Add activation thresholds: qualified-signal defaults and views

Admin-configurable thresholds (min R:R, default 2.0; min confidence,
default 70%) defining what counts as an actionable signal:

- Admin Settings: new Activation Thresholds panel
  (GET/PUT /admin/settings/activation)
- GET /trades/activation exposes values to all users with access
- Signals/Setups: filters initialize from activation values
- Track Record: "Qualified signals only" toggle (default on) via
  min_rr/min_confidence params on /trades/performance; the
  confidence breakdown always covers the full population so the
  thresholds can be validated against outcomes
- Dashboard: "Qualified" metric and qualified-first Top Setups
- Outcome evaluator unchanged: every setup is still evaluated

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

app/services/outcome_service.py

@@ -178,12 +178,20 @@ def _confidence_bucket(score: float | None) -> str | None:
     return None
 
 
-async def get_performance_stats(db: AsyncSession) -> dict:
+async def get_performance_stats(
+    db: AsyncSession,
+    min_rr: float | None = None,
+    min_confidence: float | None = None,
+) -> dict:
     """Aggregate outcome statistics over all evaluated trade setups.
 
     avg_r is the expectancy per trade in R-multiples (win = +rr_ratio,
     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.
     """
     result = await db.execute(
         select(TradeSetup).where(TradeSetup.actual_outcome.is_not(None))
@@ -195,14 +203,26 @@ async def get_performance_stats(db: AsyncSession) -> dict:
     )
     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)]
+
     by_direction: dict[str, list[TradeSetup]] = {}
     by_action: dict[str, list[TradeSetup]] = {}
     by_confidence: dict[str, list[TradeSetup]] = {}
 
-    for setup in evaluated:
+    for setup in qualified:
         by_direction.setdefault(setup.direction, []).append(setup)
         action = setup.recommended_action or "NONE"
         by_action.setdefault(action, []).append(setup)
+
+    # Confidence buckets always cover the full evaluated population
+    for setup in evaluated:
         bucket = _confidence_bucket(setup.confidence_score)
         if bucket is not None:
             by_confidence.setdefault(bucket, []).append(setup)
@@ -210,7 +230,7 @@ async def get_performance_stats(db: AsyncSession) -> dict:
     bucket_order = [label for label, _, _ in _CONFIDENCE_BUCKETS]
 
     return {
-        "overall": _bucket_stats(evaluated),
+        "overall": _bucket_stats(qualified),
         "pending": pending_count,
         "by_direction": {k: _bucket_stats(v) for k, v in sorted(by_direction.items())},
         "by_action": {k: _bucket_stats(v) for k, v in sorted(by_action.items())},