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fund-indicators/modules/yahoofinancials.py

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General fixes Added color, config file, moved packages into ./modules
2019-03-18 10:26:07 -07:00
"""
==============================
The Yahoo Financials Module
Version: 1.5
==============================
Author: Connor Sanders
Email: sandersconnor1@gmail.com
Version Released: 01/27/2019
Tested on Python 2.7, 3.3, 3.4, 3.5, 3.6, and 3.7
Copyright (c) 2019 Connor Sanders
MIT License
List of Included Functions:
1) get_financial_stmts(frequency, statement_type, reformat=True)
- frequency can be either 'annual' or 'quarterly'.
- statement_type can be 'income', 'balance', 'cash'.
- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
2) get_stock_price_data(reformat=True)
- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
3) get_stock_earnings_data(reformat=True)
- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
4) get_summary_data(reformat=True)
- reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
5) get_stock_quote_type_data()
6) get_historical_price_data(start_date, end_date, time_interval)
- Gets historical price data for currencies, stocks, indexes, cryptocurrencies, and commodity futures.
- start_date should be entered in the 'YYYY-MM-DD' format. First day that financial data will be pulled.
- end_date should be entered in the 'YYYY-MM-DD' format. Last day that financial data will be pulled.
- time_interval can be either 'daily', 'weekly', or 'monthly'. Parameter determines the time period interval.
Usage Examples:
from yahoofinancials import YahooFinancials
#tickers = 'AAPL'
#or
tickers = ['AAPL', 'WFC', 'F', 'JPY=X', 'XRP-USD', 'GC=F']
yahoo_financials = YahooFinancials(tickers)
balance_sheet_data = yahoo_financials.get_financial_stmts('quarterly', 'balance')
earnings_data = yahoo_financials.get_stock_earnings_data()
historical_prices = yahoo_financials.get_historical_price_data('2015-01-15', '2017-10-15', 'weekly')
"""
import sys
import calendar
import re
from json import loads
import time
from bs4 import BeautifulSoup
import datetime
import pytz
import random
try:
from urllib import FancyURLopener
except:
from urllib.request import FancyURLopener
# track the last get timestamp to add a minimum delay between gets - be nice!
_lastget = 0
# Custom Exception class to handle custom error
class ManagedException(Exception):
pass
# Class used to open urls for financial data
class UrlOpener(FancyURLopener):
version = 'Mozilla/5.0 (Windows; U; Windows NT 5.1; it; rv:1.8.1.11) Gecko/20071127 Firefox/2.0.0.11'
# Class containing Yahoo Finance ETL Functionality
class YahooFinanceETL(object):
def __init__(self, ticker):
self.ticker = ticker.upper() if isinstance(ticker, str) else [t.upper() for t in ticker]
self._cache = {}
# Minimum interval between Yahoo Finance requests for this instance
_MIN_INTERVAL = 7
# Meta-data dictionaries for the classes to use
YAHOO_FINANCIAL_TYPES = {
'income': ['financials', 'incomeStatementHistory', 'incomeStatementHistoryQuarterly'],
'balance': ['balance-sheet', 'balanceSheetHistory', 'balanceSheetHistoryQuarterly', 'balanceSheetStatements'],
'cash': ['cash-flow', 'cashflowStatementHistory', 'cashflowStatementHistoryQuarterly', 'cashflowStatements'],
'keystats': ['key-statistics'],
'history': ['history']
}
# Interval value translation dictionary
_INTERVAL_DICT = {
'daily': '1d',
'weekly': '1wk',
'monthly': '1mo'
}
# Base Yahoo Finance URL for the class to build on
_BASE_YAHOO_URL = 'https://finance.yahoo.com/quote/'
# private static method to get the appropriate report type identifier
@staticmethod
def get_report_type(frequency):
if frequency == 'annual':
