# 🚀 IPO Tracker — Recent + Upcoming IPOs, Lockup Expirations (`nexgendata/ipo-tracker`) Actor Track recent + upcoming IPOs across NYSE and NASDAQ. Real-time IPO calendar with pricing, share counts, valuations, lead underwriters, lockup expirations, post-IPO performance. Bloomberg IPO desk alternative for prop traders, IPO arb funds, fintech apps. Pay-per-result, no Bloomberg seat. - **URL**: https://apify.com/nexgendata/ipo-tracker.md - **Developed by:** [NexGenData](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 $500.00 / 1,000 ipo records 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 ## 🚀 IPO Tracker — Recent + Upcoming IPOs, Lockup Expirations **The institutional-grade IPO desk on tap.** Real-time IPO calendar for NYSE and NASDAQ — recent priced IPOs, upcoming scheduled IPOs, and newly filed S-1 / F-1 registration statements — with deal pricing, share counts, post-money valuations, lead underwriters, **lockup expiration dates**, and post-IPO performance for already-trading names. Built for IPO desk analysts, prop traders, IPO arbitrage funds, lockup-expiration arbitrageurs, and fintech apps that surface fresh IPO opportunities to retail. This is the actor a Bloomberg IPO desk seat (BWPO``) costs $24k/yr to access. Pay-per-result on Apify — no seat, no contract, no minimum. --- ### Why a dedicated IPO actor (and not just a stock screener) IPOs are a distinct asset class with their own data model. A generic equity screener fails them three ways: 1. **No deal economics.** Recent stock price ≠ IPO price. You need the deal size, shares offered, midpoint of the marketed price range, post-pricing valuation, **and** the implied first-day pop. None of those fields exist on a stock screener. 2. **No lifecycle status.** An IPO moves through four states — `filing` (S-1 / F-1 just filed with the SEC) → `upcoming` (priced or scheduled, not yet trading) → `priced` (priced today, opens any minute) → `trading` (already trading post-IPO, performance accruing). Each state has different traders watching it: ECM bankers watch filings, IPO subscribers watch upcomings, day traders watch priced, lockup arbs watch trading. 3. **No lockup-expiration field.** This is the single most valuable IPO data point after deal size. Insiders, VC funds, and pre-IPO employees are contractually locked out of selling for 180 days post-IPO. Lockup expiration day is the **single most-traded event** in IPO arbitrage. A stock screener gives you a current price; this actor gives you the expiration date so you can build the short-vol straddle, the borrow-and-short, or the put-spread weeks in advance. --- ### What you get A unified IPO record stream, one record per IPO event: ```json { "symbol": "CBRS", "company_name": "Cerebras Systems Inc.", "ipo_date": "2026-05-14", "ipo_status": "upcoming", "ipo_price": 120.00, "ipo_price_low": 115.00, "ipo_price_high": 125.00, "shares_offered": 28000000, "proceeds_usd": 3360000000.00, "valuation_usd": 25560000000.00, "trailing_revenue_usd": 509990000.00, "lead_underwriters": [], "exchange": "NASDAQ", "sector": "Technology", "industry": null, "current_price": null, "current_market_cap_usd": null, "gain_loss_from_ipo_price_pct": null, "lockup_expiration_date": "2026-11-10", "lockup_days": 180, "filing_date": null, "prospectus_url": "https://www.sec.gov/cgi-bin/browse-edgar?...", "is_spac": false, "stockanalysis_url": "https://stockanalysis.com/stocks/cbrs/", "data_source": "stockanalysis_calendar" } ```` That gives you everything to: - Build an IPO subscription / allocation tracker - Compute first-day pop heat-maps (current\_price vs ipo\_price) - Pre-build short books for lockup-expiration day (lockup\_expiration\_date 60-90 days out) - Run sector-tilt analytics (Tech vs Biotech IPO cadence) - Power a fintech "fresh IPOs" feed for retail brokerage apps - Feed an ECM desk's daily filings comp screen *** ### Lifecycle states explained | `ipo_status` | Meaning | Typical buyer | |---|---|---| | `filing` | S-1 / F-1 just filed with SEC. No price yet, no date yet. | ECM analysts, sell-side bankers, IPO Boutique | | `upcoming` | Priced or scheduled IPO. Has price range + date. Not yet trading. | IPO desk allocators, retail subscription apps | | `priced` | Priced today. Opens for trading any minute. | Day traders, first-day pop speculators | | `trading` | Already IPO'd. Public market data available. | Lockup arbs, lockup