Update app.py

app.py

@@ -42,21 +42,40 @@ def fetch_stock_data(ticker_symbol):
 
 def compare_to_index(stock_ratios, index_averages):
  comparison = {}
+ undervalued_count = 0
+ overvalued_count = 0
+ 
  for ratio, value in stock_ratios.items():
- # Ensure the ratio exists in the DataFrame
  if ratio in index_averages.index and value is not None:
  average = index_averages.loc[ratio]['Average']
- # Default interpretation for most ratios (higher = overvalued)
- interpretation = 'Overvalued' if value > average else 'Undervalued'
+ # Interpretation for most ratios (higher = overvalued)
+ if value > average:
+ interpretation = 'Overvalued'
+ overvalued_count += 1
+ else:
+ interpretation = 'Undervalued'
+ undervalued_count += 1
  
- # Invert interpretation for specific ratios like P/B Ratio, Book-to-Market Ratio
- if ratio in ['Book-to-Market Ratio']: # Add any other ratios where higher means undervalued
+ # Adjust interpretation for specific ratios
+ if ratio in ['Book-to-Market Ratio']: # Example: higher means undervalued
  interpretation = 'Undervalued' if value > average else 'Overvalued'
+ if interpretation == 'Undervalued':
+ undervalued_count += 1 # Correct previous count if needed
+ overvalued_count -= 1
+ else:
+ undervalued_count -= 1
+ overvalued_count += 1
  
  comparison[ratio] = f"{interpretation} (Your Ratio: {value}, S&P 500 Avg: {average})"
  else:
  comparison[ratio] = 'N/A'
- return comparison
+ 
+ # Calculate combined score
+ combined_score = undervalued_count - overvalued_count
+ comparison['Combined Score'] = combined_score
+ 
+ return comparison, combined_score
+
 
 
 
@@ -72,11 +91,17 @@ 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 = compare_to_index(stock_data, sp500_averages)
+ comparison, combined_score = compare_to_index(stock_data, sp500_averages)
  
  st.write(f"Valuation Comparison for {ticker_symbol}:")
  for ratio, result in comparison.items():
- st.write(f"{ratio}: {result}")
+ 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})")
+