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backend/app/api/portfolio.py

@@ -100,3 +100,45 @@ def delete_portfolio_item(item_id: int, db: Session = Depends(get_db), current_u
     db.delete(item)
     db.commit()
     return {"message": "Stock sold/removed successfully"}
+
+@router.get("/recommendations/{symbol}")
+def get_recommendations(symbol: str, current_user: User = Depends(get_current_user)):
+    try:
+        ticker = yf.Ticker(symbol)
+        recs = ticker.recommendations
+        
+        if recs is None or recs.empty:
+            return {"error": "No recommendation data available", "confidence": 0}
+        
+        # Get the most recent month's data (period '0m' is usually index 0)
+        latest = recs.iloc[0]
+        
+        strong_buy = int(latest.get("strongBuy", 0))
+        buy = int(latest.get("buy", 0))
+        hold = int(latest.get("hold", 0))
+        sell = int(latest.get("sell", 0))
+        strong_sell = int(latest.get("strongSell", 0))
+        
+        total = strong_buy + buy + hold + sell + strong_sell
+        
+        if total == 0:
+            return {"error": "No recommendations found", "confidence": 0}
+            
+        # Calculate confidence score
+        # Strong Buy = 100, Buy = 75, Hold = 50, Sell = 25, Strong Sell = 0
+        score = (strong_buy * 100 + buy * 75 + hold * 50 + sell * 25) / total
+        
+        return {
+            "symbol": symbol,
+            "period": str(latest.get("period", "0m")),
+            "strongBuy": strong_buy,
+            "buy": buy,
+            "hold": hold,
+            "sell": sell,
+            "strongSell": strong_sell,
+            "total": total,
+            "confidence": round(score, 1)
+        }
+    except Exception as e:
+        print(f"Error fetching recommendations for {symbol}: {e}")
+        return {"error": str(e), "confidence": 0}