🏦 Global Central-Bank Policy Rates (BIS)

Pull the official policy interest rate for 40+ countries and currency areas straight from the Bank for International Settlements (BIS) WS_CBPOL dataset — the single most authoritative, internationally-consistent source for "what is each central bank's headline rate, and what was it on any past date." Get the current and historical policy rate per country and period as clean JSON, filtered to the countries, frequency, and lookback you need. This is the cross-country rates backbone that macro funds, FX traders, and rate dashboards otherwise stitch together from dozens of separate central-bank websites in inconsistent formats.

One actor. One token. The harmonized global policy-rate series behind macro dashboards and FX desks — delivered as pay-per-record JSON, no Bloomberg or Macrobond seat.

Why use this

• One harmonized source for 40+ economies. Instead of scraping the Fed, ECB, BoE, BoJ, PBoC, and 35+ others — each with its own page format and definition — you get one consistent series for all of them, defined and maintained by the BIS.
• Current and historical. lastNObservations lets you pull just the latest print for a live dashboard or a long back-history for rate-cycle analysis and backtesting.
• Cross-country by design. The single biggest pain in rates work is comparability; the BIS series is curated specifically to be comparable across central banks.
• Daily or monthly frequency. Choose D for the precise step-changes or M for clean monthly time series.
• Official source, ETL-ready. Reads the BIS statistical data. Stable flat JSON loads straight into Snowflake, BigQuery, Postgres, or a notebook for time-series work.

What you get

Each record represents one country's policy rate at one period (date or month). Core fields:

• country — ISO country / area code (e.g. US, XM = euro area, GB, JP, CN)
• period — the observation period (date for daily frequency, year-month for monthly)
• policyRatePct — the central bank's policy rate for that country and period, in percent
• title — the human-readable series title from the BIS (identifies the country / rate)

Pull US with lastNObservations=24 monthly and you get 24 rows tracing the Fed's rate path; pull a basket of ["US","XM","GB","JP"] and you get the four series side by side.

Use cases

• Global macro & rate-differential trading — Pull ["US","XM","GB","JP","CN"] and compute live policy-rate differentials — the core driver of carry trades and FX positioning across the G10 and major EM.
• FX carry models — FX desks join policyRatePct across currency pairs to build and rebalance carry baskets, where the rate differential is the expected return indicator.
• Rate-dashboard / fintech apps — Power a "central banks at a glance" widget or alerting tool by pulling the latest observation for all covered countries on a schedule.
• Monetary-cycle backtesting — Quants pull long histories (lastNObservations large) to backtest strategies conditioned on global hiking/cutting regimes.
• Macro research & strategy notes — Strategists chart the synchronized global tightening or easing cycle across 40+ banks for client-facing commentary.
• Risk & treasury management — Corporate treasuries and asset-liability teams track policy rates in every currency they fund in to manage rate exposure.
• Economic / academic research — Researchers use the BIS harmonized series as the standard cross-country policy-rate input for monetary-policy and international-finance studies.

Sample output

{
  "country": "US",
  "period": "2026-05",
  "policyRatePct": 4.25,
  "title": "Central bank policy rate — United States"
}

Reading the data: codes, periods, and how to build differentials

The four fields are deliberately minimal, which makes them easy to model — but a few conventions are worth knowing:

• country uses ISO-style codes, with one important special case: XM is the euro area (the ECB's single rate), used instead of individual eurozone member codes. The major economies you will reach for — US, GB, JP, CN, plus the larger EM banks — are all covered in the BIS's 40+ set.
• period format follows frequency: a year-month string for monthly (M) pulls, and a date for daily (D) pulls. For evenly-spaced charting, monthly is cleanest; for pinpointing the exact day a bank hiked or cut, daily is the right choice.
• policyRatePct is the headline policy rate in percent. The single most common downstream computation is a rate differential: pull two or more countries with the same frequency and lastNObservations, align on period, and subtract one policyRatePct from another. That differential is the carry indicator behind most FX positioning and the input to cross-country rate models.
• title carries the BIS's human-readable series label, handy for chart legends and for confirming which rate a code maps to.

