Calculating YTD and MTD Performance of Mutual Funds Using PySpark

Learn how to calculate the Year-To-Date (YTD) and Month-To-Date (MTD) performance of Mutual Funds using `PySpark`. This guide guides you through the necessary code and concepts to analyze mutual fund data effectively.
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Understanding YTD and MTD in Mutual Funds

Investing in mutual funds can be a smart way to grow your wealth, but understanding their performance can sometimes be complicated. Two important measures of mutual fund performance are Year-To-Date (YTD) and Month-To-Date (MTD) returns.

YTD measures the performance since the start of the current calendar year, while

MTD measures the performance from the start of the current month.

In this guide, we will explore how to calculate YTD and MTD performance of mutual funds using PySpark, specifically focusing on how to leverage data from a NAV (Net Asset Value) history CSV file.

Sample NAV Data

Here's an example of how our CSV data might look, containing NAV values for different mutual funds on various dates:

[[See Video to Reveal this Text or Code Snippet]]

The goal is to calculate the YTD and MTD performance for each mutual fund for a specific date, in this case, February 3, 2022.

Formulas for YTD and MTD

YTD Calculation:

The formula to calculate YTD is:

[[See Video to Reveal this Text or Code Snippet]]

NAV(end): The NAV on the current date (e.g., February 3, 2022).

NAV(start): The NAV on January 1st of the same year.

MTD Calculation:

The formula for MTD is similar but focuses on the start of the current month:

[[See Video to Reveal this Text or Code Snippet]]

NAV(end): The NAV on the current date (e.g., February 3, 2022).

NAV(start): The NAV on February 1st, 2022.

Implementing the Calculation in PySpark

Now, let's see how we can implement this in PySpark.

Initial Setup

We will start by loading our CSV data into a DataFrame. Assuming you have already done this, we can use the PySpark library to perform our calculations.

MTD Calculation Example

To compute the MTD performance, we can filter and group our data as follows:

[[See Video to Reveal this Text or Code Snippet]]

This will yield an output similar to:

[[See Video to Reveal this Text or Code Snippet]]

Spark 3+ Enhancements

For users of Spark version 3 or above, you can streamline this process by using the max_by and min_by functions, which simplifies our aggregation logic:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

The above steps will help you calculate both the YTD and MTD performance of mutual funds using PySpark. By filtering and aggregating your NAV data effectively, you can gain valuable insights into the performance of your investments.

Feel free to explore the nuances of these calculations, and happy analyzing!

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