# Point-in-Time Data > Data recorded as it was actually known on each historical date — first-print economic releases, as-of universe membership, unrevised financials — rather than the revised series that exists only in hindsight. - Canonical: https://hadalinstruments.com/glossary/point-in-time-data/ - Term set: https://hadalinstruments.com/glossary/ --- Most historical datasets are the present's version of the past. Economic series are revised: headline employment and GDP figures are restated in subsequent releases, sometimes drastically, so the "historical" value a standard database shows for a given month is a number that nobody possessed in that month. Corporate financials are restated. Index membership lists are published as they stand today, not as they stood on each date. Prices are retroactively adjusted. A backtest run on such data is simulating a market participant who traded on information from the future — politely, a [look-ahead bias](/glossary/look-ahead-bias/); precisely, a fiction. Point-in-time data is the corrective: every value is stored with the timestamp at which it became *knowable*, and queries return what was knowable as of the simulated date. The first print of a release, not its final revision — because the first print is what moved the market. The universe as constituted on the day, including the members that later delisted — because their absence is exactly the conditioning that [survivorship bias](/glossary/survivorship-bias/) smuggles in. The distinction is institutionalised where the stakes are understood: central-bank archival databases preserve data *vintages* — the full sequence of what each series looked like at each publication date — precisely because the revised series and the tradable series are different objects. The discipline is expensive and unglamorous. It multiplies storage, complicates every query, and produces backtests with worse numbers, since the flattering revisions and the buried failures are exactly what it removes. That is the point. A simulation on revised data measures a strategy against a memory of history — tidied, corrected, and survivor-only. A simulation on point-in-time data measures it against something resembling the history that occurred. Maintaining the distinction requires knowing what a dataset contains and when each part of it arrived, which is the concern of [tick data provenance](/glossary/tick-data-provenance/) extended across every input a strategy consumes. ## Commonly confused with Neighbouring concepts that get used interchangeably, and the distinction that actually separates them. - **Historical data** — Most historical datasets are the present's version of the past — revised, corrected, and tidied. Point-in-time data is the past's version of itself. The two disagree precisely where a strategy would have been trading on the difference. - **The revised series** — The first print is what moved the market; the revision is what the database remembers. For a backtest the first print is the tradable object and the revision is a fact about publishing history, not about the market anyone traded. - **[Tick data provenance](https://hadalinstruments.com/glossary/tick-data-provenance/)** — Provenance is about what happened to a dataset since capture — cleaning, backfill, merges. Point-in-time is about what was knowable at each historical moment. You need provenance discipline to achieve point-in-time honesty, and the two are not the same claim. - **A data vintage** — A vintage is one snapshot: what a series looked like at one publication date. Point-in-time data is the full sequence of vintages, queried as of a simulated date. Central-bank archival databases keep vintages precisely because the revised series and the tradable series are different objects. ## How to measure it in your own data A definition you cannot test is a definition you have to take on trust. This is the shortest honest route from the concept to a number you computed yourself. - **Records you need** — For every input, the value together with the timestamp at which it became knowable — not the timestamp of the period it describes. For revised series that means first prints; for universes it means membership as constituted on the day, including the members that later delisted. - **What you compute** — Query the dataset as of each simulated date and compare against what your backtest actually consumed. Any value that differs is a value the simulation had and the market did not. - **What the answer tells you** — Expect the honest version to produce worse numbers, and treat that as the measurement working. The flattering revisions and the buried failures are exactly what the discipline removes. A simulation on revised data measures a strategy against a memory of history — tidied, corrected and survivor-only. If switching to point-in-time changes nothing, verify that the switch actually happened. ## Questions and answers ### Why does using revised economic data count as look-ahead bias? Because the revised figure did not exist on the date the simulation trades it. Headline employment and GDP numbers are restated in later releases, sometimes substantially, so the value a standard database shows for a given month is one nobody possessed