Update geo_macro.py

geo_macro.py

@@ -304,7 +304,7 @@ class UnifiedMarketDataDownloader:
             close_data.columns = [ticker_to_name.get(col, col) for col in close_data.columns]
             
             # Remove any columns that failed to download
-            close_data = close_data.dropna(axis=1, how='all')
+            close_data = close_data(axis=1, how='all')
             
             print(f"   ✅ Successfully downloaded {len(close_data.columns)} series")
             return close_data
@@ -359,7 +359,7 @@ class UnifiedMarketDataDownloader:
                         df = pd.DataFrame(data['observations'])
                         df['value'] = pd.to_numeric(df['value'], errors='coerce')
                         df['date'] = pd.to_datetime(df['date'])
-                        series = df.set_index('date')['value'].dropna()
+                        series = df.set_index('date')['value']
                         
                         if len(series) > 10:
                             economic_data[name] = series
@@ -372,31 +372,32 @@ class UnifiedMarketDataDownloader:
                 continue
         
         return economic_data
-    
+
     def _merge_all_data(self, market_data, economic_data, start_date, end_date):
-        """Merge all data sources into single DataFrame with proper filling"""
+        """Merge all data sources into single DataFrame with ffill + bfill"""
         
-        # Create base date range (business days)
+        # Create a full daily date range (including weekends/holidays)
         date_range = pd.date_range(start=start_date, end=end_date, freq='D')
         
-        # Start with market data
-        unified = market_data.copy()
-        unified = unified.reindex(date_range)
+        # Initialize unified DataFrame with full date index
+        unified = pd.DataFrame(index=date_range)
+        
+        # Add market data (already date-indexed from yfinance)
+        for col in market_data.columns:
+            unified[col] = market_data[col].reindex(date_range)
         
-        # Add economic data
+        # Add FRED economic data
         for name, series in economic_data.items():
             unified[name] = series.reindex(date_range)
         
-        # Forward fill then backward fill (handles weekends/holidays)
+        # Forward-fill, then backward-fill to handle leading/trailing NaNs
         unified = unified.ffill().bfill()
         
-        # Drop any columns that are still all NaN
+        # Remove any columns that are still entirely NaN (e.g., failed downloads)
         unified = unified.dropna(axis=1, how='all')
         
         return unified
 
-
-
 '''
 
 # ======================