORD, self.ORD + 2))
+        prices = {
+            "AAA": (
+                ords,
+                [100.0, 94.0],
+                [101.0, 96.0],
+                [99.0, 89.0],
+                [100.0, 94.0],
+                [1, 1],
+            )
+        }
+        candidate = _sim_cand(
+            "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0
+        )
+
+        sim = bt._simulate_portfolio(
+            [candidate],
+            prices,
+            None,
+            "hold",
+            30,
+            initial_stop_refresh_fn=lambda *_: 90.0,
+        )
+
+        assert sim is not None
+        assert sim["stop_refresh_same_bar_hits"] == 1
+        assert sim["worst_trade_r"] == pytest.approx(-2.0)
+
+    def test_post_stop_state_suppresses_same_episode_candidate(self):
+        closes = [100.0, 94.0, 96.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        candidates = [
+            _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0),
+            _sim_cand("AAA", self.ORD + 1, entry=94.0, stop=89.0, target=110.0),
+        ]
+
+        sim = bt._simulate_portfolio(
+            candidates,
+            prices,
+            None,
+            "hold",
+            30,
+            post_stop_reentry_fn=lambda *_: None,
+        )
+
+        assert sim is not None
+        assert sim["trades"] == 1
+        assert sim["post_stop_events"] == 1
+        assert sim["post_stop_reentries"] == 0
+        assert sim["post_stop_states_open_at_end"] == 1
+
+    def test_post_stop_callback_can_reenter_same_day(self):
+        closes = [100.0, 94.0, 96.0]
+        prices = {"AAA": _sim_prices(self.ORD, closes)}
+        initial = _sim_cand(
+            "AAA", self.ORD, entry=100.0, stop=95.0, target=120.0
+        )
+
+        def immediate_reentry(sym, current_ord, _state, bar):
+            return _sim_cand(
+                sym,
+                current_ord,
+                entry=bar.close,
+                stop=bar.close - 5.0,
+                target=bar.close + 15.0,
+            )
+
+        sim = bt._simulate_portfolio(
+            [initial],
+            prices,
+            None,
+            "hold",
+            30,
+            post_stop_reentry_fn=immediate_reentry,
+            include_trades=True,
+        )
+
+        assert sim is not None
+        assert sim["trades"] == 2
+        assert sim["post_stop_reentries"] == 1
+        assert sim["reentry_events"][0]["wait_sessions"] == 0
+        assert sim["trade_details"][1]["is_reentry"] is True
+        assert sim["trade_details"][1]["reentry_wait_sessions"] == 0
+
    def test_sma50_policy_exits_on_close_break(self):
        closes = [100.0] * 56 + [90.0, 91.0]
        prices = {"AAA": _sim_prices(self.ORD, closes)}
@@ -722,6 +1038,7 @@ def test_build_recommendation_prefers_production_monitor_headline():
    })
    assert rec["headline"] is not None
    assert "3x ATR trailing exit" in rec["headline"]
+    assert "after the gate fails" in rec["headline"]
    assert any(item["topic"] == "production" for item in rec["items"])


@@ -736,6 +1053,15 @@ def test_backtest_target_model_is_small_and_validated():
        bt.validate_backtest_target_model("legacy_range_grid_touch")


+def test_backtest_cadence_is_small_validated_and_session_based():
+    assert bt.validate_backtest_cadence(" WEEKLY ") == "weekly"
+    assert bt.validate_backtest_cadence("daily") == "daily"
+    assert bt.backtest_step_sessions("weekly") == 5
+    assert bt.backtest_step_sessions("daily") == 1
+    with pytest.raises(ValueError, match="Unknown backtest cadence"):
+        bt.validate_backtest_cadence("monthly")
+
+
 def _flat_window_records():
    return [
        SimpleNamespace(
@@ -829,6 +1155,110 @@ def test_replay_ticker_candidates_carry_gate_fields():
