Prediction Market Odds Scraper – Kalshi + Polymarket

Get Kalshi and Polymarket prediction-market odds in one call and one schema: every market's YES probability, bid and ask, spread, lifetime and 24 h volume, liquidity, close time, category and, on request, the live order book, with a platform column so rows from both exchanges sit side by side. Built for traders comparing venues, researchers building cross-platform datasets, AI agents that need one tool for "what are the odds on X" and dashboards that track both exchanges without maintaining two integrations. Plain HTTPS calls to Kalshi's and Polymarket's public APIs with retries and a silent-failure check; one exchange having a bad day never stops the other.

What you get

• One row per market, same columns on both exchanges – platform, id, title, page link, yes_prob (0–1), YES/NO bid and ask, spread, volume_usd, volume_24h_usd, liquidity_usd, open interest, ISO close time, status, category, both outcomes with prices, the parent event and the scrape timestamp. Compare a Fed-decision market on Kalshi against its Polymarket twin without translating field names.
• Order books on demand – the top N price levels on the YES and the NO side as [price, size] pairs from Kalshi's order book endpoint and Polymarket's CLOB, read at the same moment as the row.
• Filters that work the same everywhere – a search term, a status (active, closed, all), a minimum volume, a sort order (24 h volume, volume, liquidity, close time) and a per-platform row cap, so a scheduled run costs the same every time.

Sample rows

Two rows from one run with includeOrderbook: true and orderbookDepth: 3, one per platform.

{
  "platform": "kalshi",
  "market_id": "KXAAAGASD-26SEP10-4.2400",
  "ticker_or_slug": "KXAAAGASD-26SEP10-4.2400",
  "title": "Will average **gas prices** be above $4.2400?",
  "url": "https://kalshi.com/markets/kxaaagasd",
  "yes_prob": 0.99,
  "yes_bid": 0.98,
  "yes_ask": 1.0,
  "no_bid": 0.0,
  "no_ask": 0.02,
  "spread": 0.02,
  "volume_usd": 8568.64,
  "volume_24h_usd": 8568.64,
  "liquidity_usd": 0.0,
  "open_interest": 8094.64,
  "close_time": "2026-09-10T03:59:00Z",
  "status": "active",
  "category": "Economics",
  "outcomes": [{ "name": "Yes", "price": 0.99 }, { "name": "No", "price": 0.01 }],
  "source_event": "KXAAAGASD-26SEP10",
  "scraped_at": "2026-09-09T15:21:16+00:00",
  "orderbook": { "yes": [[0.96, 18.91], [0.97, 872.34], [0.98, 2000.0]], "no": [] }
}

{
  "platform": "polymarket",
  "market_id": "2252244",
  "ticker_or_slug": "will-there-be-no-change-in-fed-interest-rates-after-the-september-2026-meeting-615",
  "title": "Will there be no change in Fed interest rates after the September 2026 meeting?",
  "url": "https://polymarket.com/event/fed-decision-in-september-762/will-there-be-no-change-in-fed-interest-rates-after-the-september-2026-meeting-615",
  "yes_prob": 0.465,
  "yes_bid": 0.46,
  "yes_ask": 0.47,
  "no_bid": 0.53,
  "no_ask": 0.54,
  "spread": 0.01,
  "volume_usd": 25074718.13,
  "volume_24h_usd": 690706.29,
  "liquidity_usd": 408917.26,
  "open_interest": null,
  "close_time": "2026-09-16T00:00:00Z",
  "status": "active",
  "category": "Politics",
  "outcomes": [{ "name": "Yes", "price": 0.465 }, { "name": "No", "price": 0.535 }],
  "source_event": "fed-decision-in-september-762",
  "scraped_at": "2026-09-09T15:21:16+00:00",
  "orderbook": {
    "yes": [[0.44, 47928.09], [0.45, 28019.27], [0.46, 41567.22]],
    "no": [[0.51, 90932.08], [0.52, 100092.28], [0.53, 129408.82]]
  }
}

