⁦SuperQuant feature tutorial · fixed teaching example⁩

Data sources & CSV import

Validate the source and coverage before research uses a dataset.

Before you begin: Importing can change available research data. Export your workspace before replacement.

⁦How to use it⁩

  1. Open Data sources and inspect available connection/import options.
  2. Read the expected CSV fields, market, interval and UTC requirements.
  3. Choose a teaching file only deliberately; inspect validation errors and gaps.
  4. Check the imported label, coverage and fingerprint before running a test.

⁦Worked example and expected outcome⁩

Teaching case: an hourly CSV has a missing hour and an unknown funding field.

Data sources & CSV import: worked example and outcomeTeaching case: an hourly CSV has a missing hour and an unknown funding field.. Can you identify the gap and which cost assumption remains unsupported?. A validated dataset with its remaining coverage limits visible.Worked exampleTeaching case: an hourly CSV has a missing hourand an unknown funding field.Check your understandingCan you identify the gap and which costassumption remains unsupported?Expected outcomeA validated dataset with its remaining coveragelimits visible.

⁦This is a fixed teaching example, not a current quote, personal account result or trade recommendation.⁩

⁦Check your understanding⁩

Can you identify the gap and which cost assumption remains unsupported?

⁦If it does not work⁩

Correct the file or use synthetic teaching data; renaming a file does not verify its provenance.

⁦Try it in SuperQuant⁩

⁦Sign in or create an account to open this lesson in the Academy, then use its safe in-app handoff when the feature is available to your workspace.⁩

⁦Sign in and continue this tutorial⁩