        assert c.get("action") is not None
        assert "risk_level" in c
        assert c["target_model"] == bt.PRODUCTION_GTL_TARGET_MODEL
+        assert c["ranking_period"][0] == "week"
+
+    daily_cands = bt._replay_ticker(
+        "OSC",
+        bars,
+        dict(DEFAULT_RECOMMENDATION_CONFIG),
+        dict(ACTIVATION_DEFAULTS),
+        cadence="daily",
+    )
+    assert len(daily_cands) > len(cands)
+    assert all(c["ranking_period"][0] == "date" for c in daily_cands)
+
+
+def test_slim_replay_can_retain_shorts_for_ranking_universe(monkeypatch):
+    setup = {
+        "entry": 100.0,
+        "stop": 95.0,
+        "target": 110.0,
+        "rr": 2.0,
+        "confidence": 80.0,
+        "primary_prob": 0.6,
+        "best_prob": 0.7,
+        "momentum": 0.1,
+        "meets_core": True,
+        "action": "BUY_MODERATE",
+        "risk_level": "MEDIUM",
+    }
+    monkeypatch.setattr(
+        bt,
+        "_window_setups",
+        lambda *_args, **_kwargs: [
+            {**setup, "direction": "long"},
+            {**setup, "direction": "short", "stop": 105.0, "target": 90.0},
+        ],
+    )
+    count = bt.MIN_LOOKBACK + bt.HORIZON
+    first_ord = date(2025, 1, 1).toordinal()
+    columns = (
+        list(range(first_ord, first_ord + count)),
+        [100.0] * count,
+        [101.0] * count,
+        [99.0] * count,
+        [100.0] * count,
+        [1_000_000] * count,
+    )
+
+    long_only = bt._replay_candidates_for_period(
+        "AAA", columns, {}, {}, None, date.min, "daily"
+    )
+    full_ranking_universe = bt._replay_candidates_for_period(
+        "AAA", columns, {}, {}, None, date.min, "daily", True
+    )
+    dual_ranking_replay = bt._replay_candidates_for_period(
+        "AAA", columns, {}, {}, None, date.min, "daily", True, True
+    )
+
+    assert [row["direction"] for row in long_only] == ["long"]
+    assert {row["direction"] for row in full_ranking_universe} == {
+        "long",
+        "short",
+    }
+    assert len(dual_ranking_replay) == 2
+    assert sum(
+        bool(row.get("_universe_rank_observation"))
+        for row in dual_ranking_replay
+    ) == 1
+
+    monkeypatch.setattr(bt, "_window_setups", lambda *_args, **_kwargs: [])
+    rank_only = bt._replay_candidates_for_period(
+        "AAA", columns, {}, {}, None, date.min, "daily", True, True
+    )
+    assert len(rank_only) == 1
+    assert rank_only[0]["direction"] == "rank_only"
+    assert rank_only[0]["_rank_only"] is True
+    assert rank_only[0]["_universe_rank_observation"] is True
+
+
+def test_daily_replay_uses_exact_date_ranking_periods():
+    candidates = [
+        {
+            "iso_week": (2026, 1),
+            "ranking_period": ("date", date(2026, 1, 5).toordinal()),
+            "momentum": 0.10,
+        },
+        {
+            "iso_week": (2026, 1),
+            "ranking_period": ("date", date(2026, 1, 5).toordinal()),
+            "momentum": 0.20,
+        },
+        {
+            "iso_week": (2026, 1),
+            "ranking_period": ("date", date(2026, 1, 6).toordinal()),
+            "momentum": 0.90,
+        },
+        {
+            "iso_week": (2026, 1),
+            "ranking_period": ("date", date(2026, 1, 6).toordinal()),
+            "momentum": 0.30,
+        },
+    ]
+
+    bt._assign_momentum_percentiles(candidates)
+
+    assert [row["momentum_percentile"] for row in candidates] == [0.0, 100.0, 100.0, 0.0]


 async def _seed_oscillating_ticker(session, symbol: str, n: int = 160) -> None:
@@ -870,12 +1300,20 @@ async def test_run_backtest_smoke(session):
    assert report["params"]["cost_per_side_pct"] == pytest.approx(bt.COST_PER_SIDE * 100)
    assert report["params"]["target_model"] == bt.PRODUCTION_GTL_TARGET_MODEL
    assert report["params"]["is_production_target_model"] is True
