diff --git a/src/screener/t_atr_ema_v2.py b/src/screener/t_atr_ema_v2.py
index 3c29f5d..c7a1371 100644
--- a/src/screener/t_atr_ema_v2.py
+++ b/src/screener/t_atr_ema_v2.py
@@ -80,25 +80,41 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
             last_update = row["window_start"]
 
             try:
+                # Get historical data
                 df = get_stock_data(ticker, start_date, end_date, interval)
-
-                # VALIDATION CHECKS - BEGIN
-                required_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
+                
+                # Enhanced validation with debugging
                 if df.empty:
-                    print(f"⚠️ {ticker}: Empty DataFrame from get_stock_data()")
+                    print(f"⚠️ {ticker}: Empty DataFrame")
                     continue
                     
-                missing_cols = set(required_columns) - set(df.columns)
-                if missing_cols:
-                    print(f"⚠️ {ticker}: Missing columns {missing_cols} in DataFrame")
-                    print(f"Actual columns: {df.columns.tolist()}")
+                # Ensure DataFrame has required columns and proper types
+                required_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
+                
+                # Print column info for debugging
+                print(f"\nProcessing {ticker}")
+                print(f"Columns present: {df.columns.tolist()}")
+                
+                # Convert columns to numeric if needed
+                for col in ['open', 'high', 'low', 'close', 'volume']:
+                    if col in df.columns:
+                        df[col] = pd.to_numeric(df[col], errors='coerce')
+                
+                # Verify data validity
+                if df['close'].isnull().any():
+                    print(f"⚠️ {ticker}: Contains null values in close price")
+                    continue
+                    
+                if len(df) < 50:
+                    print(f"⚠️ {ticker}: Insufficient data points ({len(df)})")
                     continue
 
-                if 'close' not in df.columns:
-                    print(f"⚠️ {ticker}: 'close' column missing, cannot process")
-                    print(f"Available columns: {df.columns.tolist()}")
+                # Calculate indicator with validated data
+                results = indicator.calculate(df.copy())  # Use copy to prevent modifications
+                
+                if results.empty:
+                    print(f"⚠️ {ticker}: No valid indicator results")
                     continue
-                # VALIDATION CHECKS - END
 
                 results = indicator.calculate(df)
                 last_row = results.iloc[-1]
diff --git a/src/screener/t_sunnyband.py b/src/screener/t_sunnyband.py
index 59a23e9..af44d63 100644
--- a/src/screener/t_sunnyband.py
+++ b/src/screener/t_sunnyband.py
@@ -88,9 +88,18 @@ def get_stock_data(ticker: str, start_date: datetime, end_date: datetime, interv
         df = pd.DataFrame(
             result.result_rows,
             columns=['date', 'open', 'high', 'low', 'close', 'volume']
-        ).dropna(subset=['close'])  # Filter out rows with null close prices
-        
-        # Convert date column if needed
+        )
+
+        # Convert numeric columns
+        numeric_columns = ['open', 'high', 'low', 'close', 'volume']
+        for col in numeric_columns:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # Handle null values
+        if df['close'].isnull().any():
+            print(f"Warning: Found null values in close prices")
+            df = df.dropna(subset=['close'])
+
         if df.empty or 'close' not in df.columns:
             return pd.DataFrame()