momentum gate: long-only + wire the percentile onto live setups

Part 1 — long-only. The momentum edge is long top-momentum; the gate was
qualifying shorts on high-momentum names (fighting the trend), which showed as
the -0.13R Short(qual.) drag. While the gate is active, shorts no longer qualify
(backend qualification, backtest _momentum_qualifies, and the frontend mirror).

Part 2 — production wiring. Live setups now carry a real momentum rank, so the
dashboard, the Track Record's qualified stats, and outcome evaluation all gate on
the same value instead of deferring to floors:
- new momentum_service.compute_momentum_percentiles: 12-1 momentum per ticker,
  ranked across the universe into a {symbol: percentile} map.
- the daily R:R scan ranks the universe up front and stores each setup's
  percentile (new trade_setups.momentum_percentile column, migration 010).
- enhance_trade_setup mutates the same row, so the percentile is preserved;
  _trade_setup_to_dict + TradeSetupResponse expose it to the API.

Until a fresh scan runs, pre-existing setups have a null percentile and the gate
falls back to floors for them (longs) / excludes them (shorts) — they fill in on
the next scan. 341 backend tests pass; frontend build clean.

Needs the alembic upgrade (migration 010) on deploy.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/rr_scanner_service.py

@@ -81,8 +81,13 @@ async def scan_ticker(
     symbol: str,
     rr_threshold: float = 1.5,
     atr_multiplier: float = 1.5,
+    momentum_percentile: float | None = None,
 ) -> list[TradeSetup]:
-    """Scan a single ticker for trade setups meeting the R:R threshold."""
+    """Scan a single ticker for trade setups meeting the R:R threshold.
+
+    ``momentum_percentile`` is the ticker's 12-1 momentum rank across the universe
+    (computed by the caller), stored on each setup so the activation gate can
+    select the top slice."""
     ticker = await _get_ticker(db, symbol)
 
     records = await query_ohlcv(db, symbol)
@@ -169,6 +174,7 @@ async def scan_ticker(
                     rr_ratio=round(best_candidate_rr, 4),
                     composite_score=round(composite_score, 4),
                     detected_at=now,
+                    momentum_percentile=momentum_percentile,
                 ))
 
     if levels_below:
@@ -202,6 +208,7 @@ async def scan_ticker(
                     rr_ratio=round(best_candidate_rr, 4),
                     composite_score=round(composite_score, 4),
                     detected_at=now,
+                    momentum_percentile=momentum_percentile,
                 ))
 
     available_directions = {s.direction for s in setups}
@@ -249,6 +256,16 @@ async def scan_all_tickers(
     tickers = list(result.scalars().all())
     total = len(tickers)
 
+    # Rank the universe by 12-1 momentum up front so each new setup carries its
+    # ticker's percentile (used by the activation gate). Best-effort.
+    try:
+        from app.services import momentum_service
+
+        percentiles = await momentum_service.compute_momentum_percentiles(db)
+    except Exception:
+        logger.exception("Momentum ranking refresh failed")
+        percentiles = {}
+
     all_setups: list[TradeSetup] = []
     for index, ticker in enumerate(tickers):
         if progress_callback is not None:
@@ -267,7 +284,8 @@ async def scan_all_tickers(
                 logger.exception("Error refreshing scores for %s", ticker.symbol)
 
             setups = await scan_ticker(
-                db, ticker.symbol, rr_threshold, atr_multiplier
+                db, ticker.symbol, rr_threshold, atr_multiplier,
+                momentum_percentile=percentiles.get(ticker.symbol),
             )
             all_setups.extend(setups)
         except Exception:
@@ -410,4 +428,5 @@ def _trade_setup_to_dict(setup: TradeSetup, symbol: str, current_price: float |
         "outcome_date": setup.outcome_date,
         "evaluated_at": setup.evaluated_at,
         "current_price": current_price,
+        "momentum_percentile": setup.momentum_percentile,
     }