I Built a Prediction Market Insider Trading Detector in One Afternoon (Because George Santos)

This morning I woke up to news that George Santos is under federal investigation for insider trading on Kalshi. By end of day I had a working anomaly detector monitoring 410 politically-sensitive prediction markets with a live dashboard.

Here's how it happened, what I found, and how you can run it yourself.

What Santos Actually Did

The short version is almost embarrassingly simple. The day before Trump's State of the Union address, Santos posted a video on X saying he'd be in the gallery. Traders on Kalshi piled into "yes he'll attend" contracts and the odds shot up.

What Santos didn't mention was that he'd already placed bets that he wouldn't attend. When he posted "Watching SOTU from an airport tv was not part of the plan! FML" while Trump was speaking, those odds cratered and Santos walked away with tens of thousands of dollars.

Kalshi caught it because they have internal account data. They knew which account placed those trades. They froze it, referred the matter to the DOJ and the CFTC, and here we are.

The thing that stuck with me reading the story was a simpler question: what could you see from the outside?

The Public API Question

Kalshi has a solid public REST API. No auth required for market data. I'd poked at it before for other projects, so I knew the shape of it. The question was whether the historical trade data was rich enough to detect the Santos pattern after the fact.

The Santos pattern, stripped down, is:

1. Someone with private knowledge establishes a position


2. A public statement moves the market significantly


3. Their position was already on the right side of that move

You can't see who placed trades from the public API. But you can see that unusual volume concentration happened before a market moved. That's the tipping-point signal -- not proof of anything, but a flag worth examining.

So I started pulling on the thread.

What I Found in the API

After some exploration (the historical endpoint pagination is a bit of an adventure), I found that Kalshi's public API exposes:

• Full trade history per market -- ticker, price, contract size, timestamp, block trade flag


• Candlestick data at 1-minute, 1-hour, and 1-day intervals


• Market metadata including series, category, open/close time, volume

What it doesn't expose: user identity, order placement timestamps (only fill time), or anything that would let you name a specific trader.

The Santos SOTU markets themselves turned out to be inaccessible -- they were structured as multivariate markets with a different ticker format that doesn't surface cleanly through the standard search. But that's fine. The Santos case is the argument for why this tool should exist, not the data it runs on.

What I found instead was more interesting.

The Watchlist

I spent about an hour mapping out which Kalshi series carry genuine MNPI risk -- markets where someone with access to non-public government information would have a meaningful trading edge.

The list got long fast:

Executive Actions -- Cabinet departures (KXCABOUT), next AG (KXNEXTAG), next SecDef, next DNI, pardons, insurrection act invocation, martial law. Anyone in the White House personnel office or inner circle knows these things before markets do.

SCOTUS -- resignation markets, court size change, next justice confirmation. Clerks and justices themselves know when a retirement is coming.

Economic Data -- Fed rate decisions, CPI, GDP. BLS and BEA staff have the data weeks before release.

Geopolitical -- Greenland acquisition, Panama Canal, Taiwan recognition, Zelensky-Putin talks. NSC and State Department staff work these negotiations in real time.

Congressional -- impeachment, house/senate control, government shutdown, veto override. Congressional whips know vote counts before anyone else.

That's 33 series, 410 open markets, and a combined trade history of over 123,000 records from just the first collection run.

The Anomaly Scorer

With data flowing in, I built a three-signal scorer:

Volume Z-Score -- compares recent trade volume against a rolling baseline. A spike of 8+ standard deviations above normal is a strong signal. This is the Santos pattern in statistical form: someone piling into a position before a market-moving event.

Block Trade Ratio -- the API flags block trades (large privately-negotiated contracts) separately. Heavy block trade concentration on a political market before an announcement is suspicious in a way that retail chatter isn't.

Price Divergence -- detects sudden directional price movement inconsistent with gradual drift. The signature of a market that already knows something.

The compound score combines all three with a block trade modifier. Thresholds: yellow at 25, red at 60.

What Flagged on Day One

. Issues and PRs welcome. If you work in fintech, policy, or prediction markets and want to talk about where this goes, I'm easy to find.

Built June 3, 2026. The same day the Santos story broke. Sometimes timing is everything.