US federal fiscal signals (debt/deposit momentum, deficit tracker, anomalies)

Name: US federal fiscal signals (debt/deposit momentum, deficit tracker, anomalies)
Creator: US Federal Fiscal Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: fiscal, debt, deficit, federal-budget, volatility, momentum, anomaly-detection, forecasting, signals, fred

Daily-quiet quarterly fiscal signals derived from FRED's US federal finance series: 30-period annualized change volatility, 3-month momentum, year-over-year change, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and the quarterly federal balance (receipts minus expenditures — the deficit tracker). Covers total federal public debt, federal current expenditures, federal current receipts, and the federal surplus or deficit. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED; underlying data from US Treasury Fiscal Service, Bureau of Economic Analysis, and Office of Management and Budget).

Source: US Federal Fiscal Signals (derived)1,002 lignesMis à jour: 22/09/2026

fiscaldebtdeficitfederal-budgetvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

98.5

Attribution

Federal Reserve Bank of St. Louis (FRED; derived signals by Frontier Data Hub)

Schéma

Colonne	Type	Description
date	string	Observation date (FRED API field date; YYYY-MM-DD).
country	string
country_code	string
series_id	string	FRED series ID, e.g. GFDEBTN, FGEXPND, FGRECPT, FYFSD; resolves to the series page at https://fred.stlouisfed.org/series/<id>.
series_label	string	Official FRED series title as published for the series.
value	float	Observation value as published by FRED for this series (GFDEBTN in millions of dollars, FGEXPND and FGRECPT in billions of dollars, FYFSD in millions of dollars; negative FYFSD values are deficits); see the series notes for methodology and revisions.
volatility_30d	float
momentum_3m	float
yoy_change_pct	float
anomaly_flag	integer
forecast_1m	float
rank	integer
balance_q	float

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	yoy_change_pct	forecast_1m	rank	balance_q
1901-06-30	United States	USA	FYFSD	Federal Surplus or Deficit [-]	63	—	—	—	—	—	—
1902-06-30	United States	USA	FYFSD	Federal Surplus or Deficit [-]	77	—	14	22.222222222222232	—	—	—
1903-06-30	United States	USA	FYFSD	Federal Surplus or Deficit [-]	45	—	-32	-41.55844155844156	—	—	—
1904-06-30	United States	USA	FYFSD	Federal Surplus or Deficit [-]	-43	—	-88	-195.55555555555557	—	—	—
1905-06-30	United States	USA	FYFSD	Federal Surplus or Deficit [-]	-23	—	20	-46.51162790697675	—	—	—

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

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Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/fiscal_signals/us_federal_fiscal_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fiscal_signals/us_federal_fiscal_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/fiscal_signals/us_federal_fiscal_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.