# APAC IPO Calendar Sweep — Pan-Asia Listings Tracker (`nexgendata/apac-ipo-calendar-sweep`) Actor Pan-Asia IPO aggregator across HKEX + TWSE + TSE + KRX + NSE/BSE + SGX + ASEAN. Forward + recent listings with sponsors, cornerstones, offer prices, FX-normalised proceeds, post-IPO returns. Hedge funds, IPO traders, financial journalists. - **URL**: https://apify.com/nexgendata/apac-ipo-calendar-sweep.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $400.00 / 1,000 ipos This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## APAC IPO Calendar Sweep — Pan-Asia Listings Tracker **One pull. Every IPO across HKEX, TWSE, TSE, KRX, NSE/BSE, SGX and ASEAN — upcoming book-builds, this week's debuts, last quarter's vintage, post-IPO returns, sponsor league tables.** If you cover Asia as one bloc — long/short PMs, allocators, IPO traders, syndicate-desk strategists, the macro-Asia journalist — you have probably stitched this dataset together yourself, one exchange at a time, from HKEXnews + TWSE MOPS + JPX TDnet + KRX KIND + SEBI EDIFAR + SGX OPERA + IDX prospectus library + Bursa Malaysia + SET + HOSE + PSE EDGE. This actor does that stitching for you and bills per IPO row. ### Who this is built for If any of these is your day job, this actor was built for you: - **Pan-Asia long/short hedge fund PMs and allocators** running multi-jurisdiction Asia books. You can't subscribe to Bloomberg, Refinitiv Eikon, Dealogic, S&P Global Market Intelligence, Asia Pacific Loan Market Association, *Mergermarket Asia*, *The Edge Singapore Premium IPO*, *Korea Economic Daily IPO Database*, *Nikkei Asia IPO Tracker* and *Business Standard India IPO Database* — but you need the merged feed. - **IPO traders running the Asia book** at sell-side syndicate desks or proprietary trading shops. You need to know what's pricing tomorrow in Hong Kong, what's listing next week in Tokyo, what's mid-book-build in Mumbai, and what just broke -27% on its debut in Seoul. All in one query. - **Family offices and SWFs (GIC, Khazanah, Temasek, Mubadala, KIA peers)** scanning cornerstone-investor allocations across pan-Asia deals. The cornerstone book is the single highest-signal datapoint in Asian primary markets — every pan-Asia mega-deal lists Hillhouse, Boyu, GIC, Norges Bank, Capital Group, Schroders, OrbiMed, Sequoia China, BlackRock and the China sovereign funds. - **Financial journalists covering Asia primary markets** — Reuters, *Bloomberg Asia*, *Nikkei Asia*, *South China Morning Post*, *Mint*, *Korea JoongAng Daily*, *Channel News Asia Business*, *DealStreetAsia* and the Asia equity-research desks that feed them. - **Sector PMs** building cross-Asia cohort studies. Semiconductors? Pull every Taiwan TPEx IPO + Korea KOSDAQ K-fabless + HK Chapter 18C + Japan TSE Growth-segment semi listing in one query. China-consumer? Pull Mixue, Chabaidao, Laopu Gold, Bawang Chaji, Chery Auto, Seres in one query. Biotech? Pull every HK Chapter 18A pre-revenue biotech + KOSDAQ Growth-tier K-biotech + India Lupin/Sun-Pharma-class IPO in one query. - **Allocators in Hillhouse / Boyu / Sequoia China / OrbiMed / Lake Bleu / Hony / Primavera / Bain Capital Asia / KKR Asia / Goldman Sachs Asia Special Situations-class funds** scanning the comp set — who anchored what, at what valuation, at what return. If you have ever budgeted USD 24,000+/year for a Bloomberg Terminal Asia primary-markets seat or USD 18,000+/year for a Dealogic Asia-Pacific subscription, you are the target buyer for this actor. Same fields, same coverage, same lifecycle granularity — billed per IPO row instead of per seat per year. ### What you get per row Every row is a single APAC IPO event. The schema is harmonised across all 11 markets so you can pivot, group-by and roll-up without per-country special-casing: - **`country`** — `Hong Kong`, `Taiwan`, `Japan`, `Korea`, `India`, `Singapore`, `Indonesia`, `Malaysia`, `Thailand`, `Vietnam`, `Philippines` - **`country_iso`** — ISO-2 country code (`HK`, `TW`, `JP`, `KR`, `IN`, `SG`, `ID`, `MY`, `TH`, `VN`, `PH`) - **`exchange`** — `HKEX`, `TWSE`, `TPEx`, `TSE`, `KOSPI`, `KOSDAQ`, `NSE/BSE`, `SGX`, `IDX`, `Bursa Malaysia`, `SET`, `HOSE`, `PSE` - **`company_name`** — issuer legal name in English / romanised - **`stock_code`** — exchange-assigned ticker if listed (e.g. `2097.HK` for Mixue Bingcheng, `285A.T` for Kioxia, `SWIGGY.NS` for Swiggy, `5326.KL` for 99 Speed Mart, `AADI.JK` for Adaro Andalan) - **`sector`** — GICS-style 11-sector taxonomy (Technology / Healthcare / Financials / Consumer Discretionary / Consumer Staples / Industrials / Energy / Real Estate / Materials / Communication Services / Utilities) - **`business_description`** — 1-3 line plain-English summary of what the company does - **`listing_status`** — `Upcoming` / `Pricing` / `Trading` / `Withdrawn` / `Postponed` - **`filing_date`** — date prospectus / Application Proof / DRHP / Lodgement was filed - **`issue_date`** — pricing / book-close date (offer price finalised, allotment confirmed) - **`listing_date`** — first-day-of-trading date in ISO-8601 (`YYYY-MM-DD`) - **`offer_price_range`** — indicative price range from the red-herring prospectus - **`offer_price`** — final offer price in local currency - **`currency`** — ISO 4217 (`HKD`, `TWD`, `JPY`, `KRW`, `INR`, `SGD`, `IDR`, `MYR`, `THB`, `VND`, `PHP`) - **`shares_offered`** — number of new shares in the offering - **`proceeds_local_million`** — gross proceeds raised, local currency millions - **`proceeds_usd_million`** — same, normalised to USD millions at recent FX - **`valuation_local_million`** — post-money market cap at listing, local currency millions - **`valuation_usd_million`** — same, USD millions - **`sponsors`** — list of HKEX-sanctioned listing sponsors / SEBI book-running lead managers / SGX issue managers / JPX joint global coordinators (CICC, Goldman Sachs, Morgan Stanley, Nomura, Daiwa, Mirae Asset, Mizuho, Kotak Mahindra, Citi, JP Morgan, DBS, Maybank, Mandiri Sekuritas, etc.) - **`cornerstone_investors`** — list of named cornerstone investors - **`prospectus_url`** — link to the official prospectus filing on the relevant regulator portal (HKEXnews, MOPS Taiwan, JPX, KIND Korea, SEBI EDIFAR, MAS OPERA, IDX, Bursa Malaysia, SET, HSX, PSE EDGE) - **`first_day_pop_pct`** — first-day close vs offer price (signed percentage) - **`current_price`** — latest local-currency market price - **`gain_loss_from_ipo_pct`** — total return from IPO price (signed percentage) - **`source_actor`** — the underlying NexGenData country-specific IPO actor that contributed this row (e.g. `nexgendata/hkex-ipo-calendar`, `nexgendata/sg-sgx-ipo-calendar`) - **`data_source`** — provenance tag ### Concrete worked examples **Example 1 — "Every APAC IPO in the last 90 days, ranked by deal size."** ```json { "country": "ALL", "status": "listed", "lookbackDays": 90, "limit": 100 } ```` Returns the recent vintage across every covered exchange — typically 30-80 IPOs depending on cycle. Sort the output by `proceeds_usd_million` descending in your downstream notebook to get the league table. This is the canonical end-of-quarter screen for Asia primary-market strategy notes. **Example 2 — "The forward pipeline of confirmed Asia mega-deals (USD 500M+)."** ```json { "country": "ALL", "status": "approved", "lookaheadDays": 180, "minProceedsUsdMillion": 500, "limit": 50 } ``` Returns CATL HK A+H secondary (~$4.6bn), Chery Auto HK (~$1.2bn), Hengrui Pharma HK (~$1.4bn), LG Electronics India NSE (~$1.4bn), NTT DC REIT Singapore (~$655M), Sakura Internet TSE secondary (~$300M), Powerchip Taiwan secondary (~$565M), Tokyo Metro TSE recently completed (~$2.3bn historical), Kakao Pay Securities Korea (~$630M) and similar. Defines the H2 2026 Asia-IPO mega-deal pipeline. **Example 3 — "Every Indian IPO this quarter."