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/qualification.py

@@ -0,0 +1,42 @@
+"""Shared definition of a 'qualified' (actionable) trade setup.
+
+A single predicate, driven by the admin activation config, used by the
+performance stats (server) and mirrored on the frontend. Beyond raw R:R and
+confidence, an actionable setup must show genuine conviction: a high-conviction
+recommended action, a clean (conflict-free) read, and a probable primary target.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+HIGH_CONVICTION_ACTIONS = {"LONG_HIGH", "SHORT_HIGH"}
+
+
+def best_target_probability(setup: Any) -> float:
+    """Highest probability among a setup's targets, 0 if none."""
+    targets = getattr(setup, "targets", None) or []
+    probs = [float(t.get("probability", 0.0)) for t in targets if isinstance(t, dict)]
+    return max(probs, default=0.0)
+
+
+def setup_qualifies(setup: Any, config: dict) -> bool:
+    """Whether a setup clears the activation gate.
+
+    ``setup`` is duck-typed: any object exposing rr_ratio, confidence_score,
+    recommended_action, risk_level and a ``targets`` list of dicts.
+    """
+    if setup.rr_ratio < config["min_rr"]:
+        return False
+    if (setup.confidence_score or 0.0) < config["min_confidence"]:
+        return False
+    if config.get("require_high_conviction"):
+        if (setup.recommended_action or "") not in HIGH_CONVICTION_ACTIONS:
+            return False
+    if config.get("exclude_conflicts"):
+        if (setup.risk_level or "") != "Low":
+            return False
+    min_tp = float(config.get("min_target_probability", 0.0))
+    if min_tp > 0 and best_target_probability(setup) < min_tp:
+        return False
+    return True