Update app.py

app.py

@@ -43,15 +43,23 @@ def fetch_stock_data(ticker_symbol):
 def compare_to_index(stock_ratios, index_averages):
  comparison = {}
  for ratio, value in stock_ratios.items():
- # Check if the ratio exists in the index_averages DataFrame
- if ratio in index_averages.index:
+ # Ensure the ratio exists in the DataFrame
+ if ratio in index_averages.index and value is not None:
  average = index_averages.loc[ratio]['Average']
- comparison[ratio] = 'Higher' if value > average else 'Lower'
+ # Default interpretation for most ratios (higher = overvalued)
+ interpretation = 'Overvalued' if value > average else 'Undervalued'
+ 
+ # 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
+ interpretation = 'Undervalued' if value > average else 'Overvalued'
+ 
+ comparison[ratio] = f"{interpretation} (Your Ratio: {value}, S&P 500 Avg: {average})"
  else:
- comparison[ratio] = 'N/A' # Ratio not available in index averages
+ comparison[ratio] = 'N/A'
  return comparison
 
 
+
 # Load S&P 500 averages
 sp500_averages = load_sp500_averages(sp500_averages_path)
 
@@ -66,10 +74,10 @@ if ticker_symbol:
  stock_data = fetch_stock_data(ticker_symbol)
  comparison = compare_to_index(stock_data, sp500_averages)
  
- st.write(f"Financial Ratios for {ticker_symbol}:")
- for ratio, value in stock_data.items():
- status = comparison.get(ratio, 'N/A')
- st.write(f"{ratio}: {value} ({status} than S&P 500 average)")
+ st.write(f"Valuation Comparison for {ticker_symbol}:")
+ for ratio, result in comparison.items():
+ st.write(f"{ratio}: {result}")
+