Lesson 7 of 12
Structured learning draftScenarios with ML
In AI & Algorithmic Trading, the way a learner handles scenarios shapes how ML is used and evaluated. Scenario analysis changes assumptions to examine outcomes. This advanced lesson focuses on a decision or output that another person can inspect.
Learning objectives
- Explain scenarios in the context of AI & Algorithmic Trading.
- Apply ML to a bounded practical task.
- Evaluate the result using explicit quality criteria.
Scenarios: from context to evidence
Scenarios connects records and assumptions to a reconciled decision in AI & Algorithmic Trading.
Define the purpose, intended user and ML constraints.
Use base, downside and upside without invented probabilities.
Compare the observed result with a normal case, boundary case and stated limitation.
Scenario analysis changes assumptions to examine outcomes. For ML, distinguish performing an operation from demonstrating that it suits the stated purpose. Use base, downside and upside without invented probabilities. Record assumptions that could change the conclusion.
Apply scenarios deliberately
- State the AI & Algorithmic Trading task and the decision it supports.
- Prepare a small ML case with a known input and difficult boundary.
- Use base, downside and upside without invented probabilities.
- Compare the observed result with the expected behaviour and explain differences.
- Save the evidence, limitation and next action in a review record.
A worked ML evidence path
A four-step worked example for applying scenarios to ML, including a boundary test and revision.
Preserve the original ML case and expected result.
Confirm the basic path behaves as expected.
Expose an assumption in the scenarios method.
Change the method, rerun both cases and record the limitation.
| Review point | Evidence |
|---|---|
| Purpose | The specific ML outcome and intended user |
| Method | The scenarios decision, input and version or context |
| Result | Observed output plus a checked boundary case |
| Limitation | What the result does not establish and the next safe action |
Common mistakes
- Using ML before defining what scenarios must achieve.
- Checking only the easiest AI & Algorithmic Trading example.
- Reporting a result without its input, assumptions or limitation.
Practice activity
Apply the lesson
For AI & Algorithmic Trading, complete a bounded ML task demonstrating scenarios. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.
Check your understanding
In AI & Algorithmic Trading, which evidence best supports a scenarios result produced with ML?
Lesson summary
- For AI & Algorithmic Trading, scenarios means: Scenario analysis changes assumptions to examine outcomes.
- A credible ML result includes a checked boundary, not only a successful example.
- The next lesson builds on this scenarios evidence record.
Sources and further reading
- National Payments SystemCentral Bank of Kenya - accessed 2026-08-21
- Financial educationOECD - accessed 2026-08-21
Personal study note