Quant Finance Roadmap — From Beginner to Hireable
Quant finance roadmap from beginner to hireable: probability, derivatives pricing and rigorous backtesting. Kaidoro adapts it to your background and deadline.
This is a starting point — make it yours
Use this goal to build your own roadmap — tailored to you and starting fresh.
Probability and statistics
The mathematical core. Everything downstream assumes it.
Master probability fundamentals~20h
Random variables, distributions, expectation, variance, covariance, conditional probability, Bayes' theorem, the Central Limit Theorem.
Done when: you can derive the variance of a sum of correlated variables.
Learn statistical inference~14h
Maximum likelihood estimation, hypothesis testing, confidence intervals, and what heavy tails do to all of it.
Done when: you can explain why financial returns break the normality assumption.
Build a Monte Carlo simulation engine~14h
Simulate correlated random paths. Understand why error shrinks as 1/√n.
Done when: quadrupling the simulations halves your error, and you predicted that.
Stochastic processes
How randomness evolves over time.
Learn Brownian motion and Itô's lemma~18h
Wiener processes, geometric Brownian motion, and why stock prices are modelled as log-normal.
Done when: you can write down the GBM SDE and explain each term.
Study martingales and the no-arbitrage argument~16h
Risk-neutral measure, and why an option's price doesn't depend on the expected direction of the stock.
Done when: that fact stops feeling paradoxical.
Derivatives pricing
Where the maths becomes a product.
Implement Black-Scholes and the Greeks~14h
The closed-form price, then delta, gamma, vega, theta. Verify against a Monte Carlo price.
Done when: both methods agree to within simulation error.
Build a binomial tree pricer~12h
Handles American options, which Black-Scholes cannot. Show it converges to Black-Scholes as steps increase.
Done when: the convergence plot looks right.
Back out implied volatility~10h
Newton's method against market prices. Plot the volatility smile.
Done when: you can explain why the smile contradicts Black-Scholes.
Portfolios and backtesting
Where most people fool themselves.
Build a Markowitz portfolio optimiser~14h
Mean-variance optimisation with real constraints — no short selling, position limits. Plot the efficient frontier.
Done when: diversification visibly beats every individual asset.
Build a backtester that doesn't lie to you~18h
Walk-forward validation, transaction costs, and ruthless attention to look-ahead bias.
Done when: you can name three ways your own backtest could still be wrong.
Test a statistical arbitrage strategy~16h
Cointegration, pairs trading, mean reversion. Measure the Sharpe ratio honestly.
Done when: you can tell whether the edge survives costs.
Interview preparation
A different skill from building things.
Drill mental maths and estimation~12h
Two-digit multiplication, percentages, squares to 20, Fermi estimation. Timed, out loud.
Done when: you can do it while explaining your reasoning.
Work through probability puzzles~16h
Conditional probability, expected value, symmetry arguments, card and dice problems.
Done when: you spot the conditioning structure before reaching for algebra.
Practise explaining your projects~8h
Two minutes per project: what, why, the hard part, what you'd do differently.
Done when: you can do it without notes and invite the follow-up question.