Prerequisite

This example requires the following dependencies software and libraries.

1. LSEG Workspace application with active Workspace account
2. Python environment in Conda with following libraries installed (I'm using Python version 3.10.11)
      - LSEG Data Library for Python: lseg.data==2.0.1
      - pandas==2.2.2
      - xlsxwriter==3.2.0
      - dateutil==2.9.0.post0
3. Internet connection

Please contact your LSEG account representative to help you to access LSEG Workspace credentials. You can generate/manage the AppKey by following the steps in the LSEG Data Library for Python Quick Start page.

Code Overview

The script is structured with modular functions for better readability and maintainability.

The key components are as below. For step 2, 3 and 5, their full code can be found in GitHub URL at the top right of this article.

0) Preparation step
1 ) Helper Functions
2) Index Data Retrieval and Processing
3) Calculating Dividend Impact
4) Multithreading
5) Excel Export
6) Example Usage

0) Preparation step

Import necessary libraries, open session to retrieve data from LSEG.

Then, define constant variables for fields and columns name to be used

1 ) Helper Functions

• get_lseg_data(universe, fields, parameters): This function handles data retrieval from LSEG, incorporating retry logic to ensure robustness.

• create_error_dataframe(index_ric, error_message): Create a Dataframe to store any errors encountered.

2) Index Data Retrieval and Processing

Full code can be found in GitHub URL at the top right of this article

• _fetch_index_data(index_ric): Fetches basic information about the index, including its constituents, currency, and divisor.
• _fetch_constituent_dividends(index_ric, index_constituent_rics, index_currency, valuation_date_str, use_prev_fx_rate): Retrieves dividend data (ex-dates, amounts, payment types) for all constituents of the index.
• _apply_fx_rates_to_dividends(div_details, index_currency, fx_rates, market_data_date, use_prev_fx_rate, locker): Handles currency conversion for dividends, ensuring they are expressed in the index currency.
• _calculate_index_divisor(index_ric, index_close_price, index_constituent_rics, index_currency, start_time, constituent_shares_df): Calculates the index divisor if it's not directly available.

3) Calculating Dividend Impact

Full code can be found in GitHub URL at the top right of this article

• get_dividend_details(index_ric, use_prev_fx_rate, fx_rates, market_data_date, locker): This function orchestrates the retrieval and processing of dividend data, calculates the impact of dividends on index points, and applies necessary FX rate conversions.
  

4) Multithreading

• get_dividends_details(index_rics_constituents, use_prev_fx_rate, valuation_date): Utilizes ThreadPoolExecutor to process multiple indices concurrently, improving performance.

5) Excel Export

Full code can be found in GitHub URL at the top right of this article

• write_sheet(wb, index_info_df, div_summary_df, div_details_item, sheetname, title, fx_rates_info): Exports the results to an Excel file, including formatting, charts, and summaries.

The example sheet of output Excel File is as below

6) Example Usage

Output of the script

The script produces 4 output DataFrames. The first output are Dataframes of index's information.

The second output are DataFrames of Dividend details.

The third output is error details dataframe, which is empty because there's no error occured here.

The forth output is a list of FX rates as below.

Key Considerations

These are things that being handled in the script. 

• Error Handling: The script includes error handling to catch potential issue like network problems or missing data.
• Efficiency: Multithreading is used to process multiple indices concurrently, enhancing performance.
• Data Consistency: The script handles currency conversions to ensure consistent reporting in the index currency.
• Maintainability: The modular structure with functions improves code organization and maintainability.

Conclusion

This article provides a practical example of how to use the LSEG data library and Python to retrieve and analyze dividend information for index constituents. The provided script can be adapted and extended to meet specific analytical requirement, offering a valuable tool for any developers wotking with financial data.