Welcome! 👋
This repository is a curated guide to preparing for quantitative finance interviews across Quant Trader, Quant Researcher, and Quant Analyst roles.
It’s designed for:
- 🎓 students and new graduates
- 🔁 career switchers
- 💼 early and experienced professionals moving into quant
The goal is simple:
👉 give you the highest-signal resources + a clear preparation roadmap
This repo focuses on signal over noise:
- what to study
- how interviews differ by role
- which resources are actually worth your time
- how to prepare in a structured way
⭐ If this repo helps you, please star it: it helps more people discover it.
- 🚀 Start Here
- 🧠 Quant Roles Explained
- 🏦 Types of Firms
- 📚 What to Study
- 📖 Best Resources
- 🗺️ Suggested Preparation Roadmap
- 📅 Study Plans
- 📄 Resume, Projects & Strategy
- 🎯 What Interviews Actually Look Like
⚠️ Common Mistakes- ❓ FAQ
- 🧪 Practice Platforms
- 🤝 Contributing
- ⭐ Final Advice
- Learn basic probability & expected value Focus on understanding how to model simple situations (not memorizing formulas).
- Start solving simple brainteasers Avoid jumping into very hard puzzles — build intuition first.
- Practice mental math daily (10–15 min) This compounds quickly and becomes a major advantage.
- Use structured resources (see below)
Do not start by trying to solve the hardest Jane Street-style problems immediately.
- Focus on:
- ⚡ Mental math speed (this is often a filter)
- 🎲 Probability intuition (expected value, quick reasoning)
- 🧠 Brainteasers / games
- Practice under time pressure
- Focus on:
- 📊 Statistics & probability (deep understanding)
- 🐍 Python / data analysis
- 📈 Modeling & ML basics
- Build small projects
- Focus on:
- 📊 Probability & statistics
- 💻 Python / SQL
- 📈 Basic finance intuition
- Do:
- Jane Street Probability Guide
- Green Book (Xinfeng Zhou)
- Mental math daily
- Timed practice sets
| Role | What You Do | What Is Tested |
|---|---|---|
| Quant Trader | Make trading decisions in real-time | Mental math, probability, decision-making |
| Quant Researcher | Build models & strategies | Stats, ML, coding, probability |
| Quant Analyst / Strat | Data + finance modeling | Python, SQL, probability, finance basics |
A lot of candidates prepare too generically.
A future quant trader should not prepare exactly like a future quant researcher.
| Firm Type | Interview Style |
|---|---|
| Prop Trading (Jane Street, Optiver, IMC) | Fast-paced, mental math heavy, games |
| Hedge Funds (Citadel, Two Sigma) | More modeling, coding, deeper probability |
| Banks | Slower pace, more finance + general quant |
Interview prep should be aligned with the type of firm you are targeting.
For example:
- prop trading usually rewards speed + clarity
- hedge funds often reward depth + technical strength
- banks are often broader and slightly less specialized in style
Not all topics are equally important — and more importantly, they are tested very differently in quant interviews.
The goal is not just to “know” these topics, but to understand how they are used in practice.
- 🎲 Probability (expected value, conditional probability)
- 📊 Statistics (distributions, variance, estimation)
- ⚡ Mental Math (speed & accuracy)
- 💻 Programming (Python / C++ / LeetCode-style)
- 🧠 Brainteasers & logic problems
- 📈 Markets basics (for trading roles)
This is the core of most quant interviews, especially for trading roles.
- Expected value (EV)
- Conditional probability
- Basic distributions
- Symmetry and simplification
- Logical modeling of situations
- Games (dice, cards, coins)
- Decision-making under uncertainty
- “What would you do?” scenarios
- Estimation of outcomes
👉 Your ability to model a problem clearly and reason step-by-step, not memorization of formulas.
- Focus on understanding structure, not formulas
- Re-solve problems until the reasoning becomes intuitive
- Practice explaining your thought process out loud
Mental math is often a filtering stage in trading interviews.
If you struggle here, you may not reach later rounds.
