Global reserve-adequacy signals (FX buffer gauges)
Annual reserve-adequacy signals derived from the IMF DataMapper (74 economies, 1980 ->): the Fund's official adequacy metrics — reserves/ARA metric, import cover (months), and reserves/short-term-debt — with 1-year and 3-year changes, 30-year change volatility, 3-sigma anomaly flags vs a trailing-10-year baseline, naive-drift 1-year forecasts, per-year cross-country volatility ranks, 10-year buffer z-scores, and adequate / thin-buffer / rapid-depletion flags. The balance-sheet buffer companion to the FX and external vulnerability signals. Country codes normalized to verified ISO alpha-3. Raw data: International Monetary Fund (DataMapper, keyless API).
Quality
Attribution
International Monetary Fund, DataMapper (derived signals by Frontier Data Hub)
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Reference year (January 1). The DataMapper publishes annual data. (unit: ISO date) |
| country | string | Country name (canonical English short name). |
| country_code | string | ISO 3166-1 alpha-3 country code. |
| series_id | string | IMF DataMapper indicator code: Reserves_ARA, Reserves_M, or Reserves_STD. |
| series_label | string | IMF DataMapper indicator label, e.g. 'Ratio of reserve/ARA metric'. |
| value | float | IMF reserve-adequacy metric value: for Reserves_ARA, the ratio of reserves to the ARA metric (unit: ratio); for Reserves_M, import cover (unit: months of imports); for Reserves_STD, reserves to short-term debt (unit: ratio). (unit: ratio or months (per series_id)) |
| change_1y | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| buffer_z_10y | float | |
| adequate_flag | integer | |
| thin_flag | integer | |
| depletion_flag | integer |
Sample rows
| date | country | country_code | series_id | series_label | value | change_1y | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | buffer_z_10y | adequate_flag | thin_flag | depletion_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000-01-01 | Argentina | ARG | Reserves_ARA | Ratio of reserves to the IMF Assessing Reserve Adequacy (ARA) metric | 0.4642473496 | — | — | — | 0 | — | — | — | 0 | 1 | 0 |
| 2000-01-01 | Armenia | ARM | Reserves_ARA | Ratio of reserves to the IMF Assessing Reserve Adequacy (ARA) metric | 1.250933927 | — | — | — | 0 | — | — | — | 1 | 0 | 0 |
| 2000-01-01 | Bulgaria | BGR | Reserves_ARA | Ratio of reserves to the IMF Assessing Reserve Adequacy (ARA) metric | 1.515750876 | — | — | — | 0 | — | — | — | 0 | 0 | 0 |
| 2000-01-01 | Belarus | BLR | Reserves_ARA | Ratio of reserves to the IMF Assessing Reserve Adequacy (ARA) metric | 0.3700037152 | — | — | — | 0 | — | — | — | 0 | 1 | 0 |
| 2000-01-01 | Chile | CHL | Reserves_ARA | Ratio of reserves to the IMF Assessing Reserve Adequacy (ARA) metric | 1.503246554 | — | — | — | 0 | — | — | — | 0 | 0 | 0 |
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/reserve_adequacy_signals/global_reserve_adequacy_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/reserve_adequacy_signals/global_reserve_adequacy_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/reserve_adequacy_signals/global_reserve_adequacy_signals
Tip: fetch /llms.txt for the full machine-readable catalog.