mirror of
https://github.com/andrewkdinh/fund-indicators.git
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891 lines
37 KiB
Python
891 lines
37 KiB
Python
"""
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==============================
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The Yahoo Financials Module
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Version: 1.5
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==============================
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Author: Connor Sanders
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Email: sandersconnor1@gmail.com
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Version Released: 01/27/2019
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Tested on Python 2.7, 3.3, 3.4, 3.5, 3.6, and 3.7
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Copyright (c) 2019 Connor Sanders
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MIT License
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List of Included Functions:
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1) get_financial_stmts(frequency, statement_type, reformat=True)
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- frequency can be either 'annual' or 'quarterly'.
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- statement_type can be 'income', 'balance', 'cash'.
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- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
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2) get_stock_price_data(reformat=True)
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- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
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3) get_stock_earnings_data(reformat=True)
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- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
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4) get_summary_data(reformat=True)
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- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
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5) get_stock_quote_type_data()
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6) get_historical_price_data(start_date, end_date, time_interval)
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- Gets historical price data for currencies, stocks, indexes, cryptocurrencies, and commodity futures.
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- start_date should be entered in the 'YYYY-MM-DD' format. First day that financial data will be pulled.
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- end_date should be entered in the 'YYYY-MM-DD' format. Last day that financial data will be pulled.
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- time_interval can be either 'daily', 'weekly', or 'monthly'. Parameter determines the time period interval.
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Usage Examples:
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from yahoofinancials import YahooFinancials
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#tickers = 'AAPL'
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#or
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tickers = ['AAPL', 'WFC', 'F', 'JPY=X', 'XRP-USD', 'GC=F']
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yahoo_financials = YahooFinancials(tickers)
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balance_sheet_data = yahoo_financials.get_financial_stmts('quarterly', 'balance')
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earnings_data = yahoo_financials.get_stock_earnings_data()
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historical_prices = yahoo_financials.get_historical_price_data('2015-01-15', '2017-10-15', 'weekly')
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"""
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import sys
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import calendar
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import re
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from json import loads
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import time
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from bs4 import BeautifulSoup
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import datetime
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import pytz
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import random
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try:
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from urllib import FancyURLopener
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except:
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from urllib.request import FancyURLopener
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# track the last get timestamp to add a minimum delay between gets - be nice!
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_lastget = 0
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# Custom Exception class to handle custom error
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class ManagedException(Exception):
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pass
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# Class used to open urls for financial data
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class UrlOpener(FancyURLopener):
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version = 'Mozilla/5.0 (Windows; U; Windows NT 5.1; it; rv:1.8.1.11) Gecko/20071127 Firefox/2.0.0.11'
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# Class containing Yahoo Finance ETL Functionality
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class YahooFinanceETL(object):
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def __init__(self, ticker):
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self.ticker = ticker.upper() if isinstance(ticker, str) else [t.upper() for t in ticker]
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self._cache = {}
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# Minimum interval between Yahoo Finance requests for this instance
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_MIN_INTERVAL = 7
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# Meta-data dictionaries for the classes to use
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YAHOO_FINANCIAL_TYPES = {
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'income': ['financials', 'incomeStatementHistory', 'incomeStatementHistoryQuarterly'],
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'balance': ['balance-sheet', 'balanceSheetHistory', 'balanceSheetHistoryQuarterly', 'balanceSheetStatements'],
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'cash': ['cash-flow', 'cashflowStatementHistory', 'cashflowStatementHistoryQuarterly', 'cashflowStatements'],
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'keystats': ['key-statistics'],
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'history': ['history']
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}
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# Interval value translation dictionary
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_INTERVAL_DICT = {
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'daily': '1d',
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'weekly': '1wk',
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'monthly': '1mo'
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}
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# Base Yahoo Finance URL for the class to build on
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_BASE_YAHOO_URL = 'https://finance.yahoo.com/quote/'
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# private static method to get the appropriate report type identifier
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@staticmethod
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def get_report_type(frequency):
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if frequency == 'annual':
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report_num = 1
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else:
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report_num = 2
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return report_num
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# Public static method to format date serial string to readable format and vice versa
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@staticmethod
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def format_date(in_date):
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if isinstance(in_date, str):
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form_date = int(calendar.timegm(time.strptime(in_date, '%Y-%m-%d')))
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else:
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form_date = str((datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=in_date)).date())
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return form_date
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# Private Static Method to Convert Eastern Time to UTC
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@staticmethod
