replace EV activation gate with cross-sectional 12-1 momentum ranking

The 5-year backtest confirmed the EV gate adds negative value (high threshold =
worst expectancy) and that 12-1 month momentum is the one price signal with a
plausible, right-signed cross-sectional IC (~0.05). So "qualified" now means:
clears the R:R + confidence floors AND the ticker ranks in the top
`min_momentum_percentile` of the universe by 12-1 momentum that week.

- qualification.py: drop expected_value_r / the EV gate; add a momentum-percentile
  gate (duck-typed `momentum_percentile`, only enforced when attached + threshold
  set, else defers to floors). Mirrored in frontend qualification.ts.
- activation config/schema: min_expected_value -> min_momentum_percentile
  (default 80 = top quintile). ActivationSettings, DashboardPage (ranks/【shows】
  momentum instead of EV), and the BacktestPanel sweep follow.
- backtest: rank each ISO week's universe by 12-1 momentum, assign a percentile,
  and qualify the top slice; the sweep now sweeps the percentile cutoff.

Also offload the backtest's per-ticker compute to a worker thread so the heavy
~5y run no longer blocks the API event loop (the "backend offline" flicker).

Production setups don't carry momentum_percentile yet — wiring the scanner to
attach it (a universe momentum-rank step) is the next step; until then the live
gate defers to floors while the backtest measures the momentum selection. 330
backend tests pass; frontend build clean.

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

app/services/admin_service.py

@@ -43,7 +43,7 @@ SUPPORTED_TICKER_UNIVERSES = {"sp500", "nasdaq100", "nasdaq_all"}
 # confidence are floors; high-conviction / clean-read / target-probability are
 # optional tighteners (off by default — turn on to be more selective).
 _ACTIVATION_FLOAT_KEYS: dict[str, str] = {
-    "min_expected_value": "activation_min_expected_value",
+    "min_momentum_percentile": "activation_min_momentum_percentile",
     "min_rr": "activation_min_rr",
     "min_confidence": "activation_min_confidence",
     "min_target_probability": "activation_min_target_probability",
@@ -53,7 +53,7 @@ _ACTIVATION_BOOL_KEYS: dict[str, str] = {
     "exclude_conflicts": "activation_exclude_conflicts",
 }
 ACTIVATION_DEFAULTS: dict[str, float | bool] = {
-    "min_expected_value": 0.15,
+    "min_momentum_percentile": 80.0,
     "min_rr": 1.2,
     "min_confidence": 55.0,
     "min_target_probability": 0.0,
@@ -201,8 +201,8 @@ async def update_activation_config(
     db: AsyncSession, updates: dict[str, float | bool]
 ) -> dict[str, float | bool]:
     """Update the activation gate. Accepts public keys; only supplied keys change."""
-    if "min_expected_value" in updates and not -1.0 <= updates["min_expected_value"] <= 10.0:
-        raise ValidationError("min_expected_value must be between -1 and 10 (R units)")
+    if "min_momentum_percentile" in updates and not 0 <= updates["min_momentum_percentile"] <= 100:
+        raise ValidationError("min_momentum_percentile must be between 0 and 100")
     if "min_rr" in updates and updates["min_rr"] < 0:
         raise ValidationError("min_rr must be >= 0")
     if "min_confidence" in updates and not 0 <= updates["min_confidence"] <= 100: