Global Stock Exchange Meta Scraper — 25 Exchanges, One Call

One API call for global equities snapshots — replaces stitching 25 individual exchange feeds.

Pulls aggregated snapshots across 25 of the world's stock exchanges in a single run. Wraps NexGenData's full suite of single-exchange screeners into one unified meta-actor with a normalized output schema — so you stop maintaining 25 separate Apify integrations and start consuming one.

---

Why this actor exists

If you cover global equities, you've felt the pain: every exchange has a different feed, a different ticker convention, a different update cadence, a different rate-limit policy. Most teams end up stitching together 10–25 different scrapers and writing brittle adapter code to glue the outputs into one dashboard.

This actor does that stitching once, server-side, behind a single Apify run. You pick the exchanges you care about, the depth you want, and the output shape (one row per exchange, or one row per stock). You get back a clean, normalized dataset every time.

Coverage — all 25 exchanges in one place

Region	Exchanges
Asia-Pacific	KOSPI (KR), TWSE (TW), NSE (IN), BSE (IN), TSE (JP), HKEX (HK), SGX (SG), HOSE (VN), ASX (AU)
Greater China	STAR Market, ChiNext, Beijing SE, Eastmoney A-Shares, Chinese ADRs
Europe	LSE (UK), Xetra / DAX (DE)
Americas	TSX (CA), B3 (BR), BMV (MX)
MENA / Africa	Tadawul (SA), BIST (TR), JSE (ZA)
SE Asia	IDX (ID), SET (TH), Bursa Malaysia, PSE (PH)

Every exchange is backed by a dedicated NexGenData single-exchange screener (linked below) — you keep getting the freshest possible data per market.

Use cases

• Hedge fund position-sizing — pull a global snapshot every morning, feed it into your sizing model, know your cross-region exposure before US open.
• Cross-border ETF research — top-N by market cap across 25 exchanges in one run becomes your candidate set for thematic ETF construction.
• Sector rotation across regions — per_stock mode gives you a unified sector field across exchanges so you can rank semiconductors / energy / banks globally.
• Daily institutional briefings — per_exchange_summary returns the top 5 gainers, top 5 losers, top 10 by market cap, advancers/decliners, and median move per exchange — drop it straight into a Slack digest or a morning newsletter.
• Cross-border M&A and venture monitoring — see which markets are seeing breakouts in real time, not a week after the fact.

Two output modes

per_exchange_summary (default — best for dashboards & briefings)

One dataset row per selected exchange:

{
  "exchange": "TSE_JAPAN",
  "country": "Japan",
  "region": "Asia-Pacific",
  "currency": "JPY",
  "snapshot_ts": "2026-05-23T13:42:11Z",
  "stock_count": 50,
  "top_gainers": [{"symbol": "...", "name": "...", "price": 0, "change_pct": 0, "market_cap": 0}],
  "top_losers":  [{"symbol": "...", "name": "...", "price": 0, "change_pct": 0, "market_cap": 0}],
  "top_by_market_cap": [...],
  "median_change_pct": 0.42,
  "advancers": 28,
  "decliners": 19,
  "unchanged": 3,
  "error": null
}

per_stock (best for screeners & sector rotation)

One row per stock across all selected exchanges, normalized into the same schema:

{
  "exchange": "HKEX",
  "country": "Hong Kong",
  "region": "Asia-Pacific",
  "currency": "HKD",
  "symbol": "0700.HK",
  "name": "Tencent Holdings",
  "price": 412.6,
  "change_pct": 1.8,
  "market_cap": 3960000000000,
  "volume": 8230000,
  "sector": "Communication Services",
  "raw": { /* original sub-actor row, untouched */ }
}

The raw field always carries the exact row from the underlying single-exchange screener — useful if you need a field we haven't normalized yet.

Input schema

Field	Type	Default	Notes
exchanges	string[]	all 25	Pick any subset.
outputMode	string	per_exchange_summary	Or per_stock.
topN	integer	50	Per exchange. Max 500.
maxParallel	integer	5	Concurrent sub-actor runs.
subActorTimeoutSecs	integer	300	Per-exchange timeout; failures don't poison the rest of the run.

How it works

Approach A (call sub-actors): this meta-actor invokes each selected single-exchange screener via Actor.call(), fetches the resulting datasets, normalizes fields across heterogeneous sub-actor schemas into a unified shape, and pushes one item per exchange (summary mode) or one item per stock (per-stock mode) to its own dataset. Failures on individual exchanges are isolated — the row carries an error field and the rest of the run continues.

Underlying single-exchange screeners (all by NexGenData)

If you only care about one market, use the dedicated screener directly — same data, lower cost:

• KOSPI / KOSDAQ — Korea
• TWSE — Taiwan
• NSE — India
• BSE — India
• B3 — Brazil
• HKEX / Hang Seng — Hong Kong
• SGX — Singapore
• HOSE — Vietnam
• TSE — Japan
• LSE — UK
• Xetra / DAX — Germany
• TSX — Canada
• ASX — Australia
• Tadawul — Saudi Arabia
• BMV — Mexico
• JSE — South Africa
• IDX — Indonesia
• SET — Thailand
• Bursa — Malaysia
• BIST — Turkey
• PSE — Philippines
• STAR Market — China
• ChiNext — China
• Chinese ADRs
• Beijing Stock Exchange
• Eastmoney A-Shares

Also see

• Global Equities Screener MCP — Model Context Protocol server that exposes this data to AI agents (Claude, ChatGPT, Cursor) as tools.

Pricing

Pay-per-event billing. See the actor's pricing tab on Apify Store for the current rates.

Support

Bug reports, schema requests, or new-exchange suggestions: open an issue on the actor's Apify Store page.