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StationFX Financial Conditions Composite (NFCI + STLFSI)
x402
StationFX Financial Conditions Composite (NFCI + STLFSI) is a paid API for AI agents from stationfx.com, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-02).
Returns a pre-computed weekly composite of the Chicago Fed NFCI and St. Louis Fed Financial Stress Index, normalized and averaged into a single financial conditions signal where positive = tighter/more stressed and negative = looser/less stressed.
Price / call$0.008
Success—
StatusUnknown
How this service works
Average of Chicago Fed NFCI and St. Louis Fed Financial Stress Index, normalized to a common scale. Positive = tighter/more stressed than average, negative = looser/less stressed. Combines two complementary stress measures into a single composite signal. Pre-computed weekly.
Output
Returns a JSON array of weekly observations, each containing the composite stress value (average of normalized NFCI and STLFSI), along with derived statistics: month-over-month and year-over-year absolute and percentage changes, 12-month and 5-year z-scores, 5-year percentile rank, 3-month and 12-month rolling averages, and a binary above-trend flag. Positive values indicate tighter-than-average or more-stressed conditions; negative values indicate looser/less-stressed.
Example use cases
Weekly macro risk dashboard update
“Fetch the latest composite financial stress reading — the one that blends NFCI and STLFSI — for the past 4 weeks so I can update my macro risk dashboard and see if we're in stressed or loose territory right now.”
Year-over-year financial conditions comparison
“Pull the StationFX NFCI-STLFSI composite from January 2023 to today and show me the year-over-year changes so I can compare how financial conditions have evolved over the past two years.”
Recession signal monitoring
“I want to check whether US financial conditions are flashing stress right now — get the composite signal for the last 6 months and flag any weeks where the value was above trend or in the top quartile of the 5-year range.”
Technical details
Endpoint
GEThttps://stationfx.com/economic-data/cross-signal/financial-conditions-composite-nfci-stlfsi?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a single, composite view of US financial conditions that reconciles two major Fed-produced stress indices (NFCI and STLFSI) into one normalized signal. Ideal for macro risk monitoring, portfolio stress overlays, and systematic strategy timing where you want a weekly, pre-computed, already-normalized composite rather than managing the raw indices separately. Prefer over individual NFCI or STLFSI endpoints when you want a blended signal that reduces single-index noise.
Side effects
- Requested date range outside available data history returns empty array or partial results
- Invalid date format (not YYYY-MM-DD) may return a 400 error
- Invalid fmt parameter value returns an error or defaults to json
- Missing required fields in input object returns a validation error
- Payment not processed (x402 protocol failure) results in 402 response blocking data access
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": "M",
"source_key": "SERIES_ID"
}
}
}Tags
x402
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