fin_data.py

import yfinance as yf
import pandas as pd
from datetime import timedelta

def calculate_ratio(stock: yf.Ticker, ps_series: pd.Series) -> pd.Series:
    """
    Calculate ps_series / share_price * 100 for each date in ps_series.
    Uses the full 5y price history fetched once, then asof() lookup —
    finds the last available trading day on or before each target date,
    capped at today so future fiscal year-ends don't return None.
    """
    if ps_series.empty:
        return pd.Series([], dtype=float)

    try:
        hist = stock.history(period='5y')
        if hist.empty:
            return pd.Series([None] * len(ps_series), index=ps_series.index)
        # Strip timezone so index comparison works cleanly
        close = hist['Close'].copy()
        close.index = close.index.tz_localize(None) if close.index.tzinfo else close.index
    except Exception as e:
        print(f"calculate_ratio: could not fetch history: {e}")
        return pd.Series([None] * len(ps_series), index=ps_series.index)

    today = pd.Timestamp.today().normalize()
    ratio_results = []

    for date, ps_val in ps_series.items():
        try:
            # Cap future dates at today so asof() doesn't return NaN
            lookup = min(pd.Timestamp(date).normalize(), today)
            price = close.asof(lookup)
            if pd.isna(price) or price == 0:
                ratio_results.append(None)
            else:
                ratio_results.append((ps_val / price) * 100)
        except Exception:
            ratio_results.append(None)

    return pd.Series(ratio_results, index=ps_series.index)


def get_data(stock): 

        # Initialize all variables to an empty Series
        EPS = pd.Series([])
        DPS = pd.Series([])
        EPS_RATIO = pd.Series([])
        DPS_RATIO = pd.Series([])
        TR = pd.Series([])
        NI = pd.Series([])
        TA = pd.Series([])
        TD = pd.Series([])
        TE = pd.Series([])
        TL = pd.Series([])

        try:
            EPS = stock.financials.T['Basic EPS'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di EPS: {e}")

        try:
            DPS = stock.dividends.resample('YE').sum().sort_index(ascending=True)
            DPS = DPS.iloc[:-1].tail(5)
            DPS.index = DPS.index.date
        except Exception as e:
            print(f"Errore durante il recupero di DPS: {e}")
            
        try:
            EPS_RATIO = calculate_ratio(stock, EPS)
        except Exception as e:
            print(f"Errore durante il recupero di EPS_RATIO: {e}")

        try:
            DPS_RATIO = calculate_ratio(stock, DPS)
        except Exception as e:
            print(f"Errore durante il recupero di DPS_RATIO: {e}")

        try:
            TR = stock.financials.T['Total Revenue'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di TR: {e}")

        try:
            NI = stock.financials.T['Net Income'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di NI: {e}")

        try:
            TA = stock.balance_sheet.T['Total Assets'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di TA: {e}")

        try:
            TD = stock.balance_sheet.T['Total Debt'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di TD: {e}")

        try:
            TE = stock.balance_sheet.T['Total Equity Gross Minority Interest'].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di TE: {e}")

        try:
            bs_t = stock.balance_sheet.T
            tl_col = 'Total Liabilities Net Minority Interest' if 'Total Liabilities Net Minority Interest' in bs_t.columns else 'Total Non Current Liabilities Net Minority Interest'
            TL = bs_t[tl_col].dropna().sort_index(ascending=True).tail(5)
        except Exception as e:
            print(f"Errore durante il recupero di TL: {e}")
             
        """
        EPS = stock.financials.T['Basic EPS'].dropna().sort_index(ascending=True).tail(5)
        DPS =  stock.dividends.resample('YE').sum().sort_index(ascending=True)
        DPS = DPS.iloc[:-1].tail(5)
        DPS.index = DPS.index.date
        EPS_RATIO = calculate_ratio(stock, EPS)
        DPS_RATIO = calculate_ratio(stock, DPS)
        TR = stock.financials.T['Total Revenue'].dropna().sort_index(ascending=True).tail(5)
        NI = stock.financials.T['Net Income'].dropna().sort_index(ascending=True).tail(5)
        TA = stock.balance_sheet.T['Total Assets'].dropna().sort_index(ascending=True).tail(5)
        TD = stock.balance_sheet.T['Total Debt'].dropna().sort_index(ascending=True).tail(5)
        TE = stock.balance_sheet.T['Total Equity Gross Minority Interest'].dropna().sort_index(ascending=True).tail(5)
        #TL = stock.balance_sheet.T['Total Non Current Liabilities Net Minority Interest'].dropna().sort_index(ascending=True).tail(5)
        """

        # print(EPS)
        # print(DPS)
        # print(EPS_RATIO)
        # print(DPS_RATIO)
        # print(TR/1000000)
        # print(NI/1000000)
        # print(TA/1000000)
        # print(TD/1000000)
        # print(TE/1000000)
        # print(TL/1000000)
    
        return EPS, DPS, EPS_RATIO, DPS_RATIO, TR, NI, TA, TD, TE, TL



#print(get_data(yf.Ticker('BCAT')))