owlplanner 2025.12.20

Owl - Optimal Wealth Lab: Retirement planner with great wisdom

Owl - Optimal Wealth Lab

A retirement exploration tool based on linear programming

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TL;DR

Owl is a retirement financial planning tool that uses a 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.

There are three ways to run Owl:

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

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

• 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

• Documentation for the app user interface is available from the interface itself.
• Installation guide and software requirements can be found here.
• User guide for the underlying Python package as used in a Jupyter notebook can be found here.

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Credits

• Contributors: Robert E. Anderson (NH-RedAnt) for bug fixes and suggestions, Clark Jefcoat (hubcity) for fruitful interactions, kg333 for fixing an error in Docker's instructions, John Leonard (jleonard99) for great suggestions, website, and more to come, Benjamin Quinn (blquinn) for improvements and bug fixes, Dale Seng (sengsational) for great insights, testing, and suggestions, Josh Williams (noimjosh) for Docker image code, Gene Wood (gene1wood) for improvements and bug fixes.
• Greg Grothaus for developing ssa.tools and providing an integration with Owl.
• Owl image is from freepik.
• Historical rates are from Aswath Damodaran.
• Linear programming optimization solvers are from HiGHS and PuLP. It can also run on MOSEK if available on your computer.
• Owl planner relies on the following Python packages:
  • highspy, Matplotlib, Numpy, odfpy, openpyxl, Pandas, Plotly, PuLP, Scipy, Seaborn, toml, and Streamlit for the front-end.

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 - Martin-D. Lacasse

Disclaimers: This code is for educatonal 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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