Datumline methods

Portfolio Estimates

sizing, comparables and path-to-target on an assumption ledger that re-marks

The problem

Estimates live in spreadsheets where a hard-coded 0.62 sits inside a formula three tabs away, nobody remembers which inputs were measured and which were guessed, and when one input changes, nobody can say which conclusions moved. AI-written estimates are worse: fluent numbers with no ledger at all.

What it is

Compose a research-backed estimate for any space (market size, comparables, path to a target) where every number lives in an assumption ledger and every output is re-marked when an input changes.

Delivered as a download: method documents, templates and standard-library Python scripts with tests that fail when the method's own logic is broken. Synthetic example data only.