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/models/trade_setup.py

@@ -26,6 +26,9 @@ class TradeSetup(Base):
     )
 
     confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    # Ticker's 12-1 momentum percentile across the universe at detection time
+    # (0–100, 100 = strongest). Drives the activation gate's core selection.
+    momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
     targets_json: Mapped[str | None] = mapped_column(Text, nullable=True)
     conflict_flags_json: Mapped[str | None] = mapped_column(Text, nullable=True)
     recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)