161 lines
4.3 KiB
Python
Executable file
161 lines
4.3 KiB
Python
Executable file
#!/usr/bin/env python3
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import pandas as pd
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import os
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from datetime import datetime, timedelta
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from yahooquery import Ticker
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from top100_extractor import programming_crime_list
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import requests
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from bs4 import BeautifulSoup
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from top100_extractor import programming_crime_list
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from PIL import Image
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import yfinance as yf
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from yahoo_fin import stock_info as si
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def get_market_cap(ticker1):
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ticker = Ticker(ticker1)
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summary = ticker.summary_detail
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market_cap = summary[ticker1.upper()]['marketCap']
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return market_cap
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<<<<<<< HEAD
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=======
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def get_earnings(ticker):
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aapl = Ticker(ticker)
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performance = aapl.earning_history
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return performance
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>>>>>>> 87c225170b8caaa835e939886712744ade00a165
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def get_analyst_estimates(ticker):
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estimates = si.get_analysts_info(ticker)
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next_5_years_estimates = estimates["Growth Estimates"].iloc[4].dropna()
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return next_5_years_estimates[1]
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def get_company_data(ticker):
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df = pd.read_csv(os.path.join(os.path.dirname(__file__), r'companies.csv'))##
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if ticker in df['ticker'].unique():
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company_row = df[df['ticker'] == ticker]
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company_name = company_row.iloc[0]['company name']
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sector = company_row.iloc[0]['industry']
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ceo = company_row.iloc[0]['ceo']
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logo = company_row.iloc[0]['logo']
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return company_name, sector, ceo, logo
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else:
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x = "not available"
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y = "not available"
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z = "not available"
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f = "not available"
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return x, y, z, f
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def get_stock_data(ticker):
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stock = Ticker(ticker)
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stock_data = stock.history(period='max', interval='1d')
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ratios = stock.valuation_measures
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earnings_annual = stock.balance_sheet(frequency="a")
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earnings_last_4q = stock.balance_sheet(frequency='q')
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pd.options.display.float_format = '{:,.2f}'.format
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earnings_last_4q = earnings_last_4q
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earnings_annual = earnings_annual
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balance_sheet = earnings_annual
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if isinstance(balance_sheet, str):
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return
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balance_sheet = pd.concat(
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[earnings_annual, earnings_last_4q], ignore_index=True)
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income_statement = stock.income_statement(frequency="a")
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All_Data = "Companies_Data"
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if not os.path.exists(All_Data):
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os.mkdir(All_Data)
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folder_name = os.path.join(All_Data, ticker + "_Data")
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if not os.path.exists(folder_name):
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os.mkdir(folder_name)
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price_data = pd.DataFrame({'Closing Price': stock_data['close']})
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earnings_data = pd.DataFrame(get_earnings(ticker))
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ratios_data = pd.DataFrame({})
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balance_sheet_data = pd.DataFrame({})
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company_name, sector, ceo, logo = get_company_data(ticker)
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company_data = pd.DataFrame({
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'company_name': [company_name],
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'sector': [sector],
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'ceo': [ceo],
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'logo': [logo]
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})
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estimated_growth = get_analyst_estimates(ticker)
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earnings_next5Y = pd.DataFrame({
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'company_name': [ticker],
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'5Y Growth estimate': [estimated_growth]
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})
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market_cap = get_market_cap(ticker)
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market_cap_file = pd.DataFrame({
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'company_name': [ticker],
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'Market capitalization': [market_cap]
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})
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ratios_components = ['asOfDate', 'PeRatio',
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'PegRatio', 'PsRatio', 'PbRatio']
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balance_sheet_components = ['asOfDate', 'CurrentAssets',
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'CurrentDebt', 'CashAndCashEquivalents', 'TotalAssets', 'TotalDebt']
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for balance in balance_sheet_components:
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if balance in balance_sheet.columns:
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balance_sheet_data[balance] = balance_sheet[balance]
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for ratio in ratios_components:
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if ratio in ratios.columns:
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ratios_data[ratio] = ratios[ratio]
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price_data.to_csv(os.path.join(folder_name, ticker+"_price_history.csv"))
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ratios_data.to_csv(os.path.join(folder_name, ticker+"_current_ratios.csv"))
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balance_sheet_data.to_csv(os.path.join(folder_name, ticker + "_balance_sheet_4Y+4Q.csv"))
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company_data.to_csv(os.path.join(folder_name, ticker + "meta_data.csv"))
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earnings_next5Y.to_csv(os.path.join(folder_name, ticker + "5YGrowthEstimates.csv"))
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market_cap_file.to_csv(os.path.join(folder_name, ticker + "MarketCap.csv"))
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earnings_data.to_csv(os.path.join(folder_name, ticker + "earnings.csv"))
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def download_all():
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for company in programming_crime_list:
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get_stock_data(company)
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if __name__ == '__main__':
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download_all()
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