by Jennifer French Kamrin, Jeremy Orloff
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Suggested foundations for probability and statistical reasoning and financial time series. Specialist prerequisites vary by resource.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Check whether a relationship can be estimated consistently over time.
Read the fitted relationship and its residuals.
Assess uncertainty rather than treating an estimate as a fact.
Study long-run relationships without confusing them with correlation.
Check the assumptions behind reported estimates.
Choose a quant role, build the mathematical core, and turn your first research question into an auditable Python project.
Connect probability, linear algebra and stochastic calculus to the quantities a quant model must estimate or price.
Find your direction, refresh probability, then connect the mathematics to a financial model.
Move from conditioning and expected values to timed problems with a defensible solution.