by Mark Joshi
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Suggested foundations for trading research and honest backtests and risk measurement and stress. Specialist prerequisites vary by resource.
Use only information available at the simulated decision time.
Separate model selection from the final evaluation.
Represent losses as a distribution rather than one forecast.
Distinguish a loss quantile from the average loss in the tail.
Reconcile signals, executions, positions and P&L.
Include the costs that can erase a paper strategy.
Compare risk estimators and their assumptions.
Choose a quant role, build the mathematical core, and turn your first research question into an auditable Python project.
Find your direction, refresh probability, then connect the mathematics to a financial model.