- Speed + accuracy
- Comfort with fractions, decimals, percentages
- Quick expected value calculations
- Timed arithmetic tests (e.g. “80 questions in 8 minutes”)
- Fast calculations during probability problems
- Real-time decision-making tasks
👉 Your ability to stay accurate under time pressure
- Practice daily (10–15 minutes)
- Track speed and accuracy over time
- Focus on consistency, not just peak performance
Statistics becomes more important for Quant Research / Analyst roles.
- Distributions and moments
- Estimation and inference
- Regression basics
- Variance and bias
- Interpreting data
- Explaining models
- Reasoning about uncertainty in datasets
👉 Your ability to reason about data and uncertainty, not just recall definitions.
- Focus on intuition behind concepts
- Work through applied examples
- Be able to explain ideas simply
Programming is critical for some roles, less relevant for others.
- Data structures and algorithms (for coding rounds)
- Writing clean, correct code
- Problem-solving under constraints
- LeetCode-style questions
- Data manipulation tasks
- Simple modeling or simulation problems
👉 Your ability to solve problems clearly and efficiently in code
- Focus on core patterns (arrays, hash maps, graphs, etc.)
- Practice writing code without over-relying on libraries
- Prioritize understanding over volume
These are used to test thinking process, not just answers.
- Breaking down complex problems
- Making reasonable assumptions
- Structuring your reasoning
- Open-ended puzzles
- Estimation problems
- “Think out loud” questions
👉 Your ability to reason clearly under uncertainty
- Practice explaining your reasoning step-by-step
- Focus on structure, not clever tricks
- Learn to simplify problems
Not always heavily tested, but useful context.
- Basic market mechanics (bid/ask, market making)
- Risk vs reward thinking
- Expected value in trading contexts
- Simple market scenarios
- Decision-making questions
- Discussions about strategies
👉 Your intuition and reasoning, not deep finance knowledge
- Focus on intuition rather than theory
- Understand simple trading scenarios
- Connect probability to real-world decisions
These topics are not tested independently.
Strong candidates are able to:
- combine them
- apply them under time pressure
- explain their reasoning clearly
That is what interviews are really evaluating.
Most candidates don’t lack resources: they lack a strategy for using them.
The goal is not to use as many resources as possible,
but to use a small number of high-quality ones in the right way.
⭐ One of the highest ROI resources
Best for: probability + brainteasers
How to use it effectively:
- Do selected problems, not necessarily cover-to-cover
- Focus on understanding the reasoning deeply
- Re-do problems multiple times until they become intuitive
Common mistake:
- ❌ Treating it as a textbook to read passively
- ❌ Rushing through problems without mastering them
Broader coverage across quant topics
Good complementary practice after the Green Book
How to use it:
- Use as a second layer for additional exposure
- Don’t rely on it as your main resource
👉 https://www.janestreet.com/probability-markets/
One of the most relevant resources for interview-style thinking
Why it’s valuable:
- Reflects how top firms think about problems
- Focuses on reasoning rather than formulas
How to use it:
- Go through it early in your prep
- Make sure you understand why each solution works
High-quality probability-style questions
How to use it:
- Great for deepening intuition
- Use after basic foundations are in place
Structured problem bank
How to use it:
- Good for building volume and consistency
- Useful once you want more repetition
👉 https://quantbrainteasers.com
Structured practice across probability, brainteasers, and role-specific prep
How to use it:
- Use for organized, role-specific practice
- Especially useful if you want a more guided workflow
👉 https://arithmetic.zetamac.com
One of the simplest and most effective tools
How to use it:
- Practice daily (10–15 minutes)
- Track your score over time
- Focus on consistency, not just peak performance
👉 https://www.tradinginterview.com
👉 https://www.tradermaths.com/math-tests
Good additional sources for realistic drills
Best general-purpose coding platform
When it matters:
- Critical for Quant Research / Dev roles
- Less relevant for pure trading roles
How to use it effectively:
- Focus on core patterns, not volume
- Prioritize:
- arrays / strings
- hash maps
- binary search
- graphs
- heaps
- basic dynamic programming
Common mistake:
- ❌ Doing random problems without pattern recognition
- ❌ Over-indexing on LeetCode for roles where it’s not central
A collection of challenging but structured problems combining math, logic, and programming
Why it’s useful:
- Develops problem-solving intuition and structured thinking
- Many problems rely on clever insights rather than brute force
- Great training for breaking down unfamiliar problems
Important note:
- Not interview-style questions, but excellent for building core thinking skills
- Can become technical/programming-heavy if overused
How to use it:
- Use selectively to sharpen reasoning and creativity
- Don’t treat it as your main interview prep source
- Jerry Qin
- Brainstellar
- QuantBrainteasers
The goal is not to use everything.