+    assert report["params"]["entry_cadence"] == "weekly"
+    assert report["params"]["step_sessions"] == 5
+    assert report["params"]["production_reentry_policy"] == "gate_reset"
    assert "net_avg_r" in report["overall_all"]

    # ablation baseline reproduces the qualified set exactly, and every row
    # carries the hold-to-horizon grading alongside the target model
    ablation = {r["variant"]: r for r in report["gate_ablation"]}
    assert ablation["all_floors"]["total"] == report["overall_qualified"]["total"]
+
+    daily_report = await bt.run_backtest(session, cadence="daily")
+    assert daily_report["params"]["entry_cadence"] == "daily"
+    assert daily_report["params"]["step_sessions"] == 1
+    assert daily_report["candidates"] > report["candidates"]
    for row in report["gate_ablation"]:
        assert "hold_net_avg_r" in row

@@ -0,0 +1,200 @@
+from datetime import date, timedelta
+
+import pytest
+
+from scripts.run_daily_reentry_matrix import (
+    PrecomputedDailyEngine,
+    ReentryPolicy,
+    _live_universe_rank_map,
+)
+
+
+RANKING_KEY = "strategy_rank"
+ORD = date(2025, 1, 6).toordinal()
+
+
+def _candidate(day_ord: int, *, stop: float = 91.0, rank: float = 81.0) -> dict:
+    return {
+        "qualified": True,
+        "direction": "long",
+        "symbol": "AAA",
+        "date": date.fromordinal(day_ord).isoformat(),
+        "entry": 100.0,
+        "stop": stop,
+        "target": 120.0,
+        RANKING_KEY: rank,
+    }
+
+
+def _state(sessions: int = 0) -> dict:
+    return {
+        "sessions_since_stop": sessions,
+        "previous_stop": 90.0,
+        "previous_rank": 80.0,
+        "gate_went_unqualified": False,
+    }
+
+
+def _call(policy: ReentryPolicy, day_ord: int, state: dict, sessions: int):
+    state["sessions_since_stop"] = sessions
+    return policy("AAA", day_ord, state, object())
+
+
+def test_next_session_blocks_only_stop_day():
+    engine = PrecomputedDailyEngine([_candidate(ORD), _candidate(ORD + 1)])
+    policy = ReentryPolicy("next_session", engine, RANKING_KEY)
+    state = _state()
+
+    assert _call(policy, ORD, state, 0) is None
+    assert _call(policy, ORD + 1, state, 1) is not None
+
+
+@pytest.mark.parametrize("sessions", [2, 3, 5])
+def test_cooldown_unlocks_at_exact_boundary(sessions):
+    engine = PrecomputedDailyEngine([
+        _candidate(ORD + sessions - 1),
+        _candidate(ORD + sessions),
+    ])
+    policy = ReentryPolicy(f"cooldown_{sessions}", engine, RANKING_KEY)
+    state = _state()
+
+    assert _call(policy, ORD + sessions - 1, state, sessions - 1) is None
+    assert _call(policy, ORD + sessions, state, sessions) is not None
+
+
+def test_gate_reset_requires_failure_before_requalification():
+    engine = PrecomputedDailyEngine([_candidate(ORD), _candidate(ORD + 2)])
+    policy = ReentryPolicy("gate_reset", engine, RANKING_KEY)
+    state = _state()
+
+    assert _call(policy, ORD, state, 0) is None
+    assert _call(policy, ORD + 1, state, 1) is None
+    emitted = _call(policy, ORD + 2, state, 2)
+    assert emitted is not None
+    assert emitted["_reentry_reason"] == "gate_failed_then_requalified"
+
+
+def test_strict_gate_reset_ignores_stop_day_failure():
+    engine = PrecomputedDailyEngine([
+        _candidate(ORD + 1),
+        _candidate(ORD + 3),
+    ])
+    policy = ReentryPolicy("strict_gate_reset", engine, RANKING_KEY)
+    state = _state()
+
+    # An unqualified stop-day close alone does not reset the strict policy.
+    assert _call(policy, ORD, state, 0) is None
+    assert state["gate_went_unqualified"] is False
+    assert _call(policy, ORD + 1, state, 1) is None
+
+    # A later unqualified close establishes the reset; only then may the next
+    # qualified setup re-enter.
