initial commit

gradio_app.py

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+import yfinance as yf
    # Max Rows and Columns
    pd.set_option('display.max_columns', 500)
    pd.set_option('display.max_rows', None)
    
    # Two Decimals
    pd.options.display.float_format = '{:.2f}'.format
    
    # The stock symbol
    symbol = ticker
    ticker = yf.Ticker(symbol)
    company_name = ticker.info.get("longName", "N/A")
    
    # Get historical share prices
    price_history = ticker.history(period='5y')
    
    # Extract the closing prices at the end of each fiscal year
    # We will reindex to have end-of-year dates that match the years of the income statements
    year_end_prices = price_history.resample('A').last()['Close']
    year_end_prices.index = year_end_prices.index.year
    
    # Get the number of shares from the info or financials
    shares_outstanding = ticker.info.get('sharesOutstanding')
    
    # Get the annual income statement data
    income_statement = ticker.financials
    
    # Transpose the income statement to have years as rows
    income_statement = income_statement.T
    
    # Extract net income and calculate EPS
    income_statement['EPS'] = income_statement['Net Income'] / shares_outstanding
    
    # Convert the 'Date' index to a year
    income_statement.index = income_statement.index.year
    
    # Combine EPS and closing prices into one DataFrame
    pe_ratios_df = pd.DataFrame({
        'Year': income_statement.index,
        'EPS': income_statement['EPS'],
        'Revenue': income_statement['Total Revenue'],
        'Closing Price': year_end_prices.reindex(income_statement.index).values
    })
    
    # Calculate the P/E ratio
    pe_ratios_df['P/E Ratio'] = pe_ratios_df['Closing Price'] / pe_ratios_df['EPS']
    
    # Calculate Market Capitalization for each year (Closing Price * Shares Outstanding)
    pe_ratios_df['Market Cap'] = pe_ratios_df['Closing Price'] * shares_outstanding
    
    # Calculate the P/S ratio
    pe_ratios_df['P/S Ratio'] = pe_ratios_df['Market Cap'] / pe_ratios_df['Revenue']
    
    # Create DataFrame with last 10 years
    current_year = datetime.now().year
    
    # Create a list of the last 10 years
    years = list(range(current_year - 9, current_year + 1))
    
    # Create a DataFrame
    df_years = pd.DataFrame({'Year': years})
    
    # Get the historical dividend data
    dividend_data = ticker.dividends
    
    # Convert the Series to a DataFrame
    dividend_df = dividend_data.reset_index()
    
    # Rename the columns for clarity
    dividend_df.columns = ['Date', 'Dividend']
    
    # Extract the year from the Date column
    dividend_df['Year'] = dividend_df['Date'].dt.year
    
    # Group by Year and get the last dividend of each year
    dividend_df = dividend_df.groupby('Year').last().reset_index()
    
    # Merge DataFrames
    dividend_df = pd.merge(df_years, dividend_df, on='Year', how='left')
    
    # Replace NaN with last years value
    dividend_df['Dividend'] = dividend_df['Dividend'].ffill()
    
    # Sort the DataFrame by Year
    dividend_df = dividend_df.sort_values(by='Year')
    
    # Calculate the 3-year percentage change
    dividend_df['Dividend Growth (3y)'] = dividend_df['Dividend'].pct_change(periods=3) * 100
    
    # Calculate the 5-year percentage change
    dividend_df['Dividend Growth (5y)'] = dividend_df['Dividend'].pct_change(periods=5) * 100
    
    # Calculate the 10-year percentage change
    dividend_df['Dividend Growth (10y)'] = dividend_df['Dividend'].pct_change(periods=10) * 100
    
    # Drop Columns
    dividend_df = dividend_df.drop(columns=['Dividend'])
    
    print(dividend_df)
    
    # Get the historical stock price data for the last 10 years
    price_history = ticker.history(period='10y')
    
    # Initialize a list to accumulate the annual data
    value_data = []
    
    # Start with an initial investment value of 1 share
    initial_value = price_history['Close'].iloc[0]
    cumulative_value = initial_value
    
    # Track the last year processed to ensure we only capture the final value for each year
    last_year = price_history.index[0].year
    
