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Worked example

Running the shipped quant-interview-question-generator engine on the input below produces exactly this output. Continuous integration recomputes it against the engine bundle on every build, so these numbers cannot drift from the code.

Input

{
  "tool": "quant-interview-question-generator",
  "difficulty": "medium",
  "n": 5,
  "seed": 7
}

Output40 lines

{
  "questions": [
    {
      "id": "p-m-003",
      "topic": "probability",
      "difficulty": "medium",
      "prompt": "X and Y are i.i.d. uniform on [0,1]. What is P(|X − Y| < 0.1)?",
      "solution": "Geometric: area of |X−Y|<0.1 strip in unit square = 1 − (0.9)^2 = 0.19."
    },
    {
      "id": "s-m-003",
      "topic": "stats",
      "difficulty": "medium",
      "prompt": "What is the bias-variance decomposition of MSE?",
      "solution": "MSE(θ̂) = Bias(θ̂)² + Var(θ̂). Total error trades off systematic miss against estimator volatility."
    },
    {
      "id": "p-m-001",
      "topic": "probability",
      "difficulty": "medium",
      "prompt": "You flip a fair coin until you get two heads in a row. What's the expected number of flips?",
      "solution": "Let E be the expected count. E = (1/2)(1+E_H) + (1/2)(1+E). E_H = (1/2)(1) + (1/2)(1+E). Solve: E = 6."
    },
    {
      "id": "s-m-001",
      "topic": "stats",
      "difficulty": "medium",
      "prompt": "You estimate Sharpe = 1.0 over 252 days. Construct an approximate 95% CI on the annualized Sharpe.",
      "solution": "SE(SR) ≈ √((1 + 0.5·SR²)/n). SR_ann = 1, n = 252 → SE ≈ √(1.5/252) ≈ 0.077; 95% CI ≈ 1 ± 0.15."
    },
    {
      "id": "p-m-004",
      "topic": "probability",
      "difficulty": "medium",
      "prompt": "A drunk takes a random step left/right on a number line, starting at 0. After 100 steps, what is the standard deviation of his position?",
      "solution": "Var = n = 100, σ = 10. Each step is ±1 with mean 0 and variance 1."
    }
  ],
  "totalInBank": 45
}

Frequently asked questions

What does the Quant Interview Question Generator methodology page document?
Curation criteria, topic taxonomy, and difficulty calibration for the AI Fin Hub Quant Interview Question Generator. It states the formulas, assumptions, data sources, limitations, and reproducibility steps behind the Quant Interview Question Generator, in the Finance category.
When was the Quant Interview Question Generator methodology last reviewed?
This methodology was last reviewed on 2026-05-08. The matching tool is at https://aifinhub.io/quant-interview-question-generator/.
Are the Quant Interview Question Generator numbers reproducible?
Yes. This page embeds a worked example whose output is the verbatim result of running the shipped quant-interview-question-generator engine on a fixed input; the embedded JSON is recomputed and diffed against the engine in CI, so the numbers cannot drift from the code.

Methodology · Tool · Last updated 2026-05-08

How Quant Interview Question Generator works

How the Quant Interview Question Generator builds reproducible practice sets from a curated bank.

Curation

Every question is hand-written, not LLM-generated. Solutions are checked by a human author. Topics map to the five canonical pillars at first-tier prop firms and hedge funds:

  • Probability — discrete and continuous, expectations, conditional, Brownian.
  • Statistics — estimators, hypothesis testing, multiple testing.
  • Derivatives — Black-Scholes, Greeks, parity, vol surfaces.
  • Microstructure — order books, impact, Kyle's lambda, VPIN, execution.
  • Regression — OLS, BLUE conditions, multicollinearity, regularization, IV.

Difficulty calibration

  • Easy: solvable in 2–5 minutes by a candidate with one course in the topic.
  • Medium: requires a non-trivial step or insight; 5–15 minutes.
  • Hard: non-obvious derivation, multi-step, or unfamiliar setup; 15–25 minutes.

Selection algorithm

Filter the bank by selected topics and difficulty. Apply a Fisher-Yates shuffle seeded with the user-supplied seed (LCG). Slice the first n entries. Same inputs → same set.

References

  • Joshi, M. S. (2008). Quant Job Interview Questions and Answers, 2nd ed. ISBN: 978-0-9879549-1-5.
  • Crack, T. F. (2024). Heard on the Street: Quantitative Questions from Wall Street Job Interviews, 23rd ed. ISBN: 978-0-9941311-3-9.
  • Wilmott, P. (2007). Frequently Asked Questions in Quantitative Finance. Wiley. ISBN: 978-0-470-05826-8.

Limitations

  • Bank size is bounded — repeated runs with the same filters will eventually exhaust unique combinations.
  • Real interviews include market-making and behavioural rounds we don't cover.
  • Solutions are intentionally terse; consult a textbook for the full derivation.

External resources