owlplanner 2026.4.7

Owl - Optimal Wealth Lab: Retirement planner with great wisdom

Owl - Optimal Wealth Lab

A retirement exploration tool based on mixed-integer linear programming

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TL;DR

Owl is a retirement financial planning tool that uses a mixed-integer linear programming optimization algorithm to provide guidance on retirement decisions such as contributions, withdrawals, Roth conversions, and more. Users can select varying return rates to perform historical back testing, stochastic rates for performing Monte Carlo analyses, or fixed rates either derived from historical averages, or set by the user.

Owl is designed for US retirees as it considers US federal tax laws, ACA marketplace premiums (pre-65), Medicare premiums, rules for 401k including required minimum distributions, maturation rules for Roth accounts and conversions, social security rules, etc.

Among open-source retirement planning tools, Owl stands out for its unmatched capabilities and the rigor of its financial modeling. While the full API rewards those willing to invest time, the Streamlit web interface provides an accessible entry point for all users.

There are three ways to run Owl (from easiest to more complex):

1. Streamlit Hub: Run Owl remotely as hosted on the Streamlit Community Cloud at owlplanner.streamlit.app.

2. Docker Container: Run Owl locally on your computer using a Docker image. Follow these instructions for using this option.

3. Self-hosting: Run Owl locally on your computer using Python code and libraries. Follow these instructions to install from the source code and self-host on your own computer.

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Documentation

Document	Description
docs/modeling-capabilities.md	Summary of modeled components, assumptions, and limitations
INSTALL.md	Installation guide, Python environment setup, and developer build steps
USER_GUIDE.md	Python API usage with examples for Jupyter notebooks and scripts
PARAMETERS.md	Complete reference for TOML case file parameters
RATE_MODELS.md	Available rate models (historical, stochastic, bootstrap, etc.)
papers/owl.pdf	Mathematical foundations

Documentation for the app user interface is also available from the Streamlit UI.

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Credits and Acknowledgements

See CREDITS.md.

Bugs and Feature Requests

Please submit bugs and feature requests through GitHub if you have a GitHub account or directly by email. Or just drop me a line to report your experience with the tool.

Privacy

This app does not store or forward any information. All data entered is lost after a session is closed. However, you can choose to download selected parts of your own data to your computer before closing the session. These data will be stored strictly on your computer and can be used to reproduce a case at a later time.

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Copyright © 2024-2026 - Martin-D. Lacasse

Disclaimers: This code is for educational purposes only and does not constitute financial advice.

Code output has been verified with analytical solutions when applicable, and comparative approaches otherwise. Nevertheless, accuracy of results is not guaranteed.

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