+    assert _call(policy, ORD + 2, state, 2) is None
+    assert state["gate_went_unqualified"] is True
+    emitted = _call(policy, ORD + 3, state, 3)
+    assert emitted is not None
+    assert emitted["_reentry_reason"] == (
+        "post_stop_gate_failed_then_requalified"
+    )
+
+
+def test_improved_gate_reset_requires_better_stop_and_non_weaker_rank():
+    engine = PrecomputedDailyEngine([
+        _candidate(ORD + 1, stop=89.0, rank=82.0),
+        _candidate(ORD + 2, stop=92.0, rank=79.0),
+        _candidate(ORD + 3, stop=92.0, rank=81.0),
+    ])
+    policy = ReentryPolicy("gate_reset_improved", engine, RANKING_KEY)
+    state = _state()
+
+    assert _call(policy, ORD, state, 0) is None
+    assert _call(policy, ORD + 1, state, 1) is None
+    assert _call(policy, ORD + 2, state, 2) is None
+    emitted = _call(policy, ORD + 3, state, 3)
+    assert emitted is not None
+    assert emitted["_reentry_reason"] == "gate_reset_with_improved_stop_and_rank"
+
+
+def test_two_session_confirmation_excludes_stop_day_close():
+    engine = PrecomputedDailyEngine([
+        _candidate(ORD),
+        _candidate(ORD + 1),
+        _candidate(ORD + 2),
+    ])
+    policy = ReentryPolicy("two_session_confirmation", engine, RANKING_KEY)
+    state = _state()
+
+    assert _call(policy, ORD, state, 0) is None
+    assert _call(policy, ORD + 1, state, 1) is None
+    emitted = _call(policy, ORD + 2, state, 2)
+    assert emitted is not None
+    assert emitted["_reentry_reason"] == "two_qualified_post_stop_closes"
+
+
+def _rank_observation(
+    symbol: str,
+    *,
+    raw: float,
+    residual: float,
+    volatility: float,
+) -> dict:
+    return {
+        "symbol": symbol,
+        "date": date.fromordinal(ORD).isoformat(),
+        "ranking_period": ("date", ORD),
+        "momentum": raw,
+        "residual_momentum": residual,
+        "vol_6m": volatility,
+    }
+
+
+def test_live_universe_rank_uses_each_ticker_once_and_residual_when_available():
+    observations = [
+        _rank_observation("AAA", raw=0.1, residual=0.3, volatility=0.1),
+        _rank_observation("BBB", raw=0.3, residual=0.1, volatility=0.2),
+        _rank_observation("CCC", raw=0.2, residual=0.2, volatility=0.3),
+    ]
+    first_benchmark_day = date.fromordinal(ORD) - timedelta(days=300)
+    benchmark = {
+        first_benchmark_day + timedelta(days=offset): 100.0
+        for offset in range(252)
+    }
+
+    ranks = _live_universe_rank_map(observations, benchmark, 0.8)
+
+    assert ranks[("AAA", date.fromordinal(ORD).isoformat())] == {
+        "momentum_percentile": 100.0,
+        "volatility_percentile": 0.0,
+        "strategy_rank": 80.0,
+    }
+    assert ranks[("BBB", date.fromordinal(ORD).isoformat())][
+        "momentum_percentile"
+    ] == 0.0
+    assert ranks[("CCC", date.fromordinal(ORD).isoformat())][
+        "strategy_rank"
+    ] == 60.0
+
+
+def test_live_universe_rank_uses_raw_fallback_before_benchmark_is_ready():
+    observations = [
+        _rank_observation("AAA", raw=0.1, residual=0.3, volatility=0.1),
+        _rank_observation("BBB", raw=0.3, residual=0.1, volatility=0.2),
+    ]
+
+    ranks = _live_universe_rank_map(observations, {}, 0.8)
+
+    assert ranks[("AAA", date.fromordinal(ORD).isoformat())][
+        "momentum_percentile"
+    ] == 0.0
+    assert ranks[("BBB", date.fromordinal(ORD).isoformat())][
+        "momentum_percentile"
+    ] == 100.0
+
+
+def test_live_universe_rank_rejects_duplicate_ticker_date():
+    observation = _rank_observation(
+        "AAA", raw=0.1, residual=0.2, volatility=0.1
+    )
+
+    with pytest.raises(ValueError, match="one observation"):
+        _live_universe_rank_map([observation, dict(observation)], {}, 0.8)
@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import sqlalchemy as sa
+from alembic.migration import MigrationContext
+from alembic.operations import Operations
+
+
+def _load_migration_module():
+    path = (
+        Path(__file__).resolve().parents[2]
+        / "alembic"
+        / "versions"
+        / "022_add_paper_trade_reentry_gate_reset.py"
+    )