    # Iterate over each date in the price history to calculate the value including dividends
    for i in range(len(price_history)):
        current_date = price_history.index[i]
        current_price = price_history['Close'].iloc[i]
        current_year = current_date.year
        
        # Check if any dividends were paid out on the current date
        if current_date in dividend_df['Date'].values:
            dividend = dividend_df[dividend_df['Date'] == current_date]['Dividend'].values[0]
            # Calculate the value increase due to dividends
            cumulative_value += (dividend / current_price) * cumulative_value
        
        # Update the cumulative value with the price change
        if i < len(price_history) - 1:
            next_price = price_history['Close'].iloc[i + 1]
            cumulative_value *= (next_price / current_price)
        
        # At the end of each year, record the value including dividends
        if current_year != last_year:
            # Append the cumulative value at the end of the previous year
            value_data.append({'Year': last_year, 'Value Including Dividends': cumulative_value})
            last_year = current_year
    
    # Append the final value at the end of the last year in the dataset
    value_data.append({'Year': last_year, 'Value Including Dividends': cumulative_value})
    
    # Convert the accumulated list to a DataFrame
    value_including_dividends_df = pd.DataFrame(value_data)
    
    # Calculate the percentage increase in value including dividends
    value_including_dividends_df['Value Increase'] = value_including_dividends_df['Value Including Dividends'].pct_change() * 100
    
    # Get the historical dividend data
    dividend_data = ticker.dividends
    
    # Convert the Series to a DataFrame
    dividend_df2 = dividend_data.reset_index()
    
    # Rename the columns for clarity
    dividend_df2.columns = ['Date', 'Dividend']
    
    # Extract the year from the Date column
    dividend_df2['Year'] = dividend_df2['Date'].dt.year
    
    # Aggregate the annual dividends by summing them up
    annual_dividends = dividend_df2.groupby('Year')['Dividend'].sum().reset_index()
    
    # Calculate the year-over-year dividend increase
    annual_dividends['Dividend Increase'] = annual_dividends['Dividend'].diff() > 0
    
    # Calculate the cumulative number of increases
    annual_dividends['No of Increases'] = annual_dividends['Dividend Increase'].cumsum()
    
    # Drop the 'Dividend Increase' column if not needed in the final output
    annual_dividends = annual_dividends.drop(columns=['Dividend Increase'])
    
    # Get the annual income statement data to find EPS
    income_statement = ticker.financials.T
    
    # Extract the EPS (Earnings Per Share)
    annual_eps = (income_statement['Net Income'] / ticker.info['sharesOutstanding']).reset_index()
    annual_eps.columns = ['Year', 'EPS']
    
    # Convert the index to years for the EPS DataFrame
    annual_eps['Year'] = annual_eps['Year'].dt.year
    
    # Align the DataFrames on the 'Year' column
    aligned_dividends_eps = pd.merge(annual_dividends, annual_eps, on='Year', how='outer')
    
    # Calculate the Payout Ratio
    aligned_dividends_eps['Payout Ratio (%)'] = (aligned_dividends_eps['Dividend'] / aligned_dividends_eps['EPS']) * 100
    
    aligned_dividends_eps.sort_values('Year', axis=0, ascending=True, inplace=True)
    
    # Get the income statement data
    income_statement = ticker.financials
    
    # Transpose the data to make it easier to read
    income_statement = income_statement.T
    
    # Rename the columns for clarity
    income_statement = income_statement.rename(columns={'Total Revenue': 'Revenue'})
    income_statement = income_statement.rename(columns={'Gross Profit': 'Earnings'})
    
    # Convert Index to Year and rename it to Year and sort by it
    income_statement['Year'] = income_statement.index.year
    income_statement = income_statement.sort_values(by='Year')
    
    # Calculate Growth Columns
    income_statement['Revenue Growth'] = income_statement['Revenue'].pct_change() * 100
    income_statement['EBITDA Growth'] = income_statement['EBITDA'].pct_change() * 100
    
    # Calculate Margin Columns
    income_statement['EBITDA Margin'] = (income_statement['EBITDA']/income_statement['Revenue']).pct_change() * 100
    
    # Calculate Gross Profit and Gross Profit Margin and Gross Profit Growth
    income_statement['GP'] = income_statement['Revenue'] - income_statement['Cost Of Revenue']
    income_statement['GP Margin'] = (income_statement['GP'] / income_statement['Revenue']) * 100
    
