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())