+    spec = importlib.util.spec_from_file_location("migration_022", path)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_upgrade_grandfathers_only_preexisting_initial_stops():
+    migration = _load_migration_module()
+    engine = sa.create_engine("sqlite://")
+
+    with engine.begin() as connection:
+        connection.execute(
+            sa.text(
+                """
+                CREATE TABLE paper_trades (
+                    id INTEGER PRIMARY KEY,
+                    status VARCHAR NOT NULL,
+                    close_reason VARCHAR,
+                    closed_at DATETIME
+                )
+                """
+            )
+        )
+        connection.execute(
+            sa.text(
+                """
+                INSERT INTO paper_trades (id, status, close_reason, closed_at)
+                VALUES
+                    (1, 'closed', 'stop', '2026-07-01 12:00:00'),
+                    (2, 'closed', 'manual', '2026-07-02 12:00:00'),
+                    (3, 'open', NULL, NULL)
+                """
+            )
+        )
+
+        context = MigrationContext.configure(connection)
+        migration.op = Operations(context)
+        migration.upgrade()
+
+        historical = connection.execute(
+            sa.text(
+                """
+                SELECT closed_at, reentry_gate_failed_at,
+                       reentry_gate_requalified_at
+                FROM paper_trades
+                WHERE id = 1
+                """
+            )
+        ).one()
+        assert historical[1] == historical[0]
+        assert historical[2] == historical[0]
+
+        unaffected = connection.execute(
+            sa.text(
+                """
+                SELECT reentry_gate_failed_at, reentry_gate_requalified_at
+                FROM paper_trades
+                WHERE id IN (2, 3)
+                ORDER BY id
+                """
+            )
+        ).all()
+        assert unaffected == [(None, None), (None, None)]
+
+        connection.execute(
+            sa.text(
+                """
+                INSERT INTO paper_trades (id, status, close_reason, closed_at)
+                VALUES (4, 'closed', 'stop', '2026-07-18 12:00:00')
+                """
+            )
+        )
+        new_stop = connection.execute(
+            sa.text(
+                """
+                SELECT reentry_gate_failed_at, reentry_gate_requalified_at
+                FROM paper_trades
+                WHERE id = 4
+                """
+            )
+        ).one()
+        assert new_stop == (None, None)
@@ -48,6 +48,74 @@ async def test_create_and_list_open(session):
    assert row["current_price"] == 110.0  # marked to the latest close


+async def test_create_trade_enforces_post_stop_gate_reset_at_service_boundary(session):
+    blocked_id = await _seed(session, "LOCKQ", close=100.0)
+    released_id = await _seed(session, "FREEQ", close=100.0)
+    today = date.today()
+
+    def stopped_trade(ticker_id: int, *, gate_reset_complete: bool) -> PaperTrade:
+        closed_on = today - timedelta(days=10)
+        reset_at = datetime.combine(
+            closed_on + timedelta(days=1),
+            datetime.min.time(),
+            tzinfo=timezone.utc,
+        )
+        return PaperTrade(
+            user_id=1,
+            ticker_id=ticker_id,
+            direction="long",
+            entry_price=100.0,
+            shares=10.0,
+            stop_loss=95.0,
+            target=115.0,
+            status="closed",
+            opened_at=datetime.combine(
+                closed_on - timedelta(days=1), datetime.min.time(), tzinfo=timezone.utc
+            ),
+            close_price=95.0,
+            closed_at=datetime.combine(
+                closed_on, datetime.min.time(), tzinfo=timezone.utc
+            ),
+            close_reason="stop",
+            reentry_gate_failed_at=reset_at if gate_reset_complete else None,
+            reentry_gate_requalified_at=(
+                reset_at + timedelta(days=1) if gate_reset_complete else None
+            ),
+        )
+
+    session.add_all(
+        [
+            stopped_trade(blocked_id, gate_reset_complete=False),
+            stopped_trade(released_id, gate_reset_complete=True),
+        ]
+    )
+    await session.commit()
