Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 
-@st.experimental_singleton
+@st.cache_resource
 def get_sp500_list():
     table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
     return table[0]['Symbol'].tolist()
@@ -14,12 +14,12 @@ def fetch_stock_data(ticker_symbol):
     ticker = yf.Ticker(ticker_symbol)
     info = ticker.info
     financials = {
-        'P/E Ratio': info.get('forwardPE'),
-        'P/B Ratio': info.get('priceToBook'),
-        'P/S Ratio': info.get('priceToSalesTrailing12Months'),
-        'Debt to Equity Ratio': info.get('debtToEquity'),
-        'Return on Equity': info.get('returnOnEquity'),
-        'Book-to-Market Ratio': 1 / info.get('priceToBook') if info.get('priceToBook') else None
+        'P/E Ratio': info.get('forwardPE', None),
+        'P/B Ratio': info.get('priceToBook', None),
+        'P/S Ratio': info.get('priceToSalesTrailing12Months', None),
+        'Debt to Equity Ratio': info.get('debtToEquity', None),
+        'Return on Equity': info.get('returnOnEquity', None),
+        'Book-to-Market Ratio': 1 / info.get('priceToBook', float('nan')) if info.get('priceToBook') else None
     }
     return financials, info
 
@@ -51,8 +51,11 @@ def color_combined_score(value):
     return f'background-color: {color};'
 
 def filter_incomplete_stocks(df, required_columns):
-    df = df.dropna(subset=required_columns)
-    return df
+    # Ensure all required columns exist in the DataFrame
+    for column in required_columns:
+        if column not in df.columns:
+            df[column] = pd.NA
+    return df.dropna(subset=required_columns)
 
 st.title('S&P 500 Stock Comparison Tool')
 
@@ -62,7 +65,6 @@ sp500_averages = load_sp500_averages('sp500_averages.csv')
 scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
 required_columns = ['P/E Ratio', 'P/B Ratio', 'P/S Ratio', 'Debt to Equity Ratio', 'Return on Equity', 'Book-to-Market Ratio']
 
-# Ensure filtering is done correctly with complete data
 scores_df_filtered = filter_incomplete_stocks(scores_df, required_columns)
 scores_df_sorted = scores_df_filtered.sort_values(by='Combined Score', ascending=False)
 
@@ -85,10 +87,10 @@ with col2:
             st.write(f"**{info.get('longName', 'N/A')}** ({ticker_symbol})")
             st.write(info.get('longBusinessSummary', 'N/A'))
             
-            # Iterate over required_columns instead of required_ratios
             for ratio in required_columns:
                 value = stock_data.get(ratio, 'N/A')
                 average = sp500_averages.loc[ratio, 'Average'] if ratio in sp500_averages.index else 'N/A'
                 status = comparison.get(ratio, 'N/A')
                 st.write(f"{ratio}: {value} (Your Ratio) | {average} (S&P 500 Avg) - {status}")
 
+