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Backtesting & Validation Checklist

Forecast Calibration Review Checklist

A trading or risk model that outputs probabilities is only useful if those probabilities are honest. This checklist reviews forecast calibration, the property that a stated probability matches the real frequency.

By AI Fin Hub Research · AI Fin Hub Team
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Forecast Scoring Sandbox

Paste a forecast stream (probability + outcome) and see Brier score with full decomposition, log loss, reliability diagram, and bootstrap confidence.

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Checklist Progress

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Checklist Sections

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Section 1

Phase 1: Proper scoring

3 items

Section 2

Phase 2: Reliability

3 items

Section 3

Phase 3: Discrimination

2 items

Section 4

Phase 4: Sample sufficiency

3 items

Pro Tips

Small moves that make the checklist easier to finish

Accuracy hides overconfidence. A model that is right most of the time but says ninety-nine percent when it means seventy will size your bets disastrously, which is exactly what a proper scoring rule catches.
Calibration and discrimination are different skills. A forecast can rank outcomes perfectly and still state dishonest probabilities, and only the second property is fixable with a recalibration map.
Distrust a calibration verdict on thin data. With few observations per bucket, the reliability curve is mostly noise, so confirm the sample before concluding a model is well or badly calibrated.

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Planning estimates only — not financial, tax, or investment advice.