A quantitative analyst ('quant') uses math, statistics and code to price financial instruments, measure risk, and build trading or portfolio models. Most quants hold a master's degree (60%) or bachelor's (35%) in math, stats, physics, engineering, CS or financial engineering, so this roadmap builds that foundation for free. You will move in three stages: (1) math, probability and Python, (2) finance, derivatives pricing, time series and risk, which makes you hireable, and (3) advanced stochastic calculus, C++, machine learning and production-quality model work. The key idea throughout: a model
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Every stage from beginner to expert, with lessons, practice drills, projects, interview questions and free resources. Free with a Rolearch account.
Stage 1 of 3 · about 16 weeks
Be fluent in the math, probability and Python that every quant model rests on.
Calculus and linear algebra
Optimization, risk and pricing all reduce to derivatives, integrals and matrices. Portfolio weights are a vector, covariance is a matrix, and finding the best portfolio is calculus.
Probability and statistics
Markets are uncertain, so everything you do is expectation, variance, distributions and inference. Mistakes here become real losses.
Python for data and numerics
Python is the everyday research language for quants. You will use NumPy, pandas and matplotlib to test ideas fast.
SQL and Excel basics
Data lives in databases and quick checks happen in spreadsheets. O*NET lists SQL Server and Excel as common tools.
Regression and basic econometrics
Regression is the workhorse for factor models and relationships between assets. You must know its assumptions to know when it lies.
Then 2 more stages (about 72 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.