Automating Python Scripts with the Data Library Using Windows Task Scheduler

August 05, 2026

Raksina Samasiri

Yao Koffi Kouassi

Introduction

Writing a Python script that retrieves data from the data platform is often the first milestone for developers using the Data Library.

The next challenge is usually operational rather than technical:

How can I run the script automatically every day without manually opening a terminal and executing it?

Whether you're generating daily reports, updating watchlists, refreshing dashboards, or exporting market data, automation can save time and eliminate repetitive tasks.

In this article, we'll build a simple automation workflow using:

The Data Library for Python
LSEG Workspace (for the Desktop Sessions of Data Library)
A Windows batch file
Windows Task Scheduler

The example intentionally keeps the implementation simple so that you can understand the core automation pattern and then adapt it to your own workflow. The project is designed as a bare minimum demonstration consisting of a Python script, a batch file, and Task Scheduler configuration examples.

A Note Before We Begin

This article was inspired by a question we frequently receive from developers:

"I already have a Python script that uses the Data Library. Can I run it automatically every morning?"

The solution presented here demonstrates one way to automate that workflow using LSEG Workspace, a Windows batch file, and Windows Task Scheduler.

While this approach works well for many personal, departmental, and proof-of-concept workflows, it should be viewed as a practical example rather than an officially recommended LSEG scheduling architecture.

As with any automated process, be sure to evaluate the operational, security, monitoring, and support requirements of your own environment before moving to production.

Prerequisites

Before getting started, ensure that you have:

Windows 10 or Windows 11
Python 3.9+
LSEG Workspace installed
LSEG Data Library for Python

This example uses a Desktop Session, so Workspace must be installed and authenticated before the Python script can connect successfully.

Solution Overview

The workflow looks like this:

    	
            Windows Task Scheduler

↓

Batch File

↓

Launch Workspace

↓

Execute Python Script

↓

Retrieve Data

↓

Save output to CSV file

The idea is straightforward:

Task Scheduler launches a batch file.
The batch file starts Workspace and waits for initialization.
The Python script retrieves data using the Data Library.
Results are saved locally.

Project Structure

The implementation guide provides a quick start path, while additional documentation explains scheduling and production considerations.

The project contains the following files:

    	
            ├── lseg_market_data_example.py

├── run_market_data.bat

├── IMPLEMENTATION_GUIDE.md

├── TASK_SCHEDULER_SETUP.md

├── PRODUCTION_CHECKLIST.md

└── README.md

Step 1: Create a Simple Data Retrieval Script

The heart of the workflow is a small Python script.

The example script:

Opens an LSEG session
Retrieves market data for IBM and Microsoft
Retrieves the closing price field
Saves the results to a date-stamped CSV file
Returns a success or failure exit code

The actual project includes basic exception handling to return either a success (0) or failure (1) exit code.

The simplified version looks like this:

    	
            from datetime import datetime

from lseg import data as ld
ld.open_session()
data = ld.get_data(

    universe=['IBM.N', 'MSFT.O'],

    fields=['TR.PriceClose']

)
filename = f"market_data_{datetime.now():%Y%m%d}.csv"

data.to_csv(filename)
ld.close_session()

Step 2: Test the Script Manually

Before introducing automation, always test the script manually.

    	
            python lseg_market_data_example.py

    	
            Expected output:
Session opened

Data saved to market_data_20260804.csv

Done

You should see a CSV file generated in the working directory.

This step helps verify:

Workspace connectivity
Data Library access
File creation
Permissions

Always confirm the script works manually before attempting to automate it.

Step 3: Create a Batch File

Next, create a Windows batch file that launches LSEG Workspace and executes your Python script.

In this example, the batch file performs three actions:

Launches LSEG Workspace
Waits for Workspace to initialize
Runs the Python script

Create a file named: run_market_data.bat

with the following content:

    	
            @echo off
REM Launch LSEG Workspace

start "" "C:\Program Files\Refinitiv\Refinitiv Workspace\RefinitivWorkspace.exe"
REM Wait 1 min 0 seconds for Workspace to initialize

timeout /t 60 /nobreak
REM Execute Python script using conda environment

call conda activate ldlib
python "%~dp0lseg_market_data_example.py"
exit /b %ERRORLEVEL%

Understanding the Batch File

Launch Workspace

    	
            start "" "C:\Program Files\Refinitiv\Refinitiv Workspace\RefinitivWorkspace.exe"

This starts LSEG Workspace.

Since this example uses a Desktop Session, Workspace must be running and authenticated before the Data Library can connect.

Note: The installation path may differ depending on your environment. Update the path if Workspace is installed elsewhere. And please login to the Workspace before running the script

First-Time Setup

Before scheduling the script for the first time, launch Workspace manually and sign in with your account.

When signing in, enable the: "Sign me in automatically" option shown on the Workspace login screen.

This allows Workspace to automatically restore your authenticated session when launched by the batch file, avoiding the need for manual login each time the scheduled task runs.