report_num = 1
else:
report_num = 2
return report_num
# Public static method to format date serial string to readable format and vice versa
@staticmethod
def format_date(in_date):
if isinstance(in_date, str):
form_date = int(calendar.timegm(time.strptime(in_date, '%Y-%m-%d')))
else:
form_date = str((datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=in_date)).date())
return form_date
# Private Static Method to Convert Eastern Time to UTC
@staticmethod
def _convert_to_utc(date, mask='%Y-%m-%d %H:%M:%S'):
utc = pytz.utc
eastern = pytz.timezone('US/Eastern')
date_ = datetime.datetime.strptime(date.replace(" 0:", " 12:"), mask)
date_eastern = eastern.localize(date_, is_dst=None)
date_utc = date_eastern.astimezone(utc)
return date_utc.strftime('%Y-%m-%d %H:%M:%S %Z%z')
# Private method to scrape data from yahoo finance
def _scrape_data(self, url, tech_type, statement_type):
global _lastget
if not self._cache.get(url):
now = int(time.time())
if _lastget and now - _lastget < self._MIN_INTERVAL:
time.sleep(self._MIN_INTERVAL - (now - _lastget) + 1)
now = int(time.time())
_lastget = now
urlopener = UrlOpener()
# Try to open the URL up to 10 times sleeping random time if something goes wrong
max_retry = 10
for i in range(0, max_retry):
response = urlopener.open(url)
if response.getcode() != 200:
time.sleep(random.randrange(10, 20))
else:
response_content = response.read()
soup = BeautifulSoup(response_content, "html.parser")
re_script = soup.find("script", text=re.compile("root.App.main"))
if re_script is not None:
script = re_script.text
self._cache[url] = loads(re.search("root.App.main\s+=\s+(\{.*\})", script).group(1))
response.close()
break
else:
time.sleep(random.randrange(10, 20))
if i == max_retry - 1:
# Raise a custom exception if we can't get the web page within max_retry attempts
raise ManagedException("Server replied with HTTP " + str(response.getcode()) +
" code while opening the url: " + str(url))
data = self._cache[url]
if tech_type == '' and statement_type != 'history':
stores = data["context"]["dispatcher"]["stores"]["QuoteSummaryStore"]
elif tech_type != '' and statement_type != 'history':
stores = data["context"]["dispatcher"]["stores"]["QuoteSummaryStore"][tech_type]
else:
stores = data["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
return stores
# Private static method to determine if a numerical value is in the data object being cleaned
@staticmethod
def _determine_numeric_value(value_dict):
if 'raw' in value_dict.keys():
numerical_val = value_dict['raw']
else:
numerical_val = None
return numerical_val
# Private method to format date serial string to readable format and vice versa
def _format_time(self, in_time):
form_date_time = datetime.datetime.fromtimestamp(int(in_time)).strftime('%Y-%m-%d %H:%M:%S')
utc_dt = self._convert_to_utc(form_date_time)
return utc_dt
# Private method to return the a sub dictionary entry for the earning report cleaning
def _get_cleaned_sub_dict_ent(self, key, val_list):
sub_list = []
for rec in val_list:
sub_sub_dict = {}
for k, v in rec.items():
if k == 'date':
sub_sub_dict_ent = {k: v}
else:
numerical_val = self._determine_numeric_value(v)
sub_sub_dict_ent = {k: numerical_val}
sub_sub_dict.update(sub_sub_dict_ent)
sub_list.append(sub_sub_dict)
sub_ent = {key: sub_list}
return sub_ent
# Private method to process raw earnings data and clean
def _clean_earnings_data(self, raw_data):
cleaned_data = {}
earnings_key = 'earningsData'
financials_key = 'financialsData'
for k, v in raw_data.items():
if k == 'earningsChart':
sub_dict = {}
for k2, v2 in v.items():
if k2 == 'quarterly':
sub_ent = self._get_cleaned_sub_dict_ent(k2, v2)
elif k2 == 'currentQuarterEstimate':
numerical_val = self._determine_numeric_value(v2)