short-sellers, momentum funds | The actor returns all four states in a single pull. Filter by `status` to narrow. *** ### Input filters | Input | Type | Description | |---|---|---| | `limit` | integer | Max records to return (0 = all, typically 250+). | | `status` | enum | `all` / `upcoming` / `priced` / `trading` / `filing` | | `date_range` | enum | `all` / `last_30d` / `last_90d` / `last_180d` / `upcoming` | | `min_proceeds_usd` | integer | Filter out small deals. Use $100M for institutional IPOs, $1B for mega IPOs. | | `exchange` | enum | `all` / `NYSE` / `NASDAQ` / `AMEX` / `OTC` | | `sector` | enum | All 11 GICS sectors | | `exclude_spacs` | boolean | Strip blank-check / acquisition-corp names | *** ### Use cases for institutional capital #### IPO desk analysts Daily fresh pull of upcoming IPOs + recent filings. Sort by sector, deal size, lead underwriter. Replace your manual Renaissance Capital / IPO Boutique workflow with a JSON feed you can pipe into Snowflake / BigQuery. #### Lockup expiration arbitrageurs This is where the actor pays for itself ten times over. Every record has `lockup_expiration_date` computed as `ipo_date + 180 days` (or pulled from the source when available). Run a query for IPOs with `lockup_expiration_date` between now and now+30 days, cross-reference with current short interest, and you have your watch list. The trade: when 180-day lockup expires, insider supply hits the market and stocks typically drop 1-3% on heavy volume. Pre-position short, or sell weekly calls into the event. #### IPO arbitrage funds Subscribe to allocated IPOs at the IPO price, sell into the first-day pop. The actor surfaces `ipo_price` vs `current_price` for every recent IPO so you can compute the realized pop distribution, build base rates, and size your next allocation. #### Fintech apps surfacing IPOs to retail Power "Upcoming IPOs This Week" feeds on Robinhood-style apps. The actor's `upcoming` status filter combined with `date_range: upcoming` gives you a clean retail-facing list, refreshed daily. #### Prop traders First-day-pop dispersion trading. Recent IPOs with `gain_loss_from_ipo_price_pct` give you cohort statistics for sizing your next IPO debut play. #### Pre-IPO funding round backfill Combine with our **Startup Funding Tracker** (sister actor) to chain a private company's full lifecycle — from Series B funding through S-1 filing through priced IPO. This is the only fully-linked private-to-public data pipeline on Apify. #### ECM bankers / corporate development Daily monitor of competitive filings. New S-1 filed in your sector? You see it in your next morning pull, no Bloomberg seat required. *** ### Pricing — pay per IPO record | Event | Cost | |---|---| | Actor start | **$0.01** | | Per IPO record returned | **$0.50** | A 5-record smoke test costs **$2.51** total. A daily 50-record IPO pull costs **$25.01/day** or roughly **$9,100/year** — about 38% of a single Bloomberg Terminal seat, with full programmatic access and zero seat licensing. IPO records are priced higher ($0.50/record) than our regional stock screener records ($0.25/record) because each IPO is a discrete, high-value event signal. A single lockup-expiration date can drive a 5-figure trade; a single new S-1 filing can trigger a 6-figure long-short pair construction. We anchor the price to the event value, not the data byte count. *** ### Data sources & reliability 1. **stockanalysis.com IPO calendar** (primary) — clean server-rendered HTML tables. The recent IPOs table contains ~200 trailing IPOs with prices, current quotes, and post-IPO performance. The upcoming calendar has full deal detail (price range, shares offered, deal size, post-money valuation, trailing revenue). The filings table has 400+ newly filed S-1 / F-1 registrations. 2. **NASDAQ IPO calendar** (cross-reference) — JSON endpoints used for symbol + date validation when ambiguity arises. 3. **SEC EDGAR full-text search** (prospectus links) — every record carries a deterministic SEC EDGAR search URL so you can drop straight into the prospectus / S-1 / 424B4 filing without a separate lookup. 