by Mohamed Aazi
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Suggested foundations for financial machine learning and probability and statistical reasoning. Specialist prerequisites vary by resource.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Assess uncertainty rather than treating an estimate as a fact.
Learn statistical learning, establish simple baselines, and evaluate financial predictions without leaking the future.
Connect probability, linear algebra and stochastic calculus to the quantities a quant model must estimate or price.