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>

tests/unit/test_qualification.py

@@ -0,0 +1,81 @@
+"""Unit tests for the activation qualification predicate."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+from app.services.qualification import best_target_probability, setup_qualifies
+
+FULL_GATE = {
+    "min_rr": 2.0,
+    "min_confidence": 70.0,
+    "min_target_probability": 60.0,
+    "require_high_conviction": True,
+    "exclude_conflicts": True,
+}
+
+
+def _setup(**kwargs):
+    base = dict(
+        rr_ratio=3.0,
+        confidence_score=80.0,
+        recommended_action="LONG_HIGH",
+        risk_level="Low",
+        targets=[{"probability": 65.0}],
+    )
+    base.update(kwargs)
+    return SimpleNamespace(**base)
+
+
+class TestSetupQualifies:
+    def test_clean_high_conviction_setup_passes(self):
+        assert setup_qualifies(_setup(), FULL_GATE) is True
+
+    def test_low_rr_fails(self):
+        assert setup_qualifies(_setup(rr_ratio=1.5), FULL_GATE) is False
+
+    def test_low_confidence_fails(self):
+        assert setup_qualifies(_setup(confidence_score=60.0), FULL_GATE) is False
+
+    def test_moderate_action_fails_when_high_conviction_required(self):
+        assert setup_qualifies(_setup(recommended_action="LONG_MODERATE"), FULL_GATE) is False
+
+    def test_neutral_action_fails(self):
+        assert setup_qualifies(_setup(recommended_action="NEUTRAL"), FULL_GATE) is False
+
+    def test_short_high_passes(self):
+        assert setup_qualifies(_setup(recommended_action="SHORT_HIGH"), FULL_GATE) is True
+
+    def test_non_low_risk_fails_when_excluding_conflicts(self):
+        assert setup_qualifies(_setup(risk_level="Medium"), FULL_GATE) is False
+        assert setup_qualifies(_setup(risk_level="High"), FULL_GATE) is False
+
+    def test_low_target_probability_fails(self):
+        assert setup_qualifies(_setup(targets=[{"probability": 40.0}]), FULL_GATE) is False
+
+    def test_no_targets_fails_when_probability_required(self):
+        assert setup_qualifies(_setup(targets=[]), FULL_GATE) is False
+
+    def test_conviction_filters_can_be_disabled(self):
+        relaxed = {
+            "min_rr": 2.0,
+            "min_confidence": 70.0,
+            "min_target_probability": 0.0,
+            "require_high_conviction": False,
+            "exclude_conflicts": False,
+        }
+        # Moderate action, medium risk, no targets — still passes on rr+confidence alone
+        s = _setup(recommended_action="LONG_MODERATE", risk_level="Medium", targets=[])
+        assert setup_qualifies(s, relaxed) is True
+
+    def test_missing_confidence_treated_as_zero(self):
+        assert setup_qualifies(_setup(confidence_score=None), FULL_GATE) is False
+
+
+class TestBestTargetProbability:
+    def test_returns_max(self):
+        s = _setup(targets=[{"probability": 40.0}, {"probability": 72.0}, {"probability": 55.0}])
+        assert best_target_probability(s) == 72.0
+
+    def test_empty_is_zero(self):
+        assert best_target_probability(_setup(targets=[])) == 0.0