def get_stock_info(stock_symbol):
ticker_acao = Ticker(stock_symbol)
resultado = ticker_acao.get_modules(['financialData', 'quoteType'])
codigo = itemgetter(stock_symbol)(resultado)
retorno = {'result': codigo}
return json.dumps(retorno), 200
def create_stocks():
# read ticker symbols from the text area
StockTextarea = request.form['StockTextarea'].upper().replace(" ", "")
"""# read ticker symbols from the text area
volume = request.form['volume']"""
#print('Volume flag: ' + volume)
is_error = False
if StockTextarea.strip() == '':
flash('Please, Enter tickers on ticker Area')
is_error = True
symbol = StockTextarea.split(',')
symbol_index = StockTextarea.split(',')
symbol_index.append('NDAQ')
symbol_index.append('^DJI')
symbol_index.append('^GSPC')
if len(symbol) > 10:
flash('Please, Enter 10 tickers maximum')
is_error = True
# Get Start Date
SD = ObtainDate(request.form['datefrom'])
#Print if there is a error with the date SD
if SD == '':
is_error = True
# Get End Date
ED = ObtainDate(request.form['dateto'])
#Print if there is a error with the date ED
if ED == '':
is_error = True
# Get Current Date
TD = datetime.date.today()
#Print if there is a error with the date ED
if SD.strftime("%Y%m%d") > TD.strftime("%Y%m%d"):
flash('The start date can not higher than today')
is_error = True
#Print if there is a error with the date ED
if ED.strftime("%Y%m%d") > TD.strftime("%Y%m%d"):
flash('The end date can not higher than today')
is_error = True
#Print if there is a error with the date ED
if ED < SD:
flash('The end date can''t be less than the start date')
is_error = True
# Get number of days<
dt = (ED - SD)
countDays = dt.days
if countDays > 27:
flash('The count of the days can not be greater than 27 days')
is_error = True
if is_error:
return redirect(url_for('Index'))
#return redirect(url_for('Index',PstockTextarea =request.form['StockTextarea'],P_StartDate=request.form['datefrom'],P_EndDate=request.form['dateto']))
list_yec = []
tree_stocks = {}
#Set tickers
m = Ticker(symbol_index)
#Get index prices
list_NDAQ = []
list_NDAQ.append(m.price['NDAQ']['shortName'])
list_NDAQ.append("{:.2f}".format(m.price['NDAQ']['regularMarketPrice']))
list_NDAQ.append("{:.2f}".format(m.price['NDAQ']['regularMarketChange']))
list_NDAQ.append("{:.2f}".format(m.price['NDAQ']['regularMarketChangePercent'] * 100))
list_DJI = []
list_DJI.append(m.price['^DJI']['shortName'])
list_DJI.append("{:.2f}".format(m.price['^DJI']['regularMarketPrice']))
list_DJI.append("{:.2f}".format(m.price['^DJI']['regularMarketChange']))
list_DJI.append("{:.2f}".format(m.price['^DJI']['regularMarketChangePercent'] * 100))
list_GSPC = []
list_GSPC.append(m.price['^GSPC']['shortName'])
list_GSPC.append("{:.2f}".format(m.price['^GSPC']['regularMarketPrice']))
list_GSPC.append("{:.2f}".format(m.price['^GSPC']['regularMarketChange']))
list_GSPC.append("{:.2f}".format(m.price['^GSPC']['regularMarketChangePercent'] * 100))
tree_stocks['NDAQ'] = list_NDAQ
tree_stocks['DJI'] = list_DJI
tree_stocks['GSPC'] = list_GSPC
#End Index
list_dates = []
for stock in symbol:
try:
#get prices info
f = web.DataReader(stock, 'yahoo', SD, ED)
l_count_stock = len(f)
list_yec = []
try:
#get earnig date
yec_date = (datetime.date.fromtimestamp(m.get_modules('calendarEvents')[stock]['earnings']['earningsDate'][0]))
yec_count = (yec_date - TD).days
if yec_count > 1:
yec_desc = str(yec_count) + ' days to earnings call'
else:
if yec_count >= 0:
yec_desc = str(yec_count) + ' day to earnings call'
else:
yec_desc = ''
yec_count = ''
list_yec.append(yec_count)
list_yec.append(yec_desc)
except:
list_yec = []
try:
#get Name and Industry
l_current_price = m.financial_data[stock]['currentPrice']
current_price = "{:.2f}".format(l_current_price)
except:
l_current_price = 0
current_price = ''
try:
#get Name and Industry
stock_name = m.price[stock]['longName']
stock_industry = 'Industry: ' + m.asset_profile[stock]['industry']
except:
stock_name = ''
stock_industry = ''
#reset values
ChangeStock = ''
ChangeStock_percent = ''
ChangeStock_pre = ''
ChangeStock_percent_pre = ''
ChangeStock_post = ''
ChangeStock_percent_post = ''
