Working With Kalshi's Public Market Data

Public market data from an event exchange is more approachable than most financial data. It is small, it is well structured, and the questions it answers are human-readable. That makes it a good place to learn how to build a data pipeline properly.

This page is about the shape of the work. It is not about authenticated endpoints, and nothing here involves an account: this site reads public data only and never handles exchange credentials.

What public market data typically gives you

A market list. Every listed contract with its ticker, its title, its status, its strike and its close time. This is the spine of everything else.

Current pricing. Best bid, best offer, last trade, and usually volume and open interest. Enough to compute a mid and a spread. Volume versus open interest explains why those two are not interchangeable.

Order book depth. Resting size at each price level, where exposed. This is what lets you estimate slippage rather than guess at it.

Trade history. Executed prices and sizes with timestamps.

Settlement results. How resolved markets ended, which is the raw material for any calibration work.

The three things beginners get wrong

Polling everything, constantly. A full market list is large and mostly static. The prices on markets nobody is trading do not change. Pull the list occasionally, pull prices for the subset you care about frequently, and back off when nothing is moving. Rate limits and polling is the whole discipline in one page.

Treating last trade as the price. It is a historical fact, not a quote. A dataset built on last-trade values will be full of prices from days ago presented as current. Store bid, offer and last separately and always know which one a chart is showing.

Storing every tick forever. You will accumulate an enormous amount of data that mostly says "nothing happened". Sample sensibly, keep the full resolution only around events, and compress aggressively. Storing time series cheaply covers the patterns.

Timestamps are the hard part

Every record needs to carry the time the exchange observed it, not the time you fetched it. Those two differ by your polling interval, and if you compare a Kalshi price captured at one moment with another venue's price captured ninety seconds later, the difference you measure includes ninety seconds of drift.

For cross-venue work this is the single largest source of phantom opportunity. Stale quotes and timing is about detecting it, and detecting stale data is about building the alarm.

Be a good client

Identify your client honestly in a user agent. Respect documented rate limits and back off on errors rather than retrying in a tight loop. Cache anything that does not change. Do not scrape pages when an endpoint exists.

None of this is legal advice and terms of service change; read the current ones. The general principle stands regardless: a data pipeline that behaves well keeps working, and one that does not eventually stops.

What to build first

Not a trading system. Build a recorder: something that captures a small set of markets at a sensible interval and writes them down with honest timestamps. Run it for a month. Then look at what you have and see whether the thing you assumed was true actually is.

That order — record, then look, then model — is the one that saves the most wasted effort. Backtesting honestly explains what goes wrong when you skip the first two steps.

Next

What the CFTC licence means for the venue and for you.