in that month. The strategy is reacting to information from the future, politely described. ### What is a data vintage? The full sequence of what a series looked like at each publication date, rather than only its current state. Central-bank archival databases preserve vintages deliberately, because the revised series and the series anyone could have traded are different objects and conflating them destroys the second. ### Is point-in-time discipline worth the cost? Point-in-time discipline is expensive and unglamorous — it multiplies storage, complicates every query, and produces worse backtest numbers. That last part is the argument for it rather than against. The numbers get worse because the flattery is being removed, and a result that survives the removal is worth more than the one that preceded it. ### Does this only affect economic data? No. Corporate financials are restated, index membership lists are published as they stand today rather than as they stood on each date, and prices are retroactively adjusted. Every one of those is the same failure: a value stored under the timestamp of the period it describes rather than the moment it became knowable. ## If this has already cost you Whether a series has been quietly revised since the dates it describes is a property of the file, and it is checkable. - [Dataset Forensics Assay](https://hadalinstruments.com/assays/#dataset-forensics-assay) “Can I trust the data file my research stands on?” Will not establish: That the data is correct. Structure can prove a file defective; it cannot prove it faithful to the market — only a second independent source can, and the report names that limit. Intake is not open yet, so none of these can be commissioned today. They are listed here so you know the measurement exists and what it would and would not settle — the [launch list](https://hadalinstruments.com/pricing/#waitlist) hears first. ## Related terms Derived from the links this entry makes and the entries that link back to it. - [Implementation Shortfall](https://hadalinstruments.com/glossary/implementation-shortfall/) The difference between the return of a paper portfolio traded instantly at the decision price and the return actually realised, including the cost of the quantity that never filled at all. - [Look-Ahead Bias](https://hadalinstruments.com/glossary/look-ahead-bias/) The use, at a simulated decision point, of any information that would not have been available at that moment in live trading. - [Non-Farm Payrolls (NFP)](https://hadalinstruments.com/glossary/non-farm-payrolls/) The monthly US employment release — published by the Bureau of Labor Statistics, typically at 8:30 a.m. Eastern on the first Friday of the month — which functions as the largest recurring scheduled volatility event in foreign exchange. - [Survivorship Bias](https://hadalinstruments.com/glossary/survivorship-bias/) A distortion introduced when a dataset includes only the entities that survived to the present, so that failures vanish from history and everything measurable looks better than it was. - [Tick Data Provenance](https://hadalinstruments.com/glossary/tick-data-provenance/) The documented history of a market dataset: where each tick was captured, when, through what aggregation, and every alteration — cleaning, backfill, merge, adjustment — applied to it since capture. ## In the research Point-in-Time Data comes up in ten research notes on this site, and this entry lists three of them. - [Why do two dashboards show different numbers?](https://hadalinstruments.com/research/why-do-two-dashboards-show-different-numbers/) Usually both are right by their own hidden definitions — the real defect is that neither number's derivation can be produced on demand. A ten-minute test. - [Why do two data feeds give different backtests?](https://hadalinstruments.com/research/why-do-two-data-feeds-give-different-backtests/) In spot FX there is no single tape: every feed is one venue's filtered, aggregated history. Where feeds diverge, why results move, and how to diff yours. - [The break that cleared neither bar](https://hadalinstruments.com/research/the-break-that-cleared-neither-bar/) A joint change-point detector flagged one break across twelve market channels, then tested it against two independent bars and cleared neither. ## Cite This Definition APA BibTeX HTML Hadal Instruments. (2026). Point-in-Time Data. Hadal Glossary. https://hadalinstruments.com/glossary/point-in-time-data/ Version 2b360a7, 2026-08-28. @misc{hadal_2026_point-in-time-data, author = {Hadal Instruments}, title = {Point-in-Time Data}, year = {2026}, url = {https://hadalinstruments.com/glossary/point-in-time-data/}, howpublished = {Hadal Glossary}, version = {2b360a7}, note = {Pre-launch publication; version dated 2026-08-28} } Source: Hadal Instruments, Point-in-Time Data. Original Research Copy Citation **Version 2b360a7** identifies the commit that last changed this page in Hadal's content repository. That repository is not public, so the identifier does not resolve externally — it is published so a citation pins one specific state rather than a moving page. To obtain the exact version cited, use the [press and research route](https://hadalinstruments.com/press/).