** ```json { "country": "IN", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "limit": 100 } ``` Drills into the world's most active IPO market by deal count. India NSE+BSE prices 200+ IPOs/year across mainboard + SME segments; this query returns the institutional-grade mainboard slice for the current quarter — Swiggy, Hyundai Motor India, NTPC Green, Vishal Mega Mart, plus forward names like LG Electronics India. **Example 4 — "ASEAN-only IPO pipeline for the next 6 months."** ```json { "country": "ASEAN", "status": "approved", "lookaheadDays": 180, "limit": 50 } ``` Returns SGX (NTT DC REIT, Centurion Accommodation REIT, Info-Tech Systems), IDX Indonesia (forward), Bursa Malaysia, SET Thailand, HOSE Vietnam (Masan High-Tech Materials), PSE Philippines pipeline. Useful for the ASEAN-only allocator who doesn't trade North Asia. **Example 5 — "Every pre-revenue HK Chapter 18A biotech vs Korea KOSDAQ K-biotech in 2024-2026."** ```json { "country": "ALL", "sector": "Healthcare", "status": "all", "lookbackDays": 730, "lookaheadDays": 180, "limit": 200 } ``` Returns the cross-Asia biotech IPO cohort — HK 18A vintage (Duality Bio, Hansoh Pharmaceutical Innovation) alongside KOSDAQ Growth-tier K-biotech (DXVX class) for cohort comparison. ### Why pan-Asia coverage is uniquely hard (and uniquely valuable) Asia primary-market data has historically been fragmented across 11+ regulators, 7+ alphabets/scripts, 9+ currencies, and several different lifecycle taxonomies. SEBI uses DRHP / RHP. HKEX uses Application Proof / PHIP. SGX uses Lodgement / Registration. TSE uses TDnet filings. KRX uses KIND disclosures. TWSE uses Public Information Observation. Bursa uses Mainboard / ACE / LEAP. Each has its own prospectus naming convention, its own sponsor/lead-manager nomenclature, its own quiet-period rules, its own cornerstone-investor disclosure regime. Anyone who has built this dataset from scratch knows: the merged-feed problem is 80% of the work. This actor solves it. Every row is normalised to one schema, one currency-translation framework (we expose both local and USD), one lifecycle taxonomy (`Upcoming` → `Pricing` → `Trading` → optional `Withdrawn` / `Postponed`), and one sector taxonomy (GICS-style 11-sector). ### The 11 markets we cover - **Hong Kong (HKEX)** — Main Board + GEM. World #1 IPO venue by proceeds in 5 of the last 15 years (2009-2012, 2018). Active Chapters: 8A Weighted Voting Rights (Alibaba secondary, Xiaomi, Meituan), 18A pre-revenue biotech (InnoCare, BeiGene, Duality Bio class), 18C Specialist Technology Companies (Black Sesame, Plus AI, WeRide forward). 2024-2026 wave: A+H secondaries (Midea, S.F. Holding, CATL forward, Hengrui forward), China consumer brands (Mixue, Chabaidao, Laopu Gold). - **Taiwan (TWSE + TPEx)** — Main board + Taipei Exchange (OTC) + TPEx Innovation Board. Home to the world's most strategic semiconductor IPO pipeline — TSMC supply chain (Phison, Realtek, Powerchip class). - **Japan (TSE)** — Prime / Standard / Growth markets post-2022 restructure. Largest 2024-2026 deals: Tokyo Metro ($2.3bn), Kioxia ($770M), Rakuten Bank secondary, Sakura Internet GPU-cloud expansion. - **Korea (KRX — KOSPI + KOSDAQ)** — KOSPI main-board (Samsung-class) + KOSDAQ growth (K-biotech, K-fabless, K-pop entertainment IPOs). Recent: HD Hyundai Marine Solution (97% first-day pop), LG Electronics secondary. - **India (NSE / BSE)** — World's most active IPO market by deal count, ~200+ listings/year across mainboard + SME. Recent: Hyundai Motor India ($3.3bn — India's largest IPO ever), Swiggy ($1.4bn), NTPC Green ($1.2bn), Vishal Mega Mart, LG Electronics India forward. - **Singapore (SGX — Mainboard + Catalist)** — Quality over quantity (5-30 IPOs/year). 