#!/usr/bin/env python # -*- coding: utf-8 -*- # # yfinance - market data downloader # https://github.com/ranaroussi/yfinance # # Copyright 2017-2019 Ran Aroussi # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import time as _time import datetime as _datetime import requests as _requests import pandas as _pd import numpy as _np import re as _re try: from urllib.parse import quote as urlencode except ImportError: from urllib import quote as urlencode from . import utils import json as _json # import re as _re # import sys as _sys from . import shared _BASE_URL_ = 'https://query2.finance.yahoo.com' _SCRAPE_URL_ = 'https://finance.yahoo.com/quote' _ROOT_URL_ = 'https://finance.yahoo.com' class TickerBase(): def __init__(self, ticker, session=None): self.ticker = ticker.upper() self.session = session self._history = None self._base_url = _BASE_URL_ self._scrape_url = _SCRAPE_URL_ self._fundamentals = False self._info = None self._analysis = None self._sustainability = None self._recommendations = None self._major_holders = None self._institutional_holders = None self._mutualfund_holders = None self._isin = None self._news = [] self._shares = None self._calendar = None self._expirations = {} self._earnings_dates = None self._earnings_history = None self._earnings = { "yearly": utils.empty_df(), "quarterly": utils.empty_df()} self._financials = { "yearly": utils.empty_df(), "quarterly": utils.empty_df()} self._balancesheet = { "yearly": utils.empty_df(), "quarterly": utils.empty_df()} self._cashflow = { "yearly": utils.empty_df(), "quarterly": utils.empty_df()} # accept isin as ticker if utils.is_isin(self.ticker): self.ticker = utils.get_ticker_by_isin(self.ticker, None, session) def stats(self, proxy=None): # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} if self._fundamentals: return ticker_url = "{}/{}".format(self._scrape_url, self.ticker) # get info and sustainability data = utils.get_json(ticker_url, proxy, self.session) return data def history(self, period="1mo", interval="1d", start=None, end=None, prepost=False, actions=True, auto_adjust=True, back_adjust=False, proxy=None, rounding=False, tz=None, timeout=None, **kwargs): """ :Parameters: period : str Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max Either Use period parameter or use start and end interval : str Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo Intraday data cannot extend last 60 days start: str Download start date string (YYYY-MM-DD) or _datetime. Default is 1900-01-01 end: str Download end date string (YYYY-MM-DD) or _datetime. Default is now prepost : bool Include Pre and Post market data in results? Default is False auto_adjust: bool Adjust all OHLC automatically? Default is True back_adjust: bool Back-adjusted data to mimic true historical prices proxy: str Optional. Proxy server URL scheme. Default is None rounding: bool Round values to 2 decimal places? Optional. Default is False = precision suggested by Yahoo! tz: str Optional timezone locale for dates. (default data is returned as non-localized dates) timeout: None or float If not None stops waiting for a response after given number of seconds. (Can also be a fraction of a second e.g. 0.01) Default is None. **kwargs: dict debug: bool Optional. If passed as False, will suppress error message printing to console. """ if start or period is None or period.lower() == "max": if end is None: end = int(_time.time()) elif isinstance(end, _datetime.datetime): end = int(_time.mktime(end.timetuple())) else: end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d'))) if start is None: if interval == "1m": start = end - 604800 # Subtract 7 days else: start = -631159200 elif isinstance(start, _datetime.datetime): start = int(_time.mktime(start.timetuple())) else: start = int(_time.mktime( _time.strptime(str(start), '%Y-%m-%d'))) params = {"period1": start, "period2": end} else: period = period.lower() params = {"range": period} params["interval"] = interval.lower() params["includePrePost"] = prepost params["events"] = "div,splits" # 1) fix weired bug with