Eikon Data API
| Last update | Mar 2019 |
| Operating System | Windows |
| Interpreter | Python 3.5 or higher |
| Desktop version | Eikon - 4.0.36 or higher. |
Start with the Eikon Data API for Python in 4 steps
- Run the Eikon application
- Create an App Key for your application
- Install the Python library for Eikon Data API
- Let's have some fun !
1. Run the Eikon application
The Eikon application integrates a Data API proxy that acts as an interface between the Eikon Data API Python library and the Eikon Data Platform. For this reason, the Eikon application must be running when you use the Eikon Data API Python library. When launched, the Eikon application starts listening for local websocket connections on port 9000 or the next available port if port 9000 is already occupied. You need to have a valid Eikon user account to be able to launch the Eikon application.
2. Create an App Key for your application
Every application using the Eikon Data API must identify itself thanks to a Key called Application Key or App Key for short. This Key, that is a unique identifier for your application, must be created using the App Key Generator Eikon app that you can find in the Eikon App Library or via the toolbar of Eikon. Here is how to run it and how to create an App Key for a new application:
- In the Eikon toolbar type "App Key" and run the App Key Generator app:
- In the App Key Generator app indicate the name of your new application, select the APIs it uses and click on the "Register New App" button:
- Read the "Terms & Conditions" and click on the "Accept" button
- Copy the App Key that you will use later in your new application:
3. Install the Python library for the Eikon Data API
The Eikon Data API Python library is an ease of use library, which conveniently wraps the raw message transcription between Eikon and Python and provides user friendly data retrieval calls. The data output from the Eikon Data API Python library is available as Pandas DataFrames or as JSON objects.
Note: if you do not have a Python environment, please refer to the Setting up a Python development environment tutorial to set one up before following the next steps.
Run the following command to install the Eikon package from the Python Package Index (PyPI):
pip install eikon
Note: This command must be run at the prompt of the command-line interpreter (CMD). If the "Script/" folder of your Python installation is not included in your PATH environment variable, you may even have to navigate to this folder before you run the pip command.
Below is an example of the expected output
C:\>
C:\>cd Python36-32\Scripts
C:\Python36-32\Scripts>pip install eikon
Collecting eikon
Using cached eikon-0.1.11-py3-none-any.whl
Collecting websocket-client (from eikon)
Using cached websocket_client-0.46.0-py2.py3-none-any.whl
...
Installing collected packages: six, websocket-client, zope.interface, pytz, date
time, appdirs, numpy, python-dateutil, pandas, certifi, chardet, idna, urllib3,
requests, eikon
Successfully installed appdirs-1.4.3 certifi-2018.1.18 chardet-3.0.4 datetime-4.2 eikon-0.1.11 idna-2.6 numpy-1.14.0 pandas-0.22.0 ython-dateutil-2.6.1 pytz-2017.3 requests-2.18.4 six-1.11.0 urllib3-1.22 websocket-client-0.46.0 zope.interface-4.4.3
C:\Python36-32\Scripts>
import eikon as ek
ek.set_app_key('8e5a3xxxxxxxxxxxxxxxxxxxxxxxxxxxx21b031c')
Note: In earlier versions of the Python library for Eikon Data APIs, the set_app_key function was called set_app_id. The set_app_id function is now deprecated but still works. If you use an earlier version of the Python library you can call set_app_id to set the App Key of your application.
Great, you have imported the library and set the App Key of your application. You are now ready to send data retrieval requests.
The following instruction retrieves news headlines on Deutsche Lufthansa AG (equity RIC: LHAG.DE), between 09:00 and 18:00 GMT on the 5th of Apr 2017.
ek.get_news_headlines('R:LHAG.DE', date_from='2019-03-06T09:00:00', date_to='2019-03-06T18:00:00')
out:
| versionCreated | text | storyId | sourceCode | |
| 2019-03-15 16:35:55.731 | 2019-03-15 16:35:55.731 | Tagesvorschau 18.03.2019 | urn:newsml:reuters.com:20190315:nAWP3vd2n8:1 | NS:AWP |
| 2019-03-15 14:40:56.000 | 2019-03-15 15:19:15.000 | SunExpress Boeing 737 MAX siparişlerinde karar... | urn:newsml:reuters.com:20190315:nL8N2124TT:1 | NS:RTRS |
| 2019-03-15 14:53:31.000 | 2019-03-15 14:53:31.000 | SunExpress sticks to Boeing 737 MAX orders des... | urn:newsml:reuters.com:20190315:nFWN2120AN:1 | NS:RTRS |
| 2019-03-15 14:53:04.000 | 2019-03-15 14:53:04.000 | FIRMEN-BLICK-Ferienflieger SunExpress hält an ... | urn:newsml:reuters.com:20190315:nL8N2124UW:1 | NS:RTRS |
| 2019-03-15 12:28:02.876 | 2019-03-15 12:28:02.876 | Austrian Airlines: Annual results 2018: Passen... | urn:newsml:reuters.com:20190315:nNRA880x8g:1 | NS:ENPNWS |
| 2019-03-15 12:28:02.318 | 2019-03-15 12:28:02.318 | Traffic results for February 2019: More Than 8... | urn:newsml:reuters.com:20190315:nNRA880x8j:1 | NS:ENPNWS |
| 2019-03-15 12:28:02.299 | 2019-03-15 12:28:02.299 | Lufthansa Group to Buy 20 Boeing 787 Dreamline... | urn:newsml:reuters.com:20190315:nNRA880x7p:1 | NS:ENPNWS |
| 2019-03-15 12:05:06.205 | 2019-03-15 12:05:06.205 | Lufthansa to buy 40 long-haul aircraft to redu... | urn:newsml:reuters.com:20190315:nNRA880mz3:1 | NS:DATMTR |
| 2019-03-15 10:38:43.685 | 2019-03-15 10:38:43.685 | DJ Why Flag Carriers Struggle to Create Low-Co... | urn:newsml:reuters.com:20190315:nDJR15tblb:2 | NS:DJN |
| 2019-03-15 09:03:35.394 | 2019-03-15 09:03:35.394 | Lufthansa und Wirecard-Aktie knicken ein | urn:newsml:reuters.com:20190315:nNRA87z2u9:1 | NS:DERSPI |
Now, let's display the latest news story satisfying the news search expression "EU AND POL", which represents news on the European Union politics.
