diff --git a/src/utils/data_utils.py b/src/utils/data_utils.py
index 89ebff9..53f9faf 100644
--- a/src/utils/data_utils.py
+++ b/src/utils/data_utils.py
@@ -139,69 +139,3 @@ def process_signal_data(ticker: str, signal_data: dict, current_volume: int,
         })
     
     return entry_data
-
-        
-        result = client.query(query)
-        
-        if not result.result_rows:
-            return pd.DataFrame()
-            
-        # Create base DataFrame
-        df = pd.DataFrame(
-            result.result_rows,
-            columns=['date', 'open', 'high', 'low', 'close', 'volume']
-        )
-
-        # Convert numeric columns
-        numeric_columns = ['open', 'high', 'low', 'close', 'volume']
-        for col in numeric_columns:
-            df[col] = pd.to_numeric(df[col], errors='coerce')
-
-        # Convert date column
-        df['date'] = pd.to_datetime(df['date'])
-        
-        # Set date as index for resampling
-        df.set_index('date', inplace=True)
-        
-        # Resample based on interval
-        if interval == 'daily':
-            rule = '1D'
-        elif interval == '5min':
-            rule = '5T'
-        elif interval == '15min':
-            rule = '15T'
-        elif interval == '30min':
-            rule = '30T'
-        elif interval == '1hour':
-            rule = '1H'
-        else:
-            rule = '1D'  # Default to daily
-            
-        resampled = df.resample(rule).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        }).dropna()
-        
-        # Reset index to get date as column
-        resampled.reset_index(inplace=True)
-        
-        # Filter to requested date range
-        mask = (resampled['date'] >= start_date + timedelta(days=89)) & (resampled['date'] <= end_date)
-        resampled = resampled.loc[mask]
-        
-        # Handle null values
-        if resampled['close'].isnull().any():
-            print(f"Warning: Found null values in close prices")
-            resampled = resampled.dropna(subset=['close'])
-
-        if resampled.empty or 'close' not in resampled.columns:
-            return pd.DataFrame()
-            
-        return resampled
-
-    except Exception as e:
-        print(f"Error fetching {ticker} data: {str(e)}")
-        return pd.DataFrame()