Yahoo! - returning 60m for 30m bars if params["interval"] == "30m": params["interval"] = "15m" # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} # Getting data from json url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker) session = self.session or _requests data = None try: data = session.get( url=url, params=params, proxies=proxy, headers=utils.user_agent_headers, timeout=timeout ) if "Will be right back" in data.text or data is None: raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n" "Our engineers are working quickly to resolve " "the issue. Thank you for your patience.") data = data.json() except Exception: pass # Work with errors debug_mode = True if "debug" in kwargs and isinstance(kwargs["debug"], bool): debug_mode = kwargs["debug"] err_msg = "No data found for this date range, symbol may be delisted" if data is None or not type(data) is dict or 'status_code' in data.keys(): shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if "many" not in kwargs and debug_mode: print('- %s: %s' % (self.ticker, err_msg)) return utils.empty_df() if "chart" in data and data["chart"]["error"]: err_msg = data["chart"]["error"]["description"] shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if "many" not in kwargs and debug_mode: print('- %s: %s' % (self.ticker, err_msg)) return shared._DFS[self.ticker] elif "chart" not in data or data["chart"]["result"] is None or \ not data["chart"]["result"]: shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if "many" not in kwargs and debug_mode: print('- %s: %s' % (self.ticker, err_msg)) return shared._DFS[self.ticker] # parse quotes try: quotes = utils.parse_quotes(data["chart"]["result"][0], tz) except Exception: shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if "many" not in kwargs and debug_mode: print('- %s: %s' % (self.ticker, err_msg)) return shared._DFS[self.ticker] # 2) fix weired bug with Yahoo! - returning 60m for 30m bars if interval.lower() == "30m": quotes2 = quotes.resample('30T') quotes = _pd.DataFrame(index=quotes2.last().index, data={ 'Open': quotes2['Open'].first(), 'High': quotes2['High'].max(), 'Low': quotes2['Low'].min(), 'Close': quotes2['Close'].last(), 'Adj Close': quotes2['Adj Close'].last(), 'Volume': quotes2['Volume'].sum() }) try: quotes['Dividends'] = quotes2['Dividends'].max() except Exception: pass try: quotes['Stock Splits'] = quotes2['Dividends'].max() except Exception: pass try: if auto_adjust: quotes = utils.auto_adjust(quotes) elif back_adjust: quotes = utils.back_adjust(quotes) except Exception as e: if auto_adjust: err_msg = "auto_adjust failed with %s" % e else: err_msg = "back_adjust failed with %s" % e shared._DFS[self.ticker] = utils.empty_df() shared._ERRORS[self.ticker] = err_msg if "many" not in kwargs and debug_mode: print('- %s: %s' % (self.ticker, err_msg)) if rounding: quotes = _np.round(quotes, data[ "chart"]["result"][0]["meta"]["priceHint"]) quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64) quotes.dropna(inplace=True) # actions dividends, splits = utils.parse_actions(data["chart"]["result"][0], tz) # Yahoo bug fix - it often appends latest price even if after end date if end and not quotes.empty: endDt = _pd.to_datetime(_datetime.datetime.fromtimestamp(end)) if quotes.index[quotes.shape[0]-1] > endDt: quotes = quotes.iloc[0:quotes.shape[0]-1] # combine df = _pd.concat([quotes, dividends, splits], axis=1, sort=True) df["Dividends"].fillna(0, inplace=True) df["Stock Splits"].fillna(0, inplace=True) # index eod/intraday df.index = df.index.tz_localize("UTC").tz_convert( data["chart"]["result"][0]["meta"]["exchangeTimezoneName"]) if params["interval"][-1] == "m": df.index.name = "Datetime" elif params["interval"] == "1h": pass else: df.index = _pd.to_datetime(df.index.date) if tz is not None: df.index = df.index.tz_localize(tz) df.index.name = "Date" # duplicates and missing rows cleanup df.dropna(how='all', inplace=True) df = df[~df.index.duplicated(keep='first')] self._history = df.copy() if not actions: df.drop(columns=["Dividends", "Stock Splits"], inplace=True) return