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.
Quality
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)
Schema
| Column | 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 |
Sample rows
| 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 | — |
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/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)API endpoint: https://datazimuts.com/v1/datasets/tradeprice_signals/us_trade_price_signals
Tip: fetch /llms.txt for the full machine-readable catalog.