4. **Computed lockup expiration** — when not present in the primary source, we compute lockup as `ipo_date + 180 calendar days`. This is the SEC-customary lockup window for the overwhelming majority of US IPOs. If your strategy is sensitive to the exact lockup terms (some IPOs run 90-day, 270-day, or staggered releases), cross-reference the prospectus\_url field. The actor is resilient to single-source outages — if stockanalysis.com returns 4xx/5xx for one of the three tables, the other two still emit records. *** ### How to run **Apify Console** — paste your filter inputs, hit Run, download the dataset as JSON or CSV. **Apify API** (Python): ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/ipo-tracker").call(run_input={ "limit": 50, "status": "upcoming", "date_range": "upcoming", "min_proceeds_usd": 100000000, "exchange": "all", }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["symbol"], item["company_name"], item["ipo_date"], item["proceeds_usd"], item["valuation_usd"], item["lockup_expiration_date"]) ``` **Schedule it** — pair with an Apify scheduled task. Daily 06:30 ET (pre-market) gives you a fresh IPO calendar snapshot every trading day; or trigger weekly Friday post-close for a clean weekly summary. *** ### FAQ **How fresh is the data?** stockanalysis.com refreshes its IPO calendar within minutes of NYSE / NASDAQ tape updates. Run the actor every 1-4 hours during market hours to stay current. **Does it cover SPACs?** Yes, included by default. Toggle `exclude_spacs: true` to strip "Acquisition Corp" / "Capital Acquisition" / "blank check" entries — useful for operational-IPO analytics where SPAC noise distorts averages. **What about international IPOs (LSE, HKEX, TSX)?** Not in this actor — US listings only (NYSE / NASDAQ / AMEX). For HKEX IPOs use our HKEX Hang Seng Stock Screener; for TSX, our TSX Canada screener. London IPOs are on our roadmap. **What about direct listings and SPAC mergers (de-SPACs)?** Direct listings (Spotify, Slack, Coinbase) are included with `ipo_status: trading` and `proceeds_usd: 0` (no primary capital raised). De-SPAC business combinations are flagged via `is_spac: true` on the SPAC vehicle side. **How accurate is the computed lockup\_expiration\_date?** Default 180-day SEC-customary window is accurate for ~85% of US IPOs. The remaining 15% include staggered releases (e.g., 25% at 90 days, 75% at 180 days) and longer windows (270 days for some biotech and SPAC PIPEs). For trade execution, always cross-reference the prospectus. **Can I get the lead underwriters?** Currently `lead_underwriters` is emitted but typically empty in the primary source feed. We're adding underwriter enrichment via SEC EDGAR S-1 cover-page parsing in v0.1. For now, click the `prospectus_url` to pull underwriters from the deal prospectus directly. **Can I get history (e.g., 2024 IPO performance)?** Not yet — the actor returns the trailing ~6 month rolling window from stockanalysis.com. For historical IPO datasets, contact us about a custom data pipeline. *** ### Built by NexGenData We build data infrastructure for global equity desks. 220+ actors live on Apify covering US, China, Hong Kong, Japan, Korea, India, Brazil, MENA, ASEAN, and emerging markets. IPO data complements our broader US-equity stack — combine this actor with our Finviz screener, SEC filings trackers, and institutional holdings actors to build a fully connected pre-IPO-through-post-IPO data pipeline. *** ### Explore the NexGenData Equity Data Suite This actor is part of NexGenData's global equity data fleet. IPOs sit at the intersection of private and public markets — pair the IPO Tracker with NexGenData's pre-IPO funding tracker, US equity screener, and SEC institutional-holdings trackers to build a fully connected lifecycle pipeline: #### Pre-IPO and private capital - 🔗 **[Startup Funding Tracker](https://apify.com/nexgendata/startup-funding-tracker)** — Pre-IPO funding rounds (Series A → late-stage). Natural complement: chain a company's full lifecycle from seed through S-1 through priced IPO. #### Public equity peers - 🔗 **[Chinese ADRs Stock Screener](https://apify.com/nexgendata/chinese-adrs-stock-screener)** — US-listed Chinese companies (NYSE / NASDAQ ADRs). Many recent IPOs are Chinese ADRs — chase the post-IPO performance here. - 🔗 **[Finviz Stock Screener](https://apify.com/nexgendata/finviz-stock-screener)** — US equity (NYSE / NASDAQ / AMEX) with 70+ filters. Cross-reference recent IPOs against the broader US universe. #### Post-IPO institutional intelligence - 🔗 **[SEC Form 13F Holdings Tracker](https://apify.com/nexgendata/sec-form-13f-holdings-tracker)** — Quarterly institutional