Without automatic sign-in enabled:

Workspace may stop at the login screen
The Desktop Session may not initialize
The Python script may fail to connect to the Data Platform

Important: Test the workflow manually at least once after enabling automatic sign-in. Verify that launching Workspace automatically signs you in before configuring Windows Task Scheduler.

Wait for Initialization

    	
            timeout /t 60 /nobreak

This gives Workspace time to start and establish the necessary session.

Depending on your machine and environment, you may need to increase or decrease the delay.

Run Python

Using the Correct Python Environment

One of the most common issues when scheduling Python scripts is that Windows Task Scheduler executes a different Python interpreter than the one you used during development.

For example:

The Data Library may be installed in a Conda environment
The scheduled task may execute the system Python installation
The script then fails because required packages cannot be found

To avoid this problem, always ensure that the batch file uses the same environment that was used to develop and test the script.

Option 1: Conda Environment

If you're using Conda, activate the environment before running the script.

(In this example Conda environment named "ldlib" is being used, you can check available Conda environments by running command "conda env list")

Example script:

    	
            call conda activate ldlib

python "%~dp0lseg_market_data_example.py"

Option 2: Virtual Environment (venv)

If you're using a virtual environment, call the Python executable directly.

Typical virtual environment paths look like any of the below:

C:\Projects\venv\Scripts\python.exe
C:\Users\username\project\.venv\Scripts\python.exe

Example script:

    	
            "C:\Projects\venv\Scripts\python.exe" "%~dp0lseg_market_data_example.py"

Option 3: Conda Environment Using Full Path

Instead of activating the environment, you can execute its Python interpreter directly.

This approach is often more reliable when running through Task Scheduler because it does not depend on Conda initialization.

Example script:

Option 4: Base Python Installation

If the Data Library is installed in your main Python installation, you can execute Python directly.

    	
            "C:\Users\username\AppData\Local\Programs\Python\Python311\python.exe" "%~dp0lseg_market_data_example.py"

or, if Python has already been added to the system PATH.

    	
            python "%~dp0lseg_market_data_example.py"

Using the full Python path removes ambiguity and ensures that the scheduled task always uses the expected environment.

Note: If the scheduled task fails with messages such as ModuleNotFoundError: No module named 'lseg', the task is likely using a different Python interpreter than the one where the Data Library was installed. The first thing to verify is the Python executable being used by the batch file.

Step 4: Test the Batch File

Before scheduling anything, execute the batch file "run_market_data.bat" manually, then

Verify that:

Workspace launches
The Python script runs
Market data is retrieved
CSV files are created

If this step works successfully, you're ready to move on to scheduling.

Step 5: Configure Windows Task Scheduler

Windows Task Scheduler allows the batch file to run automatically.

Open taskschd.msc

Then Create a task with settings similar to:

    	
            Name:

Market Data Retrieval



Trigger:

Weekdays



Time:

07:00 AM



Action:

Run run_market_data.bat

The full step-by-step configuration is included in the accompanying Task Scheduler guide.(TASK_SCHEDULER_SETUP.md)

Once configured, Windows will automatically execute the workflow according to the schedule you define.

Example Output

After a successful run, you'll find a date-stamped CSV file such as: market_data_20260804.csv, the generated file can then be used for reporting, analytics, dashboards, or downstream processing.

Example output:

Start Simple, Then Improve

The example project is intentionally minimal. It demonstrates the automation pattern without adding unnecessary complexity. The project documentation outlines several enhancements that should be considered before moving a workflow into production.

Examples include:

Logging: Replace simple console output with log files.
Data Validation: Check for:
- Empty datasets
- Missing instruments
- Unexpected row counts
Configuration Files: Move hard-coded instruments and fields into external configuration files.
Monitoring and Alerts: Notify users if data retrieval fails.
Retry Logic: Handle temporary connectivity issues gracefully.
Unit Testing: Validate expected behaviour before deploying changes.

These enhancements can be introduced incrementally as requirements evolve.

Common Use Cases

Once the automation workflow is working, you can replace the sample request with your own.

Examples include:

Daily pricing reports
AI watchlist updates
Portfolio monitoring
Economic indicator tracking
Dashboard refreshes
Excel exports
Power BI data feeds

The scheduling mechanism stays exactly the same. Only the Python script changes.

Key Takeaways

The Data Library can be integrated into automated workflows using Desktop Sessions.
A simple Windows batch file can launch Workspace and execute Python code.
Windows Task Scheduler provides an easy way to run jobs on a schedule.
Using the full path to the Python interpreter helps avoid environment-related issues.
Start with a simple proof of concept and gradually add production features as needed.

Conclusion

Automation doesn't need to be complicated.

With a small Python script, a batch file, and Windows Task Scheduler, you can transform a manual data retrieval process into a repeatable workflow that runs automatically.

The example shown here is intentionally minimal, making it easy to understand, customize, and extend. Once you've verified the basic workflow, you can gradually add logging, monitoring, validation, and other production features to meet the needs of your environment