StationFX St. Louis Fed Financial Stress Index

x402

StationFX St. Louis Fed Financial Stress Index 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).

Retrieves the St. Louis Fed Financial Stress Index (STLFSI), a composite weekly measure of U.S. financial market stress built from 18 series including rates, spreads, and equity measures.

Price / call$0.005

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StatusUnknown

How this service works

St. Louis Fed composite stress index measuring strain in U.S. financial markets. Constructed from 18 weekly data series including rates, spreads, and equity measures. Zero = normal conditions, positive = above-average stress. Weekly frequency, useful for crisis detection and risk-off signals.

Output

Returns an array of weekly observations, each containing the raw STLFSI value (zero = normal, positive = above-average stress), month-over-month and year-over-year changes, 12-month and 5-year z-scores, 5-year percentile rank, 3-month and 12-month rolling averages, and an above-trend binary flag. Supports filtering by date range and field selection.

Example use cases

Crisis detection for portfolio risk system

“Fetch the St. Louis Fed Financial Stress Index weekly readings from 2024-01-01 to today and flag any weeks where the value was above zero — I need to know when stress was elevated this year.”

Macro dashboard stress monitoring

“What's the most recent STLFSI reading and how does it compare to the past 5 years in terms of percentile rank? I want to understand if financial conditions are unusually tight right now.”

Historical stress analysis for 2020 COVID period

“Get the St. Louis Fed Financial Stress Index data from 2020-01-01 to 2020-12-31 so I can see how financial stress peaked and recovered during the COVID crisis.”

Technical details

Endpoint

GEThttps://stationfx.com/economic-data/financial-conditions/st-louis-fed-financial-stress-index?utm_source=zero.xyz

When to prefer

Use this endpoint when you need a single, authoritative composite measure of U.S. financial system stress rather than monitoring individual credit spreads, rates, or equity volatility separately. It is ideal for macro risk dashboards, crisis detection triggers, risk-off signal generation, and historical stress comparisons. Prefer this over individual spread or rate series when you want a holistic, Fed-constructed summary of financial conditions.

Side effects

Invalid date format returns a 400-level error
date_from after date_to returns empty data or error
Requesting fields that don't exist may return null values or an error
Data may lag by one week due to weekly publication frequency
No data before STLFSI series inception date (~1994)

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": "W",
      "source_key": "STLFSI4"
    }
  }
}

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