    # Calculate Gross Profit Growth
    income_statement['GP Growth'] = income_statement['GP'].pct_change() * 100
    
    # Format EBITDA
    income_statement['EBITDA MEUR'] = income_statement['EBITDA'] / 1000000
    
    income_df = income_statement[['Year','EBITDA MEUR', 'Revenue Growth', 'GP Margin', 'GP Growth', 'EBITDA Margin', 'EBITDA Growth']]
    
    # Get the cashflow data
    cashflow_data = ticker.cashflow
    
    # Transpose the data to have years as rows
    cashflow_statement = cashflow_data.T
    
    # Get the number of shares outstanding from the info
    shares_outstanding = ticker.info.get('sharesOutstanding')
    
    # Extract Cash Flow from Operations and CapEx
    cashflow_operations = cashflow_statement['Operating Cash Flow']
    capex = cashflow_statement['Capital Expenditure']
    cashflow = cashflow_statement['Free Cash Flow']
    
    # Calculate Cash Flow per Share
    cashflow_per_share = cashflow_operations / shares_outstanding
    
    # Calculate Free Cash Flow (FCF)
    cashflow = cashflow_operations + capex  # CapEx is typically a negative number in the cash flow statement
    
    # Convert the 'Date' index to a year
    cashflow.index = cashflow.index.year
    
    # Create a DataFrame for display
    cashflow_df = pd.DataFrame({
        'Year': cashflow.index,
        'Cashflow Operations': cashflow_operations.values,
        'Cashflow per Share': cashflow_per_share,
        #'Capital Expenditures': capex.values,
        'Free Cashflow': cashflow.values
    })
    
    # Get the annual balance sheet data
    balance_sheet = ticker.balance_sheet
    
    # Transpose the data to have years as rows
    balance_sheet = balance_sheet.T
    
    # Extract Total Stockholder Equity
    total_equity = balance_sheet['Stockholders Equity']
    
    # Get the number of shares outstanding from the info
    shares_outstanding = ticker.info.get('sharesOutstanding')
    
    # Calculate Book Value per Share
    book_value_per_share = total_equity / shares_outstanding
    
    # Extract Total Debt and Cash Equivalents
    total_debt = balance_sheet['Total Debt']
    cash_and_equivalents = balance_sheet['Cash And Cash Equivalents']
    
    # Calculate Net Financial Liabilities
    net_financial_liabilities = total_debt - cash_and_equivalents
    
    # Convert the index to years
    net_financial_liabilities.index = net_financial_liabilities.index.year
    
    # Create a DataFrame for display
    net_financial_liabilities_df = pd.DataFrame({
        'Year': net_financial_liabilities.index,
        'Bookvalue per Share': book_value_per_share,
        'Total Debt': total_debt.values,
        'Cash': cash_and_equivalents.values,
        'Net Financial Liabilities': net_financial_liabilities.values
    })
    
    # Merge DataFrames
    df = pd.merge(pe_ratios_df, annual_dividends, on='Year', how='left')
    df = pd.merge(df, value_including_dividends_df, on='Year', how='inner')
    df = pd.merge(df, income_df, on='Year', how='inner')
    df = pd.merge(df, dividend_df, on='Year', how='left')
    df = pd.merge(df, cashflow_df, on='Year', how='inner')
    df = pd.merge(df, net_financial_liabilities_df, on='Year', how='inner')
    
    return company_name, df
    gr.Markdown("# Stock Data Viewer\nEnter a stock ticker symbol to view the historical data.")
    
    with gr.Row():
        ticker_input = gr.Textbox(label="Enter Ticker Symbol")
    with gr.Row():
        submit_btn = gr.Button("Submit")
    
    ticker_headline = gr.Markdown("")
    
    with gr.Row():
        data_output = gr.Dataframe()
    def show_dataframe(ticker):
        company_name, df = get_stock_data(ticker)
        headline = f"### Data for {company_name} ({ticker})"
        return headline, df
    submit_btn.click(show_dataframe, inputs=ticker_input, outputs=[ticker_headline, data_output])

requirements.txt

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+yfinance