Fetch data from the IEX API

finance, stock, market, market-data, IEX, API, iex-api
pip install iex-api-python==0.0.1


Build Status Coverage Status Python 3.6 Documentation


IEX-API-Python Logo

under construction

This module is currently being actively developed. Feedback is welcomed.


The iex-api-python module is a wrapper for the IEX API, and is designed to closely map to the organization of the original API while adding functionality. A few examples of the additional functionality are:

  • Many queries are retadurned as Pandas Dataframes.
  • Built-in support for websockets connections.
  • Option to format timestamps as datetime objects or ISO format.


Note that you must be using Python >=3.6

pip install iex-api-python

Getting Started

From the API documenation:

The IEX API is a set of services designed for developers and engineers. It can be used to build high-quality apps and services. We’re always working to improve the IEX API. Please check back for enhancements and improvements.

The API terms apply to the use of this module, as does the requirement to properly attribute the use of IEX data.


The IEX-API-Python module is designed to map closely to the API from IEX. For many of the API calls, the resulting dataset is better represented in a tabular format. For these calls, data are returned as a pandas.DataFrame.


To illustrate a few things you can do with iex-api-python, take a look at the examples below.

Fetch all stock symbols

from iex import reference
reference.symbols() # Returns a Pandas Dataframe of all stock symbols, names, and more.
     symbol        date  iexId  isEnabled  \
0         A  2018-05-16      2       True
1        AA  2018-05-16  12042       True
2      AABA  2018-05-16   7653       True
3       AAC  2018-05-16   9169       True

Get a stock price

from iex import Stock

Get a stocks price for the last year

from iex import Stock
       change  changeOverTime  changePercent    close        date     high  \
0    0.000000        0.000000          0.000  10.2760  2017-05-16  10.3982
1   -0.169075       -0.016446         -1.645  10.1070  2017-05-17  10.2854
2    0.028180       -0.013712          0.279  10.1351  2017-05-18  10.1633
3    0.075144       -0.006394          0.741  10.2103  2017-05-19  10.2760
4    0.216042        0.014626          2.116  10.4263  2017-05-22  10.4545
5   -0.046966        0.010062         -0.450  10.3794  2017-05-23  10.4874
6   -0.084539        0.001830         -0.814  10.2948  2017-05-24  10.3888