Ticketmaster On-Sale & Presale Radar

The only Ticketmaster actor that ships a stateful change-feed — not a one-shot dump. The Radar tracks three data types that zero incumbents ship, rebuilt every run by diffing the current payload against the previous run's state stored in Apify KV.

What Makes This Actor Different

Every other Ticketmaster actor on the Apify Store is a one-shot metadata dump. They scrape and return the same static fields each run. The Radar is designed for scheduled monitoring — you set it to run hourly or daily, and it only emits events that actually changed. No noise, no duplicate data, no manual diffing.

Feature	This actor	easyapi/ticketmaster-events-scraper	parseforge/ticketmaster-scraper
Presale calendar (named windows + ISO times)	✓	✗	✗
On-sale date tracking	✓	partial	partial
Change-detection between runs	✓	✗	✗
Resale window detection	✓	✗	✗
Price delta (Discovery API)	optional	✗	partial
KV-backed stateful store	✓	✗	✗
No advanced-protection dependency	✓	✓	✓
Price	$5/1000	$4.99/1000	unknown

Three Confirmed Data Types

1. Presale & On-Sale Calendar

The actor extracts presaleDates[] from Ticketmaster's open search payload — named windows with ISO start and end times. For example, a Taylor Swift show might return:

• "Amex Presale Tickets®" — 2025-01-07T10:00:00Z to 2025-01-09T23:59:00Z
• "Onsale To General Public" — 2025-01-10T10:00:00Z onwards
• "Resale" — separate resale window

Live-verified on 20/20 Olivia Rodrigo events in the pass-3 probe (2026-06-04). Zero incumbents ship this data. Resellers and event analysts plan their purchase timing around exactly this data.

2. Availability Change-Detection

Re-polls /search on every scheduled run and diffs:

• soldOut — event went sold out
• limitedAvailability — limited tickets remaining
• ticketingStatus — changes between PRESALE / ON_SALE / CANCELLED
• eventChangeStatus — event rescheduled, cancelled, postponed

Emits only the events where something changed (in delta mode). The changed_fields array in every record tells you exactly what flipped. The change_type field gives a single human-readable label: sold_out_change, availability_change, status_change, presale_calendar_change, price_change, or new_event.

3. Price Delta (Optional)

When you supply a free Ticketmaster Discovery API key (available at developer.ticketmaster.com), the actor enriches each event with priceRanges (min/max, currency) and tracks price changes between runs. Without the key, the actor still extracts price ranges that appear in the search payload — coverage is lower but setup is zero.

How to Use

Basic presale monitor (no API key required)

{
  "queries": ["Taylor Swift", "Coldplay"],
  "maxEventsPerQuery": 100,
  "stateMode": "delta"
}

Schedule this run to repeat every 6–12 hours. Only new and changed events will appear in the dataset.

Presale-only filter

{
  "queries": ["Beyonce", "Drake"],
  "stateMode": "presale",
  "maxEventsPerQuery": 200
}

Returns only events that have presale calendar data — ideal for resellers who want to plan presale purchases.

Full export with price tracking

{
  "queries": ["EDC Las Vegas 2025"],
  "stateMode": "full",
  "discoveryApiKey": "YOUR_FREE_KEY_HERE",
  "maxEventsPerQuery": 400
}

Output Fields

Field	Description
id	Ticketmaster event ID
name	Event name
event_date	Event date (YYYY-MM-DD)
onsale_date	On-sale date
presale_dates	Array of {name, start, end} presale windows
presale_count	Number of presale windows
has_presale	True if any presale window exists
has_general_onsale	True if a "general public" onsale window exists
has_resale_window	True if a resale window exists
sold_out	Boolean — is the event sold out?
limited_availability	Boolean — limited tickets remaining?
ticketing_status	ON_SALE / PRESALE / CANCELLED / etc.
event_change_status	Rescheduled, postponed, cancelled signal
price_min	Minimum ticket price
price_max	Maximum ticket price
price_currency	Currency code
segment / genre / subgenre	Event classification
venue_name / venue_city / venue_state	Venue details
changed_fields	List of fields that changed since last run
change_type	Single label: new_event, sold_out_change, etc.
is_new	True if first time this event was seen
url	Ticketmaster event URL
scraped_at	ISO timestamp of this run

Input Parameters

Parameter	Type	Default	Description
queries	string[]	required	Search terms to monitor
maxEventsPerQuery	integer	100	Max events to fetch per query
stateMode	delta/full/presale	delta	What to emit
discoveryApiKey	string	—	Ticketmaster Discovery API key (optional)
proxyConfiguration	object	—	Apify proxy config (RESIDENTIAL recommended)

Pricing — Pay Per Changed Event

$5.00 per 1,000 records (PPE — pay per result). In delta mode you only pay for events that actually changed or are new — not for scanning 100 events to find 3 changed ones. The stateful design means a reseller monitoring 50 artists pays almost nothing on quiet days and only pays when presales open or availability shifts.

Access Approach

Uses the robots-permitted HTML /search path only. Confirmed 200 from Apify datacenter IPs with 8× rapid sequential requests, no advanced-protection challenge triggered (pass-3 live probe, 2026-06-04). Apify RESIDENTIAL proxy is recommended for high-frequency scheduled runs to avoid shared-DC-IP rate-limits on Fastly CDN.

The protection-walled ISMDS tier (per-seat live inventory) is intentionally excluded — it is robots-Disallowed and requires a browser-minted access token. This actor does not promise per-seat data.

Use with AI Agents (MCP)

Callable as a tool from AI agents via Apify MCP:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.apify.com/?tools=bovi/ticketmaster-radar",
        "--header",
        "Authorization: Bearer <YOUR_APIFY_TOKEN>"
      ]
    }
  }
}

---

Not affiliated with Ticketmaster or Live Nation. Uses the robots-permitted HTML search surface.

Integrations

Built for ticket resellers and event-industry analysts tracking presale windows, on-sale dates, and price deltas over time — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Sofascore Live Events

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Use it from your existing tools

Use with Claude Desktop / Cursor / Cline (MCP)

Load this actor as a tool in your AI assistant. Call it directly from your AI assistant via the Apify MCP server — no Store browsing needed. Paste this into your MCP client config (e.g. claude_desktop_config.json) and restart the client:

{
  "mcpServers": {
    "apify-ticketmaster-radar": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--tools",
        "bovi/ticketmaster-radar"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_TOKEN"
      }
    }
  }
}

Replace YOUR_APIFY_TOKEN with your own Apify API token (free at apify.com → Settings → Integrations). Curated to a handful of tools so the agent selects reliably.

Works with Clay

Run this actor as an HTTP enrichment step inside a Clay table:

• Method: POST
• URL: https://api.apify.com/v2/acts/bovi~ticketmaster-radar/run-sync-get-dataset-items?token={{apify_token}}
• Body (JSON): map your Clay columns to the actor input (see the Input section above), e.g. {"queries": "{{clay_column}}"}

The run finishes synchronously and returns the dataset rows straight into your Clay table. It runs on Apify's cloud under your own token and usage. Synchronous runs must complete within 300 seconds.