by Antoine Garin, Daniel Kuhn, Lukas Looser, Philipp J. Schneider, Shuhan Liu
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Suggested foundations for financial machine learning and optimisation and numerical methods. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Understand identifiability and fitted model parameters.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.