fix: improve stock price query recognition — handle typos (APPL→AAPL), add share-of phrasing, add company name aliases for ticker extraction

Made-with: Cursor

agent/graph.py

@@ -169,18 +169,44 @@ def _get_client() -> anthropic.Anthropic:
 def _extract_ticker(query: str, fallback: str = None) -> str | None:
     """
     Extracts the most likely stock ticker from a query string.
-    Looks for 1-5 uppercase letters.
+    Handles typos (APPL→AAPL), company names (APPLE→AAPL), and "share of TICKER" phrasing.
     Returns fallback (default None) if no ticker found.
     Pass fallback='SPY' for market queries that require a symbol.
     """
-    words = query.upper().split()
+    # Common misspellings and aliases
+    TICKER_CORRECTIONS = {
+        "APPL": "AAPL",
+        "APPL.": "AAPL",
+        "APPLE": "AAPL",
+        "GOOG": "GOOGL",
+        "GOOGLE": "GOOGL",
+        "ALPHABET": "GOOGL",
+        "AMAZON": "AMZN",
+        "MICROSOFT": "MSFT",
+        "NVIDIA": "NVDA",
+        "TESLA": "TSLA",
+        "META": "META",
+        "FACEBOOK": "META",
+    }
+
+    message = query.strip()
+    msg_upper = message.upper()
+
+    # Pattern: "share of TICKER" or "shares of TICKER" — check first
+    share_of_match = re.search(r"share[s]?\s+of\s+([A-Z]{1,5})", msg_upper)
+    if share_of_match:
+        candidate = share_of_match.group(1)
+        return TICKER_CORRECTIONS.get(candidate, candidate)
+
+    words = msg_upper.split()
     known_tickers = {"AAPL", "MSFT", "NVDA", "TSLA", "GOOGL", "GOOG", "AMZN",
-                     "META", "NFLX", "SPY", "QQQ", "BRK", "BRKB"}
+                     "META", "NFLX", "SPY", "QQQ", "BRK", "BRKB", "VTI"}
 
     for word in words:
         clean = re.sub(r"[^A-Z]", "", word)
-        if clean in known_tickers:
-            return clean
+        corrected = TICKER_CORRECTIONS.get(clean, clean)
+        if corrected in known_tickers:
+            return corrected
 
     for word in words:
         clean = re.sub(r"[^A-Z]", "", word)
@@ -202,7 +228,7 @@ def _extract_ticker(query: str, fallback: str = None) -> str | None:
             "COULD", "SHOULD", "MIGHT", "SHALL", "ONLY", "ALSO",
             "SINCE", "WHILE", "STILL", "AGAIN", "THOSE", "OTHER",
         }:
-            return clean
+            return TICKER_CORRECTIONS.get(clean, clean)
 
     return fallback
 
@@ -777,8 +803,10 @@ async def classify_node(state: AgentState) -> AgentState:
     # Check BEFORE performance/portfolio fallback. User asking about market price of a ticker.
     stock_price_kws = [
         "stock price", "share price", "price of", "current price",
+        "share of", "shares of", "price for", "stock for", "trading for",
+        "worth today", "per share",
         "what is aapl", "what is msft", "what is nvda", "what is tsla",
-        "what is googl", "what is amzn", "what is meta",
+        "what is googl", "what is amzn", "what is meta", "what is vti",
         "trading at", "price today", "how much is", "ticker", "quote",
         "what's the stock price", "whats the stock price",
     ]