Fix: Explicit Sector Truth Tables to prevent AI Hallucination

ai/ai_reporter.py

@@ -50,6 +50,7 @@ INSTRUCTION: Start your commentary exactly with the header: "Market Commentary -
             selection_effect=attribution_report.selection_effect * 100,
             top_contributors=json.dumps(attribution_report.top_contributors, indent=2),
             top_detractors=json.dumps(attribution_report.top_detractors, indent=2),
+            sector_positioning=json.dumps(attribution_report.sector_exposure, indent=2), # Truth Table Injection
             current_date=current_date # Pass date to template
         )
         

ai/prompts.py

@@ -37,6 +37,9 @@ Write a "Trailing 30-Day Risk & Performance Attribution" report relative to the
 **Top Active Detractors (JSON)**:
 {top_detractors}
 
+**Sector Positioning (The "Truth Table")**:
+{sector_positioning}
+
 ## Guidelines for the Narrative:
 1. **Timeframe**: Use the EXACT date provided: "{current_date}".
 2. **Ticker Validation**: Use the Ticker symbols exactly as listed.

analytics/attribution.py

@@ -120,6 +120,28 @@ class AttributionEngine:
         top_contributors = build_truth_table(sorted_contrib, 5)
         top_detractors = build_truth_table(sorted_contrib.sort_values(by='contribution', ascending=True), 5)
         
+        # Build Sector Exposure Truth Table
+        sector_exposure = []
+        for sector, data in sector_groups:
+            w_p = data['wp'].sum()
+            w_b = data['wb'].sum()
+            
+            if w_p == 0.0 and w_b > 0.0:
+                status = "Excluded"
+            elif w_p < w_b:
+                status = "Underweight"
+            elif w_p > w_b:
+                status = "Overweight"
+            else:
+                status = "Neutral"
+                
+            sector_exposure.append({
+                "Sector": sector,
+                "Portfolio_Weight": f"{w_p:.2%}",
+                "Benchmark_Weight": f"{w_b:.2%}",
+                "Status": status
+            })
+
         # Narrative skeleton (to be filled by AI)
         narrative_raw = (
             f"Total Active Return: {(total_allocation + total_selection + total_interaction):.4f}. "
@@ -133,5 +155,6 @@ class AttributionEngine:
             total_active_return=(total_allocation + total_selection + total_interaction),
             top_contributors=top_contributors,
             top_detractors=top_detractors,
+            sector_exposure=sector_exposure,
             narrative=narrative_raw
         )

core/schema.py

@@ -97,4 +97,5 @@ class AttributionReport(BaseModel):
     total_active_return: float
     top_contributors: List[Dict]
     top_detractors: List[Dict]
+    sector_exposure: Optional[List[Dict]] = Field(default_factory=list, description="Sector Level Attribution (Truth Table)")
     narrative: str