StationFX GDP – Nominal Gross Domestic Product

x402

StationFX GDP – Nominal Gross Domestic Product is a paid API for AI agents from stationfx.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).

Returns U.S. nominal GDP quarterly time series data from the BEA, with derived stats like YoY growth, z-scores, trend flags, and rolling averages.

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Total value of goods and services produced in the U.S., nominal (current dollar) terms. Quarterly BEA estimate. Use for economic size comparisons, debt-to-GDP calculations, and long-run growth trend analysis. Quarterly frequency with full history back to 1947.

Output

Returns an array of quarterly observations, each containing the observation date, raw nominal GDP value in current dollars, month-over-month and year-over-year absolute and percentage changes, 12-month and 5-year z-scores, a percentile rank over the trailing 5 years, a binary above-trend flag, and 3-month and 12-month rolling averages.

Example use cases

Debt-to-GDP ratio calculation

“Fetch the latest U.S. nominal GDP value from StationFX so I can divide our national debt figure by it and get the current debt-to-GDP ratio.”

Long-run GDP growth trend analysis

“Give me quarterly U.S. nominal GDP data from 1947 through today so I can chart the long-run growth trajectory and identify the fastest- and slowest-growing decades.”

Recession-era GDP contraction review

“Pull StationFX nominal GDP data for 2007 through 2010 in JSON and show me the year-over-year percent changes and z-scores so I can quantify how deep the Great Recession contraction was.”

Technical details

Endpoint

GEThttps://stationfx.com/economic-data/gdp-growth/gross-domestic-product?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need U.S. nominal (current-dollar) GDP time series data with pre-computed statistical enrichments like z-scores, trend flags, and rolling averages. It is ideal for debt-to-GDP calculations, long-run growth comparisons, and macroeconomic dashboards. Prefer it over raw BEA data pulls when you want enriched, agent-ready output without building your own derived stats. Use sibling StationFX endpoints for inflation, labor, or interest rate data.

Side effects

Invalid date format in date_from or date_to returns an error
date_from later than date_to yields empty or error response
Unsupported fmt enum value causes a 400 bad request
Requesting fields not present in the GDP series returns null or missing values
Dates before 1947 may return no data as BEA history starts then
Payment of 0.005 USDC not completed results in 402 Payment Required

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "schema": {
    "type": "object",
    "properties": {
      "data": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "date": {
              "type": "string",
              "description": "Observation date YYYY-MM-DD"
            },
            "value": {
              "type": "number",
              "description": "Raw observed value in series units"
            },
            "mom_pct": {
              "type": "number",
              "description": "Month-over-month % change"
            },
            "yoy_pct": {
              "type": "number",
              "description": "Year-over-year % change"
            },
            "zscore_5y": {
              "type": "number",
              "description": "Z-score relative to trailing 5 years"
            },
            "mom_change": {
              "type": "number",
              "description": "Month-over-month absolute change"
            },
            "yoy_change": {
              "type": "number",
              "description": "Year-over-year absolute change"
            },
            "zscore_12m": {
              "type": "number",
              "description": "Z-score relative to trailing 12 months"
            },
            "above_trend": {
              "type": "integer",
              "description": "1 if value is above long-run trend, else 0"
            },
            "pct_rank_5y": {
              "type": "number",
              "description": "Percentile rank over trailing 5 years (0-100)"
            },
            "rolling_3m_avg": {
              "type": "number",
              "description": "3-month rolling average"
            },
            "rolling_12m_avg": {
              "type": "number",
              "description": "12-month rolling average"
            },
            "trend_direction": {
              "type": "integer",
              "description": "Trend: 1 rising, -1 falling, 0 flat"
            }
          }
        },
        "description": "Observations ordered by date ascending"
      },
      "meta": {
        "type": "object",
        "description": "Series metadata: source_key (FRED series ID), units, frequency (D/W/M/Q/A), date_from, date_to, fields"
      }
    },
    "description": "Station f(x) response with metadata and pre-computed derived metrics"
  },
  "example": {
    "data": [
      {
        "date": "2024-01-01",
        "value": 5.33,
        "mom_pct": 0,
        "yoy_change": 0.5,
        "zscore_12m": 1.2,
        "trend_direction": 1
      }
    ],
    "meta": {
      "frequency": "Q",
      "source_key": "GDP"
    }
  }
}

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