+
+    with pytest.raises(ValidationError, match="requires a post-stop gate reset"):
+        await svc.create_trade(
+            session,
+            1,
+            symbol="LOCKQ",
+            direction="long",
+            entry_price=100.0,
+            shares=10.0,
+            stop_loss=95.0,
+            target=115.0,
+        )
+
+    trade = await svc.create_trade(
+        session,
+        1,
+        symbol="FREEQ",
+        direction="long",
+        entry_price=100.0,
+        shares=10.0,
+        stop_loss=95.0,
+        target=115.0,
+    )
+    assert trade.ticker_id == released_id
+
+
 async def test_close_uses_current_price(session):
    await _seed(session, "AAA", close=112.0)
    trade = await svc.create_trade(session, 1, symbol="AAA", direction="long",
@@ -607,6 +607,102 @@ async def test_get_trade_setups_can_exclude_tickers_with_open_paper_trades(
    assert [row["symbol"] for row in ticker_rows] == ["OPENQ"]


+@pytest.mark.asyncio
+async def test_get_trade_setups_applies_initial_stop_gate_reset_lock(
+    db_session: AsyncSession,
+):
+    now = datetime.now(timezone.utc)
+    if await db_session.get(User, 1) is None:
+        db_session.add(
+            User(id=1, username="u", password_hash="x", role="user", has_access=True)
+        )
+        await db_session.flush()
+
+    blocked = Ticker(symbol="STOP4")
+    released = Ticker(symbol="STOP5")
+    trailing = Ticker(symbol="TRAILQ")
+    db_session.add_all([blocked, released, trailing])
+    await db_session.flush()
+
+    for ticker in (blocked, released, trailing):
+        db_session.add(
+            TradeSetup(
+                ticker_id=ticker.id,
+                direction="long",
+                entry_price=100.0,
+                stop_loss=95.0,
+                target=115.0,
+                rr_ratio=3.0,
+                composite_score=80.0,
+                detected_at=now,
+            )
+        )
+
+    def closed_trade(
+        ticker: Ticker,
+        reason: str,
+        *,
+        gate_reset_complete: bool = False,
+    ) -> PaperTrade:
+        closed_on = now.date() - timedelta(days=10)
+        return PaperTrade(
+            user_id=1,
+            ticker_id=ticker.id,
+            direction="long",
+            entry_price=100.0,
+            shares=10.0,
+            stop_loss=95.0,
+            target=115.0,
+            status="closed",
+            opened_at=datetime.combine(
+                closed_on - timedelta(days=1), datetime.min.time(), tzinfo=timezone.utc
+            ),
+            close_price=95.0,
+            closed_at=datetime.combine(
+                closed_on, datetime.min.time(), tzinfo=timezone.utc
+            ),
+            close_reason=reason,
+            reentry_gate_failed_at=(
+                now - timedelta(days=9) if gate_reset_complete else None
+            ),
+            reentry_gate_requalified_at=(
+                now - timedelta(days=8) if gate_reset_complete else None
+            ),
+        )
+
+    db_session.add_all(
+        [
+            closed_trade(blocked, "stop"),
+            closed_trade(released, "stop", gate_reset_complete=True),
+            closed_trade(trailing, "trailing"),
+        ]
+    )
+    await db_session.flush()
+
+    default_symbols = {
+        row["symbol"] for row in await get_trade_setups(db_session)
+    }
+    assert {"STOP4", "STOP5", "TRAILQ"}.issubset(default_symbols)
+
+    available_symbols = {
+        row["symbol"]
+        for row in await get_trade_setups(
+            db_session,
+            exclude_reentry_gate_locked_tickers=True,
+        )
+    }
+    assert "STOP4" not in available_symbols
+    assert {"STOP5", "TRAILQ"}.issubset(available_symbols)
+
+    annotated = await get_trade_setups(
+        db_session,
+        symbol="STOP4",
+        include_reentry_gate_lock=True,
+    )
+    assert len(annotated) == 1
+    assert annotated[0]["reentry_gate_reset_required"] is True
+
+
 async def _seed_stale_setup_with_current_scores(db_session: AsyncSession) -> TradeSetup:
    """Stored setup frozen at scan time (conf 82, neutral) vs. current context
    (bullish sentiment, composite 96) that yields live confidence 97.
