Update app.py

app.py

@@ -77,5 +77,98 @@ with col2:
 
 
 
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+
+# Define the path to your CSV file with S&P 500 averages
+sp500_averages_path = 'sp500_averages.csv'
+
+# Define the function to load S&P 500 averages from a CSV file
+@st.experimental_memo
+def get_sp500_list():
+    table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
+    return table[0]['Symbol'].tolist()
+
+# Define the function to load S&P 500 averages from a CSV file
+sp500_averages_path = 'sp500_averages.csv'
+def load_sp500_averages(filepath):
+    return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')
+
+# Define the function to fetch financial data for a single stock
+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
+    }
+    return financials
+
+# Define the function to compare stock ratios to S&P 500 averages
+def compare_to_index(stock_ratios, index_averages):
+    comparison = {}
+    score = 0
+    for ratio, value in stock_ratios.items():
+        if pd.notna(value):
+            average = index_averages.loc[ratio, 'Average']
+            if value < average:  # For ratios where lower is better
+                comparison[ratio] = 'Undervalued'
+                score += 1
+            elif value > average:  # For ratios where higher is not better
+                comparison[ratio] = 'Overvalued'
+                score -= 1
+        else:
+            comparison[ratio] = 'Data not available'
+    return comparison, score
+
+# Define the function to calculate combined scores for stocks
+def calculate_combined_scores_for_stocks(stocks, index_averages):
+    scores = []
+    for ticker_symbol in stocks:
+        stock_data = fetch_stock_data(ticker_symbol)
+        comparison, score = compare_to_index(stock_data, index_averages)
+        scores.append({'Stock': ticker_symbol, 'Combined Score': score})
+    return pd.DataFrame(scores)
+
+# User interface in Streamlit
+st.title('S&P 500 Stock Comparison Tool')
+
+# Fetch the current S&P 500 list and load the averages
+sp500_list = get_sp500_list()
+sp500_averages = load_sp500_averages(sp500_averages_path)
+
+# Calculate combined scores for all S&P 500 stocks
+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([1, 3])
+
+# First column for the sorted overview
+with col1:
+    st.subheader("Stock Overview")
+    st.dataframe(scores_df_sorted.style.applymap(lambda x: 'background-color: green' if x > 0 
+                                                 else ('background-color: red' if x < 0 else '')))
+
+# 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=sp500_list)
+    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)
+            
+            st.write(f"**{ticker_symbol} - {yf.Ticker(ticker_symbol).info['longName']}**")
+            st.write("Financial Ratios compared to S&P 500 averages:")
+            for ratio, status in comparison.items():
+                st.write(f"{ratio}: {status}")
+
+