saveName='earningDiff',
hlines=[],
title='earningDiff')
MakePlot(merged_stock_earn['e_over_p_diff'],
merged_stock_earn[j],
xname='e_over_p_diff',
yname=j,
saveName='e_over_p_diff',
hlines=[],
title='e_over_p_diff')
return merged_stock_earn
# collecting spy
sqlcursor = SQL_CURSOR()
ts = ALPHA_TIMESERIES()
j = 0
# reading in the spy data
spy, j = ConfigTable('SPY', sqlcursor, ts, readType, hoursdelay=2)
AddInfo(spy, spy, debug=debug)
# processing new earnings
connectionCal = SQL_CURSOR('earningsCalendar.db')
connectionCalv2 = SQL_CURSOR('earningsCalendarForTraining.db')
fd = ALPHA_FundamentalData()
my_3month_calendar = GetUpcomingEarnings(fd, ReDownload)
print(my_3month_calendar)
it = 0
preLoaded = []
if not ReDownload:
def GenerateSignal(ticker,
out_file_name='out_bull_instructions.csv',
price_targets=[]):
connectionCal = SQL_CURSOR('earningsCalendarv2.db')
fd = ALPHA_FundamentalData()
sqlcursor = SQL_CURSOR()
ts = ALPHA_TIMESERIES()
api = ALPACA_REST()
stockInfoQuarter, stockInfoAnnual, company_overview = CollectEarnings(
ticker, connectionCal)
# annual balance sheet
balance_sheet_annual = fd.get_balance_sheet_annual(ticker)[0]
balance_sheet_annual['fiscalDateEnding'] = pd.to_datetime(
balance_sheet_annual['fiscalDateEnding'], errors='coerce')
for d in balance_sheet_annual.columns:
if d not in ['fiscalDateEnding', 'totalAssets']:
balance_sheet_annual[d] = pd.to_numeric(balance_sheet_annual[d],
errors='coerce')
# quarterly income statement
income_statement_quarterly = fd.get_income_statement_quarterly(ticker)[0]
for d in income_statement_quarterly.columns:
if d not in ['fiscalDateEnding', 'reportedCurrency']:
income_statement_quarterly[d] = pd.to_numeric(
income_statement_quarterly[d], errors='coerce')
for d in ['fiscalDateEnding']:
income_statement_quarterly[d] = pd.to_datetime(
income_statement_quarterly[d], errors='coerce')
if debug:
print(income_statement_quarterly)
print(income_statement_quarterly.dtypes)
tstock_info, j = ConfigTable(ticker, sqlcursor, ts, 'full')
spy, j = ConfigTable('SPY', sqlcursor, ts, 'compact')
#print(spy.columns)
est = pytz.timezone('US/Eastern')
today = datetime.now(tz=est) + maindatetime.timedelta(minutes=-40)
#today = datetime.utcnow() + maindatetime.timedelta(minutes=-30)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-04:00")
five_days = (
today +
maindatetime.timedelta(days=-7)).strftime("%Y-%m-%dT%H:%M:%S-04:00")
minute_prices = []
ntry = 0
while ntry < 3:
try:
minute_prices = runTicker(api,
ticker,
timeframe=TimeFrame.Minute,
start=five_days,
end=d1)
break
except (requests.exceptions.ConnectionError):
ntry += 1
# may want to restrict to NYSE open times
try:
spy = AddInfo(spy, spy)
tstock_info = AddInfo(tstock_info, spy, AddSupport=True)
except (ValueError, KeyError):
print('Error processing adding info %s' % ticker)
recent_quotes = getQuotes(api, ticker)
if debug:
print(tstock_info[[
'adj_close', 'sma20', 'sma20cen', 'vwap10cen', 'vwap10'
]][50:-10])
earn = Earnings(ticker, income_statement_quarterly, company_overview,
balance_sheet_annual, stockInfoQuarter, stockInfoAnnual,
tstock_info, minute_prices, recent_quotes)
#earn.BuildPDF()
earn.WriteCSV(out_file_name, price_targets)