Getting hired at Hudson River Trading (HRT) is no easy feat As one of the most prestigious proprietary trading firms in the world, they only recruit the very best Their interview process is notoriously rigorous, designed to thoroughly assess your skills in areas like programming, statistics, and finance.
To land your dream job at this elite trading firm, preparation is key By knowing what to expect in the HRT interview and formulating winning responses, you can confidently tackle any question thrown your way
In this comprehensive guide, we reveal the top 25 Hudson River Trading interview questions along with detailed examples of strong answers. From brainteasers to technical queries, these are the essential questions you need to ace your upcoming HRT interview.
Overview of the Hudson River Trading Interview Process
Before we get into the specific questions, let’s quickly go over what to expect from the whole HRT interview process:

Online coding assessment – Typically 23 algorithmic and data structure problems on platforms like HackerRank. Aims to screen candidates for basic CS aptitude.

Technical phone screens—one to two rounds that focus on math, statistics, probability, and financial ideas Gauge your technical abilities.

Onsite interviews – Conducted virtually. 45 rounds covering both technical and behavioral aspects. Assess overall job fit.

Case study – Analyze a business case and present recommendations. Tests analytical and problemsolving abilities.
The process is quite extensive, usually lasting 24 weeks from initial online test to final decision. Thorough preparation and practice are vital to stand out amongst the competition.
Now let’s look at some of the most frequently asked and tricky questions:
Programming and Coding
These questions test your programming knowledge and ability to write clean, efficient code. Brush up on languages like C++, Python, and Java and practice coding challenges.
1. Find the nth Fibonacci number recursively

Example: Write a recursive function to find the nth Fibonacci number. The first two Fibonacci numbers are 0 and 1. To generate the next number, we sum the previous two.

Strong Answer:
def fibonacci(n): if n == 0: return 0 elif n == 1: return 1 else: return fibonacci(n1) + fibonacci(n2)
This recursive solution finds the nth Fibonacci number by calling itself on the two preceding numbers and summing the result. I initialize the base cases for 0 and 1, then use recursion to find the higher term.
2. Reverse a linked list iteratively and recursively

Example: Implement a function to reverse a singly linked list both iteratively and recursively.

Strong Answer:
Iterative:
def reverse_linked_list(head): prev = None curr = head while curr: next = curr.next curr.next = prev prev = curr curr = next return prev
Recursive:
def reverse_list_recursive(head): if not head or not head.next: return head new_head = reverse_list_recursive(head.next) head.next.next = head head.next = None return new_head
The iterative approach uses a prev/curr pointer to flip the next pointer of each node to reverse direction. The recursive solution recurring on the sublist and linking back in reverse order.
3. Find duplicates in an array

Example: Given an array of integers, return all duplicates. The array can contain multiple duplicates.

Strong Answer:
def find_duplicates(arr): duplicates = set() seen = set() for val in arr: if val in seen: duplicates.add(val) seen.add(val) return list(duplicates)
I create a hash set to store duplicates and another to track seen values. Iterating over the array, any value already in the seen set must be a duplicate, so I add to duplicates. Finally, return duplicates as a list.
This provides O(n) time complexity by utilizing hash sets for constant time lookup.
Financial Modeling and Statistics
HRT looks for strong quantitative skills. Be ready for probability, statistics, modeling, and market analysis questions.
4. What is the expected value and variance of a binomial distribution?

Example: Derive the formulas for expected value and variance of a binomial distribution with parameters n and p.

Strong Answer: For a binomial distribution with n trials and probability p of success on each trial,
The expected value is: E(X) = np
Since there are n trials, and probability p of success on each, the expected number of successes is np.
The variance is: Var(X) = np(1 – p)
Using the formula Var(X) = E(X^2) – [E(X)]^2
E(X^2) = np(1p) + n(n1)p^2
E(X) = np
Plugging this into the variance formula gives np(1p).
5. Calculate the valueatrisk for an asset

Example: You have $1 million invested in an asset with an average daily return of 0.02% and a standard deviation of returns of 1%. Calculate the oneday 5% valueatrisk.

Strong Answer:
Using the common parametric method, the ValueatRisk formula is:
VaR = Asset Value x (Mean Return – Zscore x Standard Deviation)
Where:
 Asset Value = $1,000,000
 Mean Return = 0.02% = 0.0002
 Standard Deviation = 1% = 0.01
 Zscore for 95% confidence = 1.645
Plugging this in:
VaR = $1,000,000 x (0.0002 – 1.645 x 0.01) = $16,450
Therefore, there is a 5% chance the asset will lose more than $16,450 in one day.
6. Explain Monte Carlo simulation and an application of it

Example: What is Monte Carlo simulation? Give an example of how it can be applied in finance.

