Update app.py

app.py

@@ -2,34 +2,20 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 
-# Predefined list of S&P 500 stocks for selection
-stocks = ['AAPL', 'MSFT', 'AMZN', 'GOOGL', 'FB', 'BRK.B', 'JNJ', 'V', 'PG', 'JPM',
-          'UNH', 'MA', 'INTC', 'VZ', 'HD', 'T', 'DIS', 'MRK', 'PFE', 'BAC', 
-          'KO', 'WMT', 'MCD', 'ABT', 'CSCO', 'PEP', 'NFLX', 'XOM', 'CVX', 'NKE', 
-          'LLY', 'ADBE', 'CMCSA', 'ORCL', 'CRM', 'TMO', 'ACN', 'ABBV', 'AVGO', 'TXN',
-          'COST', 'DHR', 'MDT', 'NEE', 'PYPL', 'AMGN', 'HON', 'LIN', 'PM', 'BA',
-          'UNP', 'IBM', 'QCOM', 'LMT', 'BMY', 'SBUX', 'MMM', 'GE', 'CAT', 'CVS',
-          'WFC', 'SCHW', 'RTX', 'AMT', 'GS', 'DE', 'C', 'MS', 'GILD', 'UPS', 
-          'BLK', 'MO', 'MDLZ', 'INTU', 'TGT', 'AXP', 'ANTM', 'ISRG', 'SYK', 'CI', 
-          'PGR', 'BKNG', 'CL', 'SPGI', 'MMC', 'BDX', 'ADP', 'CME', 'USB', 'TJX', 
-          'ZTS', 'FIS', 'GM', 'CB', 'CHTR', 'PLD', 'SO', 'COP', 'DUK', 'EL']
-
-# Assuming you have an updated CSV with S&P 500 averages for financial ratios
-sp500_averages_path = 'sp500_averages.csv'
-
+# Load S&P 500 averages from a CSV file
 def load_sp500_averages(filepath):
-    # Load the CSV without specifying an index column name
     return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')
 
-
+# Fetch financial data for a single stock
 def fetch_stock_data(ticker_symbol):
-    # Fetch financial data for a single stock
     ticker = yf.Ticker(ticker_symbol)
     info = ticker.info
     
+    # Calculate Book-to-Market Ratio
     pb_ratio = info.get('priceToBook')
     book_to_market_ratio = 1 / pb_ratio if pb_ratio and pb_ratio > 0 else None
     
+    # Extract relevant financial information, including Book-to-Market Ratio
     financials = {
         'P/E Ratio': info.get('forwardPE'),
         'P/B Ratio': pb_ratio,
@@ -38,104 +24,58 @@ def fetch_stock_data(ticker_symbol):
         'Return on Equity': info.get('returnOnEquity'),
         'Book-to-Market Ratio': book_to_market_ratio,
     }
-    return financials
-
-def compare_to_index(stock_ratios, index_averages):
-    comparison = {}
-    undervalued_count = 0
-    overvalued_count = 0
-    
-    for ratio, value in stock_ratios.items():
-        if ratio in index_averages.index and value is not None:
-            average = index_averages.loc[ratio]['Average']
-            # Interpretation for most ratios (higher = overvalued)
-            if value > average:
-                interpretation = 'Overvalued'
-                overvalued_count += 1
-            else:
-                interpretation = 'Undervalued'
-                undervalued_count += 1
-            
-            # 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'
     
-    # Calculate combined score
-    combined_score = undervalued_count - overvalued_count
-    comparison['Combined Score'] = combined_score
-    
-    return comparison, combined_score
+    return financials, info
 
+# ... rest of your existing functions ...
 
-def calculate_combined_scores_for_stocks(stocks, index_averages):
-    scores = []
+# User interface in Streamlit
+st.title('S&P 500 Stock Comparison Tool')
 
-    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
+# Check if companies are in the S&P 500
+@st.cache
+def get_sp500_list():
+    table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
+    return table[0]['Symbol'].tolist()
 
+sp500_list = get_sp500_list()
 
 # Load S&P 500 averages
 sp500_averages = load_sp500_averages(sp500_averages_path)
 
-# User interface in Streamlit
-st.title('S&P 500 Stock Comparison Tool')
-
-# Fetch combined scores and overview if not already in session state
-if 'scores_df' not in st.session_state:
-    st.session_state['scores_df'] = calculate_combined_scores_for_stocks(stocks, sp500_averages)
-
-# Sort the DataFrame by combined score for the overview
-scores_df_sorted = st.session_state['scores_df'].sort_values(by='Combined Score', ascending=False)
+# Calculate combined scores for stocks in the S&P 500
+scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
+scores_df_sorted = scores_df.sort_values(by='Combined Score', ascending=False)
 
 # Use columns for side-by-side layout
-col1, col2 = st.columns([2, 3])
+col1, col2 = st.columns([1, 3])
 
 # First column for the sorted overview
 with col1:
     st.subheader("Stock Overview")
-    for _, row in scores_df_sorted.iterrows():
-        color = "green" if row['Combined Score'] > 0 else "red" if row['Combined Score'] < 0 else "grey"
+    # Create a DataFrame for the sidebar with color-coded combined scores
+    scores_df_sorted['color'] = scores_df_sorted['Combined Score'].apply(
+        lambda x: 'green' if x > 0 else 'red' if x < 0 else 'grey')
+    for index, row in scores_df_sorted.iterrows():
+        color = row['color']
         st.markdown(f"<span style='color: {color};'>{row['Stock']}: {row['Combined Score']}</span>", unsafe_allow_html=True)
 
 # Second column for detailed financial ratios and company information
 with col2:
     st.subheader("Stock Details")
-    ticker_symbol = st.selectbox('Select a stock for details', options=scores_df_sorted['Stock'].tolist())
+    # Dropdown to select stock for details
+    ticker_symbol = st.selectbox('Select a stock for details', options=sp500_list)
     if ticker_symbol:
         with st.spinner(f'Fetching data for {ticker_symbol}...'):
-            stock_data = fetch_stock_data(ticker_symbol)
+            stock_data, info = fetch_stock_data(ticker_symbol)
             comparison, _ = compare_to_index(stock_data, sp500_averages)
             
-            # Fetch additional information such as company name and description
-            company_info = yf.Ticker(ticker_symbol).info
-            company_name = company_info.get('longName')
-            company_description = company_info.get('longBusinessSummary')
-            
-            st.write(f"**{company_name}**")
-            st.write(company_description)
+            # Display company name and description
+            st.write(f"**{info.get('longName')}**")
+            st.write(info.get('longBusinessSummary'))
             
-            st.write(f"**Financial Ratios for {ticker_symbol}:**")
-            for ratio, result in comparison.items():
-                if ratio != 'Combined Score':  # Avoid repeating the combined score
-                    st.write(f"{ratio}: {result}")
+            # Display financial ratios in a table
+            st.table(pd.DataFrame.from_dict(stock_data, orient='index', columns=['Value']))