US trade price signals (import/export prices, terms of trade, China price trend spread)
Monthly US trade-price signals derived from FRED: BLS import and export price indices for all commodities plus the China-origin import price index, with 1-month and year-on-year percent changes, 30-month annualized change volatility, 3-month momentum, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-series volatility rank, the terms of trade (export/import prices) and the China-vs-world import price trend spread. 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: U.S. Bureau of Labor Statistics, International Price Program.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Labor Statistics, International Price Program; derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD, first day of the reference month). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID: IR (import prices), IQ (export prices) or CHNTOT (China-origin import prices); resolves 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 Labor Statistics, International Price Program). |
| value | float | Price index value as published by FRED for this series; see the series notes for the index base period and methodology. |
| mom_change_pct | float | |
| yoy_change_pct | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| terms_of_trade | float | |
| china_price_trend_spread | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | mom_change_pct | yoy_change_pct | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | terms_of_trade | china_price_trend_spread |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1982-09-01 | United States | USA | IR | Import Price Index (End Use): All Commodities | 80 | — | — | — | — | 0 | — | — | — | — |
| 1982-12-01 | United States | USA | IR | Import Price Index (End Use): All Commodities | 79.9 | -0.12499999999999734 | — | — | — | 0 | — | — | — | — |
| 1983-03-01 | United States | USA | IR | Import Price Index (End Use): All Commodities | 77.7 | -2.7534418022528206 | — | — | — | 0 | — | — | — | — |
| 1983-06-01 | United States | USA | IR | Import Price Index (End Use): All Commodities | 77.8 | 0.12870012870012104 | — | — | -2.750000000000008 | 0 | — | — | — | — |
| 1983-09-01 | United States | USA | IQ | Export Price Index (End Use): All Commodities | 85.7 | — | — | — | — | 0 | — | — | 110.43814432989691 | — |
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/tradeprice_signals/us_trade_price_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/tradeprice_signals/us_trade_price_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/tradeprice_signals/us_trade_price_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.