import yfinance as yf
from yahooquery import Ticker
from yahoo_fin import stock_info as si

def fetch_eps_estimate(ticker_name, replacement_value=0.0):
    data = si.get_analysts_info(ticker_name)
    # Extract the EPS Trend data
    eps_trend = data["EPS Trend"]
    # Locate the 'Next Year' EPS estimate for the given ticker
    eps_next_year_row = eps_trend[eps_trend["EPS Trend"] == "Current Estimate"]
    eps_next_year_column = [col for col in eps_next_year_row.columns if "Next Year" in col][0]
    eps_next_year = eps_next_year_row[eps_next_year_column].values[0]
    print(eps_next_year)
    # Check for NaN or 'nan' string and replace with the replacement_value if needed
    try:
        eps_estimate = float(eps_next_year)
    except ValueError:
        eps_estimate = replacement_value
    return eps_estimate

def fetch_growth_estimate(ticker_name, replacement_value=0.0):
    data = si.get_analysts_info(ticker_name)
    growth_estimates = data["Growth Estimates"]
    growth_next_5_years_row = growth_estimates[growth_estimates["Growth Estimates"] == "Next 5 Years (per annum)"]
    growth_next_5_years = growth_next_5_years_row[ticker_name].values[0]
    # Check for NaN and replace with the replacement_value if needed
    print(growth_next_5_years)
    if growth_next_5_years != growth_next_5_years or growth_next_5_years == 'nan':
        return replacement_value
    elif isinstance(growth_next_5_years, str):
        # Check if it's a string before stripping
        return float(growth_next_5_years.strip('%')) / 100 # Convert to a decimal form
    else:
        return growth_next_5_years/100

def fetch_data(ticker_name):
    ticker = yf.Ticker(ticker_name)
    # Fetch the Forward EPS (earnings per share) using yahooquery
    eps = fetch_eps_estimate(ticker_name)
    # Fetch the number of shares outstanding
    num_shares = ticker.info['sharesOutstanding']
    # Fetch the growth rate using the provided function
    growth5 = fetch_growth_estimate(ticker_name)
    # Fetch dividend payments to calculate payout ratio
    history = ticker.history(period="5y")
    dividend = history['Dividends'].sum()
    payout_ratio = dividend / (eps * 5) if eps > 0 else 0 # Over 5 years. Assume 0 if no EPS.
    print(payout_ratio)
    return eps, num_shares, growth5, payout_ratio

def calculate_intrinsic_value(eps, growth_rate, payout_ratio, discount_rate, n, num_shares):
    perpetual_growth = 0.02 # Assumption: 3% perpetual growth rate for terminal value
    terminal_value = eps * (1 + perpetual_growth) / (discount_rate - perpetual_growth)
    intrinsic_value_per_share = (eps * (1 + growth_rate) * (1 - payout_ratio) / (discount_rate - growth_rate)) + (terminal_value / (1 + discount_rate)**n) / num_shares
    return intrinsic_value_per_share

if __name__ == "__main__":
    ticker_name = input("Enter the ticker name: ")

    # Fetch the required data
    eps, num_shares, growth_rate, payout_ratio = fetch_data(ticker_name)

    # Assumptions
    discount_rate = 0.08 # 8% WACC, can be refined further based on company-specific details
    n = 5 # Forecast for 5 years ahead

    # Calculate the intrinsic value
    intrinsic_value = calculate_intrinsic_value(eps, growth_rate, payout_ratio, discount_rate, n, num_shares)

    print(f"The intrinsic value per share for {ticker_name} is: ${intrinsic_value:.2f}")