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README

📈 Awesome Quant Interview Prep

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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

If you are a beginner

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.

If you target Quant Trading roles

Focus on:

- ⚡ Mental math speed (this is often a filter) - 🎲 Probability intuition (expected value, quick reasoning) - 🧠 Brainteasers / games

Practice under time pressure

If you target Quant Research roles

Focus on:

- 📊 Statistics & probability (deep understanding) - 🐍 Python / data analysis - 📈 Modeling & ML basics

Build small projects

📈 If you target Quant Analyst / Strat roles

Focus on:

- 📊 Probability & statistics - 💻 Python / SQL - 📈 Basic finance intuition

If you are short on time (2–4 weeks)

Do:

- Jane Street Probability Guide - Green Book (Xinfeng Zhou) - Mental math daily - Timed practice sets

🧠 Quant Roles Explained

| 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.

🏦 Types of Firms

| 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

📚 What to study

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)

🎲 Probability (most important)

This is the core of most quant interviews, especially for trading roles.

What matters:

Expected value (EV)
Conditional probability
Basic distributions
Symmetry and simplification
Logical modeling of situations

How it shows up in interviews:

Games (dice, cards, coins)
Decision-making under uncertainty
“What would you do?” scenarios
Estimation of outcomes

What is actually tested:

👉 Your ability to model a problem clearly and reason step-by-step, not memorization of formulas.

How to train effectively:

Focus on understanding structure, not formulas
Re-solve problems until the reasoning becomes intuitive
Practice explaining your thought process out loud

⚡ Mental Math (critical for trading roles)

Mental math is often a filtering stage in trading interviews. If you struggle here, you may not reach later rounds.

What matters:

Speed + accuracy
Comfort with fractions, decimals, percentages
Quick expected value calculations

How it shows up:

Timed arithmetic tests (e.g. “80 questions in 8 minutes”)
Fast calculations during probability problems
Real-time decision-making tasks

What is actually tested:

👉 Your ability to stay accurate under time pressure

How to train effectively:

Practice daily (10–15 minutes)
Track speed and accuracy over time
Focus on consistency, not just peak performance

📊 Statistics (more important for research roles)

Statistics becomes more important for Quant Research / Analyst roles.

What matters:

Distributions and moments
Estimation and inference
Regression basics
Variance and bias

How it shows up:

Interpreting data
Explaining models
Reasoning about uncertainty in datasets

What is actually tested:

👉 Your ability to reason about data and uncertainty, not just recall definitions.

How to train effectively:

Focus on intuition behind concepts
Work through applied examples
Be able to explain ideas simply

💻 Programming (role-dependent importance)

Programming is critical for some roles, less relevant for others.

What matters:

Data structures and algorithms (for coding rounds)
Writing clean, correct code
Problem-solving under constraints

How it shows up:

LeetCode-style questions
Data manipulation tasks
Simple modeling or simulation problems

What is actually tested:

👉 Your ability to solve problems clearly and efficiently in code

How to train effectively:

Focus on core patterns (arrays, hash maps, graphs, etc.)
Practice writing code without over-relying on libraries
Prioritize understanding over volume

🧠 Brainteasers & Logic

These are used to test thinking process, not just answers.

What matters:

Breaking down complex problems
Making reasonable assumptions
Structuring your reasoning

How it shows up:

Open-ended puzzles
Estimation problems
“Think out loud” questions

What is actually tested:

👉 Your ability to reason clearly under uncertainty

How to train effectively:

Practice explaining your reasoning step-by-step
Focus on structure, not clever tricks
Learn to simplify problems

📈 Markets & Finance Basics (for trading roles)

Not always heavily tested, but useful context.

What matters:

Basic market mechanics (bid/ask, market making)
Risk vs reward thinking
Expected value in trading contexts

How it shows up:

Simple market scenarios
Decision-making questions
Discussions about strategies

What is actually tested:

👉 Your intuition and reasoning, not deep finance knowledge

How to train effectively:

Focus on intuition rather than theory
Understand simple trading scenarios
Connect probability to real-world decisions

🧠 Key takeaway

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.

📖 Best Resources

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.

📘 Books

Xinfeng Zhou — A Practical Guide to Quantitative Finance Interviews (“Green Book”)

⭐ 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

Mark Joshi — Quant Job Interview Questions (“Red Book”)

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

🎲 Probability & Core Prep

Jane Street — Probability & Markets Guide

👉 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

Jerry Qin

👉 https://jerryqin.com/

High-quality probability-style questions

How to use it:

Great for deepening intuition
Use after basic foundations are in place

Brainstellar

👉 https://brainstellar.com

Structured problem bank

How to use it:

Good for building volume and consistency
Useful once you want more repetition

QuantBrainteasers

👉 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

⚡ Mental Math

Zetamac

👉 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

TradingInterview / TraderMaths

👉 https://www.tradinginterview.com 👉 https://www.tradermaths.com/math-tests

Good additional sources for realistic drills

💻 Programming

LeetCode

👉 https://leetcode.com

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

🧠 Brainteasers & Puzzles

Project Euler

👉 https://projecteuler.net/

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

Other sources

Jerry Qin
Brainstellar
QuantBrainteasers

🧠 Key principle

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.

🗺️ Suggested Preparation Roadmap

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

Step-by-step structure

Build probability fundamentals

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

Solve structured problem sets

Move to curated question sets (not random problems).

