diff --git a/src/screener/t_atr_ema.py b/src/screener/t_atr_ema.py
index 47a3192..6b1919e 100644
--- a/src/screener/t_atr_ema.py
+++ b/src/screener/t_atr_ema.py
@@ -91,7 +91,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
             
         bullish_signals = []
         
-        for ticker, current_price, current_volume, last_update in qualified_stocks:
+        for ticker, current_price, current_volume, last_update, stock_type in qualified_stocks:
             try:
                 # Get historical data based on interval
                 df = get_stock_data(ticker, start_date, end_date, interval)
@@ -110,6 +110,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
                         'target_price': signal_data['ema'],
                         'volume': current_volume,
                         'signal_date': signal_date,
+                        'stock_type': stock_type,  # Add stock type
                         'last_update': datetime.fromtimestamp(last_update/1000000000)
                     }
                     
diff --git a/src/utils/data_utils.py b/src/utils/data_utils.py
index 3b7a49e..148db30 100644
--- a/src/utils/data_utils.py
+++ b/src/utils/data_utils.py
@@ -28,7 +28,7 @@ def print_signal(signal_data: dict, signal_type: str = "🔍") -> None:
         signal_type (str): Emoji indicator for signal type (default: 🔍)
     """
     try:
-        print(f"\n{signal_type} {signal_data['ticker']} @ ${signal_data['entry_price']:.2f} on {signal_data['signal_date'].strftime('%Y-%m-%d %H:%M')}")
+        print(f"\n{signal_type} {signal_data['ticker']} ({signal_data['stock_type']}) @ ${signal_data['entry_price']:.2f} on {signal_data['signal_date'].strftime('%Y-%m-%d %H:%M')}")
         print(f"   Size: {signal_data['shares']} shares (${signal_data['position_size']:.2f})")
         print(f"   Stop: ${signal_data['stop_loss']:.2f} (7%) | Target: ${signal_data['target_price']:.2f}")
         print(f"   Risk/Reward: 1:{signal_data['risk_reward_ratio']:.1f} | Risk: ${abs(signal_data['risk_amount']):.2f}")
@@ -50,7 +50,7 @@ def get_qualified_stocks(start_date: datetime, end_date: datetime, min_price: fl
         min_volume (int): Minimum trading volume
         
     Returns:
-        list: List of tuples (ticker, price, volume, last_update)
+        list: List of tuples (ticker, price, volume, last_update, type)
     """
     try:
         start_ts = int(start_date.timestamp() * 1000000000)
@@ -60,12 +60,14 @@ def get_qualified_stocks(start_date: datetime, end_date: datetime, min_price: fl
             query = f"""
                 WITH filtered_data AS (
                     SELECT 
-                        ticker,
-                        window_start,
-                        close,
-                        volume,
-                        toDateTime(toDateTime(window_start/1000000000)) as trade_date
-                    FROM stock_db.stock_prices
+                        sp.ticker,
+                        sp.window_start,
+                        sp.close,
+                        sp.volume,
+                        t.type as stock_type,
+                        toDateTime(toDateTime(sp.window_start/1000000000)) as trade_date
+                    FROM stock_db.stock_prices sp
+                    JOIN stock_db.stock_tickers t ON sp.ticker = t.ticker
                     WHERE window_start BETWEEN {start_ts} AND {end_ts}
                     AND toDateTime(window_start/1000000000) <= now()
                     AND close BETWEEN {min_price} AND {max_price}
@@ -74,15 +76,17 @@ def get_qualified_stocks(start_date: datetime, end_date: datetime, min_price: fl
                 daily_data AS (
                     SELECT
                         ticker,
+                        stock_type,
                         toDate(trade_date) as date,
                         argMax(close, window_start) as daily_close,
                         sum(volume) as daily_volume
                     FROM filtered_data
-                    GROUP BY ticker, toDate(trade_date)
+                    GROUP BY ticker, stock_type, toDate(trade_date)
                 ),
                 latest_data AS (
                     SELECT 
                         ticker,
+                        any(stock_type) as stock_type,
                         argMax(daily_close, date) as last_close,
                         sum(daily_volume) as total_volume,
                         max(toUnixTimestamp(date)) as last_update
@@ -94,13 +98,14 @@ def get_qualified_stocks(start_date: datetime, end_date: datetime, min_price: fl
                     ticker,
                     last_close,
                     total_volume,
-                    last_update
+                    last_update,
+                    stock_type
                 FROM latest_data
                 ORDER BY ticker
             """
             
             result = client.query(query)
-            qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
+            qualified_stocks = [(row[0], row[1], row[2], row[3], row[4]) for row in result.result_rows]
             
             return qualified_stocks