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).
Qualité
Attribution
International Monetary Fund, DataMapper (derived signals by Frontier Data Hub)
Schéma
| Colonne | 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 |
Exemple de lignes
| 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 |
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/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)Point d’accès API: https://datazimuts.com/v1/datasets/reserve_adequacy_signals/global_reserve_adequacy_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.