Lesson 8 of 12

Structured learning draft

Evaluation with Backtesting

In AI & Algorithmic Trading, the way a learner handles evaluation shapes how Backtesting is used and evaluated. Evaluation compares alternatives consistently. This advanced lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain evaluation in the context of AI & Algorithmic Trading.
Apply Backtesting to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureSide-by-side comparison

Evaluation: from context to evidence

Evaluation connects records and assumptions to a reconciled decision in AI & Algorithmic Trading.

Records and assumptions

Define the purpose, intended user and Backtesting constraints.

frames

Evaluation

Include fees, timing and jurisdiction-specific effects.

produces evidence for

Reconciled decision

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Evaluation is credible only when the result can be traced back to its purpose, inputs and constraints. Evaluation is the decision layer between the starting context and evidence that the result is fit for purpose.

Evaluation compares alternatives consistently. For Backtesting, distinguish performing an operation from demonstrating that it suits the stated purpose. Include fees, timing and jurisdiction-specific effects. Record assumptions that could change the conclusion.

Apply evaluation deliberately

State the AI & Algorithmic Trading task and the decision it supports.
Prepare a small Backtesting case with a known input and difficult boundary.
Include fees, timing and jurisdiction-specific effects.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

evaluation evidence record
Review point	Evidence
Purpose	The specific Backtesting outcome and intended user
Method	The evaluation 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 Backtesting before defining what evaluation 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 Backtesting task demonstrating evaluation. 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 evaluation result produced with Backtesting?

A polished Backtesting output without its input A screenshot showing the tool opened A claim that the method always works The input, expected behaviour, normal and boundary results, and a limitation

Lesson summary

For AI & Algorithmic Trading, evaluation means: Evaluation compares alternatives consistently.
A credible Backtesting result includes a checked boundary, not only a successful example.
The next lesson builds on this evaluation evidence record.

Sources and further reading

National Payments SystemCentral Bank of Kenya - accessed 2026-08-21
Financial educationOECD - accessed 2026-08-21

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