Quantitative researcher, ML architect, and author applying classical ML and complexity science to financial markets.
Based in Fort Collins, CO
Published Rethink Press, 2022
Media CBS · Reuters · WSJ
We are drowning in data and starving for understanding.
Bad decisions aren't made from bad data. They're made from good data, poorly interpreted. The bottleneck has never been information. It has always been the framework for making sense of it.
That conviction has shaped every chapter.
Peck's career began in academic computing by managing systems infrastructure for University and regional partners, then at the Kansas Research and Education Network, where he advised state agencies on cybersecurity policy, served as registrar for the state's academic domain namespaces, and built telemetry platforms that collected and processed network data in real time.
This data-first orientation carried forward. As CTO of a web startup in the mid-2000s, Peck brought financial engineering to a domain that hadn't seen it: building bid automation and yield management systems for programmatic advertising, with real-time revenue attribution and anomaly detection underneath. The company grew from zero to $30 million in revenue in roughly eighteen months.
Classical statistical tools, applied with engineering discipline to the right problem, produce outsized results.
Managing a first-generation family office, Peck applied the same quantitative lens to a new domain: markets.
The strategies that worked became systematic. The systematic approaches became funds.Peck launched a quantitative cryptocurrency hedge fund applying the same ML discipline to digital asset markets. The trading worked. After four years, he took the fund private.
The book emerged from the same period. Managing significant crypto exposure required a risk framework that didn't exist because classical models weren't built for the asset class, so Peck built one for his own portfolio, then published it.
Cryptocurrency Risk Management was released in December 2022. The same month, FTX collapsed — the largest counterparty failure in crypto history. The book had mapped exactly that risk category months before it materialized. It reached Amazon bestseller status in the weeks that followed. CBS, Reuters, and the Wall Street Journal needed a credible voice with a framework that predated the crisis.
The quantitative tools that institutional investors use to navigate financial markets have rarely been available outside institutions. The frameworks exist. The methodology is known. The access isn't.
Financial markets are complex systems. Complex systems have structure. That structure reveals itself in the data; if you have the right framework to see it.
MacroContext is an attempt to close that gap by publishing the methodology openly, making the tools available, and continuing the research in public rather than in private.