Interactive Roadmap

Interview Prep Plan

A focused 3-month sprint to get you ready for quant trader, developer, and researcher interviews. Follow it sequentially or jump to the month that matches where you are right now.

01
Build Your Foundation
Mental math, probability, Python, and core books
02
Go Deep Technically
Options, microstructure, ML, and coding practice
03
Sharpen & Apply
Mock interviews, resume polish, and firm research

Don't just solve — verbalize

Interviewers evaluate your reasoning process, not just the answer. Practice explaining every step out loud from day one.

20 minutes a day beats marathons

Mental math fluency and probability intuition come from daily repetition, not occasional cram sessions the week before.

Recruit while still preparing

You never finish becoming ready. Applications, networking, and prep run in parallel — start both before you feel ready.

Track-specific depth matters

A trader, developer, and researcher face very different interviews. Use the track guides to calibrate where to go deep.

1

Build Your Foundation

Weeks 1–4  ·  Mental Math · Probability · Python · Books
Week 1
Mental Math & Arithmetic Fluency
  • Memorize squares 1–25 and cubes 1–15 cold — recite both lists forward and backward
  • Learn Vedic multiplication tricks: ×11, ×25, near-100 squares, and squaring numbers ending in 5
  • Practice percent changes and fraction-to-decimal conversions until they feel automatic
  • Drill 20 min/day using AggieQuant's Mental Math game — target 20+ correct in 90 seconds on Easy, then advance to Medium
Mental Math All Tracks Daily Habit
Week 2
Probability Fundamentals
  • Master sample spaces, events, and the axioms of probability — build formal notation habits early
  • Work through Bayes' theorem with 5 fully written-out examples, including false positive paradoxes
  • Practice expected value and combinatorics (combinations, permutations, stars and bars)
  • Read Heard on the Street Chapters 1–3 — attempt each problem before reading the solution
Probability All Tracks Heard on the Street
Week 3
Python & Data Foundations
  • Set up Python, Jupyter, pandas, numpy, and matplotlib in a clean virtual environment
  • Load and clean a real OHLCV CSV (daily stock data) — handle NaNs, reindex, parse dates
  • Compute rolling means, rolling standard deviation, and pairwise correlations between tickers
  • Plot a price series with buy/sell annotations — build a habit of visualizing before modeling
Python Developer Researcher
Week 4
Green Book Deep Dive
  • Read A Practical Guide to Quantitative Finance Interviews (Green Book) Chapters 1–2
  • Solve 3 problems per day — write full solutions, not just answers; review where your reasoning diverged
  • Practice saying your solution out loud: articulate assumptions, approach, and sanity checks
  • Attend AggieQuant's Probability & Brainteasers session if available — get live feedback on your process
Interview Prep Green Book All Tracks

Month 1 goal: Arithmetic should feel automatic and probability setups should feel structured. You are building reflexes, not cramming facts.

2

Go Deep Technically

Weeks 5–8  ·  Options · Microstructure · ML · Coding
Week 5
Options & Derivatives from First Principles
  • Build call and put payoff diagrams from scratch — understand intrinsic vs. extrinsic value intuitively
  • Derive risk-neutral pricing and work through the Black-Scholes derivation step by step
  • Implement Black-Scholes in Python: price calls and puts, then compute all 5 Greeks analytically
  • Plot P&L surfaces varying spot, strike, and time to expiry — make the Greeks visceral, not abstract
Derivatives Trader Researcher Python
Week 6
Market Microstructure
  • Study limit order book structure: how orders queue, match, and execute at top-of-book vs. market depth
  • Decompose bid-ask spreads into adverse selection cost, inventory cost, and order processing cost
  • Play Riverboat Broker on the AggieQuant competition portal — reflect on how pricing intuition develops
  • Read Trading and Exchanges Chapters 1–4 for a practitioner-level view of market mechanics
Microstructure Trader Trading and Exchanges
Week 7
Statistics & ML for Finance
  • Review key distributions: normal, log-normal, Poisson, and their roles in financial modeling
  • Understand non-stationarity, autocorrelation, and why standard train/test splits fail on time series
  • Build a simple momentum signal in Python — use walk-forward validation to avoid look-ahead bias
  • Read ISLR Chapters 1–4 to reinforce regression, classification, and cross-validation intuition
Statistics ML Researcher ISLR
Week 8
Coding & Green Book Finish
  • Developer track: Solve 20 medium LeetCode problems — focus on arrays, hash maps, heaps, and sorting
  • Developer track: Review C++ memory management, templates, and concurrency basics; be ready to discuss trade-offs
  • Trader/Researcher: Finish Green Book Chapters 3–6 — focus on stochastic processes and statistics sections
  • All tracks: time yourself at 20 min per problem and practice explaining your approach before coding
Coding C++ Interview Prep Developer

Month 2 goal: You should be able to explain options pricing, order book mechanics, and a basic backtest to a non-technical person. Depth comes from being able to teach it, not just recite it.

3

Sharpen & Apply

Weeks 9–12  ·  Resume · Mock Interviews · Networking · Firms
Week 9
Resume & Application Polish
  • Quantify every bullet point — replace vague action verbs with specific outcomes (e.g., "reduced latency by 40%")
  • Tailor your top section to your target track: signal the right skills first for Dev vs. Trader vs. Researcher
  • Do a peer review with 2 other members using AggieQuant's structured rubric — give and receive specific feedback
  • Map out application deadlines for target firms and prioritize based on fit and timing
Resume Career Dev All Tracks
Week 10
Mock Interviews & Feedback
  • Complete 2 full mock interview sessions with partners — rotate roles so you get reps on both sides
  • Round 1: 3 probability/mental math problems (20 min each) with 10 min of structured feedback
  • Round 2: 2 track-specific technical questions — coding for Dev, market scenarios for Trader, modeling for Researcher
  • Group debrief: identify the most common reasoning gaps and assign targeted problem sets to close them
Mock Interview Interview Prep All Tracks
Week 11
Networking & Cold Outreach
  • Build a curated list of 15–20 target professionals on LinkedIn — focus on alumni, recent hires, and researchers at target firms
  • Draft 3 personalized cold messages using the AggieQuant template — reference their work specifically, not just their firm
  • Follow up with any discovery day contacts you made earlier in the semester — a short, direct check-in
  • Prepare your 60-second verbal pitch: who you are, what you've done, and what you're looking for — practice it out loud
Networking Cold Outreach Career Dev
Week 12
Firm Research & Final Ramp
  • Deep dive on 5 target firms: research their trading style, public-facing papers, recent news, and interview reputation
  • Study each firm's interview format — some are pure math, some have live trading simulations, some emphasize coding
  • Be ready to discuss your 2–3 strongest projects in detail: hypothesis, methodology, results, and what you'd do differently
  • Final timed sprint: 30 mixed problems (math + probability + track-specific) in 60 minutes — simulate real pressure
Firm Research Final Prep Career Dev

Month 3 goal: Walk into every interview knowing your own story cold, able to run a 20-minute problem session cleanly, and with a specific reason you want to work at each firm you're targeting.

Want structured prep sessions? AggieQuant runs recurring probability sessions, mental math drills, and mock interview rounds during the semester. Check the Events page for upcoming dates, or reach out at bago2007@tamu.edu.