Kalshi Node

The Kalshi node fetches live prediction market data — market titles, executable bid/ask prices, volume, and open interest — from Kalshi ↗, the CFTC-regulated US prediction market exchange. Use it to monitor event probabilities, alert on price moves, or feed market sentiment into an LLM for analysis.

What Is Kalshi?

Kalshi is a regulated exchange where people trade on the outcomes of real-world events — Fed rate decisions, elections, economic data, weather, and more. Each market resolves YES or NO, and contract prices between 0 and 1 represent the crowd's estimated probability. A YES price of 0.72 means the market thinks there's a 72% chance the event happens.

Configuration

Finding markets (discovery flow)

Kalshi has no free-text market search — discovery is topic → series → events → markets. The seamless path:

1. discover with your topic (world cup) → returns candidate series (KXWCGAME = game winner, KXWCGOAL = player goalscorer). Player props live in a different series than game winners.
2. events with seriesTicker: KXWCGAME (and optional query: "Portugal") → returns the games with their markets inlined — no need to hand-build event/leg tickers.
3. lookup any exact market ticker for the live orderbook.

search is a shortcut that does step 1+2 in one call and returns both the markets and the resolved series (so an empty market list still tells you which series to drill into). | Status | open (default), settled, or all. | | Limit | Number of items to return. Default: 10. Max: 200. | | Include Orderbook | Fetch live bid/ask depth for the first 10 returned markets. Lookup mode always includes the orderbook. | | Description | Optional note describing what this data fetch is for. |

Bid, Ask, and Mid — Not Last Price

Every market returns the executable top of book, normalized to 0–1 probabilities:

Empty sides of the book are null rather than 0 or 1: if nobody is bidding, yesBid is null and so are yesMid and spread. Check for null before comparing prices.

Using Kalshi in a Workflow

Probability Alert Monitor

Check a Fed rate market on a schedule and alert when the bid crosses a threshold.

Sentiment-Driven Analysis

Feed Kalshi markets into an LLM to assess whether current prices represent value.

Example LLM prompt:

User: Evaluate this Kalshi market:
Title: {{kalshi.markets[0].title}}
Yes bid/ask: {{kalshi.markets[0].yesBid}} / {{kalshi.markets[0].yesAsk}}
Spread: {{kalshi.markets[0].spread}}
Volume: {{kalshi.markets[0].volume}}

Cross-Venue Comparison

Pair the Kalshi node with the Polymarket node to compare prices for the same real-world event on both venues. Both nodes output 0–1 probabilities, so prices are directly comparable.

Output

Example Output

{
  "markets": [
    {
      "ticker": "KXFED-26JUN-T4.00",
      "eventTicker": "KXFED-26JUN",
      "title": "Fed funds rate above 4.00% after June meeting?",
      "subtitle": "Above 4.00%",
      "status": "active",
      "yesBid": 0.17,
      "yesAsk": 0.19,
      "yesMid": 0.18,
      "noBid": 0.81,
      "noAsk": 0.83,
      "noMid": 0.82,
      "lastPrice": 0.23,
      "spread": 0.02,
      "volume": 48210,
      "volume24h": 1830,
      "openInterest": 12044,
      "closeTime": "2026-06-17T18:00:00Z"
    }
  ],
  "count": 1,
  "query": "KXFED-26JUN-T4.00",
  "mode": "lookup"
}

Next Steps

• Polymarket Node — Compare event prices across prediction venues.
• LLM Node — Feed market prices into an AI model for probability analysis.
• Conditional Node — Route your workflow based on bid/ask thresholds.
• Storage Node — Track probability changes over time between workflow runs.