US bank lending standards signals (SLOOS tightening, credit conditions)
Quarterly US credit-conditions signals derived from FRED's Senior Loan Officer Opinion Survey (SLOOS): net percentages of banks tightening lending standards for C&I loans, with quarter-on-quarter and year-on-year changes in percentage points, 30-quarter annualized change volatility, 1-quarter momentum, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, tightening regime flags with 4-year z-scores, and the large-firm minus small-firm tightening spread (the flight-to-quality gauge). 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 survey: Board of Governors of the Federal Reserve System.
Quality
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying survey: Board of Governors of the Federal Reserve System; derived signals by Frontier Data Hub)
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD, first day of the reference quarter). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID, e.g. DRTSCILM, DRTSCIS; resolves to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (Senior Loan Officer Opinion Survey on Bank Lending Practices, Board of Governors of the Federal Reserve System). |
| value | float | Observation value as published by FRED for this series: net percentage of domestic banks tightening standards for commercial and industrial loans (positive = tightening on net, negative = easing on net); see the series notes for methodology and revisions. |
| change_qoq | float | |
| change_yoy | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| tightening_flag | integer | |
| tightening_z_4y | float | |
| large_small_spread | float |
Sample rows
| date | country | country_code | series_id | series_label | value | change_qoq | change_yoy | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | tightening_flag | tightening_z_4y | large_small_spread |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1990-04-01 | United States | USA | DRTSCILM | Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Large and Middle-Market Firms | 54.4 | — | — | — | — | 0 | — | — | 1 | — | 1.6999999999999957 |
| 1990-04-01 | United States | USA | DRTSCIS | Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Small Firms | 52.7 | — | — | — | — | 0 | — | — | 1 | — | 1.6999999999999957 |
| 1990-07-01 | United States | USA | DRTSCILM | Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Large and Middle-Market Firms | 46.7 | -7.699999999999996 | — | — | -7.699999999999996 | 0 | — | — | 1 | — | 12.800000000000004 |
| 1990-07-01 | United States | USA | DRTSCIS | Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Small Firms | 33.9 | -18.800000000000004 | — | — | -18.800000000000004 | 0 | — | — | 1 | — | 12.800000000000004 |
| 1990-10-01 | United States | USA | DRTSCILM | Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Large and Middle-Market Firms | 54.2 | 7.5 | — | — | 7.5 | 0 | — | — | 1 | — | 13.5 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
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/sloos_signals/us_bank_lending_standards_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/sloos_signals/us_bank_lending_standards_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/sloos_signals/us_bank_lending_standards_signals
Tip: fetch /llms.txt for the full machine-readable catalog.