US bank credit cycle signals (loan growth momentum, delinquency stress regime, anomalies, forecasts)

Name: US bank credit cycle signals (loan growth momentum, delinquency stress regime, anomalies, forecasts)
Creator: Bank Credit Cycle Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: credit, bank-credit, loans, delinquency, credit-cycle, financial-stability, volatility, momentum, anomaly-detection, forecasting, signals, fred

Signals derived from FRED's US bank-credit series: 30-period annualized volatility of changes, 3-month momentum, year-over-year level change, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and a delinquency stress-regime flag (1 when the real-estate delinquency rate exceeds its trailing-5-year 75th percentile) mapped onto every row's quarter. Covers TOTLL (weekly bank credit of all commercial banks), BUSLOANS (monthly commercial & industrial loans), REVOLSL (monthly consumer credit outstanding) and DRALACBN (quarterly delinquency rate on all real-estate loans). 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).

Source: Bank Credit Cycle Signals (derived)4,627 lignesMis à jour: 22/09/2026

creditbank-creditloansdelinquencycredit-cyclefinancial-stabilityvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

99.4

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. TOTLL, BUSLOANS, REVOLSL, DRALACBN; 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 (TOTLL and BUSLOANS in billions of dollars; REVOLSL in millions of dollars; DRALACBN in percent); see the series notes for methodology and revisions.
volatility_30d	float
momentum_3m	float
yoy_change	float
anomaly_flag	integer
forecast_1m	float
rank	integer
delinq_stress_regime	integer

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	yoy_change	forecast_1m	rank
1947-01-01	United States	USA	BUSLOANS	Commercial and Industrial Loans, All Commercial Banks	11.2894	—	—	—	—	—
1947-02-01	United States	USA	BUSLOANS	Commercial and Industrial Loans, All Commercial Banks	11.5179	—	—	—	—	—
1947-03-01	United States	USA	BUSLOANS	Commercial and Industrial Loans, All Commercial Banks	11.7667	—	—	—	—	—
1947-04-01	United States	USA	BUSLOANS	Commercial and Industrial Loans, All Commercial Banks	12.0269	—	0.7374999999999989	—	—	—
1947-05-01	United States	USA	BUSLOANS	Commercial and Industrial Loans, All Commercial Banks	12.2788	—	0.7609000000000012	—	—	—

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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/credit_signals/us_bank_credit_cycle_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/credit_signals/us_bank_credit_cycle_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/credit_signals/us_bank_credit_cycle_signals

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