I'm Daniel Kaufman. I build research and analytical tools for the markets that are still being built — predictive markets, perpetual futures, commodity complexes, and compute. My focus is the collateral mechanics that determine how these instruments behave at scale, and the structural inefficiencies that show up where new listing pathways meet old margining conventions.

Kinetic Alpha is the working portfolio of that focus. Each piece started as a question I was answering for my own work and ended up as a research piece plus a dashboard you can drive. The throughline is the same every time: how should this be collateralized, what is the market still pricing wrong, and what does the trade look like once both questions are answered.

Background

My career spans risk management, derivatives, and commodity-markets infrastructure — most of it on the buy- and sell-side of energy and commodity derivatives: pricing, decomposition, collateral modeling, and the margin-framework architecture that determines what capital efficiency looks like in practice. The work on this site reflects that directly. The energy-decomposition dashboard is the tool I used to wish existed; the perpetual-futures pieces apply a decade of cleared-derivative framework thinking to new underlyings.

Across every asset class, it's the same three steps: read the rulebook, model the margin, understand the collateral. Most structural alpha in modern derivatives sits in those three steps.

Why this work, why now

Three things happened recently that made me start publishing this material rather than just using it.

The CFTC opened the perpetual-futures pathway onshore. Kalshi's BTCPERP approval, the Coinbase × Deribit pathway, CME spot-quoted futures, and the Third Circuit's ruling that event contracts qualify as swaps — the regulatory building blocks for a new generation of US-listed derivative structures, each re-opening questions about collateral, settlement, and listing that the textbook doesn't answer.

Two exchanges filed competing compute futures. CME × Silicon Data and ICE × Ornn, on rival indices — the first new major commodity in decades, commoditizing in public. The structural questions (hierarchy, dispersion, basis) map directly onto commodity-markets frameworks I've worked with for years.

AI collapsed the build cycle. Every dashboard here took days to build, not quarters — reference Python engines, parity-checked JavaScript counterparts, interactive scenario tooling, all shipped by one person end-to-end. That changes the economics of what gets built and who builds it. The question worth answering in public is what is now worth building that wasn't before.

What you'll find here

• Research pieces on emerging derivative structures — each a working hypothesis with a clean breakdown of collateral and settlement mechanics.
• Interactive dashboards with every piece — drive the inputs, watch the outputs, find the trade. The full catalog is on the dashboards page.
• Reference implementations — Python engines and parity-checked JavaScript counterparts. If a number shows up on a dashboard, it traces back to source.
• The Lab — AI-native side projects beyond financial markets. DankeSuper is the active one.

What's next

• On-chain data layer across the dashboards — marks, reference tables, and positions in a form any agent or app can consume.
• Cross-venue divergence scanning across Kalshi, Polymarket, and Manifold, on a curated question-link table.
• Portfolio-level allocator that consumes the margin framework outputs — the engine that prices risk also sizes positions.
• The Lab as a first-class feature — DankeSuper and future side projects with their own routes.

Get in touch

Always happy to compare notes on collateralization, margin frameworks, perpetual-contract design, predictive markets, or agent-native research workflows — especially in markets where the textbook ends a few pages too early.