A strong setup is often:
- 1–2 core probability resources
- 1 mental math tool (daily)
- 1 structured problem source
- coding practice if needed
Used consistently, this is more effective than jumping between many platforms.
The order in which you prepare matters a lot.
Many candidates follow a scattered approach (random problems, mixed topics, no structure), which leads to slow progress and gaps that show up during interviews.
A more effective approach is to build skills progressively, in a way that matches how interviews actually work.
- Learn probability fundamentals
- Start solving problems daily
- Add mental math practice
- Add coding (if needed)
- Practice under time pressure
- Review mistakes deeply
- Repeat
Start with expected value, conditional probability, and basic distributions.
Goal:
- understand how to model simple situations
- reason step-by-step
At this stage:
- focus on clarity, not speed
Move to curated question sets (not random problems).
Goal:
- build intuition
- recognize patterns
- understand common problem types
Key point:
- quality > quantity
Start early and stay consistent.
Goal:
- improve speed and accuracy
- get comfortable with calculations under pressure
Practical tip:
- 10–15 minutes daily is enough if done consistently
Mainly for Quant Research / Dev roles.
Goal:
- master core patterns
- write clean, correct code
Important:
- focus on understanding patterns, not solving hundreds of random problems
This is where preparation becomes realistic.
Goal:
- simulate interview conditions
- identify weak points
Key insight:
- problems that feel easy untimed often become difficult when timed
This is one of the highest ROI steps.
Goal:
- turn weaknesses into strengths
- make reasoning automatic
Practical tip:
- re-solve problems until you can do them quickly and confidently
Final stage of preparation.
Goal:
- think clearly under pressure
- communicate your reasoning effectively
Practice:
- explain your thinking out loud
- simulate real interview scenarios
Many candidates do something like:
- jump between topics
- solve random problems
- delay mental math
- avoid timed practice
- focus too much on reading instead of solving
This often leads to:
- slow progress
- inconsistent performance
- difficulty under interview conditions
Preparation should be:
- structured
- role-specific
- practice-heavy
- progressively timed
The difference between average and strong candidates is often not knowledge,
but how they structure their preparation.
A good study plan is not about doing everything:
it’s about focusing on the highest ROI activities in the right order.
Below are realistic plans depending on your timeline.
This is for:
- upcoming interviews
- tight deadlines
- candidates who already have basic foundations
Goal:
👉 maximize interview readiness quickly
Focus:
- probability fundamentals (expected value, conditional probability)
- core problem types
- start daily mental math
What to do:
- work through key sections of the Green Book
- solve 10–20 structured problems per day
- start mental math (10–15 min daily)
Priority:
- understanding > speed
Focus:
- common interview problem types
- building intuition
What to do:
- continue Green Book / structured resources
- start mixing sources (e.g. QuantBrainteasers, Brainstellar, Jerry Qin)
- begin light timed practice
Mental math:
- aim for noticeable speed improvement
Focus:
- solving under time constraints
- combining skills
What to do:
- timed sets (important)
- mixed problem sessions (probability + brainteasers + math)
- introduce mock-style practice
If relevant:
- start coding practice on LeetCode
Focus:
- performance under pressure
- communication
What to do:
- full mock interviews
- simulate real conditions (timing, stress, explanation)
- review mistakes deeply
Mental math:
- keep daily practice (non-negotiable)
This is the optimal balance for most candidates.