positions. Watch which hedge funds are loading up on freshly-IPO'd names. - 🔗 **[SEC Schedule 13D/G Activist Tracker](https://apify.com/nexgendata/sec-schedule-13dg-activist-tracker)** — 5%+ activist positions. Critical for detecting activist accumulation in post-lockup IPOs. #### Multi-asset / specialty - 🔗 **[Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server)** — Unified Model Context Protocol server exposing the full NexGenData equity fleet to Claude, Cursor & MCP-aware LLMs. #### Browse the full NexGenData catalog **[→ See all 220+ NexGenData actors at apify.com/nexgendata](https://apify.com/nexgendata?fpr=2ayu9b)** — equity screeners across 25+ markets, IPO + pre-IPO trackers, SEC filings, institutional holdings, B2B intelligence, MCP servers, and more. *** ### Why NexGenData vs. paid alternatives | Tool | Annual cost | API access | IPO coverage | |------|-------------|------------|--------------| | Bloomberg Terminal (BWPO ``) | ~$24,000/seat | Limited | Global | | FactSet IPO Monitor | ~$15,000/seat | Paid add-on | Global | | Refinitiv Eikon ECM | ~$22,000/seat | Paid add-on | Global | | Renaissance Capital IPO | ~$9,000/seat | None | US | | **NexGenData IPO Tracker** | **Pay-per-result** | **Native** | **US (NYSE + NASDAQ)** | Run this actor once per day for an institutional IPO desk workflow (~50 records/day, 250 trading days/year) and your annual cost is approximately **$9,100** — a 60-95% discount vs. the major paid alternatives, with no seat licensing, no minimum commitment, and full programmatic access via Apify's REST API or any of 1,500+ MCP / no-code integrations. *** ### 🚀 Apify affiliate program — Get free credits & save 30% Sign up to Apify via our referral link and you'll get: - **Free starter credits** to test this actor and the rest of our 220+ actor fleet - **30% off platform fees** for the life of your account **[→ Sign up here with NexGenData's affiliate link](https://apify.com/nexgendata?fpr=2ayu9b)** — same Apify, same actors, just cheaper for you and a small kickback that helps us keep building. *Built and maintained by NexGenData — institutional-grade equity, IPO, SEC filings, and B2B data infrastructure on Apify.* # Actor input Schema ## `limit` (type: `integer`): Maximum number of IPO records to return. Set to 0 to return all matching IPOs (typically 250+ across recent + upcoming + filings). Useful for testing smaller batches before running a full pull. ## `status` (type: `string`): Filter by IPO lifecycle status. 'upcoming' = priced/scheduled IPOs not yet trading; 'priced' = recently priced including same-day pricing; 'trading' = already-trading recent IPOs (last 6 months) with post-IPO performance; 'filing' = newly filed S-1 / F-1 registration statements (pre-roadshow); 'all' = every status. ## `date_range` (type: `string`): Filter by IPO date range. 'last\_30d' = IPOs that priced in the last 30 calendar days; 'last\_90d' = last 90 days; 'last\_180d' = last 180 days (full lockup window); 'upcoming' = scheduled future IPOs only; 'all' = no date filter. ## `min_proceeds_usd` (type: `integer`): Minimum total deal size (proceeds raised) in USD. Use 100000000 ($100M) to filter for institutional-size IPOs. Use 1000000000 ($1B) to filter for mega IPOs only. Set to 0 to include all sizes including micro-cap and SPAC deals. ## `exchange` (type: `string`): Filter by US listing exchange. NYSE includes large operational IPOs (Reddit, Birkenstock-tier). NASDAQ includes tech and biotech IPOs (most SPAC and biotech debuts). 'all' returns both exchanges and any AMEX / OTC listings. ## `sector` (type: `string`): Filter by GICS-style sector. 'all' returns everything. IPO sectors skew heavily Technology and Healthcare (biotech). Useful for sector-rotation IPO strategies and theme-focused IPO arb desks. ## `exclude_spacs` (type: `boolean`): If true, filters out Special Purpose Acquisition Companies (SPACs) — recognized by 'Acquisition Corp', 'Capital Acquisition', etc. in the company name. SPACs typically price at exactly $10 and add noise to operational IPO analytics. ## Actor input object example ```json { "limit": 50, "status": "all", "date_range": "all", "min_proceeds_usd": 0, "exchange": "all", "sector": "all", "exclude_spacs": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "limit": 50, "status": "all", "date_range": "all", "min_proceeds_usd": 0, "exchange": "all", "sector": "all", "exclude_spacs": false }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/ipo-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "limit": 50, "status": "all", "date_range": "all", "min_proceeds_usd": 0, "exchange": "all", "sector": "all", "exclude_spacs": False, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/ipo-tracker").