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 rowsUpdated: 9/22/2026

creditbank-creditloansdelinquencycredit-cyclefinancial-stabilityvolatilitymomentumanomaly-detectionforecastingsignalsfred

Quality

99.4

Attribution

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

Schema

Column	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

Sample rows

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	—	—	—

Download sample data

Download the full sample snapshot for this dataset (sample rows, not the complete dataset).

Download sample CSV Download sample JSON

Use with an LLM

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)

API endpoint: https://datazimuts.com/v1/datasets/credit_signals/us_bank_credit_cycle_signals

Tip: fetch /llms.txt for the full machine-readable catalog.