Quantitative Analyst
A research-backed roadmap from mathematical foundations to production quant finance — stochastic calculus, derivatives pricing, algorithmic trading, and ML for finance across 8 stages. Built for roles at Citadel, Two Sigma, DE Shaw, Jane Street, Goldman Sachs, and JPMorgan.
Mathematical Foundations
4–6 weeksThe quantitative bedrock that separates quants from data scientists — linear algebra, probability, and statistics at a graduate level.
Python & R for Quant Finance
3–4 weeksThe quant toolkit — NumPy/SciPy for numerical computing, QuantLib for derivatives, vectorbt for backtesting, and Polars for large time-series.
Financial Markets & Instruments
3–4 weeksYou cannot price what you do not understand. Deep knowledge of equities, fixed income, derivatives, and FX is the prerequisite for all pricing and risk work.
Stochastic Calculus & Derivatives Pricing
4–5 weeksThe mathematics that separates quants from everyone else — Ito calculus, Black-Scholes, stochastic volatility, and numerical pricing methods.
Risk Management & Portfolio Theory
3–4 weeksMarkowitz to CVaR — the framework every buy-side quant and risk manager uses to construct, monitor, and stress-test portfolios.
Algorithmic Trading & Alpha Research
4–5 weeksFrom signal construction to execution — factor research, backtesting methodology, execution algorithms, and the TCA loop that closes the strategy lifecycle.
Machine Learning for Finance
3–4 weeksML is no longer optional in quant research — but finance-specific methodology (purged CV, signal IC testing, feature engineering) is what makes it actually work.
Portfolio & Career
4–6 weeksThe quant job market rewards demonstrated skills over credentials — a rigorous backtested strategy, a strong GitHub, and interview preparation for brainteasers and coding tests.
Ready to start your quant path?
Mathematics first — stochastic calculus and probability theory are the foundations that separate quants from data scientists.