Building an AI agent? Start with /llms.txt for the full site index.
Macroeconomics
Central Bank of Taiwan (CBC) policy interest-rate snapshot — the discount and accommodation rates as most recently set.
GET /v2/datasets/interest-rate-snapshotinterest-rate-snapshot returns the current CBC policy rates with the date they took effect, sourced from the central bank. It is a snapshot surface, not a long daily time series.
| Field | Type | Description |
|---|---|---|
rate_name | string | Which policy rate. |
value_pct | number | Rate in percent. |
as_of | string | Date the rate took effect. |
source_role | string | cbc_official. |
| Rows | 165 |
| Window | 2000-12-29 – 2026-05-27 |
| Grade | Verified |
TODO — no real response has been captured for this dataset yet. It needs an entitled API key, or its required parameters are not yet known. Rather than show an example nobody has observed, this section stays empty.
curl "https://api.twmarketdata.com/v2/datasets/interest-rate-snapshot?rate_code=CBC_REDISCOUNT_RATE" \ -H "X-API-Key: sk_live_..."| Parameter | Required | Type | Description |
|---|---|---|---|
symbol | No | string | Ticker to filter to a single security. |
limit | No | integer | Maximum rows to return. |
A first call:
import requests resp = requests.get( "https://api.twmarketdata.com/v2/datasets/interest-rate-snapshot", params={"rate_code": "CBC_REDISCOUNT_RATE"}, headers={"X-API-Key": "sk_live_..."},)resp.raise_for_status()print(resp.json())With a date-range filter:
import requests # The full verified example — the same call with every supported filter set.resp = requests.get( "https://api.twmarketdata.com/v2/datasets/interest-rate-snapshot", params={"rate_code": "CBC_REDISCOUNT_RATE"}, headers={"X-API-Key": "sk_live_..."},)resp.raise_for_status()print(resp.json())This endpoint is GET /v2/datasets/interest-rate-snapshot. The full machine-readable schema (parameters, security, response envelope) lives in the OpenAPI spec.