Overview

When investing in a retirement fund using DCA(Dollar-Cost Averaging) strategy, does it perform better than the inflation rate?

In this project, we will find out what is the past inflation rate in Thailand using the Data Library for Python to consume data from the Workspace Desktop application.

Then we will find out how the DCA on a fund performs and plot a bar chart to compare the average return against inflation.

Introduction to the Data Library for Python

The Data Library for Python provides a set of ease-of-use interfaces offering coders uniform access to the breadth and depth of financial data and services available on the Workspace, RDP, and Real-Time Platforms. The API is designed to provide consistent access through multiple access channels and target both Professional Developers and Financial Coders. Developers can choose to access content from the desktop, through their deployed streaming services, or directly to the cloud. With the Data Library, the same Python code can be used to retrieve data regardless of which access point you choose to connect to the platform.

The Data Library are available in the following programming languages:

For more deep detail regarding the Data Library for Python, please refer to the following articles and tutorials:

Disclaimer

This article is based on Data Library Python versions 2.1.1 using the Desktop Session only.

The Coding part

Step 1: Import Data Library and other libraries

The next step is call the Data Library open_session() function to start a session with the Workspace Desktop application. You should have input your Workspace App-Key in a lseg-data.config.json configuration file in the same location as Python application file.

Please note that the LSEG Workspace desktop application integrates the API proxy that acts as an interface between the Data library and the Workspace Platform. For this reason, the Workspace application must be running when you use the Data library with Desktop Session.

Step 2: Find out the inflation rate.

We use the Data Library get_history() function to get inflation rate historical data.

This function returns data as DataFrame object.

To make this DataFrame object easier to read, we rename all columns names. Now we got the price indexes.

Next, we convert them to percent changes per year.

Step 3: Retrieve Fund NAV for the past years.

I am using Fidelity Advantage Pf Fd-Hong Kong Equity-Ord (RIC Code: LP63500988) as an example Fund data.

Fidelity Advantage Pf Fd-Hong Kong Equity-Ord Fund data

Step 4: Define your past contribution.

Please note that these are sample numbers which include random increment per year.

Step 5: Fill contribution data into NAV DataFrame using the date.

If the date could not be found in NAV DataFrame, add 1 day to the date (until the NAV is found). Then remove all the rows without a contribution.

Step 6: Add calculated data to the DataFrame. They are Total Amount Invested, Unit Invested, Total Unit Invested and Gain/Loss columns.

Once you complete this step, you are able to see how much you gain(or loss). But this still does not give you the idea of how much it grows per year on average.