2026 pipeline anchored by NTT DC REIT, Centurion Accommodation REIT, Info-Tech Systems. - **Indonesia (IDX)** — Largest ASEAN market by GDP. 2024-2026 highlights: Adaro Andalan Indonesia ($350M+), MR DIY Indonesia ($270M+). - **Malaysia (Bursa Malaysia)** — 99 Speed Mart Retail ($1bn+ — largest Malaysian IPO in a decade). - **Thailand (SET)** — Big C, Berli Jucker class, SET-mai secondary universe. - **Vietnam (HOSE)** — Frontier-Asia rising star. Masan High-Tech Materials forward. - **Philippines (PSE)** — OceanaGold Philippines mining IPO. ### Quick-start example ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("nexgendata/apac-ipo-calendar-sweep").call(run_input={ "country": "ALL", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "limit": 100 }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["country"], "|", item["company_name"], "|", item["listing_status"], "|", item.get("proceeds_usd_million"), "USD M", "|", item.get("first_day_pop_pct"), "% pop") ``` ### Sister NexGenData actors (cross-link hub) This actor is the **pan-Asia rollup tier** of the NexGenData IPO product family. Pair it with the country-specific deep-dive actors when you need maximum lifecycle granularity for a single jurisdiction: - [nexgendata/hkex-ipo-calendar](https://apify.com/nexgendata/hkex-ipo-calendar?fpr=2ayu9b) — HKEX Main Board + GEM with regulatory chapters (18A / 18C / 8A), sponsor list, cornerstone book, post-IPO return - [nexgendata/sg-sgx-ipo-calendar](https://apify.com/nexgendata/sg-sgx-ipo-calendar?fpr=2ayu9b) — Singapore Exchange Mainboard + Catalist - [nexgendata/ipo-tracker](https://apify.com/nexgendata/ipo-tracker?fpr=2ayu9b) — US IPO tracker (NYSE / Nasdaq) with lockup expirations, S-1/F-1 filings, SEC EDGAR cross-references - [nexgendata/sec-form-13f-tracker-pro](https://apify.com/nexgendata/sec-form-13f-tracker-pro?fpr=2ayu9b) — US 13F institutional-holdings tracker (who bought what) - [nexgendata/sec-form-8k-material-events-scraper](https://apify.com/nexgendata/sec-form-8k-material-events-scraper?fpr=2ayu9b) — US 8-K material-events monitoring - [nexgendata/earnings-calendar](https://apify.com/nexgendata/earnings-calendar?fpr=2ayu9b) — global earnings calendar with EPS estimates #### Pan-Asia equity screener fleet (pair with this actor) If you trade the names *after* they list, pair the IPO calendar with the post-listing screeners: - [nexgendata/hkex-hang-seng-stock-screener](https://apify.com/nexgendata/hkex-hang-seng-stock-screener?fpr=2ayu9b) — HKEX + Hang Seng Index - [nexgendata/eastmoney-china-stock-screener](https://apify.com/nexgendata/eastmoney-china-stock-screener?fpr=2ayu9b) — Shanghai + Shenzhen A-shares - [nexgendata/twse-stock-screener](https://apify.com/nexgendata/twse-stock-screener?fpr=2ayu9b) — Taiwan Stock Exchange - [nexgendata/tse-japan-stock-screener](https://apify.com/nexgendata/tse-japan-stock-screener?fpr=2ayu9b) — TSE Japan + Nikkei 225 - [nexgendata/kospi-stock-screener](https://apify.com/nexgendata/kospi-stock-screener?fpr=2ayu9b) — KOSPI + KOSDAQ (Korea) - [nexgendata/sgx-singapore-stock-screener](https://apify.com/nexgendata/sgx-singapore-stock-screener?fpr=2ayu9b) — Singapore Exchange screener - [nexgendata/idx-indonesia-stock-screener](https://apify.com/nexgendata/idx-indonesia-stock-screener?fpr=2ayu9b) — IDX Indonesia - [nexgendata/bursa-malaysia-stock-screener](https://apify.com/nexgendata/bursa-malaysia-stock-screener?fpr=2ayu9b) — Bursa Malaysia - [nexgendata/set-thailand-stock-screener](https://apify.com/nexgendata/set-thailand-stock-screener?fpr=2ayu9b) — SET Thailand - [nexgendata/hose-vietnam-stock-screener](https://apify.com/nexgendata/hose-vietnam-stock-screener?fpr=2ayu9b) — HOSE Vietnam - [nexgendata/pse-philippines-stock-screener](https://apify.com/nexgendata/pse-philippines-stock-screener?fpr=2ayu9b) — PSE Philippines ### How this compares to the incumbents | | This actor | Bloomberg Asia IPO | Refinitiv Eikon APAC | Dealogic | The Edge Singapore Premium | Korea Economic Daily IPO DB | |---|---|---|---|---|---|---| | **Cost** | Per IPO row (sub-1% of seat) | USD 24,000+/seat/year | USD 22,000+/seat/year | USD 18,000+/year | SGD 600+/year (SG only) | KRW 1.2M+/year (KR only) | | **Pan-Asia coverage** | 11 markets in one query | Yes (one seat) | Yes (one seat) | Yes (one seat) | SG only | KR only | | **Forward calendar** | Yes (announced + book-build + priced) | Yes | Yes | Yes (deal-flow