try:
#get Change
l_change = m.price[stock]['regularMarketChange']
ChangeStock = "{:.2f}".format(l_change)
l_change_percent = m.price[stock]['regularMarketChangePercent']*100
ChangeStock_percent = "{:.2f}".format(l_change_percent)
except:
ChangeStock = ''
ChangeStock_percent = ''
try:
#get Change pre
l_change_pre = m.price[stock]['preMarketChange']
ChangeStock_pre = "{:.2f}".format(l_change_pre)
l_change_percent_pre = m.price[stock]['preMarketChangePercent']*100
ChangeStock_percent_pre = "{:.2f}".format(l_change_percent_pre)
except:
ChangeStock_pre = ''
ChangeStock_percent_pre = ''
try:
#get Change post
l_change_post = m.price[stock]['postMarketChange']
ChangeStock_post = "{:.2f}".format(l_change_post)
l_change_percent_post = m.price[stock]['postMarketChangePercent']*100
ChangeStock_percent_post = "{:.2f}".format(l_change_percent_post)
except:
ChangeStock_post = ''
ChangeStock_percent_post = ''
date_index = l_count_stock
#count dates
if date_index > 0:
date_index = date_index - 1
tree_stock = {}
#Build List Dates
if len(list_dates) == 0:
list_dates = []
#get dates
for i in range(l_count_stock):
list_dates.append(f.index[date_index - i].strftime("%Y-%m-%d"))
#Build List Prices
list_prices = []
l_date_price = 0
#get prices
for i in range(l_count_stock):
date_price = f.loc[f.index[date_index - i],'Close']
l_date_price += date_price
list_prices.append("{:.2f}".format(date_price))
#get average stock
l_average_stock = (l_date_price/l_count_stock)
#get difference
l_difference = (l_average_stock - l_current_price)
if l_current_price != 0:
l_average_dif = (l_difference/l_current_price)*100
#tree_stocks[stock+'0'] = info_stock.info['industry']
#tree_stocks[stock+'1'] = info_stock.calendar.loc[info_stock.calendar.index[0],'Value'].strftime("%Y-%m-%d")
#tree_stocks[stock+'1'] = stock
average_list = []
average_list.append("{:.2f}".format(l_average_stock))
average_list.append('Average Price: ' + "{:.2f}".format(l_average_stock))
average_list.append("{:.2f}".format(l_difference))
average_list.append("{:.2f}".format(l_average_dif) + '%')
average_list.append('Returning to Avg: ' + "{:.2f}".format(l_difference) + ' (' + "{:.2f}".format(l_average_dif) + '%)')
average_list.append(ChangeStock)
average_list.append(ChangeStock_percent)
average_list.append(ChangeStock_post)
average_list.append(ChangeStock_percent_post)
average_list.append(ChangeStock_pre)
average_list.append(ChangeStock_percent_pre)
ChangeStock_percent_pre
tree_stock[stock+'0'] = 'DATA_FOUND'
tree_stock[stock+'1'] = stock_name
tree_stock[stock+'2'] = stock_industry
tree_stock[stock+'3'] = list_yec
tree_stock[stock+'4'] = current_price
tree_stock[stock+'5'] = average_list
#tree_stock[stock+'7'] = list_dates
tree_stock[stock+'6'] = list_prices
except:
tree_stock = {}
tree_stock[stock+'0'] = 'NO_DATA_FOUND'
tree_stock[stock+'1'] = 'There is no information for the symbol'
tree_stocks[stock] = tree_stock
continue
tree_stocks[stock] = tree_stock
tree_stocks['DATES'] = list_dates
return render_template('create_stocks.html', pTreeStocks=tree_stocks)
#tickers.income_statement(frequency="q")
#See list of allowable modules
#aapl = Ticker('aapl')
#All modules will get data for all the modules
#aapl.all_modules
#You can get multiple modules in the same call
#https://github.com/dpguthrie/yahooquery/blob/master/README.md#accessing-multiple-modules
modules = [
'cashflowStatementHistory', 'cashflowStatementHistoryQuarterly',
'balanceSheetHistory', 'balanceSheetHistoryQuarterly',
'incomeStatementHistory', 'incomeStatementHistoryQuarterly'
]
financial_statement_data = tickers.get_modules(modules)
cash_flow_annual_list = []
balance_sheet_annual_list = []
income_statement_annual_list = []
cash_flow_quarterly_list = []
balance_sheet_quarterly_list = []
income_statement_quarterly_list = []
cash_flow_annual_column_name_list = []
balance_sheet_annual_column_name_list = []
income_statement_annual_column_name_list = []
cash_flow_quarterly_column_name_list = []
balance_sheet_quarterly_column_name_list = []
def get_financials(exchange='SGX', delimiter='.SI'):
'''
Query stock financials (quarterly and annual).
