def generate_header(dd_input2):
global q_display
if dd_input2 is None:
raise PreventUpdate
sym = Stock(dd_input2, token=token)
q_display = sym.get_quote()
#stock_header_dict[dd_input2] = {k: v for k, v in q_display.items() if k in ['symbol', 'open', 'close', 'high', 'low', 'latestPrice', 'latestVolume', 'companyName']}
return """{0} ({1})""".format(
q_display['companyName'],
q_display['symbol']), """Latest Price Sold: {0}""".format(
get_data_points(
q_display['symbol'],
"QUOTE-LATESTPRICE")), "Open Price: {0}".format(
get_data_points(
q_display['symbol'],
"QUOTE-OPEN")), """Close Price: {0}""".format(
get_data_points(q_display['symbol'], "QUOTE-CLOSE")
), """High Price: {0}""".format(
get_data_points(
q_display['symbol'],
"QUOTE-HIGH")), """Low Price: {0}""".format(
get_data_points(q_display['symbol'],
"QUOTE-LOW")
), """Latest Volume Traded: {0}""".format(
get_data_points(q_display['symbol'],
"QUOTE-LATESTVOLUME"))
def get_cash_flow(self, stock=None) -> int:
"""
Get cash flow for a stock
Parameters:
stock (str): ticker string of the stock
Returns:
(int): the cash flow of the stock
"""
if self.pre_built:
return self.cash_flow
self._check_args(stock)
return get_data_points(stock, SC.CASH_FLOW)
def get_price_to_sales(self, stock=None) -> float:
"""
Get price to sales ratio of a given stock
Parameters:
stock (str): ticker string of the stock
Returns:
(float): price to sales of the stock
"""
if self.pre_built:
return self.price_to_sales
self._check_args(stock)
return get_data_points(stock, SC.PRICE_TO_SALES)
def get_price_to_book(self, stock=None) -> float:
"""
Get the price to book value ratio of a given stock
Parameters:
stock (str): ticker string of the stock
Returns:
(float): the price of the stock divided by the book value per share of the stock
"""
if self.pre_built:
return self.price_to_book
self._check_args(stock)
return get_data_points(stock, SC.PRICE_TO_BOOK)
def get_debt_to_equity(self, stock=None) -> float:
"""
Get the debt to equity ratio of a given stock
Parameters:
stock (str): ticker string of the stock
Returns:
(float): the total liabilities of the stock divided by shareholder equity
"""
if self.pre_built:
return self.debt_to_equity
self._check_args(stock)
return get_data_points(stock, SC.DEBT_TO_EQUITY)
def get_beta(self, stock=None) -> float:
"""
Get the beta value of a given stock
Refers to the volatility of a stock
beta > 1: more volatile
beta < 1: less volatile
Parameters:
stock (str): ticker string of the stock
Returns:
(float): the beta value of the stock
"""
if self.pre_built:
return self.beta
self._check_args(stock)
return get_data_points(stock, SC.BETA)
def build(self, stock):
"""
Preload all important metrics to avoid making multiple api calls for the same information
Parameters:
stock (str): ticker string of the stock
"""
self.stock = Stock(stock)
self.pe_ratio = self.stock.get_quote()[SC.PE_RATIO]
self.price_to_book = get_data_points(stock, SC.PRICE_TO_BOOK)
self.debt_to_equity = get_data_points(stock, SC.DEBT_TO_EQUITY)
self.peg = get_data_points(stock, SC.PEG)
self.beta = get_data_points(stock, SC.BETA)
self.cash_flow = get_data_points(stock, SC.CASH_FLOW)
self.price_to_sales = get_data_points(stock, SC.PRICE_TO_SALES)
self.pre_built = True
def get_info(message):
if message in [
'address', 'ZIP', 'EMPLOYEES', 'EXCHANGE', 'WEBSITE', 'PHONE',
'WEEK52HIGH', 'WEEK52LOW'
]:
return get_data_points(suggestions[0],
key=message,
token="sk_9520be7c9fdb4ae5ad5055d91ce95721",
output_format='pandas')
else:
stock = Stock(suggestions[0],
token="sk_9520be7c9fdb4ae5ad5055d91ce95721")
quote = stock.get_quote()
if message == 'companyName':
return quote['companyName']
elif message == 'logo':
return 'url: ' + stock.get_logo()['url']
elif message == 'latestPrice':
return quote['latestPrice']
elif message == 'avgTotalVolume':
return quote['avgTotalVolume']
elif message == 'news':
news = stock.get_news()
return '\n'.join([
'title: ' + n['headline'] + '\nurl: ' + n['url']
for n in news[:5]
])
elif message == 'history_price':
history_price = stock.get_historical_prices()
return '\n'.join([
'date: ' + p['date'] + '\nopen: ' + str(p['open']) +
'\nclose: ' + str(p['close']) + '\nhigh: ' + str(p['high']) +
'\nlow: ' + str(p['low']) for p in history_price[-5:]
])
else: # sharesOutstanding
return stock.get_key_stats()['sharesOutstanding']
def test_no_symbol_fails(self):
with pytest.raises(TypeError):
get_data_points()
def test_get_one_data_point2(self):
data = get_data_points("AAPL", "ZIP", output_format="pandas")
assert isinstance(data, str)
def test_get_one_data_point(self):
data = get_data_points("AAPL", "ZIP")
assert isinstance(data, str)
def test_all_data_points(self):
data = get_data_points("AAPL")
assert isinstance(data, pd.DataFrame)
assert "key" in data.columns
def test_get_one_data_point_pandas(self):
# pandas should also be str
data = get_data_points("AAPL", "ZIP", output_format='pandas')
assert isinstance(data, str)
def test_all_data_points_pandas(self):
data = get_data_points("AAPL", output_format='pandas')
assert isinstance(data, pd.DataFrame)
assert "ZIP" in data.columns
def test_all_data_points(self):
data = get_data_points("AAPL")
assert isinstance(data, list)
assert isinstance(data[0], dict)
assert "weight" in data[1]
