def get_next_earning_release() -> list:
date_from = datetime.datetime.today()
date_to = get_next_working_day()
yec = YahooEarningsCalendar()
all_ticker = list()
today_earnings = yec.earnings_on(date_from)
tomorrow_earnings = yec.earnings_on(date_to)
for stock1, stock2 in zip(today_earnings, tomorrow_earnings):
if stock1['startdatetimetype'] == 'AMC':
all_ticker.append(stock1['ticker'])
if stock2['startdatetimetype'] == 'BMO':
all_ticker.append(stock2['ticker'])
return all_ticker
def update_data():
latest_date = get_latest_data()
on_date = latest_date + timedelta(days=1)
on_date = datetime(on_date.year, on_date.month, on_date.day)
yec = YahooEarningsCalendar(delay=1)
while on_date <= datetime.utcnow() - timedelta(days=1):
logger.info(f"[Earnings] Add data on {on_date}")
data = yec.earnings_on(on_date)
df = pd.DataFrame(data)
df.to_csv(CRUDE_EARNINGS_DATA_PATH, mode='a', header=False)
on_date += timedelta(days=1)
time.sleep(1)
class EarningCalendar(abc.ABC):
def __init__(self):
super().__init__()
self.yec = YahooEarningsCalendar()
constituents_df = pd.read_csv("../data/constituents.csv")
self.constituents = set(constituents_df.Symbol.values)
def earning_events_on(self, date):
'''
:param date: date of interest
:return: tuple (number of earnings reported after the market closes, number of all other earning events)
'''
earnings = self.yec.earnings_on(date)
# filter only S&P tickers
earnings = list(
filter(lambda x: x['ticker'] in self.constituents, earnings))
# after market close earnings - will be added to the next market day
after_market_close = len(
list(filter(lambda x: x['startdatetimetype'] == 'AMC', earnings)))
others = len(earnings) - after_market_close
s_and_p_matches = (after_market_close, others)
return s_and_p_matches
import pandas as pd
from datetime import datetime
from datetime import timedelta
from yahoo_earnings_calendar import YahooEarningsCalendar
import dateutil.parser
pd.set_option('display.max_columns', None)
# Downloading the earnings for a specific date
report_date = datetime.now().date()
yec = YahooEarningsCalendar()
earnings_list = yec.earnings_on(report_date)
earnings_df = pd.DataFrame(earnings_list)
earnings_df = earnings_df.drop(columns = ['gmtOffsetMilliSeconds', 'quoteType', 'timeZoneShortName'])
earnings_df.columns = ['Ticker', 'Company Name', 'DateTime', 'Type', 'EPS Estimate', 'EPS Actual', 'EPS Surprise PCT']
earnings_df = earnings_df[['Ticker', 'Company Name', 'DateTime', 'Type', 'EPS Estimate', 'EPS Actual', 'EPS Surprise PCT']]
earnings_df['DateTime'] = pd.to_datetime(earnings_df['DateTime']).dt.tz_localize(None)
print(earnings_df)
# Downloading the earnings for a range of dates
DAYS_AHEAD = 7
start_date = datetime.now().date()
end_date = (datetime.now().date() + timedelta(days=DAYS_AHEAD))
yec = YahooEarningsCalendar()
earnings_list = yec.earnings_between(start_date, end_date)
earnings_df = pd.DataFrame(earnings_list)
earnings_df = earnings_df.drop(columns = ['gmtOffsetMilliSeconds', 'quoteType', 'timeZoneShortName'])
earnings_df.columns = ['Ticker', 'Company Name', 'DateTime', 'Type', 'EPS Estimate', 'EPS Actual', 'EPS Surprise PCT']
import datetime
from yahoo_earnings_calendar import YahooEarningsCalendar
date_from = datetime.datetime.strptime('May 5 2017 10:00AM',
'%b %d %Y %I:%M%p')
date_to = datetime.datetime.strptime('May 8 2017 1:00PM', '%b %d %Y %I:%M%p')
yec = YahooEarningsCalendar()
print(yec.earnings_on(date_from))
print(yec.earnings_between(date_from, date_to))
!pip install yfinance
!pip install yahoo_earnings_calendar
# Import required library modules
import yfinance as yf
from yahoo_earnings_calendar import YahooEarningsCalendar
import pandas as pd
import numpy as np
# Try to get tickers from earnings report
# You may enter your prefered stock ticker manually.
yec= YahooEarningsCalendar()
from datetime import datetime
day1 = datetime.strptime('10/30/2020', '%m/%d/%Y')
er = yec.earnings_on(day1)
# Use this dropdown widget to select a ticker for retrieving the stock data.
from ipywidgets import Dropdown
option_list =[]
for i in range(len(er)):
option_list.append([er[i]['companyshortname']+'-'+er[i]['ticker'], er[i]['ticker']])
dropdown = Dropdown(description = 'Select one company', options = option_list)
display(dropdown)
ticker = dropdown.value
# Get the stock data from Yahoo finance.
# If your stock sticker is not found in the dropdown menu, just enter it here to replace 'ticker' variable.
data = yf.download(ticker,'2019-01-01','2020-05-22')
# Take a look at the data
(SELECT COUNT()+1 FROM (
SELECT DISTINCT date FROM close_cur AS t WHERE date < close_yst2.date)) as rank,
close as close_before
FROM close_yst2) yst3
ON cur.rank = yst3.rank+1
'''
df = pd.read_sql_query(qry, conn, parse_dates=["date"])
# print(df.head(1104))
date_from = datetime.datetime.strptime('Aug 5 2016', '%b %d %Y').date()
date_to = datetime.date.today()
yec = YahooEarningsCalendar()
list_result = yec.earnings_on(date_from)
df_table = pd.DataFrame(yec.earnings_between(date_from, date_to))
df_table = df_table[[
'companyshortname', 'startdatetime', 'startdatetimetype', 'ticker'
]]
print(df_table)
#print(yec.earnings_on(date_from)[])
#print(yec.earnings_between(date_from, date_to))
# data['4. close'].plot()
# plt.title('Intraday Times Series for the MSFT stock (1 min)')
# plt.show()
start = date(2019, 3, 19)
yec = YahooEarningsCalendar()
with f:
writer = csv.writer(f)
writer.writerow(
("Report Date", "Ticker", "Report Time", "Earnings",
"Earnings Surprise (%)", "Close Before Earnings",
"Open After Earnings", "Close After Earnings", "High After Earnings",
"Low After Earnings", "Volume Before Earnings"))
while start < date.today():
print(start)
time.sleep(2)
for stock in yec.earnings_on(start):
ticker = stock['ticker']
earningsTime = stock['startdatetimetype']
if earningsTime == 'TAS':
et = datetime.strptime(stock['startdatetime'],
"%Y-%m-%dT%H:%M:%S.%fZ")
earningsOpen = et.replace(hour=9, minute=30, second=0)
earningsClose = et.replace(hour=16, minute=00, second=0)
if et < earningsOpen:
earningsTime = 'BMO'
elif et > earningsClose:
earningsTime = 'AMC'
else:
continue