US corporate profit signals (profit momentum, economy-wide margin, profitability regime, anomalies)
Quarterly signals derived from BEA corporate-profits data (redistributed by FRED): 30-quarter annualized change volatility, 1-quarter momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, the economy-wide profit margin (profits as % of GDP) and a 20-quarter margin z-score (the profitability-regime gauge). Covers corporate profits after tax from 1947. 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: U.S. Bureau of Economic Analysis.
Quality
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Economic Analysis; 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 (CP); FRED IDs resolve to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (U.S. Bureau of Economic Analysis data). |
| value | float | Corporate profits after tax (without IVA and CCAdj) in billions of dollars, quarterly, seasonally adjusted annual rate; 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 | |
| profit_margin | float | |
| margin_z_20q | float |
Sample rows
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change_pct | anomaly_flag | forecast_1m | rank | profit_margin | margin_z_20q |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1947-01-01 | United States | USA | CP | Corporate Profits After Tax (without IVA and CCAdj) | 21.97 | — | — | — | 0 | — | — | 9.035054531098353 | — |
| 1947-04-01 | United States | USA | CP | Corporate Profits After Tax (without IVA and CCAdj) | 20.788 | — | -5.380063723258988 | — | 0 | — | — | 8.451505886944643 | — |
| 1947-07-01 | United States | USA | CP | Corporate Profits After Tax (without IVA and CCAdj) | 20.564 | — | -1.0775447373484681 | — | 0 | — | — | 8.239277200152252 | — |
| 1947-10-01 | United States | USA | CP | Corporate Profits After Tax (without IVA and CCAdj) | 22.451 | — | 9.176230305388057 | — | 0 | — | — | 8.643477256540068 | — |
| 1948-01-01 | United States | USA | CP | Corporate Profits After Tax (without IVA and CCAdj) | 23.739 | — | 5.736938221014665 | 8.051888939462915 | 0 | — | — | 8.933100526074162 | — |
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/profit_signals/us_corporate_profit_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/profit_signals/us_corporate_profit_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/profit_signals/us_corporate_profit_signals
Tip: fetch /llms.txt for the full machine-readable catalog.