Parameter:
exchange : Short name for stock exchange. Default value is SGX
delimiter : RIC code accompanying tickers
'''
# Initialise parameters #
## Tickers ##
filepath = re.split(exchange, download_path)[0]
stocks = pd.read_excel(filepath + exchange + '_TICKERS.xlsx')
target_tickers = list(stocks['TICKER'])
## Modules ##
modules = [
'balanceSheetHistory', 'balanceSheetHistoryQuarterly',
'incomeStatementHistory', 'incomeStatementHistoryQuarterly',
'cashflowStatementHistory', 'cashflowStatementHistoryQuarterly',
'defaultKeyStatistics'
]
## Errata tickers ##
errata = []
## Empty DataFrames ##
bal_sheet = pd.DataFrame([{'symbol': np.nan, 'periodType': np.nan}])
inc_statement = pd.DataFrame([{'symbol': np.nan, 'periodType': np.nan}])
cf_statement = pd.DataFrame([{'symbol': np.nan, 'periodType': np.nan}])
fin_ratios = pd.DataFrame([{'symbol': np.nan}])
# Get stock fundamnetals - Balance Sheet, Income Statement, Cash Flow #
while True:
if errata:
target_tickers = [
target_ticker for target_ticker in target_tickers
if target_ticker not in errata
]
tickers = Ticker(target_tickers)
try:
data = tickers.get_modules(modules)
except Exception as e:
## Get errata tickers ##
errata.append(
re.split('/',
re.split(r'\%s' % delimiter, str(e))[0])[-1] +
delimiter)
print(errata)
else:
## Retrieve working data for errata_tickers ##
print('--Commence processing DataFrames--')
if errata:
for ticker in errata:
errata_ticker = Ticker(ticker)
for module in modules:
try:
errata_data = errata_ticker.get_modules(module)
except:
pass
else:
## Combine dictionaries ##
for key, value in errata_data.items():
data.update({key: value})
## Merge tickers ##
target_tickers += errata
## Convert dictionaries into DataFrames ##
for target_ticker in target_tickers:
for module in modules:
### Balance Sheet ###
if re.search('balanceSheetHistory', module):
try:
bal_sheet = pd.concat([
bal_sheet,
pd.DataFrame(data[target_ticker][module]
['balanceSheetStatements'])
],
axis=0)
except:
pass
else:
bal_sheet = get_period_type(
bal_sheet, target_ticker, module)
### Income Statement ###
elif re.search('incomeStatementHistory', module):
try:
inc_statement = pd.concat([
inc_statement,
pd.DataFrame(data[target_ticker][module]
['incomeStatementHistory'])
],
axis=0)
except:
pass
else:
inc_statement = get_period_type(
inc_statement, target_ticker, module)
### Cash Flow Statement ###
elif re.search('cashflowStatementHistory', module):
try:
cf_statement = pd.concat([
cf_statement,
pd.DataFrame(data[target_ticker][module]
['cashflowStatements'])
],
axis=0)
except:
pass
else:
cf_statement = get_period_type(
cf_statement, target_ticker, module)
### Financial Ratios ###
else:
try:
fin_ratios = pd.concat([
fin_ratios,
pd.DataFrame([data[target_ticker][module]])
],
axis=0)
except:
pass
else:
fin_ratios.loc[fin_ratios['symbol'].isnull(),
'symbol'] = target_ticker
## Drop the first row ##
bal_sheet.index = range(0, len(bal_sheet))
bal_sheet = bal_sheet.iloc[1:]
inc_statement.index = range(0, len(inc_statement))
inc_statement = inc_statement.iloc[1:]
cf_statement.index = range(0, len(cf_statement))
cf_statement = cf_statement.iloc[1:]
fin_ratios.index = range(0, len(fin_ratios))
fin_ratios = fin_ratios.iloc[1:]
## Download DataFrames ##
writer = pd.ExcelWriter(download_path + '\\' + exchange +
r'\FIN_STATEMENTS.xlsx',
engine='xlsxwriter')
bal_sheet.to_excel(writer, sheet_name='BS', index=False)
inc_statement.to_excel(writer, sheet_name='IS', index=False)
cf_statement.to_excel(writer, sheet_name='CFS', index=False)
fin_ratios.to_excel(writer, sheet_name='RATIOS', index=False)
writer.save()
print('--Complete processing--')
## Break out of while loop ##
break
return data, bal_sheet, inc_statement, cf_statement, fin_ratios
