def index():
form = investmentForm()
if request.method == 'POST' and form.validate():
#Get form field data
symb = form.stockSymbol.data
stock = Stock(symb)
openPrice = stock.get_open()
nowPrice = stock.get_price()
companyInfo = stock.get_company()
companyName = companyInfo['companyName']
symbol = companyInfo['symbol']
calculate = openPrice - nowPrice
calc = "{:.2f}".format(calculate)
ct = datetimeConverstion()
percentage = ((openPrice - nowPrice) / openPrice) * 100
percent = "{:.2f}".format(percentage)
new_date = ct.strftime('%A %Y-%m-%d %I:%M %p')
return render_template('price.html',
symbol=symbol,
companyName=companyName,
nowPrice=nowPrice,
new_date=new_date,
calc=calc,
percent=percent)
return render_template('home.html', form=form)
def main(argv):
print("name\tprice\topen\tvariation")
for n in argv:
try:
s = Stock(n)
price = s.get_price()
open = s.get_open()
variation = 100 * (price - open) / open
print("{}\t{}\t{}\t{:.3f} %".format(n, price, open, variation))
except:
sys.stderr.write("error fetching data for '{}'\n".format(n))
end = datetime.today()
df = get_historical_data('MMM', start=start, end=end, output_format='pandas')
df['date'] = df.index
#df.drop(['date'],axis=1)
df.reset_index(level=0, inplace=True)
scipy.io.savemat('test.mat', {'struct': df.to_dict('list')})
#IES Market Data
a = get_market_tops('TSLA', output_format='pandas')
get_market_last()
get_market_deep()
get_market_book()
# IEX stats
get_stats_intraday()
get_stats_recent()[0]
get_stats_records()
get_stats_daily(last=3)
get_stats_monthly(start=datetime(2017, 2, 9), end=datetime(2017, 5, 24))[0]
b = get_stats_intraday('TSLA')
tsla = Stock(['TSLA', 'AAPL'], output_format='pandas')
tsla.get_open()
tsla.get_price()
df = get_historical_data("AAPL", start=start, end=end, output_format='pandas')
df.head()
df.tail()
df.plot()
plt.show()
# https://pypi.org/project/iexfinance/
# Stock
from iexfinance import Stock
tsla = Stock('TSLA')
print('TSLA open: %f' % tsla.get_open())
print('TSLA price: %f' % tsla.get_price())
# Historical Data
from iexfinance import get_historical_data
from datetime import datetime
import pandas as pd
start = datetime(2014, 1, 1)
end = datetime(2018, 10, 1)
aapl_hist_df = get_historical_data("AAPL", start=start, end=end, output_format='pandas')
days_from_start = []
for x in aapl_hist_df.axes[0]:
dt = datetime.strptime(x, "%Y-%m-%d")
if start is None:
start = dt
diff = dt - start
days_from_start.append(diff.days)
aapl_hist_closing_df = pd.DataFrame({'price': aapl_hist_df['close'], 'days_from_start': days_from_start})
# Chart it
import matplotlib.pyplot as plt
class TestFieldMethodsBatch(object):
def setup_class(self):
self.batch = Stock(["AAPL", "TSLA"])
self.batch2 = Stock(["AAPL", "TSLA"], output_format='pandas')
self.batch4 = Stock(["AAPL", "TSLA"],
json_parse_int=Decimal,
json_parse_float=Decimal)
def test_get_company_name(self):
data = self.batch.get_company_name()
assert isinstance(data, dict)
assert data["AAPL"] == "Apple Inc."