focus) | SG only | KR only | | **Cornerstone investor lists** | Yes | Yes | Yes | Partial | Yes (SG) | Yes (KR) | | **Regulatory chapter tags (HK 18A/18C/8A)** | Yes | Partial | Partial | No | No | No | | **Per-IPO API access** | Yes | Limited (terminal-licensed) | Limited (terminal-licensed) | Yes (enterprise tier) | No | No | | **FX-normalised USD proceeds** | Yes (both local + USD) | Yes | Yes | Yes | No | No | | **Source actor traceability** | Yes (per-country lineage) | No | No | No | No | No | For pan-Asia rollup work, the major incumbents (Bloomberg, Refinitiv, Dealogic) all require a full institutional seat per analyst. This actor charges per IPO row — the per-deal economics work out to fractions of a cent vs the per-seat economics of the incumbents. ### Data sourcing The curated database underlying this actor pulls from: - HKEXnews + HKEX listing-committee hearing schedule - TWSE MOPS Public Information Observation + Taipei Exchange (TPEx) - JPX TDnet + TSE new-listing notices - KRX KIND Korea Investor's Network for Disclosure - SEBI EDIFAR + NSE/BSE listing prospectus archives - MAS OPERA Singapore prospectus registry + SGX listing notices - IDX Indonesia prospectus library - Bursa Malaysia Mainboard listing notices + sponsor reports - SET Thailand prospectus archive - HSX HOSE Vietnam listing notices - PSE EDGE Philippines disclosure portal - Sponsor PHIPs (Post-Hearing Information Packs), DRHPs, prospectuses - Bloomberg / Refinitiv / Nikkei Asia / Korea Economic Daily / The Edge Singapore / Mint / Business Standard / South China Morning Post / Channel News Asia Business IPO coverage - Cornerstone-investor press releases Records are hand-verified for deal size, sponsor list, cornerstone names and post-IPO return. ### FAQ **Q: How fresh is the data?** The curated database covers 2024-01 through the forward 2026 calendar. Forward-pipeline rows (status `Upcoming` / `Pricing`) are refreshed on the announcement cadence of the underlying exchange filings — typically T+0 to T+3 from when the prospectus is lodged. Completed-IPO rows (status `Trading`) are post-debut and stable; `current_price` and `gain_loss_from_ipo_pct` snapshot at the last refresh of the curated database. For real-time post-listing pricing pair this actor with the country-specific stock screener actors listed above (HKEX, TWSE, TSE, KOSPI, SGX, IDX, Bursa, SET, HOSE, PSE all have dedicated screeners in the NexGenData fleet). **Q: How does the `proof` / `approved` / `listed` status taxonomy map to each exchange?** We normalise across the per-exchange lifecycles. `proof` covers early-stage filings (HKEX Application Proof, SEBI DRHP, SGX Lodgement, TSE TDnet pre-listing filings, KRX KIND preliminary disclosure). `approved` covers listing-committee-approved / book-building / RHP / PHIP / pricing-day deals. `listed` covers anything trading. `all` (default) returns the full lifecycle. **Q: Why is the proceeds figure sometimes in HKD millions and sometimes in IDR billions in the underlying data?** Each row preserves the local-currency figure in `proceeds_local_million` (always normalised to millions for consistency, even where local convention is otherwise — e.g. Indonesia would natively quote Rupiah trillions) and exposes an FX-normalised USD value in `proceeds_usd_million`. Sort/filter on the USD field for cross-Asia comparison; use the local-currency field for native-market analysis. **Q: Does this actor charge for empty result sets?** No. If your filters return no matches, we push a single `no_match` diagnostic row (which IS charged as one dataset item) with the active filters echoed back so you can debug, but the typical empty-result charge is one row, not the per-IPO premium tier. Tune `lookbackDays` / `lookaheadDays` if you see this. **Q: Can I get historical (pre-2024) data?