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 '{ "limit": 50, "status": "all", "date_range": "all", "min_proceeds_usd": 0, "exchange": "all", "sector": "all", "exclude_spacs": false }' | apify call nexgendata/ipo-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/ipo-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🚀 IPO Tracker — Recent + Upcoming IPOs, Lockup Expirations", "description": "Track recent + upcoming IPOs across NYSE and NASDAQ. Real-time IPO calendar with pricing, share counts, valuations, lead underwriters, lockup expirations, post-IPO performance. Bloomberg IPO desk alternative for prop traders, IPO arb funds, fintech apps. Pay-per-result, no Bloomberg seat.", "version": "0.0", "x-build-id": "FRa9e9NuVCf2q749R" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~ipo-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-ipo-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~ipo-tracker/runs": { "post": { "operationId": "runs-sync-nexgendata-ipo-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~ipo-tracker/run-sync": { "post": { "operationId": "run-sync-nexgendata-ipo-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "limit": { "title": "Number of IPO records to return", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Maximum number of IPO records to return. Set to 0 to return all matching IPOs (typically 250+ across recent + upcoming + filings). Useful for testing smaller batches before running a full pull.", "default": 50 }, "status": { "title": "IPO status filter", "enum": [ "all", "upcoming", "priced", "trading", "filing" ], "type": "string", "description": "Filter by IPO lifecycle status. 'upcoming' = priced/scheduled IPOs not yet trading; 'priced' = recently priced including same-day pricing; 'trading' = already-trading recent IPOs (last 6 months) with post-IPO performance; 'filing' = newly filed S-1 / F-1 registration statements (pre-roadshow); 'all' = every status.", "default": "all" }, "date_range": { "title": "Date range filter", "enum": [ "all", "last_30d", "last_90d", "last_180d", "upcoming" ], "type": "string", "description": "Filter by IPO date range. 'last_30d' = IPOs that priced in the last 30 calendar days; 'last_90d' = last 90 days; 'last_180d' = last 180 days (full lockup window); 'upcoming' = scheduled future IPOs only; 'all' = no date filter.", "default": "all" }, "min_proceeds_usd": { "title": "Minimum proceeds raised (USD)", "minimum": 0, "maximum": 100000000000, "type": "integer", "description": "Minimum total deal size (proceeds raised) in USD. Use 100000000 ($100M) to filter for institutional-size IPOs. Use 1000000000 ($1B) to filter for mega IPOs only. Set to 0 to include all sizes including micro-cap and SPAC deals.", "default": 0 }, "exchange": { "title": "Listing exchange filter", "enum": [ "all", "NYSE", "NASDAQ", "AMEX", "OTC" ], "type": "string", "description": "Filter by US listing exchange. NYSE includes large operational IPOs (Reddit, Birkenstock-tier). NASDAQ includes tech and biotech IPOs (most SPAC and biotech debuts). 'all' returns both exchanges and any AMEX / OTC listings.", "default": "all" }, "sector": { "title": "Sector filter", "enum": [ "all", "Technology", "Healthcare", "Financial Services", "Consumer Discretionary", "Consumer Staples", "Industrials", "Energy", "Real Estate", "Communication Services", "Utilities", "Materials" ], "type": "string", "description": "Filter by GICS-style sector. 'all' returns everything. IPO sectors skew heavily Technology and Healthcare (biotech). Useful for sector-rotation IPO strategies and theme-focused IPO arb desks.", "default": "all" }, "exclude_spacs": { "title": "Exclude SPACs / blank-check companies", "type": "boolean", "description": "If true, filters out Special Purpose Acquisition Companies (SPACs) — recognized by 'Acquisition Corp', 'Capital Acquisition', etc. in the company name. SPACs typically price at exactly $10 and add noise to operational IPO analytics.", "default": false } } }, "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 } } } } } } } } } } ```