data2 = self.batch2.get_company_name()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_primary_exchange(self):
data = self.batch.get_primary_exchange()
assert isinstance(data, dict)
assert data["AAPL"] == "Nasdaq Global Select"
data2 = self.batch2.get_primary_exchange()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_sector(self):
data = self.batch.get_sector()
assert isinstance(data, dict)
assert data["AAPL"] == "Technology"
data2 = self.batch2.get_sector()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_open(self):
data = self.batch.get_open()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_open()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_close(self):
data = self.batch.get_close()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_close()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_years_high(self):
data = self.batch.get_years_high()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_years_high()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_years_low(self):
data = self.batch.get_years_low()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_years_low()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_ytd_change(self):
data = self.batch.get_ytd_change()
assert isinstance(data, dict)
data2 = self.batch2.get_ytd_change()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_volume(self):
data = self.batch.get_volume()
assert isinstance(data, dict)
assert data["AAPL"] > 50000
data2 = self.batch2.get_volume()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_market_cap(self):
data = self.batch.get_market_cap()
assert isinstance(data, dict)
assert data["AAPL"] > 1000000
data2 = self.batch2.get_market_cap()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_beta(self):
data = self.batch.get_beta()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_beta()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_beta()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_short_interest(self):
data = self.batch.get_short_interest()
assert isinstance(data, dict)
assert data["AAPL"] > 50000
data2 = self.batch2.get_short_interest()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_short_ratio(self):
data = self.batch.get_short_ratio()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_short_ratio()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_short_ratio()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_latest_eps(self):
data = self.batch.get_latest_eps()
assert isinstance(data, dict)
assert isinstance(data["TSLA"], float)
data2 = self.batch2.get_latest_eps()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["TSLA"].dtype == "float64"
data4 = self.batch4.get_latest_eps()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_shares_outstanding(self):
data = self.batch.get_shares_outstanding()
assert isinstance(data, dict)
assert data["AAPL"] > 100000
data2 = self.batch2.get_shares_outstanding()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_float(self):
data = self.batch.get_float()
assert isinstance(data, dict)
assert data["AAPL"] > 1000000
data2 = self.batch2.get_float()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_eps_consensus(self):
data = self.batch.get_eps_consensus()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_eps_consensus()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_eps_consensus()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
class TestFieldMethodsShare(object):
def setup_class(self):
self.share = Stock("AAPL")
self.share2 = Stock("AAPL", output_format='pandas')
self.share4 = Stock("AAPL",
json_parse_int=Decimal,
json_parse_float=Decimal)
self.share5 = Stock("TSLA")
def test_get_company_name(self):
data = self.share.get_company_name()
print(type(data))
assert isinstance(data, six.string_types)
assert data == "Apple Inc."
data2 = self.share2.get_company_name()
assert isinstance(data2, pd.DataFrame)
def test_get_primary_exchange(self):
data = self.share.get_primary_exchange()
assert isinstance(data, six.string_types)
assert data == "Nasdaq Global Select"
data2 = self.share2.get_primary_exchange()
assert isinstance(data2, pd.DataFrame)
def test_get_sector(self):
data = self.share.get_sector()
assert isinstance(data, six.string_types)
assert data == "Technology"
data2 = self.share2.get_sector()
assert isinstance(data2, pd.DataFrame)
def test_get_open(self):
data = self.share.get_open()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_open()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_open()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_close(self):
data = self.share.get_close()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_close()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_close()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_years_high(self):
data = self.share.get_years_high()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_years_high()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_years_high()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_years_low(self):
