by Andrea Barbon, Carlo Zarattini, Nikolas Anic, Ralf Seiz
Continue on SelfMadeQuant
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.