** The curated database includes selected mega-deal history back to 2019 (Alibaba HK secondary, Saudi Aramco rumored HK dual listing) for reference. For systematic pre-2024 cohort studies, contact us via the NexGenData profile page — we can build a custom historical extract on the same schema. ### Roadmap - Real-time intra-day pricing-day signals (live offer-price-finalised feed at issue-date pricing close) - Per-tranche allotment data (Hong Kong public-offer tranche oversubscription multiple, Indian retail/HNI/QIB allocation splits) - Sponsor / book-runner league-table actor (sister actor `apac-ipo-league-tables`) - Frontier-Asia coverage extension (Mongolia MSE, Kazakhstan AIX, Pakistan PSX) - Greater-China A-share IPO calendar (Shanghai STAR Market + Shenzhen ChiNext + Beijing Stock Exchange — sister actor `china-a-share-ipo-calendar` planned) ### Run NexGenData actors anywhere on the Apify platform Sign up for Apify with our affiliate link: **** — supports continued development of the NexGenData fleet. You get the same Apify free tier ($5 platform credit / month); we get a small referral commission on any paid usage. Browse the full 280+ NexGenData actor catalog at . # Actor input Schema ## `country` (type: `string`): Which APAC market(s) to sweep. 'ALL' returns the full pan-Asia book — every IPO from HKEX (Hong Kong) + TWSE/TPEx (Taiwan) + TSE (Japan) + KRX KOSPI/KOSDAQ (Korea) + NSE/BSE (India) + SGX (Singapore) + IDX (Indonesia) + Bursa Malaysia + SET (Thailand) + HOSE (Vietnam) + PSE (Philippines). 'ASEAN' = SG + ID + MY + TH + VN + PH only (excludes North Asia). Individual ISO-2 codes (HK / TW / JP / KR / IN / SG / ID / MY / TH / VN / PH) drill down to a single jurisdiction — useful when you're running country-specific allocator dashboards or sector models that don't cross-FX. ## `status` (type: `string`): Which slice of the IPO lifecycle to return. 'proof' = early-stage filings (Application Proof / draft prospectus / DRHP filed, not yet priced — typically 60-180 days before listing). 'approved' = listing-committee-approved deals with confirmed pricing date or final allotment (book-build phase, typically 5-30 days before listing). 'listed' = already-trading IPOs (post-debut). 'all' (default) = full lifecycle in one pull. Note: status semantics differ slightly across exchanges — HKEX uses Application Proof/PHIP, SEBI uses DRHP/RHP, SGX uses Lodgement/Registration, TSE uses TDnet/JPX filings, KRX uses KIND disclosures. We normalise across all of them. ## `lookbackDays` (type: `integer`): How many days of completed APAC IPOs (already-trading names) to include. Default 90 = the recent quarter — typically 30-100 completed deals across the bloc. 365 = full-year vintage study (good for league-table research: which sponsor / cornerstone / underwriter dominated the Asia primary calendar). 7-30 = daily/weekly trader dashboards. APAC collectively prices ~600-1000 IPOs/year across the 11 markets covered. ## `lookaheadDays` (type: `integer`): How many days of forward APAC IPO calendar to include. Default 90 = next-quarter pipeline of confirmed/imminent deals. 180+ days surfaces the soft pipeline (rumored / sponsor-mandated but not yet priced). Use 30 for the active book-build window only. India alone is the world's most active IPO market by deal count (~200+ NSE+BSE listings/year); China's HKEX has been the global #1 by proceeds in 5 of the last 15 years; Korea KOSDAQ runs at ~100 listings/year. The forward pipeline is dense. ## `sector` (type: `string`): Filter by sector classification. APAC IPO sector mix in 2024-2026 is heavily concentrated in (1) Consumer Discretionary — China auto / HK consumer-brand listings (Mixue Bingcheng, Chery Auto, Laopu Gold), Korea LG/Hyundai secondaries, India retail IPOs; (2) Technology — Taiwan semi supply chain, Japan AI cloud (Sakura Internet, Kioxia), HK Chapter 18C Specialist Tech; (3) Healthcare — HK Chapter 18A pre-revenue biotech wave, KOSDAQ K-biotech; (4) Industrials — China A+H industrials, Korea HD Hyundai class; (5) Real Estate — SGX data-centre REITs (NTT DC REIT, Centurion). 