Walleye Capital Interview Questions

Inside the Walleye Capital interview

Walleye Capital is a multi-strategy hedge fund whose quant researcher and trader interviews probe whether you can reason cleanly under uncertainty and turn a noisy signal into a defensible estimate. Expect probability and brain-teasers alongside real statistics, regression, and portfolio optimization.

What they test

The core is probability and combinatorics brain-teasers — coin races, Bayesian updates, dice EV and Kelly sizing, and counting arrangements. Around it sits a statistics and regression block (why we divide by n−1, factor models, signal half-life, predictive-ability tests) and a thread of portfolio optimization (mean-variance, top-K alphas under correlation limits). A practical coding layer covers streaming order statistics and clean handling of trades, prices, and paginated APIs.

The recurring shapes

Watch for the same skeletons: conditioning and Bayes on a small state space, geometric/first-success races, and linearity of expectation to dodge brute force. On the data side it is OLS and factor diagnostics, variance estimators (including catastrophic cancellation), and online quantiles over a sliding window. The optimization questions reward seeing the constraint that binds.

How to approach

State your assumptions, set up the conditioning or constraint explicitly, then compute — they care about the reasoning path, not just the number. For stats and regression, name the estimator and its bias before plugging in. For the coding rounds, default to a streaming, O(1)-update structure and write code that ingests messy real-world inputs cleanly.

The mix leans medium with a solid hard tail in stochastic and optimization problems and a handful of easy data-handling warm-ups.