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SuperQuant feature tutorial · fixed teaching example

Backtesting engine

Test explicit rules against a frozen historical sample.

Before you begin: A run uses the selected Playbook and dataset. Save your own rules before editing.

How to use it

  1. Open Research Lab’s Workbench and Select Playbook.
  2. Inspect Data & coverage, signal rules, protection and costs.
  3. Deliberately run the backtest; inspect sample dates, net result, drawdown and trades.
  4. Open result evidence and record the exact assumptions and one limitation.

Worked example and expected outcome

Teaching case: a positive gross result becomes negative after modeled costs.

Backtesting engine: worked example and outcomeTeaching case: a positive gross result becomes negative after modeled costs.. Can you trace the result to the same Playbook and dataset?. A reproducible historical run with its cost assumptions.Worked exampleTeaching case: a positive gross result becomesnegative after modeled costs.Check your understandingCan you trace the result to the same Playbookand dataset?Expected outcomeA reproducible historical run with its costassumptions.

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

Check your understanding

Can you trace the result to the same Playbook and dataset?

If it does not work

Missing coverage or an out-of-date result requires a corrected sample or a new run; it is not a forecast.

Try it in SuperQuant

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