Seven Months and 20,000 Lines of Python Later: Lessons from Building a Personal Financial Simulator with AI | WARPSimLab

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Introduction

Over the last seven months I built WARPSimLab, a personal finance and retirement simulation platform. The project grew to roughly 20,000 lines of Python spread across 65 source files. WARPSimLab includes retirement modeling, portfolio simulation, taxes, Monte Carlo analysis, historical market windows, reporting, and a desktop graphical user interface.

AI (ChatGPT) was involved throughout the project. This article describes my experience tackling a significant medium-sized software project using AI as a development partner.

Some parts of this collaboration worked remarkably well while other parts failed repeatedly. The most useful lessons came from understanding where AI’s strengths and weaknesses were, and how to mitigate issues as they arose.

Why I Built It

I bought a DOS based personal financial simulator program in the 1990’s. This simulator opened my eyes to numerous financial concepts such as the power of compounded interest, the advantages and risks of stocks, and the power of time. Although this software was extremely educational, it had weaknesses such as not separating assets for married couples. I eventually lost this DOS based software, and couldn’t find a replacement. I decided to recreate this software when I retired.

Last year (2025) I became extremely interested in AI for software development. I have been following AI since taking a class in college in the 1980’s. AI was the tool that was going to change the world in 5 years, and remained that way for the next 40 years. The last few years I watched AI change from a research project to another tool in a software engineer’s toolbox. I needed a project to learn how to design and build software with AI.

I decided to use ChatGPT to create a personal financial simulator. I started with the free, anonymous version of ChatGPT, then moved to the free, logged in version, and finally picked up a monthly subscription. Although obvious now, I wasn’t sure how a simulator worked. So, I started with a command line interface to a custom financial simulator. After a few months I wondered if projects such as mine had already been built. I found numerous commercial and free personal financial simulators online. Unfortunately, none of them satisfied all of my requirements. I wanted an open source (free) personal financial software package that was targeted at education rather than advice. I wanted this simulator to not be web based, requiring users to upload their personal information to some remote server. I wanted it to be reasonably feature complete - simulating different tax and resource buckets, using different retirement withdrawal schemes, providing Monte Carlo and historical window risk analysis, and including sequence of return risk. Finally, I wanted the software to be an exploration and what-if tool, allowing users to discover how finances work for themselves.

7 months, 65 files and 20,000 lines of Python later, WARPSimLab was finally mature enough to go public.

A (supervised) division of labor

Coding with AI has been interesting. Generally speaking, we divided labor as follows:

Working With AI:

I have been responsible for the feature set. This includes such mundane detail as layout of reports, whether to implement Monte Carlo and historical window algorithms, how complicated and complete the tax simulation would be, etc.

I have been responsible for the architecture. WARPSimLab is designed in layers or components. Currently there is the GUI layer, the simulator layer, the plots layer, and the reports layer. I architected the boundaries between each layer and the internal architecture of each layer.

I designed (and re-designed and re-designed) the GUI main menu look and feel, along with all of the submenus.

I oversaw the refactor (and refactor and refactor) of the simulator core, the plots code, the reports code, the Scenario Explorer, etc.

I designed the current WARPSimLab webpage. I went through versions inspired by Picasso, Leary, Rembrandt, software and hardware companies’ websites, and finally banking and financial companies websites. I selected and polished the current design.

AI’s responsibilities:

AI has been my coding assistant. AI has created whole files consisting of hundreds of complex lines of Python in a few dozen seconds.

AI has implemented GUI code (tkinter) well enough that I really haven’t needed to learn the interface. It has written the code for the dialogs, and the code just works.

AI has created testing code for the project. This includes deterministic, feature, invariant and module tests. The total code base for these tests are now over 22,000 lines of Python.

AI has been a good brainstorming partner. Questions such as “how could I implement…” are generally answered in seconds. “Should I implement… “ or “What...