start = datetime(2013, 11, 13)
#end = datetime(2017, 5, 24)
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()
def test_intraday_json(self):
js = get_stats_intraday()
assert isinstance(js, dict)
def test_intraday_pandas(self):
df = get_stats_intraday(output_format='pandas')
assert isinstance(df, DataFrame)
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())