How the columns map

Unified column	Kalshi	Polymarket
market_id, ticker_or_slug	market ticker	Gamma market id, market slug
title	title	question
url	kalshi.com/markets/<series>	polymarket.com/event/<event>/<market>
yes_prob	last trade price in dollars (0 until the first trade; use bid/ask then)	price of the first outcome (the YES side, or the first named side on sports markets)
yes_bid, yes_ask	yes_bid_dollars, yes_ask_dollars	bestBid, bestAsk
no_bid, no_ask	no_bid_dollars, no_ask_dollars	1 - bestAsk, 1 - bestBid
spread	yes_ask - yes_bid	spread
volume_usd, volume_24h_usd	contracts traded (each settles at $1)	USD
liquidity_usd	liquidity_dollars	liquidityNum
open_interest	open contracts	null
close_time	close_time	endDate
status	active / closed / resolved (settled)	active / closed / resolved (an outcome priced 1)
category	series category (Sports, Politics, Economics, Climate and Weather…)	top-level event tag (Politics, Sports, Crypto, Geopolitics…)
outcomes	Yes / No from the last price	both outcome names and prices
source_event	event ticker	event slug
orderbook.yes / .no	YES bids / NO bids, best level last	YES bids / NO bids (mirrored from the CLOB asks), best level last

Rows carry the unified columns only. If you need every raw field of one exchange (rules text, sub-titles, strike values, token ids, tags), use the single-platform Kalshi Markets Scraper or Polymarket Markets Scraper.

Filters

Input	Default	What it does
platforms	["kalshi", "polymarket"]	Which exchanges to scrape. Each is scraped independently with the same settings.
searchTerm	""	Keep only markets whose title contains this text (case-insensitive). On Kalshi the ticker is searched too, so NFL finds the KXNFLGAME markets.
status	active	active (trading now), closed (Kalshi closed + settled, Polymarket closed) or all.
minVolume	0	Skip markets whose lifetime volume_usd is below this number.
sortBy	volume_24h	volume_24h, volume, liquidity (highest first), close_time (soonest first) or none.
maxMarketsPerPlatform	100	Rows kept per platform after filtering and sorting.
includeOrderbook	false	Fetch the live YES/NO order book for every row.
orderbookDepth	10	Price levels per side (1–100).

Kalshi's API lists markets in no useful order, so a Kalshi run without a search term ranks a pool of up to 3,000 open markets by sortBy; with a search term it walks the whole exchange (up to 50,000 markets, about 15 seconds) so rare terms are found. Polymarket is sorted server-side and cut at maxMarketsPerPlatform.

Pricing

Pay per event, no subscription: $1.50 per 1,000 markets and $2 per 1,000 order books. A default run (100 markets per platform, no order books) costs $0.30; the same run with order books costs $0.70. Set a maximum charge on the run and the Actor stops cleanly when it is reached, charging only for rows that were actually saved.

Use it from your tools

• API and SDKs – call it via the Apify API or the official Python/JavaScript clients: one call to start the run, one to fetch the merged dataset as JSON or CSV.
• Schedules – run it hourly or daily inside Apify and push new odds to Google Sheets, a webhook or your own storage automatically.
• n8n, Make and Zapier – trigger runs and pipe cross-platform odds into a workflow through Apify's integration for each.
• AI agents and MCP – this Actor is eligible for agentic use via Apify's MCP server and supports pay-per-event pricing, so an agent can call it mid-task and pay only per market it actually reads.
• Webhooks – fire on run finished to kick off the next step in a pipeline as soon as fresh odds land.

Details

• Sources: Kalshi's public trade API v2 (api.elections.kalshi.com) and Polymarket's public Gamma and CLOB APIs (gamma-api.polymarket.com, clob.polymarket.com). No authentication, no proxies, no browser, no personal data (Polymarket wallet addresses are never output).
• Freshness: scraped_at is the UTC timestamp of the run. Kalshi prices are read live. Polymarket Gamma prices come from a CDN that caches for up to 5 minutes; order books are read live from the CLOB, so turn them on when you need the exact current bid and ask.
• Reliability: 429 and 5xx responses are retried with exponential backoff (5 tries) and a 200 without the expected data counts as a failure. Each platform runs on its own: if one fails, the run pushes an error row (platform, error, errorMessage) and continues with the other. A run fails only when it produced no rows and hit errors; a filter with no matching markets is a successful, empty run.
• Run stats: the STATS record in the run's key-value store holds market, order-book, request and error counts per platform and per category (network, rate_limit, blocked, not_found, other).
• Speed: a default run finishes in about 10 seconds (five API calls). Order books add roughly a quarter of a second per market.
• Output: one dataset row per market. The Overview table shows platform, title, YES probability, 24 h volume, close time and link. Export as JSON, CSV or Excel, fetch through the Apify API, or schedule runs and pipe them into Google Sheets, Make, Zapier or your own code.