df # ------------------------ def _get_fundamentals(self, proxy=None): def cleanup(data): df = _pd.DataFrame(data).drop(columns=['maxAge']) for col in df.columns: df[col] = _np.where( df[col].astype(str) == '-', _np.nan, df[col]) df.set_index('endDate', inplace=True) try: df.index = _pd.to_datetime(df.index, unit='s') except ValueError: df.index = _pd.to_datetime(df.index) df = df.T df.columns.name = '' df.index.name = 'Breakdown' # rename incorrect yahoo key df.rename(index={'treasuryStock': 'Gains Losses Not Affecting Retained Earnings'}, inplace=True) df.index = utils.camel2title(df.index) return df # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} if self._fundamentals: return ticker_url = "{}/{}".format(self._scrape_url, self.ticker) # get info and sustainability data = utils.get_json(ticker_url, proxy, self.session) # holders try: resp = utils.get_html(ticker_url + '/holders', proxy, self.session) holders = _pd.read_html(resp) except Exception: holders = [] if len(holders) >= 3: self._major_holders = holders[0] self._institutional_holders = holders[1] self._mutualfund_holders = holders[2] elif len(holders) >= 2: self._major_holders = holders[0] self._institutional_holders = holders[1] elif len(holders) >= 1: self._major_holders = holders[0] # self._major_holders = holders[0] # self._institutional_holders = holders[1] if self._institutional_holders is not None: if 'Date Reported' in self._institutional_holders: self._institutional_holders['Date Reported'] = _pd.to_datetime( self._institutional_holders['Date Reported']) if '% Out' in self._institutional_holders: self._institutional_holders['% Out'] = self._institutional_holders[ '% Out'].str.replace('%', '').astype(float) / 100 if self._mutualfund_holders is not None: if 'Date Reported' in self._mutualfund_holders: self._mutualfund_holders['Date Reported'] = _pd.to_datetime( self._mutualfund_holders['Date Reported']) if '% Out' in self._mutualfund_holders: self._mutualfund_holders['% Out'] = self._mutualfund_holders[ '% Out'].str.replace('%', '').astype(float) / 100 # sustainability d = {} try: if isinstance(data.get('esgScores'), dict): for item in data['esgScores']: if not isinstance(data['esgScores'][item], (dict, list)): d[item] = data['esgScores'][item] s = _pd.DataFrame(index=[0], data=d)[-1:].T s.columns = ['Value'] s.index.name = '%.f-%.f' % ( s[s.index == 'ratingYear']['Value'].values[0], s[s.index == 'ratingMonth']['Value'].values[0]) self._sustainability = s[~s.index.isin( ['maxAge', 'ratingYear', 'ratingMonth'])] except Exception: pass # info (be nice to python 2) self._info = {} try: items = ['summaryProfile', 'financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail'] for item in items: if isinstance(data.get(item), dict): self._info.update(data[item]) except Exception: pass # For ETFs, provide this valuable data: the top holdings of the ETF try: if 'topHoldings' in data: self._info.update(data['topHoldings']) except Exception: pass try: if not isinstance(data.get('summaryDetail'), dict): # For some reason summaryDetail did not give any results. The price dict usually has most of the same info self._info.update(data.get('price', {})) except Exception: pass try: # self._info['regularMarketPrice'] = self._info['regularMarketOpen'] self._info['regularMarketPrice'] = data.get('price', {}).get( 'regularMarketPrice', self._info.get('regularMarketOpen', None)) except Exception: pass try: self._info['preMarketPrice'] = data.get('price', {}).get( 'preMarketPrice', self._info.get('preMarketPrice', None)) except Exception: pass self._info['logo_url'] = "" try: domain = self._info['website'].split( '://')[1].split('/')[0].replace('www.', '') self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain except Exception: pass # events try: cal = _pd.DataFrame( data['calendarEvents']['earnings']) cal['earningsDate'] = _pd.to_datetime( cal['earningsDate'], unit='s') self._calendar = cal.T self._calendar.index = utils.camel2title(self._calendar.index) self._calendar.columns = ['Value'] except Exception: pass # analyst