Goal:

build intuition
recognize patterns
understand common problem types

Key point:

quality > quantity

Introduce daily mental math

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

Add coding preparation (if relevant)

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

Introduce time pressure

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

Review mistakes deeply

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

Simulate interview conditions

Final stage of preparation.

Goal:

think clearly under pressure
communicate your reasoning effectively

Practice:

explain your thinking out loud
simulate real interview scenarios

⚠️ Common wrong approach

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

🧠 Key takeaway

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.

📅 Study Plans

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.

🔥 4-Week Crash Plan (High Intensity)

This is for:

upcoming interviews
tight deadlines
candidates who already have basic foundations

Goal: 👉 maximize interview readiness quickly

Week 1 — Foundations + Structure

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

Week 2 — Pattern Recognition

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

Week 3 — Pressure + Integration

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

Week 4 — Interview Simulation

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)

📈 8-Week Plan (Most Effective for Most People)

This is the optimal balance for most candidates.

Goal: 👉 build strong fundamentals + reach interview-level performance

Weeks 1–2 — Foundations

Focus:

probability basics
expected value
simple brainteasers

What to do:

structured learning (Green Book, guides)
untimed problem solving
start mental math daily

Weeks 3–5 — Structured Practice

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

Weeks 6–7 — Pressure Phase

Focus:

speed
consistency
handling uncertainty

What to do:

timed problem sets
mock interviews
identify weak areas

Key insight:

this is where most candidates struggle

Week 8 — Refinement

Focus:

polishing performance
fixing weaknesses

What to do:

revisit weak topics
redo difficult problems
simulate full interview sessions

⚡ Quant Trader Focus (Specialization Layer)

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

📊 Quant Research Focus (Specialization Layer)

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

⚠️ Common mistakes in study plans

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

🧠 Key takeaway

A strong study plan should:

evolve from understanding → practice → speed → simulation
be consistent rather than intense and irregular
match your target role

📄 Resume, Projects & Strategy

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.

🧠 What firms are actually looking for

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

📌 The 3 strongest signals (in order)

1. 🏆 Proof of excellence

This is the most powerful signal.

Examples:

math / programming competitions
strong academic performance
selective programs
scholarships

👉 If you have this, make it immediately visible

2. 🧪 Projects with real substance

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

3. 💻 Technical skills that are actually usable

Relevant signals:

Python (NumPy, pandas, data analysis)
C++ (for low-latency / dev roles)
SQL
machine learning (if applied, not theoretical)

👉 Depth > breadth

⚠️ What most candidates do wrong

❌ 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

🧱 How to structure your resume

Keep it 1 page maximum.

Recommended structure:

Education
Experience / Projects
Skills
(Optional) Awards / Competitions

✍️ How to write strong bullet points

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%

🧠 Golden rule

👉 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

📊 Projects that actually help

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

🧪 Interview strategy (very important)

Getting the interview is step 1.

Passing it requires:

1. 🗣️ Clear thinking

explain your reasoning step by step
don’t jump to conclusions
structure your thoughts

2. ⚖️ Balance speed vs accuracy

trading roles → speed matters
research roles → depth matters

3. ❓ Asking good questions

Shows:

curiosity
understanding
maturity

🤝 Networking (realistic view)

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

⚠️ Final advice

your resume gets you the interview
your skills get you the offer

Both matter.

🎯 What Interviews Look Like

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.

🧩 The typical interview pipeline

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.

🧪 Stage 1 — Online Assessment (OA)

This is often the first filter.

What it looks like

Depending on the role:

mental math tests
probability / brainteasers
coding challenges (often via LeetCode-style problems)
logic or game-based assessments

What is being tested

speed
accuracy
basic problem-solving
ability to stay calm under time pressure

Key insight

👉 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

Common mistake

underestimating mental math
not practicing under time pressure

📞 Stage 2 — First-Round Interviews

Usually 1–3 interviews.

What it looks like

probability questions
brainteasers
mental math
sometimes coding (for research/dev roles)

Format:

interactive
conversational
often time-constrained

What is being tested

reasoning process
clarity of thought
ability to structure problems
communication

Example flow

You might get:

a probability problem
followed by variations
then deeper follow-ups

👉 Interviewers care about how you think, not just the final answer

Key insight

👉 Most candidates fail here not because they don’t know the answer, but because they cannot structure their thinking clearly

🏢 Stage 3 — Final / Onsite Interviews

This is the most important stage.

Typically:

3–6 interviews
multiple interviewers
mix of topics

What it looks like

harder probability problems
deeper discussions
trading games (for trader roles)
coding + system thinking (for dev roles)
project deep-dives (for research roles)

What is being tested

consistency
depth of understanding
ability to handle pressure
intellectual honesty

Trading roles — specific component

You may encounter:

market-making games
expected value decisions
fast-paced scenarios

They evaluate:

decision-making
risk/reward intuition
composure

Research roles — specific component

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

🧠 The most important meta-skill

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

⚠️ What interviews are NOT

not trivia tests
not pure knowledge checks
not about memorizing solutions

🔑 What actually makes the difference

Top candidates:

stay calm under pressure
communicate clearly
simplify problems
show structured reasoning

🧪 How to prepare effectively

To match real interviews, you should:

practice under time constraints
simulate interviews (very important)
explain solutions out loud
review mistakes deeply

🎯 Final takeaway

If you understand:

how interviews are structured
what each stage is testing

👉 your preparation becomes much more efficient

⚠️ Common Mistakes

❌ 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

❓ FAQ

Do I need a PhD to break into quant?

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.