Strong Answer: Monte Carlo simulation involves using random sampling and probability to model risk and uncertainty. In finance, it can be used to forecast scenarios for pricing options or estimating portfolio risk.
For example, to value an exotic option that depends on multiple underlying assets, I can run thousands of randomized simulations of the asset prices using Monte Carlo. Then calculate the option payoff for each simulation to get a distribution of potential values. The average of this distribution provides an estimate of the option’s fair value.
The key benefit is that Monte Carlo simulation can handle complex, multivariable problems that involve significant uncertainty. This makes it a versatile and powerful technique for risk analysis in finance.
Market and Trading Concepts
You need to demonstrate indepth knowledge of how markets work and what drives trading strategies. Brush up on market microstructure, statistics, and risk management.
7. How can trading algorithms exploit arbitrage opportunities?

Example: Explain what arbitrage opportunities exist in markets and how algorithmic trading firms attempt to profit from them.

Strong Answer: Arbitrage opportunities refer to price discrepancies between assets or markets that can be exploited for riskfree profit. Some examples include:

Latency arbitrage – Exploiting microsecond time lags between price updates across exchanges. Algorithms trade on temporary price differences.

Index arbitrage – Taking advantage of mismatches between an index price and its underlying stocks.

Statistical arbitrage – Using mathematical models to exploit anomalies between correlated securities.
Trading algorithms are well suited to finding and capitalizing on these fleeting arbitrage windows. Strategies include:

Colocating servers physically close to exchanges to minimize network latency.

Using direct market access (DMA) for faster order routing.

Employing highfrequency strategies to trade rapidly when opportunities arise.

Analyzing realtime data flows to identify price discrepancies.
The profits may seem small, but at high volumes they compound to significant returns.
8. What factors influence bidask spreads in markets?

Example: Discuss the key drivers that impact bidask spreads in financial markets.

Strong Answer: The main factors influencing bidask spreads are:

Liquidity – Illiquid assets have wider spreads due to higher inventory risk for market makers.

Volatility – Uncertainty increases spread as compensation for higher risk.

Transaction costs – Costs like commissions and fees are accounted for in the spread.

Competition – More market makers reduces spreads through price competition.

Information asymmetry – Private information allows dealers to widen
Hudson River Trading LLCProprietary Trading
Based on the Interview Insights at this company, the Interview Experience is a score between 1 star (very bad) and 5 stars (very good).
The number in the middle of the doughnut pie chart is the mean of all these scores. If you move your mouse over the different parts of the doughnut, you’ll see exactly how each score was calculated.
The title percentile score is based on an adjusted score based on Bayesian Estimates that is applied to the whole Company Database. This is done to account for companies that don’t have many interview insights. The confidence in a “true score” rises as more reviews are given about a business. This causes the score to move closer to its simple average and away from the average of the whole dataset. 3. 4.
Based on the Interview Insights at this company, the Interview Difficulty is a score that goes from “very difficult” (red) to “very easy” (green).
The number in the middle of the doughnut pie chart is the mean of all these scores. The higher the number, the more difficult the interviews on average. This doughnut has different parts that, when you move your mouse over them, show you the 20% breakdown of each score given.
The title percentile score is based on an adjusted score based on Bayesian Estimates that is applied to the whole Company Database. This is done to account for companies that don’t have many interview insights. That is, as a business learns more, it becomes more sure of a “true score,” which moves it closer to its own simple average and away from the overall average of the data set. 3. 5.
Based on reviews at this company, the 20% of interns getting fulltime offers chart is meant to give you a good idea of how the company hires people.
The number in the middle of the doughnut pie chart is the mean of all these scores. This doughnut has different parts that, when you move your mouse over them, show you the 20% breakdown of each score given.
It uses an adjusted score based on Bayesian Estimates to account for companies that don’t have many reviews, which is how the percentile score in the title is found. To put it simply, when a business gets more reviews, the “true score” becomes more likely to be accurate. This makes it move closer to the simple company average and away from the average of all the data points. 35%.
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FAQ
Is it hard to get into Hudson River trading?
What should I say in a trading interview?
Does Hudson River Trading pay well?
What was the interview process like at Hudson River trading?
I interviewed at Hudson River Trading There was an online test followed by multiple interviews. The online test round was mostly leet code medium questions while all the other rounds had hard questions during the interviews. I applied online.
How do I find a job at Hudson River trading?
Glassdoor has millions of jobs plus salary information, company reviews, and interview questions from people on the inside making it easy to find a job that’s right for you. Hudson River Trading interview details: 359 interview questions and 341 interview reviews posted anonymously by Hudson River Trading interview candidates.
How much do Hudson River trading employees make?
Employees at Hudson River Trading earn an average of $643k, mostly ranging from $368k to $2901k based on 41 profiles. 100% real time & verified — Our data is sourced directly from integrations with Payroll providers, ATS, HRIS & Captable softwares + Data Union partnership comprising of Recruiters, HRTech firms etc.