@@ -3,11 +3,13 @@
 import pytest

 from app.scheduler import (
+    _consume_backtest_options,
    _consume_backtest_target_model,
    _parse_frequency,
    _resume_tickers,
    _last_successful,
    configure_scheduler,
+    queue_backtest_options,
    queue_backtest_target_model,
    scheduler,
 )
@@ -24,6 +26,15 @@ def test_manual_backtest_target_model_rejects_removed_research_arms():
        queue_backtest_target_model("production_control")


+def test_manual_backtest_options_are_one_shot_and_default_back_to_weekly():
+    assert queue_backtest_options("structural_sr", "daily") == (
+        "structural_sr",
+        "daily",
+    )
+    assert _consume_backtest_options() == ("structural_sr", "daily")
+    assert _consume_backtest_options() == ("production_gtl", "weekly")
+
+
 class TestParseFrequency:
    def test_hourly(self):
        assert _parse_frequency("hourly") == {"hours": 1}
@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from app.models.paper_trade import PaperTrade
+from app.models.ticker import Ticker
+from app.models.user import User
+from app.services.trade_policy import (
+    get_reentry_gate_locks,
+    observe_reentry_gate_transitions,
+)
+from tests.conftest import _test_session_factory  # type: ignore
+
+
+@pytest.fixture
+async def session():
+    async with _test_session_factory() as db:
+        yield db
+
+
+def _stopped_trade(
+    ticker_id: int,
+    *,
+    closed_at: datetime,
+    close_reason: str = "stop",
+    gate_failed_at: datetime | None = None,
+    gate_requalified_at: datetime | None = None,
+) -> PaperTrade:
+    return PaperTrade(
+        user_id=1,
+        ticker_id=ticker_id,
+        direction="long",
+        entry_price=100.0,
+        shares=10.0,
+        stop_loss=95.0,
+        target=115.0,
+        status="closed",
+        opened_at=closed_at - timedelta(days=5),
+        close_price=95.0,
+        closed_at=closed_at,
+        close_reason=close_reason,
+        reentry_gate_failed_at=gate_failed_at,
+        reentry_gate_requalified_at=gate_requalified_at,
+    )
+
+
+async def test_observation_releases_only_evaluated_unqualified_tickers(session):
+    session.add(User(id=1, username="u", password_hash="x", role="user", has_access=True))
+    tickers = [
+        Ticker(symbol=symbol)
+        for symbol in ("FAILQ", "PASSQ", "ERRORQ", "LATEQ")
+    ]
+    session.add_all(tickers)
+    await session.flush()
+
+    stopped_at = datetime.now(timezone.utc) - timedelta(days=1)
+    trades = [
+        _stopped_trade(ticker.id, closed_at=stopped_at)
+        for ticker in tickers[:3]
+    ]
+    observed_at = datetime.now(timezone.utc)
+    trades.append(
+        _stopped_trade(
+            tickers[3].id,
+            closed_at=observed_at + timedelta(seconds=1),
+        )
+    )
+    session.add_all(trades)
+    await session.commit()
+
+    updated = await observe_reentry_gate_transitions(
+        session,
+        evaluated_ticker_ids={tickers[0].id, tickers[1].id, tickers[3].id},
+        qualified_ticker_ids={tickers[1].id},
+        observed_at=observed_at,
+    )
+
+    assert updated == {tickers[0].id}
+    locks = await get_reentry_gate_locks(session)
+    assert set(locks) == {ticker.id for ticker in tickers}
+    assert trades[0].reentry_gate_failed_at == observed_at
+    assert trades[0].reentry_gate_requalified_at is None
+    assert trades[1].reentry_gate_failed_at is None
+    assert trades[2].reentry_gate_failed_at is None
+    assert trades[3].reentry_gate_failed_at is None
+
+    requalified_at = observed_at + timedelta(days=1)
+    updated = await observe_reentry_gate_transitions(
+        session,
+        evaluated_ticker_ids={tickers[0].id},