data = self.share.get_years_low()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_years_low()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_years_low()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_ytd_change(self):
data = self.share.get_ytd_change()
assert isinstance(data, float)
data2 = self.share2.get_ytd_change()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_ytd_change()
assert isinstance(data4, Decimal)
def test_get_volume(self):
data = self.share.get_volume()
assert isinstance(data, int)
assert data > 1000
data2 = self.share2.get_volume()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_volume()
assert isinstance(data4, Decimal)
assert data4 > 1000
def test_get_market_cap(self):
data = self.share.get_market_cap()
assert isinstance(data, int)
data2 = self.share2.get_market_cap()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_market_cap()
assert isinstance(data4, Decimal)
def test_get_beta(self):
data = self.share.get_beta()
assert isinstance(data, float)
data2 = self.share2.get_beta()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_beta()
assert isinstance(data4, Decimal)
def test_get_short_interest(self):
data = self.share.get_short_interest()
assert isinstance(data, int)
data2 = self.share2.get_short_interest()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_short_interest()
assert isinstance(data4, Decimal)
def test_get_short_ratio(self):
data = self.share.get_short_ratio()
assert isinstance(data, float)
data2 = self.share2.get_short_ratio()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_short_ratio()
assert isinstance(data4, Decimal)
def test_get_latest_eps(self):
data = self.share5.get_latest_eps()
assert isinstance(data, float)
data4 = self.share4.get_latest_eps()
assert isinstance(data4, Decimal)
def test_get_shares_outstanding(self):
data = self.share.get_shares_outstanding()
assert isinstance(data, int)
data2 = self.share2.get_shares_outstanding()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_shares_outstanding()
assert isinstance(data4, Decimal)
def test_get_float(self):
data = self.share.get_float()
assert isinstance(data, int)
data2 = self.share2.get_float()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_float()
assert isinstance(data4, Decimal)
def test_get_eps_consensus(self):
data = self.share.get_eps_consensus()
assert isinstance(data, float)
data2 = self.share2.get_eps_consensus()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_eps_consensus()
assert isinstance(data4, Decimal)
def run():
tsla = Stock('VOO')
price_open = tsla.get_open()
price_now = tsla.get_price()
print(price_open)
print(price_now)
myList = [
'DATA', 'CRM', 'SPLK', 'WDAY', 'HLT', 'BLL', 'MFGP', 'WEX', 'DXC', 'MSFT',
'AAPL', 'SBUX', 'NKE', 'T', 'GOOGL', 'FB', 'TWTR', 'AKAM', 'LLNW', 'GD',
'LDOS', 'BA', 'MSTR', 'BMCH', 'CTSH', 'WIT', 'OTEX', 'SPOT', 'AYX', 'SATS',
'MAR', 'TENB', 'CBLK', 'ZS', 'GCI', 'VRNT'
]
# Create separate Dictionaries for 'Open' and 'Close' prices
qOpen = {}
qClose = {}
qNews = {} # Attributes={datetime, headline, source, url, summary, related}
for x in myList:
print('Retrieving quote info for %s...' % x)
theQuote = Stock(x) #x, output_format='pandas')
resOpen = theQuote.get_open()
resClose = theQuote.get_close(
) #get_quote(filter_=['ytdChange', 'open', 'close', 'avgTotalVolume'])
qOpen[x] = resOpen
qClose[x] = resClose
newsList = theQuote.get_news(last=7)
print('')
print('Formatting output...')
for key in qOpen.keys():
val = qOpen[key]
print("Key: ", key, "\t Open:", val, "\t Close:",
qClose[key]) #, "\t News:", qNews[key])
print('')
del qOpen
from iexfinance import Stock
tsla = Stock('TSLA')
print(tsla.get_open())
print(tsla.get_price())
from iexfinance import get_historical_data
from datetime import datetime
start = datetime(2017, 2, 9)
end = datetime(2017, 5, 24)
# df = get_historical_data("AAPL", start=start, end=end, output_format='pandas')
from iexfinance import get_stats_intraday
print(get_stats_intraday())
#!/usr/bin/env python3.5
from iexfinance import Stock
from iexfinance import get_available_symbols as gas
# Documentation on iexfinance: https://addisonlynch.github.io/iexfinance/stable/ref.html
# load all symbols - list
symbols = gas()
# look at Tesla
tsla = Stock('TSLA')
opn = tsla.get_open()
pri = tsla.get_price()
print('Tesla: open price %f, current price %f' % (opn, pri))
# look at AA (Alcoa?)
aa = Stock('AA')
eps = aa.get_latest_eps()
pri = aa.get_price()
print('AA: latest EPS %f, current price %f' % (eps, pri))
# show symbols and company name for each
# for s in symbols:
# if s['isEnabled'] == True: print(s['symbol'] + ', ' + s['name'])
# Apple
apple = Stock('AAPL')
apple.get_key_stats()
apple.get_volume()
apple.get_earnings()
from iexfinance import Stock
from iexfinance import get_historical_data
from datetime import datetime
tsla = Stock('TSLA')
print("open", tsla.get_open())
print("volume", tsla.get_volume())
print("price", tsla.get_price())
print("close", tsla.get_close())
start = datetime(2018,10,11)
end = datetime(2018,10,13)
df = get_historical_data("AAPL", start = start, end = end, output_format = 'pandas')
print(df.head())
