Learning Paths
Study topics in order, from foundations to advanced. Each path interlinks theory with practice problems.
Probability & Counting
Foundation of quantitative reasoning: counting, conditional probability, distributions, and stochastic models.
Statistics & Distributions
Distributions, estimation, hypothesis testing, regression, and signal detection for quantitative finance.
Algorithms & Data Structures
Comprehensive coding interview prep following the Neetcode roadmap: arrays, two pointers, sliding window, stacks, binary search, trees, tries, backtracking, graphs, DP, and more.
Stochastic Processes
Random walks, Brownian motion, stochastic calculus, and continuous-time models for quantitative finance.
Options & Derivatives
Pricing, hedging, and risk management for financial derivatives.
Risk & Portfolio
Risk measurement, portfolio construction, hedging strategies, and fixed income for quantitative finance.
Brainteasers & Logic
Classic logic puzzles, lateral thinking, probability brainteasers, and game theory problems from quant interviews.
Mathematical Foundations
Number theory, linear algebra, sequences, and mental math — the mathematical toolkit for quant interviews.
Behavioral Interviews
Motivation, decision-making, and self-awareness questions common in trading firm interviews.
Finance & Markets
Market mechanics, fixed income, ETFs, and financial products — the domain knowledge every quant needs.
Machine Learning & Statistical Learning
Supervised and unsupervised learning, ensemble methods, model validation, and feature engineering for quant applications.
Market Microstructure
Order books, market impact, execution algorithms, and tick data analysis — the mechanics of how prices form and trades execute.