This is a portfolio optimizer I made to help select portfolios for my clients. I'm a personal financial adviser. I generally put my clients in a mix of low-fee index funds and ETFs and I use this to set an appropriate allocation based on the client's risk tolerance.

I have been working on this as a side project to 1) improve the theoretical underpinnings of my investment recommendations and 2) improve my coding skills with frameworks like Vue and Torch. Here's the repository for the frontend: https://github.com/justinluther502/cashgraphfront.

The frontend site is Vue 2 + Vuetify, and the backend is an AWS lambda function running the Torch optimizer. I am still working out kinks in the backend, particularly cold-start issues because the Lambda function has to load the entire Torch library (~2GB). This occasionally causes the AWS API Gateway to reach max timeout before the Lambda function finishes. Any tips on addressing this would be much appreciated. My first thought is maybe to pare down the Torch library used in the Lambda function because I am only using a small subset of the full library.

The optimizer function itself is designed to overcome several classic shortcomings in Modern Portfolio Theory (MPT) (https://en.wikipedia.org/wiki/Modern_portfolio_theory). Unlike textbook implementations of MPT, this optimizer can utilize non-normal returns distributions, risk metrics besides variance, and can allow the user to specify tail behavior, such as asset correlations behaving normally most of the time but converging to 1.0 in severe downturns. Most of these "fixes" happen during the generation of custom "flavors", or asset selection universes. I have 3 flavors pre-built and there is some information on how they are constructed in the flavor selection page.

Any feedback greatly appreciated!

Justin Luther [email protected] https://www.lutherwealth.com