US-China bilateral trade signals (decoupling tracker)
US-China bilateral goods-trade signals (Census via FRED, monthly 1985 ->): import/export momentum, 30-period change volatility, 3-sigma shock flags, drift forecasts, the bilateral deficit tracker, its 5-year z-score and the export reciprocity gauge. The bilateral lens: where the decoupling story shows up in the numbers. US government data via FRED (free, keyless-by-reuse of the existing FRED key).
Quality
Attribution
U.S. Census Bureau via FRED; derived signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Reference month (FRED observation date; monthly, seasonally adjusted). |
| country | string | United States (all series are US bilateral). |
| country_code | string | ISO 3166-1 alpha-3 code: USA. |
| series_id | string | FRED series ID: IMPCH (U.S. imports of goods from China) or EXPCH (U.S. exports of goods to China). |
| series_label | string | Official FRED series title as published in the series metadata. |
| value | float | U.S. goods trade flow with China in millions of dollars, seasonally adjusted (imports on a customs basis; exports on an FAS basis); U.S. Census Bureau via FRED. |
| yoy_change_pct | float | |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| bilateral_deficit | float | |
| deficit_yoy_change | float | |
| deficit_z_5y | float | |
| export_share_of_bilateral | float |
Sample rows
| date | country | country_code | series_id | series_label | value | yoy_change_pct | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | bilateral_deficit | deficit_yoy_change | deficit_z_5y | export_share_of_bilateral |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1985-01-01 | United States | USA | EXPCH | U.S. Exports of Goods by F.A.S. Basis to Mainland China | 319.2 | — | — | — | 0 | — | -26.099999999999966 | — | — | 1.089048106448311 |
| 1985-02-01 | United States | USA | EXPCH | U.S. Exports of Goods by F.A.S. Basis to Mainland China | 222.7 | — | — | — | 0 | — | 58.30000000000001 | — | — | 0.7925266903914591 |
| 1985-03-01 | United States | USA | EXPCH | U.S. Exports of Goods by F.A.S. Basis to Mainland China | 239.5 | — | — | — | 0 | — | 53.5 | — | — | 0.8174061433447098 |
| 1985-04-01 | United States | USA | EXPCH | U.S. Exports of Goods by F.A.S. Basis to Mainland China | 265.6 | — | -16.79197994987468 | — | 0 | — | 17.69999999999999 | — | — | 0.9375220614189905 |
| 1985-05-01 | United States | USA | EXPCH | U.S. Exports of Goods by F.A.S. Basis to Mainland China | 329.3 | — | 47.86708576560397 | — | 0 | — | -34.19999999999999 | — | — | 1.115892917655032 |
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/china_trade_signals/us_china_trade_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/china_trade_signals/us_china_trade_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/china_trade_signals/us_china_trade_signals
Tip: fetch /llms.txt for the full machine-readable catalog.