Goal:
👉 build strong fundamentals + reach interview-level performance
Focus:
- probability basics
- expected value
- simple brainteasers
What to do:
- structured learning (Green Book, guides)
- untimed problem solving
- start mental math daily
Focus:
- problem solving
- pattern recognition
- increasing difficulty
What to do:
- solve curated problems daily
- mix multiple sources (e.g. QuantBrainteasers, Brainstellar)
- start light timed sessions
If relevant:
- coding practice on LeetCode
Focus:
- speed
- consistency
- handling uncertainty
What to do:
- timed problem sets
- mock interviews
- identify weak areas
Key insight:
- this is where most candidates struggle
Focus:
- polishing performance
- fixing weaknesses
What to do:
- revisit weak topics
- redo difficult problems
- simulate full interview sessions
If targeting trading roles, prioritize:
- daily mental math (non-negotiable)
- expected value & probability intuition
- fast decision-making
- timed drills
Key difference: 👉 speed and clarity matter as much as correctness
If targeting research roles, prioritize:
- probability + statistics depth
- Python + data analysis
- modeling and experimentation
- coding
Key difference: 👉 depth and rigor matter more than raw speed
- trying to cover too many topics at once
- delaying mental math practice
- avoiding timed practice until too late
- focusing on reading instead of solving
- not reviewing mistakes
A strong study plan should:
- evolve from understanding → practice → speed → simulation
- be consistent rather than intense and irregular
- match your target role
Your resume is not a formality.
👉 It is the filter that decides whether you even get an interview.
Most candidates fail here without realizing it.
Recruiters and hiring managers scan your resume in ~10–20 seconds.
They are looking for signals of:
- analytical ability
- problem-solving
- technical skills
- evidence of excellence
Not:
- long descriptions
- generic responsibilities
- buzzwords
This is the most powerful signal.
Examples:
- math / programming competitions
- strong academic performance
- selective programs
- scholarships
👉 If you have this, make it immediately visible
Not:
- “implemented X model”
- “analyzed dataset”
But:
- clear problem
- clear method
- measurable outcome
Example (weak ❌):
Built a trading strategy using Python
Example (strong ✅):
Developed a mean-reversion strategy on equities using Python, achieving a Sharpe ratio of 1.4 over a 5-year backtest
Relevant signals:
- Python (NumPy, pandas, data analysis)
- C++ (for low-latency / dev roles)
- SQL
- machine learning (if applied, not theoretical)
👉 Depth > breadth
- ❌ list responsibilities instead of outcomes
- ❌ include projects they don’t fully understand
- ❌ add too many weak or irrelevant items
- ❌ write vague bullet points with no numbers
- ❌ treat resume as a formality
Keep it 1 page maximum.
Recommended structure:
- Education
- Experience / Projects
- Skills
- (Optional) Awards / Competitions
Use this structure:
👉 Action verb + method + result
Example:
- Built a Monte Carlo simulation in Python to price options under stochastic volatility models
- Analyzed 1M+ data points to identify inefficiencies in FX markets, improving signal accuracy by 20%
👉 If you cannot explain a line in depth, remove it.
In interviews:
- they will pick random lines
- they will go deep
- they will test your understanding
Good project types:
- backtesting trading strategies
- probability simulations
- data-driven research projects
- Kaggle competitions
- building tools (even small ones)
Better:
- 1 strong project you deeply understand
than
- 5 shallow projects
Getting the interview is step 1.
Passing it requires:
- explain your reasoning step by step
- don’t jump to conclusions
- structure your thoughts
- trading roles → speed matters
- research roles → depth matters
Shows:
- curiosity
- understanding
- maturity
Networking helps, but:
👉 it will not compensate for weak preparation
Useful actions:
- reach out to people in target roles
- ask specific, thoughtful questions
- understand interview processes
- your resume gets you the interview
- your skills get you the offer
Both matter.