+        qualified_ticker_ids={tickers[0].id},
+        observed_at=requalified_at,
+    )
+
+    assert updated == {tickers[0].id}
+    assert trades[0].reentry_gate_requalified_at == requalified_at
+    assert set(await get_reentry_gate_locks(session)) == {
+        tickers[1].id,
+        tickers[2].id,
+        tickers[3].id,
+    }
+
+
+async def test_latest_stop_starts_a_new_gate_reset_episode(session):
+    session.add(User(id=1, username="u", password_hash="x", role="user", has_access=True))
+    ticker = Ticker(symbol="TWOSTOP")
+    session.add(ticker)
+    await session.flush()
+
+    first_stop = datetime.now(timezone.utc) - timedelta(days=20)
+    session.add_all(
+        [
+            _stopped_trade(
+                ticker.id,
+                closed_at=first_stop,
+                gate_failed_at=first_stop + timedelta(days=1),
+                gate_requalified_at=first_stop + timedelta(days=2),
+            ),
+            _stopped_trade(
+                ticker.id,
+                closed_at=first_stop + timedelta(days=10),
+            ),
+        ]
+    )
+    await session.commit()
+
+    assert ticker.id in await get_reentry_gate_locks(session)
+
+
+async def test_newer_non_stop_exit_supersedes_historical_stop(session):
+    session.add(User(id=1, username="u", password_hash="x", role="user", has_access=True))
+    ticker = Ticker(symbol="LATEREXIT")
+    session.add(ticker)
+    await session.flush()
+
+    stopped_at = datetime.now(timezone.utc) - timedelta(days=20)
+    old_stop = _stopped_trade(ticker.id, closed_at=stopped_at)
+    later_manual_exit = _stopped_trade(
+        ticker.id,
+        closed_at=stopped_at + timedelta(days=10),
+        close_reason="manual",
+    )
+    session.add_all([old_stop, later_manual_exit])
+    await session.commit()
+
+    assert ticker.id not in await get_reentry_gate_locks(session)
+
+    observed_at = datetime.now(timezone.utc)
+    updated = await observe_reentry_gate_transitions(
+        session,
+        evaluated_ticker_ids={ticker.id},
+        qualified_ticker_ids=set(),
+        observed_at=observed_at,
+    )
+
+    assert updated == set()
+    assert old_stop.reentry_gate_failed_at is None
Author	SHA1	Message	Date
dennisthiessen	c9c6967c9c	chore: consolidate post-stop research artifacts Deploy / lint (push) Successful in 11s Details Deploy / test (push) Successful in 1m18s Details Deploy / deploy (push) Successful in 39s Details	2026-07-17 20:46:21 +02:00
dennisthiessen	d13c54e3c7	fix: grandfather pre-cutover stop episodes	2026-07-17 20:28:42 +02:00
dennisthiessen	d858475ddb	docs: document post-stop gate reset results	2026-07-17 19:58:40 +02:00
dennisthiessen	5155d00d9e	feat: require gate reset before post-stop reentry	2026-07-17 19:30:40 +02:00
Dennis Thiessen	1a6f82bf6d	done	2026-07-17 17:42:13 +02:00
dennisthiessen	5385f46064	feat: test shorter post-stop reentry guards	2026-07-17 17:37:03 +02:00
Dennis Thiessen	cf294a7d2b	done	2026-07-17 17:23:27 +02:00
dennisthiessen	bbc7383d3a	feat: compare legacy and live ranking universes	2026-07-17 17:07:35 +02:00
dennisthiessen	9800114fc4	fix: align daily matrix ranking universe	2026-07-17 16:39:43 +02:00
Dennis Thiessen	27bc8a6631	done	2026-07-17 16:30:13 +02:00
dennisthiessen	0cd9ee7689	feat: add daily reentry policy matrix	2026-07-17 16:11:18 +02:00
Dennis Thiessen	13f57b2525	done	2026-07-17 15:22:58 +02:00
dennisthiessen	65a462271c	feat: add selectable daily backtest cadence	2026-07-17 14:41:24 +02:00
dennisthiessen	bc50ba9136	fix: harden post-stop reentry lockdown	2026-07-17 14:17:57 +02:00
dennisthiessen	1e9f2dc4fb	feat: add five-session post-stop reentry lockdown	2026-07-17 13:21:06 +02:00