nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

Python API/Template for sourcing corporate financial and macro-economic indicator historical data

Aayush_M

Hi All,

I am looking to use my local machine Python to source the following data from LSEG database as a corporate license user of the data:

1. Monthly or quarterly historical values for select macro economic indicators (example, Crude Oil Price, US Equity Index). If there is a way to just bulk pull all available macro-indicators at LSEG across all global, regional, country, sector variables (i.e., entire macro database) that will be ideal.

2. Historical values for select public corporates for their granular financial statements (including P&L, Balance Sheet, Cashflow) (e.g., Walmart)

Please do let me know if there are any templates for this.

Regards,
Aayush

Find more posts tagged with

python

historical

python api

datastream-api

economic-data

financial-statements

#content

Accepted answers

All comments

Jirapongse

@Aayush_M

Thank you for reaching out to us.

Please confirm which LSEG product you are using.If you are a Workspace user, you can use the LSEG Data Library for Python to retrieve data through your Workspace desktop session. Examples are available on GitHub.

You can also explore additional use-case examples in the CodeBook application within Workspace. These examples are located in the __Examples__ directory in CodeBook.

Typically, to retrieve data through the library, you need to know the instruments (RICs), fields, and parameters required for your data request. You can use the Data Item Browser tool to search for and identify the appropriate instruments, fields, and parameters. Once you have this information, you can use the ld.get_data() or ld.get_history() methods to retrieve the required data.

Moreover, you can use the Search API to discover and search for instruments. For more information, see the Building Search into your Application Workflow article.

Otherwise, you can contact the Helpdesk support team by submitting a query through LSEG Support and requesting the Excel formula (for example, =RDP.Data) required to retrieve the data you need. Once you have the formula, we can help convert it into Python code. You may also find the following articles useful:

Aayush_M

Thanks for your response.

I am a Workspace user.

For example - if i want to use the advanced search option, and download the historical data of all indices (959,730) as below by extracting via Python code and then export that to csv file. How would that work?
I want this Python script to be on my local GIT repo. Is that possible?

Jirapongse

@Aayush_M

You can refer to this Find content and functionality using the Data Library with Workspace Advanced Search article. However, the Search API can return a maximum of 10,000 results per request. To retrieve all results, you will need to apply additional filters to partition the dataset into smaller subsets, each containing no more than 10,000 entries.

I am not sure that the LSEG Data Library for Python using a desktop session is the appropriate solution for downloading historical data for all 900,000 instruments, as the library is subject to usage limits. Please contact your LSEG account team or sales representative to confirm whether this approach is supported and appropriate for your intended usage.

Aayush_M

yes I understand that

My requirement is to extract the data only for 30-40 indicators.

I am just looking for an automated way to do that via Python.

For example, if I have the below requirement - how can i use Python on my local platform to extract this?

Jirapongse

@Aayush_M

You can use the Advanced Search to search for RICs and then use the Data Item Browser tool to search for fields and parameters.

For example, for economic indicators, please refer to this Retrieve Cross Economic Data using Data Library for Python article.

Aayush_M

thank you

For the 2nd link you have provided above, if I use that Python code in my local Python machine i will still be able to retrieve LSEG data just using LSEG.data library? Is that the API? I am looking for a way to extract this data on my local Python machine instead of having to use LSEG Codebook app.

I have another request - i am looking to extract historical data for corporates (E.g., Walmart) for P&L, BS, Cashflow for the last 15-20 years at most granular level (e.g., monthly, quarterly, annual).

I have a list of 40 such companies for which i am looking to extract data in a csv in the same format where the rows and column headers are the same.

is there any automatic way via Python to do this?

raksina.samasiri

Hi @Aayush_M ,

Of course, you can use the LSEG Data Library on your local machine outside of the Codebook app, however, you'll need to have the Workspace application up, running, and logged in on the same machine so your local Data Library can connect to the Data Platform (with the Data API Proxy inside the Workspace application). For more detail on how to do so, you could check https://developers.lseg.com/en/api-catalog/lseg-data-platform/lseg-data-library-for-python/quick-start#getting-started-with-python guide in the developer portal

raksina.samasiri

Regarding the data, if you ask content team to provide the Workspace Excel formula to retrieve them, we could convert it to the Python code for you. As we aren't a content expert. Please let me know if you have any further questions,

Aayush_M

Company Fundamentals Industrial Balance Sheet.xlsx

Company Fundamentals Industrial Income Statement.xlsx

Company Fundamentals Industrial Cash Flow Statement.xlsx

Hi Raksina,

These are the excel files the content team provided me with. Is this sufficient for you to convert it into a Python code?

Jirapongse

@Aayush_M

There are a lot of sheets and I can provide the sample code.

In the Balance Sheet excel file, the fields are hidden in the Column B. You may select only the required fields.

We can use these fields and the parameters in the formula to create a Python code.

For example:

#Sample fields from column B
fields = [
    "TR.F.CashSTInvst",
    "TR.F.CashCashEquiv",
    "TR.F.STInvstTot",
    "TR.F.FinAssetsST",
    "TR.F.DerivFinInstrHedgeST",
    "TR.F.DerivFinInstrHedgeTot",
    "TR.F.LoansRcvblNetST",
    "TR.F.TradeAcctTradeNotesRcvblNet",
    "TR.F.TradeAcctTradeNotesRcvblGross",
    "TR.F.ProvTradeAcctTradeNotesRcvbl",
    "TR.F.UnbillUtilRevST",
    "TR.F.LoansST",
    "TR.F.IncTaxRcvblST",
    "TR.F.RcvblOthTot",
    "TR.F.ProvDoubtAcct",
    "TR.F.RcvblOverOneYrInclInSTRcvbl",
    "TR.F.LoansRcvblTot",
    "TR.F.TradeAcctTradeNotesRcvblTot",
    "TR.F.AcctNotesRcvblTradeGrossTot",
    "TR.F.ProvDoubtTradeAcctTradeNotesPbleTot",
]
#Add Date field
fields = ["TR.F.CashSTInvst.Date"] + fields

#Call the ld.get_data method
df = ld.get_data(
        universe='WMT.O',
        fields=fields,
        parameters={'Scale': '6', 'ReportingState': 'Orig', 'ReportType': 'Latest', 'IncludeOldYE': 'No', 'FXRate': 'PeriodEnd', 'Period': 'FY0', 'Sdate': '-39FY', 'Edate': '0FY', 'FRQ': 'FY', 'Curn': 'Native', 'Transpose': 'Y'}
    )
df

The output is:

You can also refer to the following articles for guidance on converting Workspace Excel formulas into Python code.

EXPLORE OUR SITES