lseg-data 2.0.1

Client for LSEG Data Platform API's

The LSEG Data Library for Python provides a set of ease-of-use interfaces offering your applications a uniform access to the breadth and depth of financial data and services available on the Data Platform.

With this library, the same Python code can be used to retrieve data whatever the access point you choose to connect your application to the Data Platform. It can be either via a direct connection, via Eikon, via LSEG Workspace, via CodeBook or even via a local Real-Time Distribution System.

The library provides several abstraction layers enabling different programming styles and technics suitable for all developers from Financial Coders to Seasoned Developers:

• Using the Access layer is the easiest way to get LSEG data. The Access layer provides simple interfaces allowing you to rapidly prototype solutions within interactive environments such as Jupyter Notebooks. It has been designed for quick experimentation with our data and for Financial Coders specific needs.
• The Content layer is the basement of the Access layer. It provides developers with interfaces suitable for more advanced use cases (synchronous function calls, async/await, event driven). The Content layer refers to logical market data objects like market data prices and quotes, fundamental & reference data, historical data, company research data and so on.
• The Delivery layer is a low abstraction layer that defines interfaces used to interact with service agnostic delivery mechanisms of the Data Platform. The Delivery layer is a foundational component of the Content layer.
• The Session layer defines interfaces allowing your application to connect to the Data Platform via different access points (either via a direct connection, via Eikon, via the LSEG Workspace, via CodeBook or even via a local Real-Time Distribution System).

Some examples...

... with the Access layer

Import the LSEG Data Library

import lseg.data as ld

Open a data session

ld.open_session()

Get pricing snapshots and fundamental data

    df = ld.get_data(
        universe=['IBM.N', 'VOD.L'], 
        fields=['BID', 'ASK', 'TR.Revenue']
    )
    print(df)

	Instrument	BID	ASK
0	IBM.N	0.00	0.0
1	VOD.L	120.02	120.1

Get Fundamental and pricing history

    df = ld.get_history(
        universe="GOOG.O",
        fields=["BID", "ASK", "TR.Revenue"],
        interval="1Y",
        start="2015-01-01",
        end="2019-10-01",
    )
    print(df)

GOOG.O	BID	ASK	Revenue
2015-12-31	759.06	758.99	74989000000
2016-12-31	772.94	772.12	90272000000
2017-12-31	1046.46	1046.4	110855000000
2018-12-31	1037.36	1036.98
2019-12-31	1336.94	1335.9

Close the session

ld.close_session()

... with the Content layer dedicated to advanced use cases

Import the LSEG Data Library

import lseg.data as ld

Open a data session

ld.open_session()

Fundamental And Reference data retrieval

from lseg.data.content import fundamental_and_reference

response = fundamental_and_reference.Definition(
    ["TRI.N", "IBM.N"],
    ["TR.Revenue", "TR.GrossProfit"]
).get_data()

print(response.data.df)

	instrument	date	TR.Revenue	TR.GrossProfit
0	TRI.N	2020-12-31T00:00:00	5984000000	5656000000
1	IBM.N	2020-12-31T00:00:00	73620000000	35574000000

Historical data retrieval

from lseg.data.content import historical_pricing

response = historical_pricing.summaries.Definition(
    universe='VOD.L',
    interval=historical_pricing.Intervals.DAILY,
    fields=['BID', 'ASK', 'OPEN_PRC', 'HIGH_1', 'LOW_1', 'TRDPRC_1', 'NUM_MOVES', 'TRNOVR_UNS']
).get_data()

print(response.data.df)

	BID	ASK	OPEN_PRC	HIGH_1	LOW_1	TRDPRC_1	NUM_MOVES	TRNOVR_UNS
2019-12-12	144.32	144.34	144.42	145.66	143.46	144.18	12631.0	8498347218.71154
2019-12-11	143.58	143.6	142.72	144.8	142.62	143.58	10395.0	8815450412.65353
2019-12-10	142.74	142.78	143.84	143.84	141.48	142.74	10311.0	8070285210.45742
...	...	...	...	...	...	...	...	...
2019-11-18	152.1	152.12	154.74	155.66	152.0	152.12	14606.0	19322988639.34
2019-11-15	154.6	154.62	160.68	160.68	154.06	154.6326	17035.0	31993013818.37456

