Update app.py

app.py

@@ -77,6 +77,19 @@ def compare_to_index(stock_ratios, index_averages):
     return comparison, combined_score
 
 
+def calculate_combined_scores_for_stocks(stocks, index_averages):
+    scores = []
+
+    for ticker_symbol in stocks:
+        try:
+            stock_data = fetch_stock_data(ticker_symbol)
+            _, combined_score = compare_to_index(stock_data, index_averages)
+            scores.append([ticker_symbol, combined_score])
+        except Exception as e:
+            print(f"Error fetching data for {ticker_symbol}: {e}")
+    
+    scores_df = pd.DataFrame(scores, columns=['Stock', 'Combined Score'])
+    return scores_df
 
 
 # Load S&P 500 averages
@@ -88,19 +101,19 @@ st.title('S&P 500 Stock Comparison Tool')
 # Selection of stock from predefined list
 ticker_symbol = st.selectbox('Select a stock', options=stocks)
 
-if ticker_symbol:
-    with st.spinner(f'Fetching data for {ticker_symbol}...'):
-        stock_data = fetch_stock_data(ticker_symbol)
-        comparison, combined_score = compare_to_index(stock_data, sp500_averages)
-        
-        st.write(f"Valuation Comparison for {ticker_symbol}:")
-        for ratio, result in comparison.items():
-            if ratio != 'Combined Score':  # Avoid repeating the combined score in the loop
-                st.write(f"{ratio}: {result}")
-        
-        # Display the combined score with interpretation
-        score_interpretation = "Undervalued" if combined_score > 0 else "Overvalued" if combined_score < 0 else "Neutral"
-        st.metric(label="Combined Valuation Score", value=f"{combined_score} ({score_interpretation})")
+# Assuming 'stocks' is your list of stock ticker symbols
+if st.button('Calculate Combined Scores'):
+    scores_df = calculate_combined_scores_for_stocks(stocks, sp500_averages)
+    
+    # Sort by 'Combined Score' to prioritize viewing
+    scores_df.sort_values(by='Combined Score', ascending=False, inplace=True)
+    
+    # Display each stock and its combined score with color coding
+    for _, row in scores_df.iterrows():
+        color = "green" if row['Combined Score'] > 0 else "red" if row['Combined Score'] < 0 else "black"
+        score_msg = f"**{row['Stock']}**: <span style='color: {color};'>{row['Combined Score']}</span>"
+        st.markdown(score_msg, unsafe_allow_html=True)
+