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def _convert_to_utc(date, mask='%Y-%m-%d %H:%M:%S'):
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utc = pytz.utc
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eastern = pytz.timezone('US/Eastern')
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date_ = datetime.datetime.strptime(date.replace(" 0:", " 12:"), mask)
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date_eastern = eastern.localize(date_, is_dst=None)
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date_utc = date_eastern.astimezone(utc)
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return date_utc.strftime('%Y-%m-%d %H:%M:%S %Z%z')
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# Private method to scrape data from yahoo finance
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def _scrape_data(self, url, tech_type, statement_type):
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global _lastget
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if not self._cache.get(url):
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now = int(time.time())
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if _lastget and now - _lastget < self._MIN_INTERVAL:
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time.sleep(self._MIN_INTERVAL - (now - _lastget) + 1)
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now = int(time.time())
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_lastget = now
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urlopener = UrlOpener()
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# Try to open the URL up to 10 times sleeping random time if something goes wrong
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max_retry = 10
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for i in range(0, max_retry):
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response = urlopener.open(url)
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if response.getcode() != 200:
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time.sleep(random.randrange(10, 20))
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else:
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response_content = response.read()
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soup = BeautifulSoup(response_content, "html.parser")
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re_script = soup.find("script", text=re.compile("root.App.main"))
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if re_script is not None:
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script = re_script.text
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self._cache[url] = loads(re.search("root.App.main\s+=\s+(\{.*\})", script).group(1))
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response.close()
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break
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else:
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time.sleep(random.randrange(10, 20))
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if i == max_retry - 1:
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# Raise a custom exception if we can't get the web page within max_retry attempts
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raise ManagedException("Server replied with HTTP " + str(response.getcode()) +
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" code while opening the url: " + str(url))
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data = self._cache[url]
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if tech_type == '' and statement_type != 'history':
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stores = data["context"]["dispatcher"]["stores"]["QuoteSummaryStore"]
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elif tech_type != '' and statement_type != 'history':
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stores = data["context"]["dispatcher"]["stores"]["QuoteSummaryStore"][tech_type]
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else:
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stores = data["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
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return stores
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# Private static method to determine if a numerical value is in the data object being cleaned
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@staticmethod
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def _determine_numeric_value(value_dict):
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if 'raw' in value_dict.keys():
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numerical_val = value_dict['raw']
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else:
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numerical_val = None
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return numerical_val
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# Private method to format date serial string to readable format and vice versa
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def _format_time(self, in_time):
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form_date_time = datetime.datetime.fromtimestamp(int(in_time)).strftime('%Y-%m-%d %H:%M:%S')
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utc_dt = self._convert_to_utc(form_date_time)
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return utc_dt
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# Private method to return the a sub dictionary entry for the earning report cleaning
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def _get_cleaned_sub_dict_ent(self, key, val_list):
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sub_list = []
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for rec in val_list:
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sub_sub_dict = {}
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for k, v in rec.items():
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if k == 'date':
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sub_sub_dict_ent = {k: v}
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else:
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numerical_val = self._determine_numeric_value(v)
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sub_sub_dict_ent = {k: numerical_val}
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sub_sub_dict.update(sub_sub_dict_ent)
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sub_list.append(sub_sub_dict)
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sub_ent = {key: sub_list}
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return sub_ent
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# Private method to process raw earnings data and clean
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def _clean_earnings_data(self, raw_data):
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cleaned_data = {}
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earnings_key = 'earningsData'
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financials_key = 'financialsData'
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for k, v in raw_data.items():
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if k == 'earningsChart':
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sub_dict = {}
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for k2, v2 in v.items():
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if k2 == 'quarterly':
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sub_ent = self._get_cleaned_sub_dict_ent(k2, v2)
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elif k2 == 'currentQuarterEstimate':
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numerical_val = self._determine_numeric_value(v2)
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sub_ent = {k2: numerical_val}
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else:
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sub_ent = {k2: v2}
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sub_dict.update(sub_ent)
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dict_ent = {earnings_key: sub_dict}
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cleaned_data.update(dict_ent)
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elif k == 'financialsChart':
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sub_dict = {}
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for k2, v2, in v.items():
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sub_ent = self._get_cleaned_sub_dict_ent(k2, v2)
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sub_dict.update(sub_ent)
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dict_ent = {financials_key: sub_dict}
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cleaned_data.update(dict_ent)
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else:
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if k != 'maxAge':
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dict_ent = {k: v}
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cleaned_data.update(dict_ent)
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return cleaned_data
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# Private method to clean summary and price reports