Real-time streaming data retrieval

from lseg.data.content import pricing

pricing_stream = ld.content.pricing.Definition(
    universe=['EUR=', 'GBP=', 'JPY=', 'CAD='],
    fields=['DSPLY_NAME', 'BID', 'ASK']
).get_stream()

pricing_stream.on_refresh(lambda pricing_stream, instrument_name, fields:
                          print(f"Refresh received for {instrument_name}: {fields}"))

pricing_stream.on_update(lambda pricing_stream, instrument_name, fields:
                         print(f"Update received for {instrument_name}: {fields}"))

pricing_stream.open()

Output:

Refresh received for EUR= : {'DSPLY_NAME': 'BARCLAYS     LON', 'BID': 1.1635, 'ASK': 1.1639}
Refresh received for GBP= : {'DSPLY_NAME': 'NEDBANK LTD  JHB', 'BID': 1.3803, 'ASK': 1.3807}
Refresh received for CAD= : {'DSPLY_NAME': 'DANSKE BANK  COP', 'BID': 1.2351, 'ASK': 1.2352}
Refresh received for JPY= : {'DSPLY_NAME': 'ASANPACIFIBK MOW', 'BID': 113.81, 'ASK': 113.83}
Update received for JPY= : {'DSPLY_NAME': 'NEDBANK LTD  JHB', 'BID': 113.81, 'ASK': 113.83}
Update received for CAD= : {'DSPLY_NAME': 'DANSKE BANK  COP', 'BID': 1.2351, 'ASK': 1.2352}
Update received for JPY= : {'DSPLY_NAME': 'ASANPACIFIBK MOW', 'BID': 113.81, 'ASK': 113.83}
Update received for EUR= : {'DSPLY_NAME': 'BARCLAYS     LON', 'BID': 1.1635, 'ASK': 1.1639}
Update received for CAD= : {'DSPLY_NAME': 'DANSKE BANK  COP', 'BID': 1.2351, 'ASK': 1.2352}

Search

from lseg.data.content import search

response = search.Definition("IBM").get_data()

print(response.data.df)

	RIC	BusinessEntity	PermID	DocumentTitle	PI
0		ORGANISATION		International Business Machines Corp, Public C...	37036
1	IBM	QUOTExEQUITY	55839165994	International Business Machines Corp, Ordinary...	1097326
2		ORGANISATION		Tiers Corporate Bond Backed Certificates Trust...	18062670
3		ORGANISATION		SG Stuttgart Vaihingen BM-Campus 1 UG haftungs...	27968389
4	0#IBMF:	QUOTExEQUITY	21481052421	Eurex International Business Machines Equity F...	48924732
5	0#IBMDF:	QUOTExEQUITY	21612423771	Euronext Amsterdam IBM Dividend Future Chain C...	259118763
6	IBMFc1	QUOTExEQUITY	21481052892	Eurex International Business Machines Equity F...	49450681
7	IBMFc2	QUOTExEQUITY	21481053949	Eurex International Business Machines Equity F...	50092347
8	IBMDFc1	QUOTExEQUITY	21613372305	Euronext Amsterdam IBM Single Stock Dividend F...	260213021
9	IBMFc3	QUOTExEQUITY	21481053950	Eurex International Business Machines Equity F...	50092348

Close the session

ld.close_session()

Learn more

To learn more about the LSEG Data Library for Python simply log into the LSEG Developer Community. By registering and logging in to the LSEG Developer Community portal you will have free access to a number of learning materials such as Quick Start guides, Tutorials, Documentation and much more.

Help and Support

If you have any questions regarding the API usage, please post them on the Refinitiv Data Q&A Forum. The LSEG Developer Community will be very pleased to help you.