def test_earningsTodayDF(self):
from pyEX import earningsTodayDF
with patch('requests.get') as mock:
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
earningsTodayDF()
def test_earningsTodayDF(self):
from pyEX import earningsTodayDF
with patch("requests.get") as mock, patch("pickle.dump"):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
earningsTodayDF()
def co_earnings_today(self, today_ymd=None):
from datetime import datetime
try_local_pull = False
if today_ymd is None or today_ymd == datetime.now().strftime("%Y%m%d"):
today_ymd = datetime.now().strftime("%Y%m%d")
df = pyex.earningsTodayDF()
if df.empty is True and os.path.isfile(self.co_earnings_path +
today_ymd + ".csv") is True:
try_local_pull = True
elif df.empty is True:
self.logger.info(
"IEXTradingApi:co_earnings_today().: no earnings for date %s!",
today_ymd)
return
elif os.path.isfile(self.co_earnings_path + today_ymd +
".csv") is False:
self.logger.info(
"IEXTradingApi:co_earnings_today(): no historical file available for date %s",
today_ymd)
return
else:
self.logger.warning(
"IEXTradingApi:co_earnings_today(): try_local_pull is True, nothing at IEX!!!"
)
try_local_pull = True
if try_local_pull is True:
# check for an existing flat csv file, maybe we did a pull already
df = pd.read_csv(self.co_earnings_path + today_ymd + ".csv",
sep=',',
header=0)
df.set_index('symbol', inplace=True)
df.reset_index(inplace=True)
else:
df.to_csv(self.co_earnings_path + today_ymd + ".csv", sep=',')
df.reset_index(inplace=True)
market_cap_billions = df['quote.marketCap'].astype(
'float64') / 1000000000.0
market_cap_billions = market_cap_billions.round(3)
df['quote.marketCap'] = market_cap_billions.apply(
lambda x: self.format_market_cap(x))
df.EPSReportDate = df.EPSReportDate.astype('str')
df.announceTime = df.announceTime.apply(
lambda x: self.format_announce_time(x))
df.estimatedChangePercent = df.estimatedChangePercent.mul(100).round(
2).astype('str') + '%'
df.estimatedEPS = '$' + df.estimatedEPS.astype('str')
df.fiscalEndDate = df.fiscalEndDate.astype('str')
df.headline = df.headline.apply(lambda x: self.format_headline(x))
df[['headline', 'headline_color']] = df.headline.apply(pd.Series)
df["quote.week52High"] = '$' + df['quote.week52High'].astype('str')
df["quote.week52Low"] = '$' + df['quote.week52Low'].astype('str')
df['quote.ytdChange'] = df['quote.ytdChange'].mul(100).round(2).astype(
'str') + '%'
df.rename(index=str,
columns={
"symbol": "Symbol",
"quote.peRatio": "P/E Ratio",
"fiscalPeriod": "Quarter",
"numberOfEstimates": "# of Estimates",
"consensusEPS": "Consensus EPS",
"headline": "Headline",
"fiscalEndDate": "Quarter End",
"estimatedEPS": "EPS Estimate",
"estimatedChangePercent": "Estimated 1-Yr Change, EPS",
"announceTime": "Announce Time",
"EPSReportDate": "Report Date",
"quote.marketCap": "Market Cap",
"quote.peRatio": "P/E Ratio",
"quote.sector": "Sector",
"quote.week52High": "52-Week High",
"quote.week52Low": "52-Week Low",
"quote.ytdChange": "YTD Change",
"yearAgo": "Year Ago EPS"
},
inplace=True)
list_of_columns = [
'Symbol', 'Report Date', 'Announce Time', 'Consensus EPS',
'Estimated 1-Yr Change, EPS', 'EPS Estimate', 'Quarter End',
'Quarter', 'Headline', 'headline_color', '# of Estimates',
'Market Cap', 'P/E Ratio', 'Sector', '52-Week High', '52-Week Low',
'YTD Change', 'Year Ago EPS'
]
df = df[list_of_columns]
output_html_filename = self.iex_html_path + 'co_earnings_' + today_ymd + '.html'
data_table = ExtendBokeh.bokeh_co_earnings_today_datatable(
dataframe=df)
ExtendBokeh.save_co_earnings_today_data_table(
data_table,
html_output_file=output_html_filename,
html_output_file_title='CoEarningsToday')