sub_ent = {k2: numerical_val}
else:
sub_ent = {k2: v2}
sub_dict.update(sub_ent)
dict_ent = {earnings_key: sub_dict}
cleaned_data.update(dict_ent)
elif k == 'financialsChart':
sub_dict = {}
for k2, v2, in v.items():
sub_ent = self._get_cleaned_sub_dict_ent(k2, v2)
sub_dict.update(sub_ent)
dict_ent = {financials_key: sub_dict}
cleaned_data.update(dict_ent)
else:
if k != 'maxAge':
dict_ent = {k: v}
cleaned_data.update(dict_ent)
return cleaned_data
# Private method to clean summary and price reports
def _clean_reports(self, raw_data):
cleaned_dict = {}
if raw_data is None:
return None
for k, v in raw_data.items():
if 'Time' in k:
formatted_utc_time = self._format_time(v)
dict_ent = {k: formatted_utc_time}
elif 'Date' in k:
try:
formatted_date = v['fmt']
except (KeyError, TypeError):
formatted_date = '-'
dict_ent = {k: formatted_date}
elif v is None or isinstance(v, str) or isinstance(v, int) or isinstance(v, float):
dict_ent = {k: v}
# Python 2 and Unicode
elif sys.version_info < (3, 0) and isinstance(v, unicode):
dict_ent = {k: v}
else:
numerical_val = self._determine_numeric_value(v)
dict_ent = {k: numerical_val}
cleaned_dict.update(dict_ent)
return cleaned_dict
# Private Static Method to ensure ticker is URL encoded
@staticmethod
def _encode_ticker(ticker_str):
encoded_ticker = ticker_str.replace('=', '%3D')
return encoded_ticker
# Private method to get time interval code
def _build_historical_url(self, ticker, hist_oj):
url = self._BASE_YAHOO_URL + self._encode_ticker(ticker) + '/history?period1=' + str(hist_oj['start']) + \
'&period2=' + str(hist_oj['end']) + '&interval=' + hist_oj['interval'] + '&filter=history&frequency=' + \
hist_oj['interval']
return url
# Private Method to clean the dates of the newly returns historical stock data into readable format
def _clean_historical_data(self, hist_data, last_attempt=False):
data = {}
for k, v in hist_data.items():
if k == 'eventsData':
event_obj = {}
if isinstance(v, list):
dict_ent = {k: event_obj}
else:
for type_key, type_obj in v.items():
formatted_type_obj = {}
for date_key, date_obj in type_obj.items():
formatted_date_key = self.format_date(int(date_key))
cleaned_date = self.format_date(int(date_obj['date']))
date_obj.update({'formatted_date': cleaned_date})
formatted_type_obj.update({formatted_date_key: date_obj})
event_obj.update({type_key: formatted_type_obj})
dict_ent = {k: event_obj}
elif 'date' in k.lower():
if v is not None:
cleaned_date = self.format_date(v)
dict_ent = {k: {'formatted_date': cleaned_date, 'date': v}}
else:
if last_attempt is False:
return None
else:
dict_ent = {k: {'formatted_date': None, 'date': v}}
elif isinstance(v, list):
sub_dict_list = []
for sub_dict in v:
sub_dict['formatted_date'] = self.format_date(sub_dict['date'])
sub_dict_list.append(sub_dict)
dict_ent = {k: sub_dict_list}
else:
dict_ent = {k: v}
data.update(dict_ent)
return data
# Private Static Method to build API url for GET Request
@staticmethod
def _build_api_url(hist_obj, up_ticker):
base_url = "https://query1.finance.yahoo.com/v8/finance/chart/"
api_url = base_url + up_ticker + '?symbol=' + up_ticker + '&period1=' + str(hist_obj['start']) + '&period2=' + \
str(hist_obj['end']) + '&interval=' + hist_obj['interval']
api_url += '&events=div|split|earn&lang=en-US&region=US'
return api_url
# Private Method to get financial data via API Call
def _get_api_data(self, api_url, tries=0):
urlopener = UrlOpener()
response = urlopener.open(api_url)
if response.getcode() == 200:
res_content = response.read()
response.close()
if sys.version_info < (3, 0):
return loads(res_content)
return loads(res_content.decode('utf-8'))
else:
if tries < 5:
time.sleep(random.randrange(10, 20))
tries += 1
