app.py

import streamlit as st
import yfinance as yf
import pandas as pd

# Correctly using st.cache_data as per Streamlit's new caching mechanism
@st.cache_data
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 path to your CSV file with S&P 500 averages
sp500_averages_path = 'sp500_averages.csv'

def load_sp500_averages(filepath):
    return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')

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, info

def compare_to_index(stock_ratios, index_averages):
    comparison = {}
    score = 0
    for ratio, value in stock_ratios.items():
        if ratio in index_averages.index and pd.notna(value):
            average = index_averages.loc[ratio, 'Average']
            comparison[ratio] = 'Undervalued' if value < average else 'Overvalued'
            score += 1 if value < average else -1
    return comparison, score

# Ensure this function is defined before it's called in the script
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')

# Load the current S&P 500 list and 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)

# Layout for displaying overview and details
col1, col2 = st.columns([1, 3])

with col1:
    st.subheader("Stock Overview")
    # Make sure to convert 'Combined Score' to numeric if it's not already
    scores_df_sorted['Combined Score'] = pd.to_numeric(scores_df_sorted['Combined Score'], errors='coerce')
    # Apply color based on 'Combined Score' value
    def color_combined_score(value):
        color = 'green' if value > 0 else 'red' if value < 0 else 'none'
        return f'background-color: {color};'

    # Use 'Styler' to apply the style function to the 'Combined Score' column
    styled_scores_df = scores_df_sorted.style.applymap(color_combined_score, subset=['Combined Score'])
    st.dataframe(styled_scores_df)

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, info = fetch_stock_data(ticker_symbol)
            comparison, _ = compare_to_index(stock_data, sp500_averages)
            
            # Display the company name and ticker symbol
            st.write(f"**{info.get('longName')}** ({ticker_symbol})")
            st.write(info.get('longBusinessSummary'))
            
            # Display each financial ratio and its comparison result
            for ratio, status in comparison.items():
                st.write(f"{ratio}: {status}")