Most candidates prepare without a clear understanding of what interviews actually look like.
This leads to:
- practicing the wrong things
- being surprised during interviews
- underperforming despite good preparation
Below is a realistic breakdown of how quant interviews typically work.
Most firms follow a structure like this:
- Online Assessment (OA)
- Phone / First-round interviews
- Onsite / Final rounds
Not all firms use all stages, but the pattern is similar.
This is often the first filter.
Depending on the role:
- mental math tests
- probability / brainteasers
- coding challenges (often via LeetCode-style problems)
- logic or game-based assessments
- speed
- accuracy
- basic problem-solving
- ability to stay calm under time pressure
👉 This is mostly a filter stage, not a deep evaluation
You don’t need to be exceptional —
you need to be fast, clean, and consistent
- underestimating mental math
- not practicing under time pressure
Usually 1–3 interviews.
- probability questions
- brainteasers
- mental math
- sometimes coding (for research/dev roles)
Format:
- interactive
- conversational
- often time-constrained
- reasoning process
- clarity of thought
- ability to structure problems
- communication
You might get:
- a probability problem
- followed by variations
- then deeper follow-ups
👉 Interviewers care about how you think, not just the final answer
👉 Most candidates fail here not because they don’t know the answer,
but because they cannot structure their thinking clearly
This is the most important stage.
Typically:
- 3–6 interviews
- multiple interviewers
- mix of topics
- harder probability problems
- deeper discussions
- trading games (for trader roles)
- coding + system thinking (for dev roles)
- project deep-dives (for research roles)
- consistency
- depth of understanding
- ability to handle pressure
- intellectual honesty
You may encounter:
- market-making games
- expected value decisions
- fast-paced scenarios
They evaluate:
- decision-making
- risk/reward intuition
- composure
You may be asked:
- to explain projects in depth
- to reason about data / models
- to write or discuss code
They evaluate:
- rigor
- technical depth
- ability to think like a researcher
Across all stages:
👉 Thinking out loud clearly
Strong candidates:
- explain assumptions
- structure their reasoning
- adapt when corrected
Weak candidates:
- jump to answers
- stay silent while thinking
- get stuck without communicating
- not trivia tests
- not pure knowledge checks
- not about memorizing solutions
Top candidates:
- stay calm under pressure
- communicate clearly
- simplify problems
- show structured reasoning
To match real interviews, you should:
- practice under time constraints
- simulate interviews (very important)
- explain solutions out loud
- review mistakes deeply
If you understand:
- how interviews are structured
- what each stage is testing
👉 your preparation becomes much more efficient
- ❌ only reading, no practice
- ❌ ignoring mental math
- ❌ practicing without time pressure
- ❌ doing only LeetCode and assuming that is enough
- ❌ not reviewing mistakes
- ❌ preparing too broadly
- ❌ overfitting to one company’s exact style
No. Many trading roles hire from bachelor’s and master’s backgrounds.
PhDs are more common in some research-heavy roles, but they are not the only route.
No. It helps for coding, but quant interviews often also require probability, expected value, mental math, and interview-style reasoning.
For many candidates, a serious prep cycle is around 4–12 weeks, depending on your starting level and target role.
Usually some combination of:
- speed
- accuracy
- clarity of reasoning
- composure under pressure
- QuantBrainteasers → structured quant interview practice
- Brainstellar → puzzle bank
- Jerry Qin → probability prep
- LeetCode → coding
- Zetamac → mental math
We welcome contributions!
To contribute:
- add high-quality resources
- include short descriptions
- avoid duplicates
- keep it practical and curated
Open a PR if you want to improve the list.
Consistency beats intensity.
A simple routine done regularly is usually more effective than chaotic bursts of preparation.
- Practice every day
- Think deeply about problems
- Simulate real interviews
- Review mistakes honestly
If you found this helpful:
- ⭐ star the repo
- share it with others
- contribute useful resources
Maintained by QuantBrainteasers
👉 https://quantbrainteasers.com