feat: apply category defaults first then AI adjusts + remove chips

Backend: merge strategy — category defaults are applied as baseline
(ai_generated=0), AI impacts override/extend them (ai_generated=1).
All N category defaults appear in the impacts list; AI covers them
explicitly with event-specific score/direction adjustments.
Prompt: instruct AI to cover ALL default instruments.

Frontend: remove default impact chips under category selector —
they now appear directly in the instrument impacts list.

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

backend/services/impact_service.py

@@ -72,13 +72,18 @@ LISTE DES CATÉGORIES DISPONIBLES (utilise uniquement ces noms) :
     # ── Default impacts from pre-selected category ────────────────────────────
     defaults_block = ""
     if preselected_defaults:
-        top = sorted(preselected_defaults, key=lambda x: x.get("sensitivity", 0), reverse=True)[:8]
+        top = sorted(preselected_defaults, key=lambda x: x.get("sensitivity", 0), reverse=True)
         lines = [
             f"  • {d['instrument_id']}: sensibilité {d.get('sensitivity',0):.0%}, "
             f"direction usuelle = {d.get('typical_direction','?')}"
+            + (f", note: {d.get('notes','')[:60]}" if d.get("notes") else "")
             for d in top
         ]
-        defaults_block = "\nImpacts par défaut de la catégorie (ajuste si besoin) :\n" + "\n".join(lines)
+        defaults_block = (
+            "\nImpacts par défaut de la catégorie — COUVRE TOUS CES INSTRUMENTS dans ta réponse "
+            "(ajuste scores/directions selon le contexte spécifique de l'événement) :\n"
+            + "\n".join(lines)
+        )
 
     return f"""Tu es un analyste macro senior spécialisé en options et trading multi-actifs.
 
@@ -241,25 +246,49 @@ def evaluate_event_impacts(
 
     cat_label = matched_cat_name or (event.get("sub_type") or "").strip() or (event.get("category") or "")
 
-    # Persist
+    # ── Merge: category defaults (baseline) + AI adjustments ─────────────────
-    to_save = []
+    # 1. Start with ALL category defaults
+    base: Dict[str, Dict] = {}
+    if matched_cat_name:
+        cat_obj = next((c for c in all_cats if c["name"] == matched_cat_name), None)
+        if cat_obj:
+            for d in cat_obj.get("default_impacts", []):
+                tid = d.get("instrument_id")
+                if tid and tid in ALL_INSTRUMENTS:
+                    base[tid] = {
+                        "source_type":   "event",
+                        "source_id":     event_id,
+                        "source_name":   event["name"],
+                        "source_date":   event["start_date"],
+                        "category_name": cat_label,
+                        "instrument_id": tid,
+                        "impact_score":  float(d.get("sensitivity", 0.5)),
+                        "direction":     d.get("typical_direction", "neutral"),
+                        "rationale":     d.get("notes", "") or f"Défaut catégorie {cat_label}",
+                        "confidence":    0.70,
+                        "ai_generated":  0,
+                    }
+
+    # 2. AI impacts override / extend the baseline
     for imp in ai_impacts:
-        if imp.get("instrument_id") not in ALL_INSTRUMENTS:
+        tid = imp.get("instrument_id")
+        if not tid or tid not in ALL_INSTRUMENTS:
             continue
-        to_save.append({
+        base[tid] = {
             "source_type":   "event",
             "source_id":     event_id,
             "source_name":   event["name"],
             "source_date":   event["start_date"],
             "category_name": cat_label,
-            "instrument_id": imp["instrument_id"],
+            "instrument_id": tid,
             "impact_score":  float(imp.get("impact_score", 0.5)),
             "direction":     imp.get("direction", "neutral"),
             "rationale":     imp.get("rationale", ""),
             "confidence":    float(imp.get("confidence", 0.7)),
             "ai_generated":  1,
-        })
+        }
 
+    to_save = list(base.values())
     save_instrument_impacts(to_save)
     saved = get_impacts_for_source("event", event_id)
 

frontend/src/pages/MarketEvents.tsx

@@ -502,20 +502,6 @@ function EventDetail({
               {matchedCat?.description && (
                 <div className="text-xs text-slate-600 mt-1.5 italic">{matchedCat.description}</div>
               )}
-              {matchedCat && nDefaults > 0 && (
-                <div className="flex flex-wrap gap-1 mt-2">
-                  {matchedCat.default_impacts.slice(0, 8).map((di: DefaultImpact) => (
-                    <span key={di.instrument_id}
-                      className={clsx('text-xs px-1.5 py-0.5 rounded border font-mono',
-                        di.typical_direction === 'bullish'  ? 'text-emerald-400 border-emerald-800/40 bg-emerald-900/20' :
-                        di.typical_direction === 'bearish'  ? 'text-red-400 border-red-800/40 bg-red-900/20' :
-                        'text-slate-400 border-slate-700 bg-slate-800/40')}>
-                      {di.typical_direction === 'bullish' ? '↑' : di.typical_direction === 'bearish' ? '↓' : '–'} {di.instrument_id}
-                      <span className="text-slate-600 ml-1">{(di.sensitivity * 100).toFixed(0)}%</span>
-                    </span>
-                  ))}
-                </div>
-              )}
             </div>
           )
         })()}