PRACTICE GUIDE JUMP TRADING
Jump Trading Stochastic Processes Practice Test
Jump Trading does not publish the shape of its stochastic processes screen, and candidates describe it as 10 to 15 questions in 30 to 60 minutes, roughly two to five minutes per question, answered by picking one of the options.
Those are ranges across firms running this format rather than Jump Trading's own figures, so treat them as the shape to train against and check the instruction screen on the day for the marking rule.
Outcry is not affiliated with Jump Trading and has no access to their assessment content. This guide describes an assessment format that candidates report publicly; the questions here are generated by Outcry and are not Jump Trading’s own.
What it screens
A quantitative trading firm known for low-latency infrastructure and research across futures, equities and crypto.
- ✓Martingale arguments and optional stopping applied to games
- ✓Gambler's ruin: absorption probabilities and expected duration
- ✓Recognising when a process is a martingale (and when the argument breaks)
- ✓Random-walk intuition: recurrence, drift, and scaling
Where it sits at Jump Trading
A quantitative trading firm known for low-latency infrastructure and research across futures, equities and crypto. What that means for the screen is that the questions tend to be drawn from the work rather than from a textbook, so the format below is the shape to train against rather than a syllabus.
Candidates also report 2 other screens at Jump Trading, covered separately on this site. Where a firm runs several, they usually sit in one round rather than spread across the process, so the pacing of the whole set matters more than any single section.
The format
Jump Trading does not publish this screen's shape, and it varies between firms, so these are the ranges candidates report rather than exact figures.
| Questions | 10 to 15 |
|---|---|
| Time | 30 to 60 minutes |
| Per question | two to five minutes |
| Negative marking | No |
| Answer style | Multiple choice or short typed answer |
| Where it sits | Research-track screen, before the technical interviews |
What it tests, with a worked example
Every example below is generated by Outcry, drawn from the same question generators the timed drills run. None of them is Jump Trading’s.
Random walks and martingales
Stopping times, the optional stopping theorem, and gambler's ruin.
Example
A trader starts with 3 chips and bets one chip at a time on a fair coin, stopping at 0 chips or at 16. What is the probability of reaching 16?
- 1/16
- 1/2
- 13/16
- 3/16
Answer 3/16
The chip count is a martingale under a fair bet and the stopping time is finite, so the expected value at the stop equals the start: P×16 + (1−P)×0 = 3. That gives P = 3/16 = 3/16 = 0.188. The coin being fair makes the game fair, not the two outcomes equally likely - the barriers are at different distances.
Inference and research integrity
What a result means, and what it would take for it to mean nothing.
Example
A return model fits 5 regressors on 120 observations and reports R² = 0.1. What is the adjusted R²?
- 0.138
- 0.1
- 0.058
- 0.061
Answer 0.061
Adjusted R² = 1 − (1 − R²)(n − 1)/(n − k − 1) = 1 − (1 − 0.1) × 119/114 = 0.061. R² can only rise when a regressor is added, however worthless, so the unadjusted 0.1 rewards throwing variables at the problem. The adjustment charges 5 degrees of freedom, and here it costs 0.039.
Applied statistics
Regression identities, standard errors, and reading a coefficient correctly.
Example
A strategy has a true annual Sharpe ratio of 0.8. How many years of returns are needed before its t-statistic against zero reaches 3?
Answer 14.1
The t-statistic of a mean return over T years is the Sharpe ratio times √T, so T = (t/S)² = (3/0.8)² = 14.1 years. The argument: a Sharpe ratio IS a t-statistic per unit of time, which is why the sample size needed scales with the inverse square of it. At Sharpe 0.8 the answer is 14.1 years, and that is under the generous assumptions - returns independent, the edge constant, no regime change, and nobody looking at the data until the end. Any of those failing pushes the requirement up. This is the arithmetic that makes track records nearly useless as evidence at low Sharpe, and it is the reason research groups reach for higher-frequency data where the same edge produces many more independent observations per year.
Stating assumptions
Research screens mark the assumption you named as much as the number you produced.
Conditional expectation
Tower property and the expectation of a stopped process, which is most of what these rounds ask.
Example
5 strategies each have return variance 9, and every pair has correlation 0.2. What is the variance of the sum of all 5 returns?
- 45
- 81
- 225
- 52.2
Answer 81
Var(ΣX) = Σ Var(X) + Σ over ordered pairs Cov(X,Y). The variances give 5 × 9 = 45. There are 5 × 4 = 20 ordered pairs, each contributing 0.2 × 9 = 1.8, so the covariance term is 36. Total = 45 + 36 = 81. Stopping at 45 is the answer only when the strategies are independent.
Variance decomposition
Signal against noise, and how much of a measured edge should be believed.
Example
A strategy has a true annual Sharpe ratio of 0.8. How many years of returns are needed before its t-statistic against zero reaches 2.5?
Answer 9.8
The t-statistic of a mean return over T years is the Sharpe ratio times √T, so T = (t/S)² = (2.5/0.8)² = 9.8 years. The argument: a Sharpe ratio IS a t-statistic per unit of time, which is why the sample size needed scales with the inverse square of it. At Sharpe 0.8 the answer is 9.8 years, and that is under the generous assumptions - returns independent, the edge constant, no regime change, and nobody looking at the data until the end. Any of those failing pushes the requirement up. This is the arithmetic that makes track records nearly useless as evidence at low Sharpe, and it is the reason research groups reach for higher-frequency data where the same edge produces many more independent observations per year.
What a good score looks like
Research screens are marked by a person more often than a machine, so a numeric cutoff is rarely visible. Candidates consistently report that a wrong answer with a stated assumption scores better than a right answer with none.
How to train for it
- 01Rebuild the standard results rather than memorising them - a two-state chain, gambler's ruin, the regression slope from correlation and standard deviations.
- 02Work under time, but slower than a trading screen. These reward a correct setup, not speed.
- 03Get comfortable with conditional expectation specifically. More of these rounds reduce to the tower property than to anything else.
TRAIN IT HERE
The drills that match each section
Martingale Mutiny
Optional stopping played as a game. Try to find a betting strategy that beats a fair coin.
Ruin Walker
Gambler's ruin with live absorption probabilities.
Distribution Lab
Recognise a distribution from how its samples behave.
SIT THE FULL BATTERY
All the sections back to back on one clock, marked the way the real screen marks them, with a by-skill breakdown at the end. Included with any pass.
Also reported at Jump Trading
Common questions
- Is the Jump Trading stochastic processes test multiple choice?
- Reported as multiple choice or short typed answer. Formats move, so treat this as the shape rather than a guarantee.
- How long is the Jump Trading stochastic processes test?
- Candidates report 10 to 15 questions in 30 to 60 minutes, roughly two to five minutes per question.
- Is there negative marking on the Jump Trading stochastic processes test?
- No. A wrong answer costs nothing beyond the mark you would have earned, so leaving an item blank is never better than guessing at it.
- How do I practise for it free?
- Every drill linked on this page is free to play, with no account, inside a daily run cap. Questions are generated fresh each run, so there is nothing to memorise between attempts.
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