#!/usr/bin/env python3

import pandas as pd
import os
from datetime import datetime, timedelta
from yahooquery import Ticker
from top100_extractor import programming_crime_list
import requests
from bs4 import BeautifulSoup
from top100_extractor import programming_crime_list
from PIL import Image
import yfinance as yf
from yahoo_fin import stock_info as si


def get_market_cap(ticker1):

    ticker = Ticker(ticker1)

    summary = ticker.summary_detail
    market_cap = summary[ticker1.upper()]['marketCap']

    return market_cap


def get_earnings(ticker):

    aapl = Ticker(ticker)

    performance = aapl.earning_history

    return performance



def get_analyst_estimates(ticker):
    estimates = si.get_analysts_info(ticker)
    next_5_years_estimates = estimates["Growth Estimates"].iloc[4].dropna()
    return next_5_years_estimates[1]


def get_company_data(ticker):

    df = pd.read_csv(os.path.join(os.path.dirname(__file__), r'companies.csv'))##

    if ticker in df['ticker'].unique():
        company_row = df[df['ticker'] == ticker]
        company_name = company_row.iloc[0]['company name']
        sector = company_row.iloc[0]['industry']
        ceo = company_row.iloc[0]['ceo']
        logo = company_row.iloc[0]['logo']

        return company_name, sector, ceo, logo
    else:
        x = "not available"
        y = "not available"
        z = "not available"
        f = "not available"
        return x, y, z, f


def get_stock_data(ticker):

    stock = Ticker(ticker)
    stock_data = stock.history(period='max', interval='1d')
    ratios = stock.valuation_measures
    earnings_annual = stock.balance_sheet(frequency="a")
    earnings_last_4q = stock.balance_sheet(frequency='q')

    pd.options.display.float_format = '{:,.2f}'.format

    earnings_last_4q = earnings_last_4q
    earnings_annual = earnings_annual

    balance_sheet = earnings_annual

    if isinstance(balance_sheet, str):
        return

    balance_sheet = pd.concat(
        [earnings_annual, earnings_last_4q], ignore_index=True)

    income_statement = stock.income_statement(frequency="a")

    All_Data = "Companies_Data"
    if not os.path.exists(All_Data):
        os.mkdir(All_Data)

    folder_name = os.path.join(All_Data, ticker + "_Data")

    if not os.path.exists(folder_name):
        os.mkdir(folder_name)

    price_data = pd.DataFrame({'Closing Price': stock_data['close']})

    earnings_data = pd.DataFrame(get_earnings(ticker))

    ratios_data = pd.DataFrame({})
    balance_sheet_data = pd.DataFrame({})
    company_name, sector, ceo, logo = get_company_data(ticker)

    company_data = pd.DataFrame({
        'company_name': [company_name],
        'sector': [sector],
        'ceo': [ceo],
        'logo': [logo]
    })

    estimated_growth = get_analyst_estimates(ticker)

    earnings_next5Y = pd.DataFrame({
        'company_name': [ticker],
        '5Y Growth estimate': [estimated_growth]
    })

    market_cap = get_market_cap(ticker)

    market_cap_file = pd.DataFrame({
        'company_name': [ticker],
        'Market capitalization': [market_cap]
    })

    ratios_components = ['asOfDate', 'PeRatio',
                         'PegRatio', 'PsRatio', 'PbRatio']
    balance_sheet_components = ['asOfDate', 'CurrentAssets',
                                'CurrentDebt', 'CashAndCashEquivalents', 'TotalAssets', 'TotalDebt']

    for balance in balance_sheet_components:

        if balance in balance_sheet.columns:
            balance_sheet_data[balance] = balance_sheet[balance]

    for ratio in ratios_components:

        if ratio in ratios.columns:
            ratios_data[ratio] = ratios[ratio]

    price_data.to_csv(os.path.join(folder_name, ticker+"_price_history.csv"))
    ratios_data.to_csv(os.path.join(folder_name, ticker+"_current_ratios.csv"))
    balance_sheet_data.to_csv(os.path.join(folder_name, ticker + "_balance_sheet_4Y+4Q.csv"))
    company_data.to_csv(os.path.join(folder_name, ticker + "meta_data.csv"))
    earnings_next5Y.to_csv(os.path.join(folder_name, ticker + "5YGrowthEstimates.csv"))
    market_cap_file.to_csv(os.path.join(folder_name, ticker + "MarketCap.csv"))
    earnings_data.to_csv(os.path.join(folder_name, ticker + "earnings.csv"))



def download_all():
    for company in programming_crime_list:
        get_stock_data(company)


if __name__ == '__main__':
    download_all()