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def _clean_reports(self, raw_data):
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cleaned_dict = {}
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if raw_data is None:
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return None
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for k, v in raw_data.items():
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if 'Time' in k:
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formatted_utc_time = self._format_time(v)
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dict_ent = {k: formatted_utc_time}
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elif 'Date' in k:
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try:
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formatted_date = v['fmt']
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except (KeyError, TypeError):
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formatted_date = '-'
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dict_ent = {k: formatted_date}
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elif v is None or isinstance(v, str) or isinstance(v, int) or isinstance(v, float):
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dict_ent = {k: v}
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# Python 2 and Unicode
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elif sys.version_info < (3, 0) and isinstance(v, unicode):
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dict_ent = {k: v}
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else:
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numerical_val = self._determine_numeric_value(v)
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dict_ent = {k: numerical_val}
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cleaned_dict.update(dict_ent)
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return cleaned_dict
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# Private Static Method to ensure ticker is URL encoded
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@staticmethod
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def _encode_ticker(ticker_str):
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encoded_ticker = ticker_str.replace('=', '%3D')
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return encoded_ticker
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# Private method to get time interval code
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def _build_historical_url(self, ticker, hist_oj):
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url = self._BASE_YAHOO_URL + self._encode_ticker(ticker) + '/history?period1=' + str(hist_oj['start']) + \
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'&period2=' + str(hist_oj['end']) + '&interval=' + hist_oj['interval'] + '&filter=history&frequency=' + \
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hist_oj['interval']
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return url
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# Private Method to clean the dates of the newly returns historical stock data into readable format
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def _clean_historical_data(self, hist_data, last_attempt=False):
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data = {}
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for k, v in hist_data.items():
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if k == 'eventsData':
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event_obj = {}
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if isinstance(v, list):
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dict_ent = {k: event_obj}
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else:
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for type_key, type_obj in v.items():
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formatted_type_obj = {}
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for date_key, date_obj in type_obj.items():
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formatted_date_key = self.format_date(int(date_key))
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cleaned_date = self.format_date(int(date_obj['date']))
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date_obj.update({'formatted_date': cleaned_date})
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formatted_type_obj.update({formatted_date_key: date_obj})
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event_obj.update({type_key: formatted_type_obj})
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dict_ent = {k: event_obj}
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elif 'date' in k.lower():
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if v is not None:
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cleaned_date = self.format_date(v)
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dict_ent = {k: {'formatted_date': cleaned_date, 'date': v}}
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else:
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if last_attempt is False:
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return None
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else:
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dict_ent = {k: {'formatted_date': None, 'date': v}}
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elif isinstance(v, list):
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sub_dict_list = []
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for sub_dict in v:
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sub_dict['formatted_date'] = self.format_date(sub_dict['date'])
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sub_dict_list.append(sub_dict)
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dict_ent = {k: sub_dict_list}
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else:
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dict_ent = {k: v}
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data.update(dict_ent)
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return data
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# Private Static Method to build API url for GET Request
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@staticmethod
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def _build_api_url(hist_obj, up_ticker):
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base_url = "https://query1.finance.yahoo.com/v8/finance/chart/"
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api_url = base_url + up_ticker + '?symbol=' + up_ticker + '&period1=' + str(hist_obj['start']) + '&period2=' + \
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str(hist_obj['end']) + '&interval=' + hist_obj['interval']
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api_url += '&events=div|split|earn&lang=en-US®ion=US'
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return api_url
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# Private Method to get financial data via API Call
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def _get_api_data(self, api_url, tries=0):
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urlopener = UrlOpener()
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response = urlopener.open(api_url)
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if response.getcode() == 200:
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res_content = response.read()
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response.close()
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if sys.version_info < (3, 0):
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return loads(res_content)
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return loads(res_content.decode('utf-8'))
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else:
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if tries < 5:
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time.sleep(random.randrange(10, 20))
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tries += 1
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return self._get_api_data(api_url, tries)
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else:
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return None
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# Private Method to clean API data
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def _clean_api_data(self, api_url):
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raw_data = self._get_api_data(api_url)
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ret_obj = {}
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ret_obj.update({'eventsData': []})
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if raw_data is None:
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return ret_obj
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results = raw_data['chart']['result']
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if results is None:
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return ret_obj
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for result in results:
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tz_sub_dict = {}
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ret_obj.update({'eventsData': result.get('events', {})})
