Add multi-factor conviction gate to activation

Make "qualified" mean an edge candidate, not just R:R + confidence.
The gate now also requires (all admin-configurable, defaults on):
- high conviction: recommended_action LONG_HIGH / SHORT_HIGH only
- clean read: risk_level Low (no contradicting signals)
- probable primary target: best target probability >= min (default 60)

- Shared predicate: app/services/qualification.py +
  frontend/src/lib/qualification.ts (mirrored)
- Activation config extended (min_target_probability,
  require_high_conviction, exclude_conflicts) with bool-aware
  get/update + validation
- /trades/performance switched to ?qualified_only=true, applying
  the full gate server-side; confidence breakdown stays unfiltered
- Dashboard "Qualified", Signals "Qualified only" toggle, and
  Track Record all use the one gate; Admin gains the new controls

Sentiment provider runtime config (prior change) included.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

app/services/outcome_service.py

@@ -23,6 +23,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models.ohlcv import OHLCVRecord
 from app.models.trade_setup import TradeSetup
+from app.services.qualification import setup_qualifies
 
 logger = logging.getLogger(__name__)
 
@@ -180,8 +181,7 @@ def _confidence_bucket(score: float | None) -> str | None:
 
 async def get_performance_stats(
     db: AsyncSession,
-    min_rr: float | None = None,
-    min_confidence: float | None = None,
+    config: dict | None = None,
 ) -> dict:
     """Aggregate outcome statistics over all evaluated trade setups.
 
@@ -189,9 +189,10 @@ async def get_performance_stats(
     loss = -1R, expired = 0R). A positive avg_r means the signals have
     been profitable on a risk-adjusted basis.
 
-    min_rr / min_confidence filter the overall, direction and action
-    breakdowns. The confidence breakdown deliberately stays unfiltered:
-    it is the instrument for validating the thresholds themselves.
+    When ``config`` (an activation-gate dict) is supplied, the overall,
+    direction and action breakdowns cover only qualified setups. The
+    confidence breakdown deliberately stays unfiltered: it is the
+    instrument for validating the gate itself.
     """
     result = await db.execute(
         select(TradeSetup).where(TradeSetup.actual_outcome.is_not(None))
@@ -203,14 +204,10 @@ async def get_performance_stats(
     )
     pending_count = len(pending_result.scalars().all())
 
-    def qualifies(setup: TradeSetup) -> bool:
-        if min_rr is not None and setup.rr_ratio < min_rr:
-            return False
-        if min_confidence is not None and (setup.confidence_score or 0.0) < min_confidence:
-            return False
-        return True
-
-    qualified = [s for s in evaluated if qualifies(s)]
+    if config is not None:
+        qualified = [s for s in evaluated if setup_qualifies(s, config)]
+    else:
+        qualified = evaluated
 
     by_direction: dict[str, list[TradeSetup]] = {}
     by_action: dict[str, list[TradeSetup]] = {}