Quant Interview Compass

Stop over-reading. Aim at practice. Stay on track.

Quant Math & Modeling
Pure math brain: foundations for puzzles, models, and ML.
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Subtopics
  • Probability Theory
  • Mathematical Statistics
  • Combinatorics
  • Time-Series Analysis
  • Machine Learning & Deep Learning
  • Applied ML for Finance
Main Sources
Stochastics, Markets & Instruments
How things trade and are priced: derivatives, fixed income, regimes, microstructure.
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Subtopics
  • Derivatives & Pricing
  • Stochastic Calculus / Pricing Math
  • Fixed Income & Rates
  • Portfolio Theory & Risk Management
  • Markets & Asset Classes (FX, Equities, Commodities, Crypto)
  • Market Microstructure
Main Sources
The Quant Outcome (The Goal)
The **Expected Output** of successfully running the Study Workflow Loop is:
  • ✅ Micro-notes (Notion)
  • 🧠 Cheat Sheets (1-page)
  • 🔨 Broken Understanding (Practice)
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Study Workflow (Run This Loop, Not CBSE Mode)

Any topic from above runs through this same loop. The goal is fast understanding and lots of practice, not endless reading.

1 Pick a micro-topic – tiny unit: “conditional probability”, “digital call payoff”, “yield curve inversion”, “order book depth”.
2 Quick read – scan the core source (Aman.ai, SMU notes, Taleb, Allocate Smartly, Portfolio Charts, QuickTake, Paradigm/Cumberland) just enough to know what it is and why it matters.
3 Micro-notes in Notion – 5–10 lines: definition, key formula, one example, one trap, one diagram idea, source link.
4 Quizlet for recall – build a few cards: formulas, definitions, patterns. Quick daily reps only.
5 Break your understanding – go to hard questions (TraderMath, QuantQuestions, prof question bank, LeetCode, Interview Query, or GPT) and push until something snaps.
6 Re-write after failing – update Notion notes with what you missed: edge cases, hidden assumptions, better explanations.
7 Cheat sheet via GPT – use your Cheat-Sheet GPT to compress the topic into a 1-page view and save it next to your notes.

Main rule: reading is allowed only to feed questions

If you catch yourself “just reading”, you’re off-script. The loop should always push you toward questions and failures, then back into better notes and cheat sheets.

  • Keep topics small so the loop is fast.
  • Touch questions early – don’t wait to “finish the chapter”.
  • Use your mistakes as the content for your cheat sheets.
Core learning: Aman.ai, SMU notes, Dynamic Hedging, Allocate Smartly, Portfolio Charts, Bloomberg QuickTake, Paradigm / Cumberland articles.
Breaking understanding: SMU question bank, TraderMath brainteasers, QuantQuestions.io, LeetCode, Interview Query, GPT as an interviewer.
Retention: Notion (micro-notes + cheat sheets) and Quizlet (flashcards).

Question Types → Where To Practice

Interviews care about how you think under questions, not how many pages you read. Use this map to jump straight into the right kind of practice.

🧠 Probability, puzzles, brainteasers
EV games, dice/coin puzzles, conditional probability, combinatorics, mental math, DRW / Jane Street style puzzles.
Practice at: QuantQuestions.io, TraderMath, LeetCode (math).
📈 Derivatives, pricing, Greeks
Payoffs, replication, put-call parity, gamma/theta/vega, barriers, digitals, volatility, basic SABR/local vol intuition.
Practice at: SMU derivatives question bank, TraderMath options guide, Wilmott / QuantNet threads (search when needed), GPT for custom payoff / hedging drills.
🌍 Macro regimes & “how would you trade this?”
“Inflation is rising and the curve inverts, what happens?”, “You have this asset in this regime: how do you detect it, how do you trade it?”.
Practice at: TraderMath scenario Qs
💻 ML, DS & Coding
PCA, regression, trees, deep learning models, model bias/variance, data structures, SQL joins, Pandas.
Practice at: Interview Query, LeetCode (DS), HackerRank (SQL).