Common analysis recipes

• Live G5 dashboard: {"countries": ["US","XM","GB","JP","CN"], "frequency": "M", "lastNObservations": 1} — one latest print per bank, refreshed on a schedule, for a "central banks at a glance" widget.
• Rate-cycle history: {"countries": ["US"], "frequency": "M", "lastNObservations": 60} — five years of the Fed's path for cycle analysis and backtests.
• Carry basket: pull the G10 monthly, align on period, and rank by policyRatePct to build a long-high-rate / short-low-rate carry basket that you rebalance each refresh.
• Decision-day watch: {"countries": ["US","XM"], "frequency": "D", "lastNObservations": 30} around expected meeting dates to capture the exact step-change.

Input parameters

Parameter	What it does
countries	ISO country / area codes to pull (e.g. US, XM = euro area, GB, JP, CN).
frequency	Observation frequency: M = monthly, D = daily.
lastNObservations	Return the most recent N data points per country.

How to use

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

run = client.actor("nexgendata/global-central-bank-policy-rates").call(run_input={
    "countries": ["US", "XM", "GB", "JP", "CN"],
    "frequency": "M",
    "lastNObservations": 24,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["country"], item["period"], item["policyRatePct"])

cURL

curl -X POST "https://api.apify.com/v2/acts/nexgendata~global-central-bank-policy-rates/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "countries": ["US", "XM", "GB", "JP"],
    "frequency": "D",
    "lastNObservations": 60
  }'

No-code (Zapier / Make)

Use the Apify "Run actor and get dataset items" action with actor ID nexgendata/global-central-bank-policy-rates, schedule it daily, and feed the latest policyRatePct per country into a Google Sheet, BI dashboard, or alert when a rate changes.

Pricing

This actor runs on Apify's pay-per-event (PPE) model — $0.10 per result (one charge per country-period observation returned), plus a sub-cent actor-start event. No subscription, no seat licence, no minimum.

Worked example:

• Latest print for 10 countries (lastNObservations=1) → 10 rows → $1.00
• 5 countries × 24 monthly observations → 120 rows → $12.00
• A 40-country, 12-observation dashboard refresh → ~480 rows → $48.00

Because billing is per observation, you tune cost precisely with lastNObservations and the size of your countries list — a daily latest-print dashboard for a handful of countries costs a few cents per run.

How this compares to Bloomberg / Macrobond

A harmonized global policy-rate series is a standard line item inside Bloomberg Terminal (~$24,000/seat/year) and Macrobond (multi-thousand-dollar annual licences) — both excellent if you need their full macro workspaces, charting, and the rest of the data universe. But if your specific need is "the policy rate for these countries, current and historical, as clean JSON", you do not need a terminal seat to get it. The BIS publishes the authoritative series openly; this actor delivers it queryable from your own code at pay-per-record cost — a 95%+ saving for the narrow-but-critical job of feeding rate differentials into a model or a dashboard.

FAQ

Q: Where does the data come from? A: The Bank for International Settlements (BIS) WS_CBPOL "central bank policy rates" statistical dataset — the BIS's curated, internationally-comparable compilation of headline policy rates.

Q: Which countries are covered? A: 40+ economies and currency areas, including the US, euro area (XM), UK (GB), Japan (JP), China (CN), and the major developed and emerging-market central banks tracked by the BIS.

Q: What does XM mean? A: XM is the code for the euro area — the ECB's single policy rate covering all eurozone members. Use it instead of individual euro-member country codes.

Q: Daily vs monthly — which should I use? A: Use D (daily) when you need to capture the exact dates of rate step-changes; use M (monthly) when you want a clean, evenly-spaced time series for charting or modeling.

Q: How current is the latest observation? A: The series reflects the BIS's latest published update. Schedule the actor on Apify to refresh after expected central-bank decision dates so your dashboard captures new moves promptly.

Q: Can I get a long history for backtesting? A: Yes — raise lastNObservations to pull a deep back-history per country for rate-cycle and strategy backtesting.

Schema stability & versioning

This actor follows NexGenData's additive-only schema contract. New fields may be added over time (appearing as new JSON keys, defaulting to null for older runs), but existing fields are never renamed or removed without a major-version bump and advance notice. Field semantics — percent units, period formats, country codes — are never silently changed. Build production time-series pipelines on this output with confidence.

Compliance & legal

• This actor reformats public BIS statistical data. It is not affiliated with, endorsed by, or sponsored by the BIS or any central bank.
• The rates are reported facts, not financial advice.
• For any regulated decision — trading, treasury, risk — verify against the primary BIS source and apply appropriate review.
• You are responsible for ensuring your downstream use complies with the BIS terms of use and applicable laws.

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