optimize search query

app/main.py

@@ -64,32 +64,26 @@ def calculate_score(item: Dict, search_query: str) -> int:
     symbol_lower = item['symbol'].lower()
     query_lower = search_query.lower()
 
-    # Exact symbol match
+    # Base priority calculations
     if symbol_lower == query_lower:
-        return PRIORITY_STRATEGIES['exact_symbol_match']
+        base_score = PRIORITY_STRATEGIES['exact_symbol_match']
+    elif symbol_lower.startswith(query_lower):
+        base_score = PRIORITY_STRATEGIES['symbol_prefix_match']
+    elif name_lower == query_lower:
+        base_score = PRIORITY_STRATEGIES['exact_name_match']
+    elif name_lower.startswith(query_lower):
+        base_score = PRIORITY_STRATEGIES['name_prefix_match']
+    elif query_lower in symbol_lower:
+        base_score = PRIORITY_STRATEGIES['symbol_contains']
+    elif query_lower in name_lower:
+        base_score = PRIORITY_STRATEGIES['name_contains']
+    else:
+        base_score = len(PRIORITY_STRATEGIES)
 
-    # Symbol prefix match
-    if symbol_lower.startswith(query_lower):
-        return PRIORITY_STRATEGIES['symbol_prefix_match']
+    # Apply penalty if the symbol contains a dot
+    dot_penalty = 1 if '.' in symbol_lower else 0
 
-    # Exact name match
-    if name_lower == query_lower:
-        return PRIORITY_STRATEGIES['exact_name_match']
-    
-    # Name prefix match
-    if name_lower.startswith(query_lower):
-        return PRIORITY_STRATEGIES['name_prefix_match']
-    
-    # Symbol contains query
-    if query_lower in symbol_lower:
-        return PRIORITY_STRATEGIES['symbol_contains']
-    
-    # Name contains query
-    if query_lower in name_lower:
-        return PRIORITY_STRATEGIES['name_contains']
-    
-    # Fallback
-    return len(PRIORITY_STRATEGIES)
+    return base_score + dot_penalty
 
 
 @contextmanager
@@ -116,13 +110,14 @@ with db_connection(STOCK_DB) as cursor:
   cursor.execute("SELECT DISTINCT symbol FROM stocks")
   symbols = [row[0] for row in cursor.fetchall()]
 
-  cursor.execute("SELECT symbol, name, type FROM stocks")
+  cursor.execute("SELECT symbol, name, type, marketCap FROM stocks")
   raw_data = cursor.fetchall()
   stock_list_data = [{
     'symbol': row[0],
     'name': row[1],
     'type': row[2].capitalize(),
-  } for row in raw_data]
+    'marketCap': row[3],
+  } for row in raw_data if row[3] is not None]
 #------End Stocks DB------------#
 
 #------Start ETF DB------------#
@@ -1805,16 +1800,23 @@ async def get_stock(
     # Precompile case-insensitive regex for faster matching
     search_pattern = re.compile(re.escape(query.lower()), re.IGNORECASE)
 
-    # Optimized filtering and sorting
-    results = sorted(
-        (
-            item for item in searchbar_data 
-            if search_pattern.search(item['name']) or search_pattern.search(item['symbol'])
-        ),
-        key=lambda item: calculate_score(item, query)
-    )[:5]
-    return JSONResponse(content=orjson.loads(orjson.dumps(results)))
+    # Filter items based on the search pattern (ignore items where neither name nor symbol match)
+    filtered_data = [
+        item for item in searchbar_data
+        if search_pattern.search(item['name']) or search_pattern.search(item['symbol'])
+    ]
 
+    # Sort by the calculated score, giving exact symbol matches the highest priority,
+    # and then by descending marketCap for other matches
+    results = sorted(
+        filtered_data,
+        key=lambda item: (
+            calculate_score(item, query),
+            0 if item.get('marketCap') is None else -item['marketCap']
+        )
+    )[:5]
+
+    return JSONResponse(content=orjson.loads(orjson.dumps(results)))