feat: Specialist Desks — per asset-class fundamental configs + report catalogue

- 7 pre-seeded desks (Forex, Metals, Agri, Energy, Indices, Crypto, Bonds)
  each with default fundamental drivers, macro regime sensitivities and
  price delta thresholds
- Global report catalogue (specialist_reports) fully manual — add any report
  including non-calendar ones (e.g. Cocoa Grinding Report, ICCO)
- Many-to-many report ↔ desk linking (report_desk_links table)
- 12 default reports pre-seeded (COT, EIA, WASDE, FOMC, ECB, CPI, NFP…)
- AI scorer injects SPECIALIST DESK context block for asset classes present
  in each scoring batch (upcoming reports, key drivers, regime sensitivity)
- /specialist-desks page: desk sidebar + fundamentals editor + macro
  sensitivity tag editor + reports tab + global reports catalogue + modal
  to create/edit any report with desk assignment

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/services/ai_analyzer.py

@@ -376,6 +376,54 @@ SCORE DISTRIBUTION RULE (MANDATORY):
 - A pattern with 3+ relevant news AND favorable macro can reach 70-85/100"""
 
 
+def _build_specialist_context_block(asset_classes: set) -> str:
+    """Build a SPECIALIST DESK CONTEXT block for the given asset classes."""
+    try:
+        from services.database import get_asset_class_config, get_desk_reports
+        from datetime import datetime as _dt
+        blocks = []
+        for ac in sorted(asset_classes):
+            cfg = get_asset_class_config(ac)
+            if not cfg:
+                continue
+            reports = get_desk_reports(ac)
+            lines = [f"\n## SPECIALIST DESK — {cfg['display_name'].upper()} {cfg.get('icon','')}"]
+            if cfg.get("fundamentals"):
+                lines.append(f"Key drivers: {cfg['fundamentals'][:300]}")
+            # Macro sensitivity
+            sensitivities = cfg.get("macro_sensitivity") or []
+            if sensitivities:
+                sens_str = " | ".join(
+                    f"{s.get('regime','?')} → {s.get('effect','?')}"
+                    for s in sensitivities[:4]
+                )
+                lines.append(f"Regime sensitivity: {sens_str}")
+            # Price thresholds
+            thresh = cfg.get("price_delta_thresholds") or {}
+            if thresh:
+                lines.append(
+                    f"Price thresholds: significant_week={thresh.get('significant_week','?')}% "
+                    f"extreme_week={thresh.get('extreme_week','?')}%"
+                )
+            # Upcoming reports
+            today = _dt.utcnow().strftime("%Y-%m-%d")
+            upcoming = sorted(
+                [r for r in reports if r.get("next_date") and r["next_date"] >= today],
+                key=lambda r: r["next_date"]
+            )[:5]
+            if upcoming:
+                lines.append("Upcoming reports:")
+                for r in upcoming:
+                    lines.append(
+                        f"  • {r['name']} ({r['source']}) — {r['next_date']} [{r['cadence']}]"
+                        + (f" ⭐×{r['importance']}" if r.get("importance", 1) >= 3 else "")
+                    )
+            blocks.append("\n".join(lines))
+        return "\n".join(blocks) + "\n" if blocks else ""
+    except Exception:
+        return ""
+
+
 def score_patterns_with_context(
     patterns: List[Dict],
     recent_news: List[Dict],
@@ -783,12 +831,18 @@ Lessons: {' | '.join(str(l)[:80] for l in lessons[:3])}
 
 """
 
+    # Build specialist-desk blocks for asset classes present in this pattern set
+    _all_pattern_asset_classes = {
+        p.get("asset_class", "") for p in patterns if p.get("asset_class")
+    }
+    specialist_block = _build_specialist_context_block(_all_pattern_asset_classes)
+
     # Build the per-batch prompt template (static parts)
     prompt_header = f"""GLOBAL CONTEXT:
 - Geopolitical risk score: {geo_score.get('score', 50)}/100 ({geo_score.get('level', 'medium')})
 - Top risks: {geo_score.get('top_risks', [])}
 {macro_section}{lessons_header}{iv_context}
-{risk_context}
+{risk_context}{specialist_block}
 SCORING TEMPLATE:
 {scoring_template}