diff --git a/src/screener/t_candlestick.py b/src/screener/t_candlestick.py
index f7fa1af..d6cb447 100644
--- a/src/screener/t_candlestick.py
+++ b/src/screener/t_candlestick.py
@@ -44,11 +44,14 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
     Returns:
         list: List of tuples (signal, date, signal_data) for each signal found
     """
-    if len(df) < 5:  # Need at least 5 bars for pattern recognition
+    if len(df) < 14:  # Need at least 14 bars for ATR calculation
         return []
     
     signals = []
     
+    # Calculate ATR first
+    atr = talib.ATR(df['high'].values, df['low'].values, df['close'].values, timeperiod=14)
+    
     # Use selected patterns or all patterns if none selected
     patterns_to_scan = {k: v for k, v in CANDLESTICK_PATTERNS.items() 
                        if selected_patterns is None or k in selected_patterns}
@@ -64,7 +67,7 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
         )
     
     # Look for signals across all candles
-    for i in range(len(df)):
+    for i in range(14, len(df)):  # Start after ATR lookback period
         found_patterns = []
         
         for pattern_name, pattern_values in pattern_signals.items():
@@ -76,11 +79,15 @@ def check_entry_signal(df: pd.DataFrame, selected_patterns: list = None) -> list
             # Calculate basic target and stop levels using recent price action
             current_price = df.iloc[i]['close']
             recent_low = df.iloc[max(0, i-5):i+1]['low'].min()  # Look back 5 bars for stop
-            atr = talib.ATR(df['high'].values, df['low'].values, df['close'].values, timeperiod=14)
             
-            # Use 2x ATR for target and 1x ATR for stop
-            target_price = current_price + (2 * atr[i] if not pd.isna(atr[i]) else current_price * 0.02)
-            stop_loss = max(recent_low, current_price - (atr[i] if not pd.isna(atr[i]) else current_price * 0.01))
+            # Default to percentage-based calculations
+            atr_value = atr[i]
+            if pd.isna(atr_value):
+                target_price = current_price * 1.02  # 2% target
+                stop_loss = current_price * 0.99    # 1% stop
+            else:
+                target_price = current_price + (2 * atr_value)
+                stop_loss = max(recent_low, current_price - atr_value)
             
             signal_data = {
                 'price': current_price,