What you will learn
- Dataset provenance
- Units and chart labels
- Uncertainty
- Negative findings
Dataset provenance
Dataset provenance records where observations came from, their timestamps and transformations. A reader should be able to trace a chart point back to source data. State licensing or access constraints that affect reproduction.
Units and chart labels
Every chart needs units, period and an honest scale. Distinguish cumulative cash P&L, percentage return and risk-normalized outcomes. Similar-looking curves can represent different quantities.
Uncertainty
Present uncertainty alongside estimates and explain the method. Avoid phrasing a point estimate as an exact future expectation. The number of decimals should not exceed the practical precision supported by the data.
Negative findings
Negative findings and failed variants are part of the research record. Reporting them helps readers understand selection and prevents others from repeating a rejected approach without learning why it failed.
Worked example
A chart titled performance rises from 100 to 150 but does not say whether it is an index, account balance or cumulative profit. The visual alone cannot establish a 50% strategy return.
Try it yourself
Write a complete caption for a hypothetical net equity curve including cash-flow treatment and observation frequency.
Show the worked solution
A useful caption names the account or normalized base, dates, currency, included costs, external-cash-flow treatment and marking frequency. It should identify whether the values are simulated or observed.
Apply this to your course project
Submit a concise research memo with evidence, assumptions and a rejection case.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.