'all' = no filter. ## `minProceedsUsdMillion` (type: `number`): Lower bound on gross IPO proceeds normalised to USD millions. Use to focus on institutional-grade deals. Typical thresholds: '50' = scaleable mid-cap; '250' = significant deal; '500' = mega-deal class (Hyundai Motor India $3.3bn, Midea HK ~$4bn, Tokyo Metro ~$2.3bn, Kioxia ~$770M, 99 Speed Mart ~$900M). Leave 0 for no lower bound — useful for SME/Catalist/KOSDAQ/SET-mai watching. FX rates are conservative spot marks; portfolio users should re-FX at their own latest. ## `limit` (type: `integer`): Hard cap on total APAC IPO records returned (1-500). Each IPO is one dataset row. Premium per-record pricing reflects the pan-Asia aggregator tier — Bloomberg APAC IPO / Refinitiv Eikon / Dealogic equivalents cost USD 24,000+/seat/year for comparable coverage. APAC collectively prices 600-1000 IPOs/year across the 11 markets — most institutional desks pull 50-200 per run. Records are sorted Upcoming-first by listing\_date ascending, then completed-IPOs by listing\_date descending. ## Actor input object example ```json { "country": "ALL", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "sector": "all", "minProceedsUsdMillion": 0, "limit": 10 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "country": "ALL", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "sector": "all", "minProceedsUsdMillion": 0, "limit": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/apac-ipo-calendar-sweep").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "country": "ALL", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "sector": "all", "minProceedsUsdMillion": 0, "limit": 10, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/apac-ipo-calendar-sweep").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "country": "ALL", "status": "all", "lookbackDays": 90, "lookaheadDays": 90, "sector": "all", "minProceedsUsdMillion": 0, "limit": 10 }' | apify call nexgendata/apac-ipo-calendar-sweep --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/apac-ipo-calendar-sweep", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "APAC IPO Calendar Sweep — Pan-Asia Listings Tracker", "description": "Pan-Asia IPO aggregator across HKEX + TWSE + TSE + KRX + NSE/BSE + SGX + ASEAN. Forward + recent listings with sponsors, cornerstones, offer prices, FX-normalised proceeds, post-IPO returns. Hedge funds, IPO traders, financial journalists.", "version": "0.0", "x-build-id": "bkLkyh4bg7wXhyMY8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~apac-ipo-calendar-sweep/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-apac-ipo-calendar-sweep", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~apac-ipo-calendar-sweep/runs": { "post": { "operationId": "runs-sync-nexgendata-apac-ipo-calendar-sweep", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~apac-ipo-calendar-sweep/run-sync": { "post": { "operationId": "run-sync-nexgendata-apac-ipo-calendar-sweep", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "country": { "title": "Country / region filter", "enum": [ "ALL", "ASEAN", "HK", "TW", "JP", "KR", "IN", "SG", "ID", "MY", "TH", "VN", "PH" ], "type": "string", "description": "Which APAC market(s) to sweep. 'ALL' returns the full pan-Asia book — every IPO from HKEX (Hong Kong) + TWSE/TPEx (Taiwan) + TSE (Japan) + KRX KOSPI/KOSDAQ (Korea) + NSE/BSE (India) + SGX (Singapore) + IDX (Indonesia) + Bursa Malaysia + SET (Thailand) + HOSE (Vietnam) + PSE (Philippines). 'ASEAN' = SG + ID + MY + TH + VN + PH only (excludes North Asia). Individual ISO-2 codes (HK / TW / JP / KR / IN / SG / ID / MY / TH / VN / PH) drill down to a single jurisdiction — useful when you're running country-specific allocator dashboards or sector models that don't cross-FX.", "default": "ALL" }, "status": { "title": "Listing-pipeline status", "enum": [ "all", "proof", "approved", "listed" ], "type": "string", "description": "Which slice of the IPO lifecycle to return. 'proof' = early-stage filings (Application Proof / draft prospectus / DRHP filed, not yet priced — typically 60-180 days before listing). 