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StationFX GDPNow Real-Time GDP Growth Estimate
x402
StationFX GDPNow Real-Time GDP Growth Estimate 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 the Atlanta Fed's GDPNow real-time nowcast of current-quarter U.S. GDP growth, updated continuously as new economic data releases arrive.
Price / call$0.005
Success—
StatusUnknown
How this service works
Atlanta Fed real-time GDP growth estimate updated continuously with incoming data releases. Provides the most current read on current-quarter GDP before the official BEA estimate. Useful for nowcasting, growth surprise detection, and positioning ahead of GDP releases. Irregular frequency, updated on data release days.
Output
Returns a time-series array of GDPNow observations, each containing the observation date, raw estimated GDP growth value, month-over-month and year-over-year changes, z-scores relative to 12-month and 5-year windows, percentile rank, rolling 3-month and 12-month averages, and an above-trend binary flag. Optionally available in compact 'toon' format for agent-friendly consumption.
Example use cases
Pre-GDP release positioning check
“Grab the current GDPNow estimate with its 5-year percentile rank and z-score before the BEA GDP release this week — I want to know if growth is tracking above or below long-run trend.”
Macro dashboard nowcast refresh
“Fetch the most recent GDPNow data point along with its year-over-year percent change and 3-month rolling average so I can update my live macroeconomic dashboard.”
Growth surprise detection alert
“Pull GDPNow estimates from 2024-01-01 to today and flag any readings where the 12-month z-score exceeded plus or minus 1.5 — I'm looking for significant growth surprises.”
Technical details
Endpoint
GEThttps://stationfx.com/economic-data/gdp-growth/gdpnow?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need the most current real-time GDP growth estimate before the official BEA quarterly release, especially for nowcasting, economic surprise detection, or macro positioning. It is updated irregularly on data release days, making it ideal for agents monitoring intra-quarter GDP trajectory. Prefer it over lagged official BEA data when timeliness matters, and over general economic data APIs when you specifically need the Atlanta Fed's GDPNow model output with pre-computed statistical context (z-scores, percentile ranks, trend signals).
Side effects
- No data available for requested date range — GDPNow is only updated on economic data release days so gaps between updates are normal
- Invalid date format in date_from or date_to parameters — must be YYYY-MM-DD
- Invalid fmt parameter value — only 'json' or 'toon' accepted
- Payment failure or insufficient USDC balance for x402 micropayment
- Rate limiting if too many calls made in rapid succession
- Requested fields not found in the metric list returns empty or partial response
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": "GDPNOW"
}
}
}Tags
x402
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