diff --git a/src/screener/t_atr_ema_v2.py b/src/screener/t_atr_ema_v2.py
index ba144ab..3c29f5d 100644
--- a/src/screener/t_atr_ema_v2.py
+++ b/src/screener/t_atr_ema_v2.py
@@ -82,16 +82,23 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
             try:
                 df = get_stock_data(ticker, start_date, end_date, interval)
 
-                # **Check if DataFrame has expected columns**
+                # VALIDATION CHECKS - BEGIN
+                required_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
                 if df.empty:
-                    print(f"⚠️ No data found for {ticker}. Skipping.")
+                    print(f"⚠️ {ticker}: Empty DataFrame from get_stock_data()")
+                    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()}")
                     continue
 
-                missing_cols = [col for col in ["close", "open", "high", "low", "volume"] if col not in df.columns]
-                if missing_cols:
-                    print(f"⚠️ {ticker} data is missing columns: {missing_cols}. Skipping.")
-                    print(df.head())  # Debugging output
+                if 'close' not in df.columns:
+                    print(f"⚠️ {ticker}: 'close' column missing, cannot process")
+                    print(f"Available columns: {df.columns.tolist()}")
                     continue
+                # VALIDATION CHECKS - END
 
                 results = indicator.calculate(df)
                 last_row = results.iloc[-1]
@@ -155,4 +162,4 @@ def run_atr_ema_target_scanner(min_price: float, max_price: float, min_volume: i
             print("❌ No bullish signals found.")
 
     except Exception as e:
-        print(f"❌ Error during scan: {e}")
\ No newline at end of file
+        print(f"❌ Error during scan: {e}")
diff --git a/src/screener/t_sunnyband.py b/src/screener/t_sunnyband.py
index 130f1bb..59a23e9 100644
--- a/src/screener/t_sunnyband.py
+++ b/src/screener/t_sunnyband.py
@@ -88,9 +88,12 @@ 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
+        if df.empty or 'close' not in df.columns:
+            return pd.DataFrame()
+            
         if df['date'].dtype == object:
             df['date'] = pd.to_datetime(df['date'])