How to LSEG API code to reduce the processing time for extracting ownership data ac

NicolePangilinan

Client is experiencing slowness when using the Python LSEG API, with significantly longer processing times. We would appreciate your guidance on how to speed up the process.

Attached is their current approach (exported from Jupyter Notebook as a .txt file since the notebook cannot be attached directly). The existing script, which retrieves data for 10 companies across 8 points in time, currently takes around 30 minutes to complete.

Could you please review the code and provide recommendations on how they can improve performance and optimize data retrieval?

Please assist. Thank you.

Ownership - Current Code Approach.txt

Jirapongse

@NicolePangilinan

Thank you for reaching out to us.

Under the hood, the ld.get_data method uses the FundamentalAndReference module to retrieve data for the TR fields. This module supports asynchronous operations, allowing multiple requests to be sent to the endpoint and processed concurrently. For example:

tasks = asyncio.gather(
    fundamental_and_reference.Definition(universe=['VOD.L'],fields=['TR.F.IncAvailToComShr.date', 'TR.F.IncAvailToComShr'],parameters={"SDate": "2018-01-01", "EDate": "2023-12-31", "Frq": "CY"}).get_data_async(closure='Vodafone'),
    fundamental_and_reference.Definition(universe=['AAPL.O'],fields=['TR.F.IncAvailToComShr.date', 'TR.F.IncAvailToComShr'],parameters={"SDate": "2018-01-01", "EDate": "2023-12-31", "Frq": "CY"}).get_data_async(closure='Apple'),
    fundamental_and_reference.Definition(universe=['MSFT.O'],fields=['TR.F.IncAvailToComShr.date', 'TR.F.IncAvailToComShr'],parameters={"SDate": "2018-01-01", "EDate": "2023-12-31", "Frq": "CY"}).get_data_async(closure='Microsoft')
)

The client can also refer to this sample regarding how to use the get_data_async method.

Moreover, accessing the raw data instead of the processed data frame may also improve the performance of the application, as mentioned in this discussion.