by Ernest P. Chan
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Suggested foundations for trading research and honest backtests and probability and statistical reasoning. Specialist prerequisites vary by resource.
Use only information available at the simulated decision time.
Separate model selection from the final evaluation.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Reconcile signals, executions, positions and P&L.
Include the costs that can erase a paper strategy.
Assess uncertainty rather than treating an estimate as a fact.
Choose a quant role, build the mathematical core, and turn your first research question into an auditable Python project.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.