Quant Interview Prep
Practice real probability, stochastic processes, and algorithmic problems from Jane Street, Citadel, Two Sigma and more. In Python 3 and C++ 17.
Problems from interviews at
Six structured learning paths covering every topic tested at elite quant trading firms, ordered by prerequisite so you always know what to study next.
Bayes theorem, expected value, distributions, Markov chains, and martingales.
Start learning →μNormal distribution, central limit theorem, Sharpe ratio, and signal detection.
Start learning →{ }Dynamic programming, greedy algorithms, graphs — the building blocks of quant coding rounds.
Start learning →~Brownian motion, geometric Brownian motion, and stochastic calculus.
Start learning →ΔBlack-Scholes, Greeks, put-call parity, implied volatility, and Monte Carlo pricing.
Start learning →⊗Value at Risk, portfolio optimization, and fixed income fundamentals.
Start learning →Not just another problem bank. Every feature exists because real interviews are hard and preparation needs to match.
The same editor you use at work. Python, C++, Java, JavaScript, and Go. Time and memory limits, hidden test cases. If your code passes here, it passes on the day.
Problems tagged by firm: Jane Street brain-teasers, Citadel algorithmic sets, Two Sigma probability sequences. Know exactly what to expect.
A dependency graph shows you exactly what to learn before what. Not a random list — a directed path from probability fundamentals to exotic derivatives.
Trade against random quotes in dice and card games. Practice expected value estimation, position sizing, and risk management under time pressure.
Arithmetic drills, sequence pattern recognition, and timed quizzes. Sharpen the mental math skills every quant interview demands.
Estimate answers to Fermi questions with confidence intervals. Scored on calibration — tight hits earn more, but overconfidence is punished.
Used by candidates preparing for interviews at the top quantitative trading firms.
Alex K.
Quant Trader at a Top Prop Shop
“The market making games were exactly the kind of practice I needed. Being able to drill expected value calculations under time pressure made a real difference in my interviews.”
Sophia C.
PhD Candidate, Applied Mathematics
“I loved the progression from basic probability problems to stochastic processes. The step-by-step editorials helped me understand not just the answer, but the intuition behind each solution.”
Ryan N.
Software Engineer transitioning to Quant
“Coming from a CS background, I needed to quickly ramp up on probability and options pricing. The firm-tagged problems let me focus on exactly what each company asks.”
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