From 4f545d50410c38aa28d3ace92353a37d0ad93b4f Mon Sep 17 00:00:00 2001
From: "Bobby Abellana (aider)" <bobby@abellana.work>
Date: Fri, 7 Feb 2025 08:56:00 -0800
Subject: [PATCH] fix: Align except block with try block in get_stock_data
 function

---
 src/screener/t_sunnyband.py | 159 ++++++++++++++++++------------------
 1 file changed, 80 insertions(+), 79 deletions(-)

diff --git a/src/screener/t_sunnyband.py b/src/screener/t_sunnyband.py
index f659a45..e6e678f 100644
--- a/src/screener/t_sunnyband.py
+++ b/src/screener/t_sunnyband.py
@@ -33,89 +33,90 @@ def get_interval_choice() -> str:
 
 def get_stock_data(ticker: str, start_date: datetime, end_date: datetime, interval: str) -> pd.DataFrame:
     """Fetch stock data from the database"""
-    client = create_client()
-    
-    # Calculate proper date range (looking back from today)
-    end_date = datetime.now()
-    start_date = end_date - timedelta(days=60)  # 60 days of history
-    
-    if interval == "daily":
-        table = "stock_prices_daily"
-        date_col = "date"
-        query = f"""
-            SELECT 
-                {date_col} as date,
-                open,
-                high,
-                low,
-                close,
-                volume
-            FROM stock_db.{table}
-            WHERE ticker = '{ticker}'
-            AND {date_col} BETWEEN '{start_date.date()}' AND '{end_date.date()}'
-            ORDER BY date ASC
-        """
-    else:
-        table = "stock_prices"
-        date_col = "window_start"
-        minutes_map = {
-            "5min": 5,
-            "15min": 15,
-            "30min": 30,
-            "1hour": 60
-        }
-        minutes = minutes_map[interval]
+    try:
+        client = create_client()
         
-        # Get 5-minute bars and resample them to the desired interval
-        query = f"""
-            SELECT 
-                fromUnixTimestamp(intDiv(window_start/1000000000, 300) * 300) as interval_start,
-                min(open) as open,
-                max(high) as high,
-                min(low) as low,
-                argMax(close, window_start) as close,
-                sum(volume) as volume
-            FROM stock_db.{table}
-            WHERE ticker = '{ticker}'
-            AND window_start/1000000000 BETWEEN 
-                toUnixTimestamp('{start_date.date()}') AND 
-                toUnixTimestamp('{end_date.date()}')
-            GROUP BY interval_start
-            ORDER BY interval_start ASC
-        """
-    
-    print("\nExecuting Query:")
-    print(query)  # Debugging: Print the query to verify its correctness
-    
-    result = client.query(query)
-    if not result.result_rows:
-        print(f"No data found for {ticker}")
-        return pd.DataFrame()
+        # Calculate proper date range (looking back from today)
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=60)  # 60 days of history
         
-    df = pd.DataFrame(
-        result.result_rows,
-        columns=['date', 'open', 'high', 'low', 'close', 'volume']
-    )
+        if interval == "daily":
+            table = "stock_prices_daily"
+            date_col = "date"
+            query = f"""
+                SELECT 
+                    {date_col} as date,
+                    open,
+                    high,
+                    low,
+                    close,
+                    volume
+                FROM stock_db.{table}
+                WHERE ticker = '{ticker}'
+                AND {date_col} BETWEEN '{start_date.date()}' AND '{end_date.date()}'
+                ORDER BY date ASC
+            """
+        else:
+            table = "stock_prices"
+            date_col = "window_start"
+            minutes_map = {
+                "5min": 5,
+                "15min": 15,
+                "30min": 30,
+                "1hour": 60
+            }
+            minutes = minutes_map[interval]
+            
+            # Get 5-minute bars and resample them to the desired interval
+            query = f"""
+                SELECT 
+                    fromUnixTimestamp(intDiv(window_start/1000000000, 300) * 300) as interval_start,
+                    min(open) as open,
+                    max(high) as high,
+                    min(low) as low,
+                    argMax(close, window_start) as close,
+                    sum(volume) as volume
+                FROM stock_db.{table}
+                WHERE ticker = '{ticker}'
+                AND window_start/1000000000 BETWEEN 
+                    toUnixTimestamp('{start_date.date()}') AND 
+                    toUnixTimestamp('{end_date.date()}')
+                GROUP BY interval_start
+                ORDER BY interval_start ASC
+            """
+        
+        print("\nExecuting Query:")
+        print(query)  # Debugging: Print the query to verify its correctness
+        
+        result = client.query(query)
+        if not result.result_rows:
+            print(f"No data found for {ticker}")
+            return pd.DataFrame()
+            
+        df = pd.DataFrame(
+            result.result_rows,
+            columns=['date', 'open', 'high', 'low', 'close', 'volume']
+        )
 
-    if interval != "daily" and interval != "5min":
-        # Resample to desired interval
-        df.set_index('date', inplace=True)
-        minutes = minutes_map[interval]
-        rule = f'{minutes}T'
-        
-        df = df.resample(rule).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        }).dropna()
-        
-        df.reset_index(inplace=True)
-        
-    return df
+        if interval != "daily" and interval != "5min":
+            # Resample to desired interval
+            df.set_index('date', inplace=True)
+            minutes = minutes_map[interval]
+            rule = f'{minutes}T'
+            
+            df = df.resample(rule).agg({
+                'open': 'first',
+                'high': 'max',
+                'low': 'min',
+                'close': 'last',
+                'volume': 'sum'
+            }).dropna()
+            
+            df.reset_index(inplace=True)
+            
+        return df
 
-except Exception as e:
+    except Exception as e:
         print(f"Error fetching data for {ticker}: {str(e)}")
         return pd.DataFrame()