Quantitative Researcher — Crypto Derivatives
I build and run systematic trading strategies in crypto derivatives — end to end.
I started as an alpha researcher and ended up owning the whole stack. Over four years I have built and operated every layer of a live perpetual futures book: market data collection and storage, alpha research, cost and slippage modeling, execution, monitoring, and post-trade attribution. The system I helped build was acquired in 2025 — I moved with it, and I run and extend it today.
Most quant researchers own one or two of those layers. Owning all of them taught me where the losses actually come from, and it is rarely the alpha.
Signal construction and evaluation across a rotating universe of perpetual futures, with pre-registered evaluation criteria and a formal dead-end registry so failed approaches are not silently retried.
Slippage attribution, maker/taker counterfactuals, and a cost accounting identity that reconciles simulated to realized PnL — separating execution gap from model error.
Multi-year backtests over tens of GB of minute bars, chunked to fit memory while producing bit-identical results to full-period runs.
Four-stage reconciliation from model signal to actual fill, with state-based alerting rather than event-based.
In preparation. Notes on research methodology, cost microstructure, and the failures worth documenting — the ones that do not make it into papers.