diff --git a/Ottimizzatore ITA.py b/Ottimizzatore ITA.py
index 795a82e..d34e7b5 100644
--- a/Ottimizzatore ITA.py	
+++ b/Ottimizzatore ITA.py	
@@ -125,6 +125,26 @@ def validate_returns_frame(df_returns: pd.DataFrame, threshold: float = 0.2):
         cols = ", ".join([f"{c} ({v:.0%})" for c, v in high_na.items()])
         print(f"[warn] Colonne con >{threshold:.0%} di NaN prima del fill: {cols}")
 
+# ---------------------------------
+# Utility per mappare Asset Class -> NEO
+# ---------------------------------
+def map_neo_asset_class(asset_class: str) -> str:
+    """Mappa l'Asset Class in un valore compatibile NEO."""
+    if not asset_class:
+        return ""
+    value = asset_class.strip().lower()
+    if "azion" in value:
+        return "Equity"
+    if "obblig" in value:
+        return "Fixed Income"
+    if "immobil" in value:
+        return "Real Estate"
+    if "metalli" in value or "materie" in value:
+        return "Commodities"
+    if "cript" in value or "crypto" in value:
+        return "Crypto"
+    return asset_class
+
 # ---------------------------------
 # Utility per R^2 sull’equity line
 # ---------------------------------
@@ -346,6 +366,8 @@ except SQLAlchemyError as e:
 # =========================
 template_path = os.path.join(INPUT_DIR, 'Template_Guardian.xls')
 template_df = pd.read_excel(template_path)
+template_neo_path = os.path.join(INPUT_DIR, 'Template NEO.csv')
+template_neo_df = pd.read_csv(template_neo_path, sep=';', dtype=str).fillna("")
 
 file_path = os.path.join(INPUT_DIR, 'Universo per ottimizzatore.xlsx')
 df = pd.read_excel(
@@ -402,6 +424,8 @@ riskfree_rate = 0.02
 optimized_weights = pd.DataFrame()
 per_asset_metrics = {}
 export_rows = []
+export_rows_neo = []
+neo_portfolio_name = "VAR6_5Y"
 
 for (years, target_vol), name in volatility_targets.items():
     period_start_date = end_date - pd.DateOffset(years=years)
@@ -537,6 +561,33 @@ for (years, target_vol), name in volatility_targets.items():
             output_df = results_full_df.reindex(columns=template_cols)
         export_rows.append(output_df)
 
+        # --- File CSV NEO (uno per portafoglio) ---
+        if name == neo_portfolio_name:
+            template_neo_cols = list(template_neo_df.columns)
+            if not template_neo_df.empty:
+                template_neo_defaults = template_neo_df.iloc[0].to_dict()
+            else:
+                template_neo_defaults = {col: "" for col in template_neo_cols}
+            neo_rows = []
+            for isin, weight in weights.items():
+                if weight > 0:
+                    row = dict(template_neo_defaults)
+                    row['Asset class'] = "Equity"
+                    row['InstrumentType'] = row.get('InstrumentType') or "ISIN"
+                    row['Instrument'] = isin
+                    row['Currency'] = row.get('Currency') or "EUR"
+                    row['Side'] = row.get('Side') or "Buy"
+                    row['Quantity'] = round(float(weight * 0.95), 4)
+                    row['Price'] = ""
+                    neo_rows.append(row)
+
+            neo_full_df = pd.DataFrame(neo_rows, columns=template_neo_cols)
+            if neo_full_df.empty:
+                output_neo_df = pd.DataFrame(columns=template_neo_cols)
+            else:
+                output_neo_df = neo_full_df.reindex(columns=template_neo_cols)
+            export_rows_neo.append(output_neo_df)
+
         # --- Pie chart asset allocation: salva in Output senza mostrare ---
         asset_allocations = {asset: 0 for asset in asset_class_limits}
         for isin, weight in weights.items():
@@ -689,3 +740,14 @@ with pd.ExcelWriter(combined_path, engine='openpyxl', mode='w') as writer:
         combined_df = template_df.copy()
     combined_df.to_excel(writer, sheet_name='Pesi Ottimizzati', index=False)
 print(f"Pesi ottimizzati salvati in un unico file/sheet: '{combined_path}'.")
+
+# =========================
+# EXPORT CSV NEO
+# =========================
+neo_combined_path = os.path.join(OUTPUT_DIR, f"{date_tag} Pesi ottimizzati NEO.csv")
+if export_rows_neo:
+    neo_combined_df = pd.concat(export_rows_neo, ignore_index=True)
+else:
+    neo_combined_df = pd.DataFrame(columns=template_neo_df.columns)
+neo_combined_df.to_csv(neo_combined_path, sep=';', index=False)
+print(f"Pesi ottimizzati NEO salvati in un unico CSV: '{neo_combined_path}'.")