One person, institutional discipline, retail capital, full transparency.

I spent three years as an Investment Analyst on the Risk team at the University of Chicago's Office of Investments, where TRIP manages the university's endowment across asset classes. My work focused on risk reporting, performance analysis, and collaborating with the investment teams to understand portfolio behavior at scale.

Before that, I spent over two decades in the music industry as a talent buyer and operations manager for venues across Chicago. During that time I was actively managing my own capital — trading equities and derivatives — which gave me a practical, self-taught foundation in risk and reward that I later formalized in an institutional setting.

I hold a BS in Electrical Engineering from the University of Illinois Urbana-Champaign.

When my position was eliminated in June 2026, I had a choice: interview immediately for another institutional seat, or take the window to build something I'd been designing in the margins for years.

I chose the latter.

Three years inside a multi-asset endowment taught me how institutional allocators think about risk budgets, drawdown tolerance, and edge persistence. But it also showed me the constraints: committee timelines, mandate boxes, and the gap between conviction and execution. I wanted to apply that discipline — the measurement rigor, the framework thinking — to my own capital, at my own speed, with full transparency.

• Monte Carlo simulation engine — 3,000-path forward projections modeling the exact methodology rules (reloads, sweeps, level compounding, monthly cost drag, ruin conditions)
• Reality vs model comparison — actual performance overlaid against the simulation's probability envelope
• Execution analytics — timing edge, trim discipline, fee drag, runner capture, broken down by session, sequence, and time bucket with timestamps from raw account statements
• FIFO lot accounting — trade-group-level realized P&L with full event tracking from brokerage exports
• Archive system — every methodology run preserved and browsable for comparison over time

The site is vanilla HTML/JS/CSS with no build step, deployed on Vercel. Data pipelines are Python scripts that process brokerage exports into the analysis payloads the site consumes. No frameworks, no black boxes, no dependencies.

If you can't explain every number on the page, you don't understand your methodology well enough to survive the inevitable drawdown.

Every calculation is documented. Every metric is derived from raw trade data, not approximated. Past results are immutable — the journal is append-only, and historical numbers are never recalculated. When assumptions change, they're applied going forward with a clear audit trail.

This is not a signal service or a marketing exercise. It's a real-time record of one person applying institutional measurement discipline to retail-scale capital — with full transparency on what works, what doesn't, and what the math says about survival.