Upload 3 files

app.py

@@ -36,6 +36,10 @@ app = Flask(__name__)
 CORS(app)
 
 
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({"status": "running", "message": "StockBuddy API is live!"})
+
 # [OPT-A] Lightweight TF smoke-test instead of building & running a full LSTM
 def validate_tensorflow():
     """Quick TensorFlow sanity-check (no model created, no GPU required)."""
@@ -492,16 +496,24 @@ def diagnose():
 
         api_status = {}
         try:
-            import yfinance as yf
-            test_stock = yf.Ticker("AAPL")
-            test_df = test_stock.history(period="1d")
-            api_status["yfinance"] = {
-                "status": "OK" if not test_df.empty else "No data",
-                "has_data": not test_df.empty,
-                "error": None
+            url    = "https://www.alphavantage.co/query"
+            params = {
+                "function":   "TIME_SERIES_DAILY",
+                "symbol":     "AAPL",
+                "apikey":     stock_model.ALPHAVANTAGE_API_KEY,
+                "outputsize": "compact",
+                "datatype":   "json",
+            }
+            resp = requests.get(url, params=params)
+            rj   = resp.json()
+            api_status["alpha_vantage"] = {
+                "status_code": resp.status_code,
+                "has_data":    "Time Series (Daily)" in rj,
+                "error":       rj.get("Error Message") or rj.get("Note") or rj.get("Information")
+                               if "Time Series (Daily)" not in rj else None,
             }
         except Exception as e:
-            api_status["yfinance"] = {"error": str(e)}
+            api_status["alpha_vantage"] = {"error": str(e)}
 
         try:
             headers = {"X-Finnhub-Token": stock_model.FINNHUB_API_KEY}

model.py

@@ -44,29 +44,67 @@ FINNHUB_API_KEY     = "cu5gvghr01qqj8u6iau0cu5gvghr01qqj8u6iaug"
 #                     STOCK PRICE PREDICTION FUNCTIONS
 # =============================================================================
 
-import yfinance as yf
-
 def fetch_stock_data(symbol, outputsize="full"):
-    try:
-        stock = yf.Ticker(symbol)
-        # Use 'max' or '5y' for full, '1y' for compact
-        period = "5y" if outputsize == "full" else "1y"
-        df = stock.history(period=period)
-        
-        if df.empty:
-            raise ValueError(f"Unable to fetch data for symbol '{symbol}'. Please verify the symbol.")
-            
-        df.index = pd.to_datetime(df.index).tz_localize(None)
-        
-        latest_date = df.index[-1]
-        print(f"\nLatest closing price for {symbol} "
-              f"(as of {latest_date.strftime('%Y-%m-%d')}): ${df['Close'].iloc[-1]:.2f}")
-              
-        # Add lightweight technical indicators
-        df = add_technical_indicators(df)
-        return df
-    except Exception as e:
-        raise ValueError(f"Error fetching data for {symbol}: {e}")
+    url = "https://www.alphavantage.co/query"
+    params = {
+        "function":   "TIME_SERIES_DAILY",
+        "symbol":     symbol,
+        "apikey":     ALPHAVANTAGE_API_KEY,
+        "outputsize": outputsize,
+        "datatype":   "json",
+    }
+    response = requests.get(url, params=params)
+    data = response.json()
+
+    if "Time Series (Daily)" not in data:
+        if "Error Message" in data:
+            raise ValueError(
+                f"Symbol '{symbol}' not found. Please verify the stock symbol.")
+        elif "Note" in data:
+            raise ValueError("API request limit reached. Please try again in a minute.")
+        elif "Information" in data:
+            raise ValueError(f"Alpha Vantage API daily limit reached. Please try again tomorrow. Details: {data['Information']}")
+        else:
+            raise ValueError(
+                f"Unable to fetch data for symbol '{symbol}'. Please verify the symbol.")
+
+    ts = data["Time Series (Daily)"]
+
+    df = pd.DataFrame.from_dict(ts, orient="index")
+    df.index = pd.to_datetime(df.index)
+    df.sort_index(inplace=True)
+
+    for col in ["1. open", "2. high", "3. low", "4. close", "5. volume"]:
+        if col in df.columns:
+            df[col] = df[col].astype(float)
+
+    df = df.rename(columns={
+        "1. open":   "Open",
+        "2. high":   "High",
+        "3. low":    "Low",
+        "4. close":  "Close",
+        "5. volume": "Volume",
+    })
+
+    latest_date    = df.index[-1]
+    today          = pd.Timestamp.now().normalize()
+    market_closed_days = 0
+    if today.dayofweek >= 5:
+        market_closed_days = today.dayofweek - 4
+    elif today.hour < 16:
+        market_closed_days = 1
+    expected_latest = today - pd.Timedelta(days=market_closed_days)
+    date_diff = (expected_latest - latest_date).days
+    if date_diff > 5:
+        print(f"WARNING: Latest data for {symbol} is from "
+              f"{latest_date.strftime('%Y-%m-%d')} ({date_diff} days old).")
+
+    print(f"\nLatest closing price for {symbol} "
+          f"(as of {latest_date.strftime('%Y-%m-%d')}): ${df['Close'].iloc[-1]:.2f}")
+
+    # Add lightweight technical indicators
+    df = add_technical_indicators(df)
+    return df
 
 
 # [OPT-2] Reduced feature set: 11 → 6  (Close, RSI, SMA5, MACD, Upper_Band, ROC)

requirements.txt

@@ -11,4 +11,3 @@ transformers==4.33.2
 plotly==5.17.0
 gunicorn==21.2.0
 tf-keras==2.15.0
-yfinance==0.2.40