app.py

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

# Define the path to your CSV file
sp500_averages_path = 'sp500_averages.csv'

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):
    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,
        'P/S Ratio': info.get('priceToSalesTrailing12Months'),
        'Debt to Equity Ratio': info.get('debtToEquity'),
        'Return on Equity': info.get('returnOnEquity'),
        'Book-to-Market Ratio': book_to_market_ratio,
    }
    
    return financials, info


# Update the cache decorator
@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()

# Use the updated cache function and define the CSV path
sp500_list = get_sp500_list()
sp500_averages = load_sp500_averages(sp500_averages_path)

# 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([1, 3])

# First column for the sorted overview
with col1:
    st.subheader("Stock Overview")
    # 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")
    # 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, info = fetch_stock_data(ticker_symbol)
            comparison, _ = compare_to_index(stock_data, sp500_averages)
            
            # Display company name and description
            st.write(f"**{info.get('longName')}**")
            st.write(info.get('longBusinessSummary'))
            
            # Display financial ratios in a table
            st.table(pd.DataFrame.from_dict(stock_data, orient='index', columns=['Value']))



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}")