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ret_obj.update({'firstTradeDate': result['meta'].get('firstTradeDate', 'NA')})
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ret_obj.update({'currency': result['meta'].get('currency', 'NA')})
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ret_obj.update({'instrumentType': result['meta'].get('instrumentType', 'NA')})
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tz_sub_dict.update({'gmtOffset': result['meta']['gmtoffset']})
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ret_obj.update({'timeZone': tz_sub_dict})
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timestamp_list = result['timestamp']
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high_price_list = result['indicators']['quote'][0]['high']
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low_price_list = result['indicators']['quote'][0]['low']
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open_price_list = result['indicators']['quote'][0]['open']
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close_price_list = result['indicators']['quote'][0]['close']
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volume_list = result['indicators']['quote'][0]['volume']
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adj_close_list = result['indicators']['adjclose'][0]['adjclose']
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i = 0
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prices_list = []
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for timestamp in timestamp_list:
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price_dict = {}
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price_dict.update({'date': timestamp})
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price_dict.update({'high': high_price_list[i]})
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price_dict.update({'low': low_price_list[i]})
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price_dict.update({'open': open_price_list[i]})
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price_dict.update({'close': close_price_list[i]})
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price_dict.update({'volume': volume_list[i]})
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price_dict.update({'adjclose': adj_close_list[i]})
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prices_list.append(price_dict)
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i += 1
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ret_obj.update({'prices': prices_list})
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return ret_obj
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# Private Method to Handle Recursive API Request
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def _recursive_api_request(self, hist_obj, up_ticker, i=0):
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api_url = self._build_api_url(hist_obj, up_ticker)
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re_data = self._clean_api_data(api_url)
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cleaned_re_data = self._clean_historical_data(re_data)
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if cleaned_re_data is not None:
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return cleaned_re_data
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else:
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if i < 3:
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i += 1
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return self._recursive_api_request(hist_obj, up_ticker, i)
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else:
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return self._clean_historical_data(re_data, True)
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# Private Method to take scrapped data and build a data dictionary with
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def _create_dict_ent(self, up_ticker, statement_type, tech_type, report_name, hist_obj):
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YAHOO_URL = self._BASE_YAHOO_URL + up_ticker + '/' + self.YAHOO_FINANCIAL_TYPES[statement_type][0] + '?p=' +\
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up_ticker
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if tech_type == '' and statement_type != 'history':
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try:
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re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
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dict_ent = {up_ticker: re_data[u'' + report_name], 'dataType': report_name}
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except KeyError:
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re_data = None
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dict_ent = {up_ticker: re_data, 'dataType': report_name}
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elif tech_type != '' and statement_type != 'history':
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try:
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re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
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except KeyError:
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re_data = None
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dict_ent = {up_ticker: re_data}
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else:
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YAHOO_URL = self._build_historical_url(up_ticker, hist_obj)
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try:
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cleaned_re_data = self._recursive_api_request(hist_obj, up_ticker)
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except KeyError:
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try:
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re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
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cleaned_re_data = self._clean_historical_data(re_data)
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except KeyError:
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cleaned_re_data = None
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dict_ent = {up_ticker: cleaned_re_data}
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return dict_ent
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# Private method to return the stmt_id for the reformat_process
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def _get_stmt_id(self, statement_type, raw_data):
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stmt_id = ''
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i = 0
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for key in raw_data.keys():
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if key in self.YAHOO_FINANCIAL_TYPES[statement_type.lower()]:
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stmt_id = key
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i += 1
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if i != 1:
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return None
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return stmt_id
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# Private Method for the Reformat Process
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def _reformat_stmt_data_process(self, raw_data, statement_type):
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final_data_list = []
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if raw_data is not None:
|
|
stmt_id = self._get_stmt_id(statement_type, raw_data)
|
|
if stmt_id is None:
|
|
return final_data_list
|
|
hashed_data_list = raw_data[stmt_id]
|
|
for data_item in hashed_data_list:
|
|
data_date = ''
|
|
sub_data_dict = {}
|
|
for k, v in data_item.items():
|
|
if k == 'endDate':
|
|
data_date = v['fmt']
|
|
elif k != 'maxAge':
|
|
numerical_val = self._determine_numeric_value(v)
|
|
sub_dict_item = {k: numerical_val}
|
|
sub_data_dict.update(sub_dict_item)
|
|
dict_item = {data_date: sub_data_dict}
|
|
final_data_list.append(dict_item)
|
|
return final_data_list
|
|
else:
|
|
return raw_data
|
|
|
|
# Private Method to return subdict entry for the statement reformat process
|
|
def _get_sub_dict_ent(self, ticker, raw_data, statement_type):
|
|
form_data_list = self._reformat_stmt_data_process(raw_data[ticker], statement_type)
|
|
return {ticker: form_data_list}
|
|
|
|
# Public method to get time interval code
|
|
def get_time_code(self, time_interval):
|
|
interval_code = self._INTERVAL_DICT[time_interval.lower()]
|
|
return interval_code
|
|
|
|
# Public Method to get stock data
|
|
def get_stock_data(self, statement_type='income', tech_type='', report_name='', hist_obj={}):
|
|
data = {}
|
|
if isinstance(self.ticker, str):
|
|
dict_ent = self._create_dict_ent(self.ticker, statement_type, tech_type, report_name, hist_obj)
|
|
data.update(dict_ent)
|
|
else:
|
|
for tick in self.ticker:
|
|
try:
|
|
dict_ent = self._create_dict_ent(tick, statement_type, tech_type, report_name, hist_obj)
|
|
data.update(dict_ent)
|
|
except ManagedException:
|
|
print("Warning! Ticker: " + str(tick) + " error - " + str(ManagedException))
|
|
print("The process is still running...")
