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

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.

• Read the terms.
• Read the manual and start building.
• Attribute properly.

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
Stock("F").price()

11.4

Get a stocks price for the last year

from iex import Stock
Stock("F").chart_table(range="1y")

       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
...