First, retrieve the news headlines using the search expression, then get the story ID from the response and finally request the story.
headlines = ek.get_news_headlines('EU AND POL',1)
story_id = headlines.iat[0,2]
ek.get_news_story(story_id)
The output is the HTML of the news story.
The following commands return time series of daily price history for Microsoft Corp ordinary share between 1st of Jan and 10th of Jan 2016.
df = ek.get_timeseries(["MSFT.O"],
start_date="2016-01-01",
end_date="2016-01-10")
df
out:
| MSFT.O | HIGH | CLOSE | LOW | OPEN | COUNT | VOLUME |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2016-01-04 | 54.8000 | 54.80 | 53.39 | 54.32 | 272781.0 | 53777963.0 |
| 2016-01-05 | 55.3900 | 55.05 | 54.54 | 54.93 | 180637.0 | 34079674.0 |
| 2016-01-06 | 54.4001 | 54.05 | 53.64 | 54.32 | 225856.0 | 39518863.0 |
| 2016-01-07 | 53.4850 | 52.17 | 52.07 | 52.70 | 303271.0 | 56564852.0 |
| 2016-01-07 | 53.4850 | 52.17 | 52.07 | 52.70 | 303271.0 | 56564852.0 |
| 2016-01-08 | 53.2800 | 52.33 | 52.15 | 52.37 | 261949.0 | 48753969.0 |
The following commands retrieve fundamental data - Revenue and Gross Profit - for Google, Microsoft abd Facebook
df, err = ek.get_data(['GOOG.O','MSFT.O', 'FB.O'],
[ 'TR.Revenue','TR.GrossProfit'])
df
out:
| Instrument | Revenue | Gro Profit | |
|---|---|---|---|
| 0 | GOOG.O | 90272000000 | 55134000000 |
| 1 | MSFT.O | 85320000000 | 52540000000 |
| 2 | FB.O | 27638000000 | 23849000000 |
You can specify additional parameters and request full year revenue and gross profit for the last two years scaled to millions and converted to Euros. E.g.
df, err = ek.get_data(['GOOG.O', 'MSFT.O', 'FB.O', 'AMZN.O', 'TWTR.K'],
['TR.Revenue.date','TR.Revenue','TR.GrossProfit'],
{'Scale': 6, 'SDate': 0, 'EDate': -2, 'FRQ': 'FY', 'Curn': 'EUR'})
df
out:
| Instrument | Date | Revenue | Gross Profit | |
|---|---|---|---|---|
| 0 | GOOG.O | 2016-12-31T00:00:00Z | 85866.726400 | 52443.460800 |
| 1 | GOOG.O | 2015-12-31T00:00:00Z | 69050.621090 | 43116.928250 |
| 2 | GOOG.O | 2014-12-31T00:00:00Z | 54559.726650 | 33634.735200 |
| 3 | MSFT.O | 2016-06-30T00:00:00Z | 76837.485600 | 47316.473200 |
| 4 | MSFT.O | 2015-06-30T00:00:00Z | 84041.390600 | 54370.953940 |
| 5 | MSFT.O | 2014-06-30T00:00:00Z | 63423.691530 | 43645.649550 |
| 6 | FB.O | 2016-12-31T00:00:00Z | 26289.265600 | 22685.168800 |
| 7 | FB.O | 2015-12-31T00:00:00Z | 16508.281680 | 13868.319410 |
| 8 | FB.O | 2014-12-31T00:00:00Z | 10305.018900 | 8525.241450 |
| 9 | AMZN.O | 2016-12-31T00:00:00Z | 129350.834400 | 45393.166400 |
| 10 | AMZN.O | 2015-12-31T00:00:00Z | 98532.194860 | 32555.237550 |
| 11 | AMZN.O | 2014-12-31T00:00:00Z | 73561.930200 | 21687.989400 |
| 12 | TWTR.K | 2016-12-31T00:00:00Z | 2406.173593 | 1519.426905 |
| 13 | TWTR.K | 2015-12-31T00:00:00Z | 2042.386046 | 1370.879829 |
| 14 | TWTR.K | 2014-12-31T00:00:00Z | 1159.791603 | 790.850268 |
You can also mix fundamental data with a snapshot of market data.
df, err = ek.get_data(['VOD.L', 'FB.O'],
[ 'TR.Revenue', 'TR.GrossProfit', 'CF_LAST'])
df
Out:
| Instrument | Revenue | Gross Profit | CF_LAST | |
|---|---|---|---|---|
| 0 | VOD.L | 55945230741 | 14388764346 | 203.1992 |
| 1 | FB.O | 27638000000 | 23849000000 | 140.78 |
For further information visit the Tutorials section.