|
|
continue
|
|
return data
|
|
|
|
# Public Method to get technical stock datafrom yahoofinancials import YahooFinancials
|
|
|
|
def get_stock_tech_data(self, tech_type):
|
|
if tech_type == 'defaultKeyStatistics':
|
|
return self.get_stock_data(statement_type='keystats', tech_type=tech_type)
|
|
else:
|
|
return self.get_stock_data(tech_type=tech_type)
|
|
|
|
# Public Method to get reformatted statement data
|
|
def get_reformatted_stmt_data(self, raw_data, statement_type):
|
|
data_dict = {}
|
|
sub_dict = {}
|
|
data_type = raw_data['dataType']
|
|
if isinstance(self.ticker, str):
|
|
sub_dict_ent = self._get_sub_dict_ent(self.ticker, raw_data, statement_type)
|
|
sub_dict.update(sub_dict_ent)
|
|
dict_ent = {data_type: sub_dict}
|
|
data_dict.update(dict_ent)
|
|
else:
|
|
for tick in self.ticker:
|
|
sub_dict_ent = self._get_sub_dict_ent(tick, raw_data, statement_type)
|
|
sub_dict.update(sub_dict_ent)
|
|
dict_ent = {data_type: sub_dict}
|
|
data_dict.update(dict_ent)
|
|
return data_dict
|
|
|
|
# Public method to get cleaned summary and price report data
|
|
def get_clean_data(self, raw_report_data, report_type):
|
|
cleaned_data_dict = {}
|
|
if isinstance(self.ticker, str):
|
|
if report_type == 'earnings':
|
|
try:
|
|
cleaned_data = self._clean_earnings_data(raw_report_data[self.ticker])
|
|
except:
|
|
cleaned_data = None
|
|
else:
|
|
try:
|
|
cleaned_data = self._clean_reports(raw_report_data[self.ticker])
|
|
except:
|
|
cleaned_data = None
|
|
cleaned_data_dict.update({self.ticker: cleaned_data})
|
|
else:
|
|
for tick in self.ticker:
|
|
if report_type == 'earnings':
|
|
try:
|
|
cleaned_data = self._clean_earnings_data(raw_report_data[tick])
|
|
except:
|
|
cleaned_data = None
|
|
else:
|
|
try:
|
|
cleaned_data = self._clean_reports(raw_report_data[tick])
|
|
except:
|
|
cleaned_data = None
|
|
cleaned_data_dict.update({tick: cleaned_data})
|
|
return cleaned_data_dict
|
|
|
|
# Private method to handle dividend data requestsfrom yahoofinancials import YahooFinancials
|
|
|
|
def _handle_api_dividend_request(self, cur_ticker, start, end, interval):
|
|
re_dividends = []
|
|
test_url = 'https://query1.finance.yahoo.com/v8/finance/chart/' + cur_ticker + \
|
|
'?period1=' + str(start) + '&period2=' + str(end) + '&interval=' + interval + '&events=div'
|
|
div_dict = self._get_api_data(test_url)['chart']['result'][0]['events']['dividends']
|
|
for div_time_key, div_obj in div_dict.items():
|
|
dividend_obj = {
|
|
'date': div_obj['date'],
|
|
'formatted_date': self.format_date(int(div_obj['date'])),
|
|
'amount': div_obj.get('amount', None)
|
|
}
|
|
re_dividends.append(dividend_obj)
|
|
return sorted(re_dividends, key=lambda div: div['date'])
|
|
|
|
# Public method to get daily dividend data
|
|
def get_stock_dividend_data(self, start, end, interval):
|
|
interval_code = self.get_time_code(interval)
|
|
if isinstance(self.ticker, str):
|
|
try:
|
|
return {self.ticker: self._handle_api_dividend_request(self.ticker, start, end, interval_code)}
|
|
except:
|
|
return {self.ticker: None}
|
|
else:
|
|
re_data = {}
|
|
for tick in self.ticker:
|
|
try:
|
|
div_data = self._handle_api_dividend_request(tick, start, end, interval_code)
|
|
re_data.update({tick: div_data})
|
|
except:
|
|
re_data.update({tick: None})
|
|
return re_data
|
|
|
|
|
|
# Class containing methods to create stock data extracts
|
|
class YahooFinancials(YahooFinanceETL):
|
|
|
|
# Private method that handles financial statement extraction
|
|
def _run_financial_stmt(self, statement_type, report_num, reformat):
|
|
report_name = self.YAHOO_FINANCIAL_TYPES[statement_type][report_num]
|
|
