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Data Vintages

Macroeconomic data gets revised. GDP numbers published today will be revised next month and again the month after. If you’re backtesting a trading strategy or validating a model, you need the data as it existed at a specific point in time, not the latest revised version.

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Vintage capability by provider

ProviderCapabilityHow it works
FREDNativeFRED’s ALFRED service provides exact point-in-time snapshots going back to 1990.
BLSSimulatedBuilt from BLS revision history. Reconstructs what was published as of a given date.
BEASimulatedBuilt from BEA revision history. Same reconstruction approach as BLS.
All othersUnavailableReturns the current snapshot with a fallbackReason explaining the limitation.

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Using vintages in API calls

Pass a vintage object when fetching observations or running forecasts: 
{
  "vintage": {
    "mode": "specific",
    "date": "2024-06-15"
  }
}
If the provider doesn’t support vintages, you still get data back, but the response’s vintage audit object will have vintageCapability: "unavailable" and a fallbackReason explaining what happened.

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Audit metadata

Every response that touches vintage data includes a vintage audit block: 
{
  "vintage": {
    "mode": "specific",
    "date": "2024-06-15",
    "provider": "fred",
    "source": "fred/series/observations",
    "vintageCapability": "native",
    "fallbackReason": null
  }
}
Use vintageCapability to decide how much to trust the data for backtesting:
  • "native" - exact point-in-time data from the provider
  • "simulated" - reconstructed from revision history
  • "unavailable" - current snapshot, not a true vintage