by Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Álvaro Cartea
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Suggested foundations for probability and statistical reasoning and financial machine learning. Specialist prerequisites vary by paper.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Assess uncertainty rather than treating an estimate as a fact.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Move from conditioning and expected values to timed problems with a defensible solution.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.