if reformat:
|
|
raw_data = self.get_stock_data(statement_type, report_name=report_name)
|
|
data = self.get_reformatted_stmt_data(raw_data, statement_type)
|
|
else:
|
|
data = self.get_stock_data(statement_type, report_name=report_name)
|
|
return data
|
|
|
|
# Public Method for the user to get financial statement data
|
|
def get_financial_stmts(self, frequency, statement_type, reformat=True):
|
|
report_num = self.get_report_type(frequency)
|
|
if isinstance(statement_type, str):
|
|
data = self._run_financial_stmt(statement_type, report_num, reformat)
|
|
else:
|
|
data = {}
|
|
for stmt_type in statement_type:
|
|
re_data = self._run_financial_stmt(stmt_type, report_num, reformat)
|
|
data.update(re_data)
|
|
return data
|
|
|
|
# Public Method for the user to get stock price data
|
|
def get_stock_price_data(self, reformat=True):
|
|
if reformat:
|
|
return self.get_clean_data(self.get_stock_tech_data('price'), 'price')
|
|
else:
|
|
return self.get_stock_tech_data('price')
|
|
|
|
# Public Method for the user to return key-statistics data
|
|
def get_key_statistics_data(self, reformat=True):
|
|
if reformat:
|
|
return self.get_clean_data(self.get_stock_tech_data('defaultKeyStatistics'), 'defaultKeyStatistics')
|
|
else:
|
|
return self.get_stock_tech_data('defaultKeyStatistics')
|
|
|
|
# Public Method for the user to get stock earnings data
|
|
def get_stock_earnings_data(self, reformat=True):
|
|
if reformat:
|
|
return self.get_clean_data(self.get_stock_tech_data('earnings'), 'earnings')
|
|
else:
|
|
return self.get_stock_tech_data('earnings')
|
|
|
|
# Public Method for the user to get stock summary data
|
|
def get_summary_data(self, reformat=True):
|
|
if reformat:
|
|
return self.get_clean_data(self.get_stock_tech_data('summaryDetail'), 'summaryDetail')
|
|
else:
|
|
return self.get_stock_tech_data('summaryDetail')
|
|
|
|
# Public Method for the user to get the yahoo summary url
|
|
def get_stock_summary_url(self):
|
|
if isinstance(self.ticker, str):
|
|
return self._BASE_YAHOO_URL + self.ticker
|
|
return {t: self._BASE_YAHOO_URL + t for t in self.ticker}
|
|
|
|
# Public Method for the user to get stock quote data
|
|
def get_stock_quote_type_data(self):
|
|
return self.get_stock_tech_data('quoteType')
|
|
|
|
# Public Method for user to get historical price data with
|
|
def get_historical_price_data(self, start_date, end_date, time_interval):
|
|
interval_code = self.get_time_code(time_interval)
|
|
start = self.format_date(start_date)
|
|
end = self.format_date(end_date)
|
|
hist_obj = {'start': start, 'end': end, 'interval': interval_code}
|
|
return self.get_stock_data('history', hist_obj=hist_obj)
|
|
|
|
# Private Method for Functions needing stock_price_data
|
|
def _stock_price_data(self, data_field):
|
|
if isinstance(self.ticker, str):
|
|
if self.get_stock_price_data()[self.ticker] is None:
|
|
return None
|
|
return self.get_stock_price_data()[self.ticker].get(data_field, None)
|
|
else:
|
|
ret_obj = {}
|
|
for tick in self.ticker:
|
|
if self.get_stock_price_data()[tick] is None:
|
|
ret_obj.update({tick: None})
|
|
else:
|
|
ret_obj.update({tick: self.get_stock_price_data()[tick].get(data_field, None)})
|
|
return ret_obj
|
|
|
|
# Private Method for Functions needing stock_price_data
|
|
def _stock_summary_data(self, data_field):
|
|
if isinstance(self.ticker, str):
|
|
if self.get_summary_data()[self.ticker] is None:
|
|
return None
|
|
return self.get_summary_data()[self.ticker].get(data_field, None)
|
|
else:
|
|
ret_obj = {}
|
|
for tick in self.ticker:
|
|
if self.get_summary_data()[tick] is None:
|
|
ret_obj.update({tick: None})
|
|
else:
|