return self._get_api_data(api_url, tries)
else:
return None
# Private Method to clean API data
def _clean_api_data(self, api_url):
raw_data = self._get_api_data(api_url)
ret_obj = {}
ret_obj.update({'eventsData': []})
if raw_data is None:
return ret_obj
results = raw_data['chart']['result']
if results is None:
return ret_obj
for result in results:
tz_sub_dict = {}
ret_obj.update({'eventsData': result.get('events', {})})
ret_obj.update({'firstTradeDate': result['meta'].get('firstTradeDate', 'NA')})
ret_obj.update({'currency': result['meta'].get('currency', 'NA')})
ret_obj.update({'instrumentType': result['meta'].get('instrumentType', 'NA')})
tz_sub_dict.update({'gmtOffset': result['meta']['gmtoffset']})
ret_obj.update({'timeZone': tz_sub_dict})
timestamp_list = result['timestamp']
high_price_list = result['indicators']['quote'][0]['high']
low_price_list = result['indicators']['quote'][0]['low']
open_price_list = result['indicators']['quote'][0]['open']
close_price_list = result['indicators']['quote'][0]['close']
volume_list = result['indicators']['quote'][0]['volume']
adj_close_list = result['indicators']['adjclose'][0]['adjclose']
i = 0
prices_list = []
for timestamp in timestamp_list:
price_dict = {}
price_dict.update({'date': timestamp})
price_dict.update({'high': high_price_list[i]})
price_dict.update({'low': low_price_list[i]})
price_dict.update({'open': open_price_list[i]})
price_dict.update({'close': close_price_list[i]})
price_dict.update({'volume': volume_list[i]})
price_dict.update({'adjclose': adj_close_list[i]})
prices_list.append(price_dict)
i += 1
ret_obj.update({'prices': prices_list})
return ret_obj
# Private Method to Handle Recursive API Request
def _recursive_api_request(self, hist_obj, up_ticker, i=0):
api_url = self._build_api_url(hist_obj, up_ticker)
re_data = self._clean_api_data(api_url)
cleaned_re_data = self._clean_historical_data(re_data)
if cleaned_re_data is not None:
return cleaned_re_data
else:
if i < 3:
i += 1
return self._recursive_api_request(hist_obj, up_ticker, i)
else:
return self._clean_historical_data(re_data, True)
# Private Method to take scrapped data and build a data dictionary with
def _create_dict_ent(self, up_ticker, statement_type, tech_type, report_name, hist_obj):
YAHOO_URL = self._BASE_YAHOO_URL + up_ticker + '/' + self.YAHOO_FINANCIAL_TYPES[statement_type][0] + '?p=' +\
up_ticker
if tech_type == '' and statement_type != 'history':
try:
re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
dict_ent = {up_ticker: re_data[u'' + report_name], 'dataType': report_name}
except KeyError:
re_data = None
dict_ent = {up_ticker: re_data, 'dataType': report_name}
elif tech_type != '' and statement_type != 'history':
try:
re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
except KeyError:
re_data = None
dict_ent = {up_ticker: re_data}
else:
YAHOO_URL = self._build_historical_url(up_ticker, hist_obj)
try:
cleaned_re_data = self._recursive_api_request(hist_obj, up_ticker)
except KeyError:
try:
re_data = self._scrape_data(YAHOO_URL, tech_type, statement_type)
cleaned_re_data = self._clean_historical_data(re_data)
except KeyError:
cleaned_re_data = None
dict_ent = {up_ticker: cleaned_re_data}
return dict_ent
# Private method to return the stmt_id for the reformat_process
def _get_stmt_id(self, statement_type, raw_data):
stmt_id = ''
i = 0
for key in raw_data.keys():
if key in self.YAHOO_FINANCIAL_TYPES[statement_type.lower()]:
stmt_id = key
i += 1
if i != 1:
return None
return stmt_id
# Private Method for the Reformat Process
def _reformat_stmt_data_process(self, raw_data, statement_type):
final_data_list = []
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
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