I am currently a high school junior from Oklahoma and have been learning Python for a little less than a year. Outside of programming, I enjoy running, playing basketball, and reading. I spend most of my time researching AI and its potential applications in finance. All of my major projects are open-source to encourage transparency and external contributions.
Most of my work lives at the intersection of:
- Empirical evaluation,
- Flexibility > abstraction,
- Explicit failure > silent corruption,
- Transparency above all else.
I care about honest methodology, conservative claims, and building tools that make exploring easier for everyone.
A lightweight internal framework for logging and analyzing LLM trading behavior:
- trade-level records
- portfolio state over time
- archived model decision text
- reproducible metric computation
Built to support evaluation and analysis, not optimization or monetization.
An exploratory evaluation of ChatGPT trading micro-cap stocks over a six month timeframe:
- Prompting consisted of daily updates and weekly deep research
- Provided blog updates every week on performance
- Full evaluation report coming soon
Note: This repo will eventually be powered by LIBB and provide a platform for all my future LLM trading experiments.
- Most projects start exploratory and evolve iteratively.
- I document constraints and limitations aggressively.
- I’m cautious about generalization and prefer scoped conclusions.
- I’m always open to criticism and advice.
If you’re interested in:
- evaluation methodology,
- LLM behavior analysis,
- applied research,
- asking questions,
- simply connecting,
feel free to reach out.
Thank you for reading!