|
ret_obj.update({tick: self.get_summary_data()[tick].get(data_field, None)})
|
|
return ret_obj
|
|
|
|
# Private Method for Functions needing financial statement data
|
|
def _financial_statement_data(self, stmt_type, stmt_code, field_name, freq):
|
|
re_data = self.get_financial_stmts(freq, stmt_type)[stmt_code]
|
|
if isinstance(self.ticker, str):
|
|
try:
|
|
date_key = re_data[self.ticker][0].keys()[0]
|
|
except (IndexError, AttributeError, TypeError):
|
|
date_key = list(re_data[self.ticker][0])[0]
|
|
data = re_data[self.ticker][0][date_key][field_name]
|
|
else:
|
|
data = {}
|
|
for tick in self.ticker:
|
|
try:
|
|
date_key = re_data[tick][0].keys()[0]
|
|
except:
|
|
try:
|
|
date_key = list(re_data[tick][0].keys())[0]
|
|
except:
|
|
date_key = None
|
|
if date_key is not None:
|
|
sub_data = re_data[tick][0][date_key][field_name]
|
|
data.update({tick: sub_data})
|
|
else:
|
|
data.update({tick: None})
|
|
return data
|
|
|
|
# Public method to get daily dividend data
|
|
def get_daily_dividend_data(self, start_date, end_date):
|
|
start = self.format_date(start_date)
|
|
end = self.format_date(end_date)
|
|
return self.get_stock_dividend_data(start, end, 'daily')
|
|
|
|
# Public Price Data Methods
|
|
def get_current_price(self):
|
|
return self._stock_price_data('regularMarketPrice')
|
|
|
|
def get_current_change(self):
|
|
return self._stock_price_data('regularMarketChange')
|
|
|
|
def get_current_percent_change(self):
|
|
return self._stock_price_data('regularMarketChangePercent')
|
|
|
|
def get_current_volume(self):
|
|
return self._stock_price_data('regularMarketVolume')
|
|
|
|
def get_prev_close_price(self):
|
|
return self._stock_price_data('regularMarketPreviousClose')
|
|
|
|
def get_open_price(self):
|
|
return self._stock_price_data('regularMarketOpen')
|
|
|
|
def get_ten_day_avg_daily_volume(self):
|
|
return self._stock_price_data('averageDailyVolume10Day')
|
|
|
|
def get_three_month_avg_daily_volume(self):
|
|
return self._stock_price_data('averageDailyVolume3Month')
|
|
|
|
def get_stock_exchange(self):
|
|
return self._stock_price_data('exchangeName')
|
|
|
|
def get_market_cap(self):
|
|
return self._stock_price_data('marketCap')
|
|
|
|
def get_daily_low(self):
|
|
return self._stock_price_data('regularMarketDayLow')
|
|
|
|
def get_daily_high(self):
|
|
return self._stock_price_data('regularMarketDayHigh')
|
|
|
|
def get_currency(self):
|
|
return self._stock_price_data('currency')
|
|
|
|
# Public Summary Data Methods
|
|
def get_yearly_high(self):
|
|
return self._stock_summary_data('fiftyTwoWeekHigh')
|
|
|
|
def get_yearly_low(self):
|
|
return self._stock_summary_data('fiftyTwoWeekLow')
|
|
|
|
def get_dividend_yield(self):
|
|
return self._stock_summary_data('dividendYield')
|
|
|
|
def get_annual_avg_div_yield(self):
|
|
return self._stock_summary_data('trailingAnnualDividendYield')
|
|
|
|
def get_five_yr_avg_div_yield(self):
|
|
return self._stock_summary_data('fiveYearAvgDividendYield')
|
|
|
|
def get_dividend_rate(self):
|
|
return self._stock_summary_data('dividendRate')
|
|
|
|
def get_annual_avg_div_rate(self):
|
|
return self._stock_summary_data('trailingAnnualDividendRate')
|
|
|
|
def get_50day_moving_avg(self):
|
|
return self._stock_summary_data('fiftyDayAverage')
|
|
|
|
def get_200day_moving_avg(self):
|
|
return self._stock_summary_data('twoHundredDayAverage')
|
|
|
|
def get_beta(self):
|
|
return self._stock_summary_data('beta')
|
|
|
|
def get_payout_ratio(self):
|
|
return self._stock_summary_data('payoutRatio')
|
|
|
|
def get_pe_ratio(self):
|
|
return self._stock_summary_data('trailingPE')
|
|
|
|
