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.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Economic Analysis; 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 (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 |
Exemple de lignes
| 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 | — |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
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/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)Point d’accès API: https://datazimuts.com/v1/datasets/profit_signals/us_corporate_profit_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.