'approved' = listing-committee-approved deals with confirmed pricing date or final allotment (book-build phase, typically 5-30 days before listing). 'listed' = already-trading IPOs (post-debut). 'all' (default) = full lifecycle in one pull. Note: status semantics differ slightly across exchanges — HKEX uses Application Proof/PHIP, SEBI uses DRHP/RHP, SGX uses Lodgement/Registration, TSE uses TDnet/JPX filings, KRX uses KIND disclosures. We normalise across all of them.", "default": "all" }, "lookbackDays": { "title": "Lookback window (days, for 'listed' / 'all' bucket)", "minimum": 1, "maximum": 1825, "type": "integer", "description": "How many days of completed APAC IPOs (already-trading names) to include. Default 90 = the recent quarter — typically 30-100 completed deals across the bloc. 365 = full-year vintage study (good for league-table research: which sponsor / cornerstone / underwriter dominated the Asia primary calendar). 7-30 = daily/weekly trader dashboards. APAC collectively prices ~600-1000 IPOs/year across the 11 markets covered.", "default": 90 }, "lookaheadDays": { "title": "Lookahead window (days, for 'proof' / 'approved' / 'all' bucket)", "minimum": 1, "maximum": 730, "type": "integer", "description": "How many days of forward APAC IPO calendar to include. Default 90 = next-quarter pipeline of confirmed/imminent deals. 180+ days surfaces the soft pipeline (rumored / sponsor-mandated but not yet priced). Use 30 for the active book-build window only. India alone is the world's most active IPO market by deal count (~200+ NSE+BSE listings/year); China's HKEX has been the global #1 by proceeds in 5 of the last 15 years; Korea KOSDAQ runs at ~100 listings/year. The forward pipeline is dense.", "default": 90 }, "sector": { "title": "GICS-style sector filter", "enum": [ "all", "Technology", "Healthcare", "Financials", "Consumer Discretionary", "Consumer Staples", "Industrials", "Energy", "Real Estate", "Materials", "Communication Services", "Utilities" ], "type": "string", "description": "Filter by sector classification. APAC IPO sector mix in 2024-2026 is heavily concentrated in (1) Consumer Discretionary — China auto / HK consumer-brand listings (Mixue Bingcheng, Chery Auto, Laopu Gold), Korea LG/Hyundai secondaries, India retail IPOs; (2) Technology — Taiwan semi supply chain, Japan AI cloud (Sakura Internet, Kioxia), HK Chapter 18C Specialist Tech; (3) Healthcare — HK Chapter 18A pre-revenue biotech wave, KOSDAQ K-biotech; (4) Industrials — China A+H industrials, Korea HD Hyundai class; (5) Real Estate — SGX data-centre REITs (NTT DC REIT, Centurion). 'all' = no filter.", "default": "all" }, "minProceedsUsdMillion": { "title": "Minimum gross proceeds (USD millions, FX-normalised)", "minimum": 0, "maximum": 100000, "type": "number", "description": "Lower bound on gross IPO proceeds normalised to USD millions. Use to focus on institutional-grade deals. Typical thresholds: '50' = scaleable mid-cap; '250' = significant deal; '500' = mega-deal class (Hyundai Motor India $3.3bn, Midea HK ~$4bn, Tokyo Metro ~$2.3bn, Kioxia ~$770M, 99 Speed Mart ~$900M). Leave 0 for no lower bound — useful for SME/Catalist/KOSDAQ/SET-mai watching. FX rates are conservative spot marks; portfolio users should re-FX at their own latest.", "default": 0 }, "limit": { "title": "Max IPO records returned", "minimum": 1, "maximum": 500, "type": "integer", "description": "Hard cap on total APAC IPO records returned (1-500). Each IPO is one dataset row. Premium per-record pricing reflects the pan-Asia aggregator tier — Bloomberg APAC IPO / Refinitiv Eikon / Dealogic equivalents cost USD 24,000+/seat/year for comparable coverage. APAC collectively prices 600-1000 IPOs/year across the 11 markets — most institutional desks pull 50-200 per run. Records are sorted Upcoming-first by listing_date ascending, then completed-IPOs by listing_date descending.", "default": 50 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```