def get_price_to_sales(self):
|
|
return self._stock_summary_data('priceToSalesTrailing12Months')
|
|
|
|
def get_exdividend_date(self):
|
|
return self._stock_summary_data('exDividendDate')
|
|
|
|
# Financial Statement Data Methods
|
|
def get_book_value(self):
|
|
return self._financial_statement_data('balance', 'balanceSheetHistoryQuarterly',
|
|
'totalStockholderEquity', 'quarterly')
|
|
|
|
def get_ebit(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'ebit', 'annual')
|
|
|
|
def get_net_income(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'netIncome', 'annual')
|
|
|
|
def get_interest_expense(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'interestExpense', 'annual')
|
|
|
|
def get_operating_income(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'operatingIncome', 'annual')
|
|
|
|
def get_total_operating_expense(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'totalOperatingExpenses', 'annual')
|
|
|
|
def get_total_revenue(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'totalRevenue', 'annual')
|
|
|
|
def get_cost_of_revenue(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'costOfRevenue', 'annual')
|
|
|
|
def get_income_before_tax(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'incomeBeforeTax', 'annual')
|
|
|
|
def get_income_tax_expense(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'incomeTaxExpense', 'annual')
|
|
|
|
def get_gross_profit(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'grossProfit', 'annual')
|
|
|
|
def get_net_income_from_continuing_ops(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory',
|
|
'netIncomeFromContinuingOps', 'annual')
|
|
|
|
def get_research_and_development(self):
|
|
return self._financial_statement_data('income', 'incomeStatementHistory', 'researchDevelopment', 'annual')
|
|
|
|
# Calculated Financial Methods
|
|
def get_earnings_per_share(self):
|
|
price_data = self.get_current_price()
|
|
pe_ratio = self.get_pe_ratio()
|
|
if isinstance(self.ticker, str):
|
|
if price_data is not None and pe_ratio is not None:
|
|
return price_data / pe_ratio
|
|
else:
|
|
return None
|
|
else:
|
|
ret_obj = {}
|
|
for tick in self.ticker:
|
|
if price_data[tick] is not None and pe_ratio[tick] is not None:
|
|
ret_obj.update({tick: price_data[tick] / pe_ratio[tick]})
|
|
else:
|
|
ret_obj.update({tick: None})
|
|
return ret_obj
|
|
|
|
def get_num_shares_outstanding(self, price_type='current'):
|
|
today_low = self._stock_summary_data('dayHigh')
|
|
today_high = self._stock_summary_data('dayLow')
|
|
cur_market_cap = self._stock_summary_data('marketCap')
|
|
if isinstance(self.ticker, str):
|
|
if cur_market_cap is not None:
|
|
if price_type == 'current':
|
|
current = self.get_current_price()
|
|
if current is not None:
|
|
today_average = current
|
|
else:
|
|
return None
|
|
else:
|
|
if today_high is not None and today_low is not None:
|
|
today_average = (today_high + today_low) / 2
|
|
else:
|
|
return None
|
|
return cur_market_cap / today_average
|
|
else:
|
|
return None
|
|
else:
|
|
ret_obj = {}
|
|
for tick in self.ticker:
|
|
if cur_market_cap[tick] is not None:
|
|
if price_type == 'current':
|
|
current = self.get_current_price()
|
|
if current[tick] is not None:
|
|
ret_obj.update({tick: cur_market_cap[tick] / current[tick]})
|
|
else:
|
|
ret_obj.update({tick: None})
|
|
else:
|
|
if today_low[tick] is not None and today_high[tick] is not None:
|
|
today_average = (today_high[tick] + today_low[tick]) / 2
|
|
ret_obj.update({tick: cur_market_cap[tick] / today_average})
|
|
else:
|
|
ret_obj.update({tick: None})
|
|
else:
|
|
ret_obj.update({tick: None})
|
|
return ret_obj |