Course 7 · Advanced
Quantitative Methods & Risk
The statistical and risk-management foundation a quant desk expects you to know cold. Learn how returns are really distributed and why the normal curve understates tail risk, do the arithmetic of expectancy and edge, build risk measures (volatility, drawdown, VaR, CVaR) and learn where each one fails, size positions with fixed-fractional and Kelly math without blowing up, and design backtests that survive contact with reality. No signals, no win-rate promises — just the tools to evaluate everything yourself.
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Finish every lesson and pass every module quiz to earn the certificate.
Curriculum
5 modules · 20 lessonsHow returns are actually distributed, why the normal curve underestimates tail risk, and the probability tools — expectation, variance, fat tails, volatility clustering — that everything downstream depends on.
Module 2
Expectancy, Edge & Sample Size
The arithmetic of edge: expected value per trade, the win-rate / reward-ratio trade-off, variance and the law of small numbers, and how many trades you actually need before a result means anything.
The family of numbers desks use to quantify downside — volatility, maximum drawdown, Value at Risk in three flavors, and Expected Shortfall — together with the failure mode of each.
Turning an edge into a survivable bet size: fixed-fractional risk, the Kelly criterion and why full Kelly is violent, fractional Kelly, and volatility targeting.
Overfitting and the multiple-testing trap, look-ahead and survivorship bias, walk-forward and out-of-sample discipline, realistic costs, and the bootstrap as an honesty check.
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