recommendations try: rec = _pd.DataFrame( data['upgradeDowngradeHistory']['history']) rec['earningsDate'] = _pd.to_datetime( rec['epochGradeDate'], unit='s') rec.set_index('earningsDate', inplace=True) rec.index.name = 'Date' rec.columns = utils.camel2title(rec.columns) self._recommendations = rec[[ 'Firm', 'To Grade', 'From Grade', 'Action']].sort_index() except Exception: pass # get fundamentals data = utils.get_json(ticker_url + '/financials', proxy, self.session) # generic patterns for key in ( (self._cashflow, 'cashflowStatement', 'cashflowStatements'), (self._balancesheet, 'balanceSheet', 'balanceSheetStatements'), (self._financials, 'incomeStatement', 'incomeStatementHistory') ): item = key[1] + 'History' if isinstance(data.get(item), dict): try: key[0]['yearly'] = cleanup(data[item][key[2]]) except Exception: pass item = key[1] + 'HistoryQuarterly' if isinstance(data.get(item), dict): try: key[0]['quarterly'] = cleanup(data[item][key[2]]) except Exception: pass # earnings if isinstance(data.get('earnings'), dict): try: earnings = data['earnings']['financialsChart'] earnings['financialCurrency'] = 'USD' if 'financialCurrency' not in data['earnings'] else data['earnings']['financialCurrency'] self._earnings['financialCurrency'] = earnings['financialCurrency'] df = _pd.DataFrame(earnings['yearly']).set_index('date') df.columns = utils.camel2title(df.columns) df.index.name = 'Year' self._earnings['yearly'] = df df = _pd.DataFrame(earnings['quarterly']).set_index('date') df.columns = utils.camel2title(df.columns) df.index.name = 'Quarter' self._earnings['quarterly'] = df except Exception: pass # shares outstanding try: # keep only years with non None data available_shares = [shares_data for shares_data in data['annualBasicAverageShares'] if shares_data] shares = _pd.DataFrame(available_shares) shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4])) shares.set_index('Year', inplace=True) shares.drop(columns=['dataId', 'asOfDate', 'periodType', 'currencyCode'], inplace=True) shares.rename( columns={'reportedValue': "BasicShares"}, inplace=True) self._shares = shares except Exception: pass # Analysis data = utils.get_json(ticker_url + '/analysis', proxy, self.session) if isinstance(data.get('earningsTrend'), dict): try: analysis = _pd.DataFrame(data['earningsTrend']['trend']) analysis['endDate'] = _pd.to_datetime(analysis['endDate']) analysis.set_index('period', inplace=True) analysis.index = analysis.index.str.upper() analysis.index.name = 'Period' analysis.columns = utils.camel2title(analysis.columns) dict_cols = [] for idx, row in analysis.iterrows(): for colname, colval in row.items(): if isinstance(colval, dict): dict_cols.append(colname) for k, v in colval.items(): new_colname = colname + ' ' + \ utils.camel2title([k])[0] analysis.loc[idx, new_colname] = v self._analysis = analysis[[ c for c in analysis.columns if c not in dict_cols]] except Exception: pass # Complementary key-statistics (currently fetching the important trailingPegRatio which is the value shown in the website) res = {} try: my_headers = {'user-agent': 'curl/7.55.1', 'accept': 'application/json', 'content-type': 'application/json', 'referer': 'https://finance.yahoo.com/', 'cache-control': 'no-cache', 'connection': 'close'} p = _re.compile(r'root\.App\.main = (.*);') r = _requests.session().get('https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self.ticker, self.ticker), headers=my_headers) q_results = {} my_qs_keys = ['pegRatio'] # QuoteSummaryStore # , 'quarterlyPegRatio'] # QuoteTimeSeriesStore my_ts_keys = ['trailingPegRatio'] # Complementary key-statistics data = _json.loads(p.findall(r.text)[0]) key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore'] q_results.setdefault(self.ticker, []) for i in my_ts_keys: # j=0 try: # res = {i: key_stats['timeSeries'][i][1]['reportedValue']['raw']} # We need to loop over multiple items, if they exist: 0,1,2,.. zzz = key_stats['timeSeries'][i] for j in range(len(zzz)): if key_stats['timeSeries'][i][j]: res = {i: key_stats['timeSeries'] [i][j]['reportedValue']['raw']} q_results[self.ticker].append(res) # print(res) # q_results[ticker].append(res) except: q_results[ticker].append({i: np.nan}) res = {'Company': ticker} q_results[ticker].append(res) except Exception: pass if 'trailingPegRatio' in res: self._info['trailingPegRatio'] = res['trailingPegRatio'] self._fundamentals = True def get_recommendations(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._recommendations if as_dict: return data.to_dict() return data def get_calendar(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._calendar if as_dict: return data.to_dict() return data def get_major_holders(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._major_holders if as_dict: return data.to_dict() return data def get_institutional_holders(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._institutional_holders if data is not None: if as_dict: return data.to_dict() return data def get_mutualfund_holders(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._mutualfund_holders if data is not None: if as_dict: return data.to_dict() return data def get_info(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._info if as_dict: return data.to_dict() return data def get_sustainability(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._sustainability if as_dict: return data.to_dict() return data def get_earnings(self, proxy=None, as_dict=False, freq="yearly"): self._get_fundamentals(proxy=proxy) data = self._earnings[freq] if as_dict: dict_data = data.to_dict() dict_data['financialCurrency'] = 'USD' if 'financialCurrency' not in self._earnings else self._earnings['financialCurrency'] return dict_data return data def get_analysis(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._analysis if as_dict: return data.to_dict() return data def get_financials(self, proxy=None, as_dict=False, freq="yearly"): self._get_fundamentals(proxy=proxy) data = self._financials[freq] if as_dict: return data.to_dict() return data def get_balancesheet(self, proxy=None, as_dict=False, freq="yearly"): self._get_fundamentals(proxy=proxy) data = self._balancesheet[freq] if as_dict: return data.to_dict() return data def get_balance_sheet(self, proxy=None, as_dict=False, freq="yearly"): return self.get_balancesheet(proxy, as_dict, freq) def get_cashflow(self, proxy=None, as_dict=False, freq="yearly"): self._get_fundamentals(proxy=proxy) data = self._cashflow[freq] if as_dict: return data.to_dict() return data def get_dividends(self, proxy=None): if self._history is None: self.history(period="max", proxy=proxy) if self._history is not None and "Dividends" in self._history: dividends = self._history["Dividends"] return dividends[dividends != 0] return [] def get_splits(self, proxy=None): if self._history is None: self.history(period="max", proxy=proxy) if self._history is not None and "Stock Splits" in self._history: splits = self._history["Stock Splits"] return splits[splits != 0] return [] def get_actions(self, proxy=None): if self._history is None: self.history(period="max", proxy=proxy) if self._history is not None and "Dividends" in self._history and "Stock Splits" in self._history: actions = self._history[["Dividends", "Stock Splits"]] return actions[actions != 0].dropna(how='all').fillna(0) return [] def get_shares(self, proxy=None, as_dict=False, *args, **kwargs): self._get_fundamentals(proxy=proxy) data = self._shares if as_dict: return data.to_dict() return data def get_isin(self, proxy=None): # *** experimental *** if self._isin is not None: return self._isin ticker = self.ticker.upper() if "-" in ticker or "^" in ticker: self._isin = '-' return self._isin # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} q = ticker self.get_info(proxy=proxy) if "shortName" in self._info: q = self._info['shortName'] url = 'https://markets.businessinsider.com/ajax/' \ 'SearchController_Suggest?max_results=25&query=%s' \ % urlencode(q) session = self.session or _requests data = session.get( url=url, proxies=proxy, headers=utils.user_agent_headers ).text search_str = '"{}|'.format(ticker) if search_str not in data: if q.lower() in data.lower(): search_str = '"|' if search_str not in data: self._isin = '-' return self._isin else: self._isin = '-' return self._isin self._isin = data.split(search_str)[1].split('"')[0].split('|')[0] return self._isin def get_news(self, proxy=None): if self._news: return self._news # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} # Getting data from json url = "{}/v1/finance/search?q={}".format(self._base_url, self.ticker) session = self.session or _requests data = session.get( url=url, proxies=proxy, headers=utils.user_agent_headers ) if "Will be right back" in data.text: raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n" "Our engineers are working quickly to resolve " "the issue. Thank you for your patience.") data = data.json() # parse news self._news = data.get("news", []) return self._news def get_earnings_dates(self, proxy=None): if self._earnings_dates is not None: return self._earnings_dates # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} page_size = 100 # YF caps at 100, don't go higher page_offset = 0 dates = None while True: url = "{}/calendar/earnings?symbol={}&offset={}&size={}".format( _ROOT_URL_, self.ticker, page_offset, page_size) session = self.session or _requests data = session.get( url=url, proxies=proxy, headers=utils.user_agent_headers ).text if "Will be right back" in data: raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n" "Our engineers are working quickly to resolve " "the issue. Thank you for your patience.") try: data = _pd.read_html(data)[0] except ValueError: if page_offset == 0: # Should not fail on first page if "Showing Earnings for:" in data: # Actually YF was successful, problem is company doesn't have earnings history dates = utils.empty_earnings_dates_df() break if dates is None: dates = data else: dates = _pd.concat([dates, data], axis=0) page_offset += page_size if dates is None: raise Exception("No data found, symbol may be delisted") dates = dates.reset_index(drop=True) # Drop redundant columns dates = dates.drop(["Symbol", "Company"], axis=1) # Convert types for cn in ["EPS Estimate", "Reported EPS", "Surprise(%)"]: dates.loc[dates[cn] == '-', cn] = "NaN" dates[cn] = dates[cn].astype(float) # Convert % to range 0->1: dates["Surprise(%)"] *= 0.01 # Parse earnings date string cn = "Earnings Date" # - remove AM/PM and timezone from date string tzinfo = dates[cn].str.extract('([AP]M[a-zA-Z]*)$') dates[cn] = dates[cn].replace(' [AP]M[a-zA-Z]*$', '', regex=True) # - split AM/PM from timezone tzinfo = tzinfo[0].str.extract('([AP]M)([a-zA-Z]*)', expand=True) tzinfo.columns = ["AM/PM", "TZ"] # - combine and parse dates[cn] = dates[cn] + ' ' + tzinfo["AM/PM"] dates[cn] = _pd.to_datetime(dates[cn], format="%b %d, %Y, %I %p") # - instead of attempting decoding of ambiguous timezone abbreviation, just use 'info': dates[cn] = dates[cn].dt.tz_localize( tz=self.info["exchangeTimezoneName"]) dates = dates.set_index("Earnings Date") self._earnings_dates = dates return dates def get_earnings_history(self, proxy=None): if self._earnings_history: return self._earnings_history # setup proxy in requests format if proxy is not None: if isinstance(proxy, dict) and "https" in proxy: proxy = proxy["https"] proxy = {"https": proxy} url = "{}/calendar/earnings?symbol={}".format(_ROOT_URL_, self.ticker) session = self.session or _requests data = session.get( url=url, proxies=proxy, headers=utils.user_agent_headers ).text if "Will be right back" in data: raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n" "Our engineers are working quickly to resolve " "the issue. Thank you for your patience.") try: # read_html returns a list of pandas Dataframes of all the tables in `data` data = _pd.read_html(data)[0] data.replace("-", _np.nan, inplace=True) data['EPS Estimate'] = _pd.to_numeric(data['EPS Estimate']) data['Reported EPS'] = _pd.to_numeric(data['Reported EPS']) self._earnings_history = data # if no tables are found a ValueError is thrown except ValueError: print("Could not find data for {}.".format(self.ticker)) return return data