by Kamil Kashif, Robert Ślepaczuk
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Suggested foundations for financial machine learning and trading research and honest backtests. Specialist prerequisites vary by paper.
Separate model selection from the final evaluation.
Establish an interpretable baseline before a flexible model.
Use only information available at the simulated decision time.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Reconcile signals, executions, positions and P&L.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.