Request a complete handoff
Request the research question, data provenance and version, configuration, code revision, decision timestamps, cost assumptions and output files. Separate an illustrative demonstration from an actual historical experiment.
Review in a fixed order
Check information timing first; inspect a sample of signals and fills; reconcile totals; then examine held-out evaluation and sensitivity. If inputs or timing are unresolved, record the result as not yet verified rather than inferring quality from a chart.
Keep a decision log
For each issue, record the question, evidence location, reviewer, status and required follow-up. Use statuses such as open, reproduced or resolved with a stated limitation. These are review states, not investment ratings.
Define a useful deliverable
Produce a brief that distinguishes confirmed calculations, assumptions and unresolved evidence. AI Quant Base offers public guides, local equity analysis and calculators for this process; no audit certification, managed workspace or institutional client engagement is implied.
Start the practical task
Use the research studio to document rules or inspect a suitable equity series. Review the input assumptions and export your work before leaving.
Open the workspace ↗Questions about this workflow?
For a content question or suggestion, describe your research objective, asset type, current stage and the step you need explained. Please do not send account credentials or trading keys.
info@aiquantbase.com ↗Sources & references
- Bailey et al. — The Probability of Backtest Overfitting (2015) ↗
- scikit-learn — Cross-validation and time series evaluation ↗
References support specific definitions or methodological context. Examples and research checklists are explanatory; no performance result is claimed.