Ballzatram

AI-guided workbenches, simulations, games, and odd tools

Stress testing

Macro scenario lab

Translate changes in inflation, rates, growth, oil, and credit into explicit upside/downside ranges with assumption traceability.

Assumption driven

Guided intake

Start with a decision, not a blank prompt

The workshop guide uses these intake points to ask sharper questions before producing structured output.

Goal

What decision should scenario lab support?

Inputs

Which dataset, ticker, scenario, or assumption set should be trusted?

Constraint

What risk limit, horizon, or caveat should shape the output?

Output contract

Every useful answer becomes a card stack

Recommendation

What the workflow suggests and why it is not automatic advice.

Evidence

The metrics, chart movement, or model signal supporting the view.

Caveat

The assumption, missing data, or model risk that could change the result.

Next step

The most useful follow-up action before exporting or sharing.

Base case

+7%

Demo expected return under neutral macro inputs

Bear case

-24%

Recession plus wider credit spread template

Shock count

5

Macro levers available in the current API schema

Professional review checklist

  • OKKeep shock magnitudes plausible for the horizon being modeled.
  • OKUse ranges where confidence is low rather than false precision.
  • OKRecord the date, data window, and owner of each scenario pack.

Empty state behavior

Choose shock magnitudes to calculate a scenario table and explain the dominant risk driver.

Correlation is not causation. Results are model-dependent and assume regime stability.
Placeholders only. No payment gate.