Last updated July 2026.

The through-line

My degree, business economics with an information-systems concentration, trained one habit above all others: treat a market as a system, resources moving through institutions under constraints and feedback, rather than as a list of tickers. Most of what I do now is an attempt to turn that lens into software; systems that systematically distill raw market data into decision-ready insight. The applied context is research infrastructure inside a wealth-management setting, but the organizing idea comes from school.

From data to information

The most durable thing I carried out of college is the distinction between data and information. A price, a wage, an interconnection queue, a locational marginal price; each is a compressed signal, a proxy for some latent condition, and it only becomes information once it is interpreted against a task, a time horizon, and a context. So the systems I build are less about surfacing more data and more about resolving it: pipelines and interfaces that take a noisy feed and return a signal an analyst can actually act on, with the provenance and uncertainty preserved rather than hidden.

Finding the constraint

The analytical move I lean on most is bottleneck-first thinking. In markets the interesting question is rarely "what is scarce?" but "what becomes scarce once the current constraint relaxes?" Compute pulls on energy, energy pulls on transmission, and the economic rent migrates up and down the value chain as each binding constraint moves. A lot of my tooling exists to trace those chains explicitly, from adoption and workload to capacity demand, bottleneck pricing, capex, and finally free cash flow, treating each link as a hypothesis to be tested rather than a narrative to be told.

Economic frameworks as a toolkit

I treat the schools of economic thought less as intellectual history and more as a set of diagnostic lenses I can point at a position: marginal analysis for the next-unit decision, institutional and political economy for who owns the scarce asset and captures the surplus, Schumpeterian dynamics for which incumbents a new paradigm quietly devalues, and threshold or regime analysis for the moments when a relationship breaks rather than bends. The financial-analysis tools I build, factor construction, valuation bridges, technical structure, macro-regime detection, are in a sense these questions encoded as software.

What I'm building toward

The near-term work is a research environment that connects a live book to the frameworks behind it, where every displayed number traces back to a real signal, second-order effects and distribution are modeled from the start rather than added as caveats, and the representation itself, the dashboard, the taxonomy, the chart, is treated as part of the analysis rather than decoration applied after it. But all of that is scaffolding for a longer ambition: eventually, a concentrated, research-driven investment firm built on a single premise, that conviction is earned through convergence.

The idea is straightforward to state and hard to execute. Agentic systems handle the first pass, continuously synthesizing fundamentals, market structure, technical data, and macro across a broad equity universe, so that human judgment is spent only where it creates the greatest edge: recognizing when independent lines of evidence agree, committing capital to that convergence, sizing to conviction after the downside is weighed, and recycling into the highest prospective return. The book is meant to stay concentrated and long-duration, oriented toward the structural themes where capital compounds for years rather than quarters. The firm itself stays deliberately quiet for now.

Reading

• Machine Learning for Algorithmic Trading by Stefan Jansen
• Principles by Ray Dalio