How to Use an LLM to Analyze Prediction Markets

Can ChatGPT analyze prediction markets on its own?

It can reason about the underlying question. It cannot price it. A language model has no connection to a live book, so when you ask what a market is trading at, it produces the most plausible looking number instead of the real one. Plausible is the problem. A hallucinated 62 cents reads exactly like a real 62 cents.

Markets also resolve, get delisted and change ticker conventions, so a model working from memory will analyze a contract that settled months ago and attach a probability to it. Pasting a screenshot helps a little and then hurts: one midpoint says nothing about whether either side is quoted or how much size sits there, and the model will build a thesis on that snapshot.

What data does an LLM need to analyze a prediction market?

Not a price. A book. The minimum useful payload is both sides quoted with sizes, the resolution rule, the timestamp, and enough volume context to judge whether the quote means anything.

Emptiness clusters exactly where a model is most tempted. Under 5 cents, 41% of markets had no bid. At 25 to 50 cents, 0% had no bid. So the longshots that look like cheap lottery tickets are the ones most likely to have nobody on the other side, and the mechanics compound it: on a Kalshi binary, NO_ask = 100 - YES_bid, so a zero YES bid means the NO leg costs 100 cents. A model pricing off the midpoint, not the ask, calls that a high probability trade at an attractive price. It is a trade with no counterparty.

Width varies by market type. The widest books measured were scalar economic markets, KXUSCPIYEAR at 57 cents, where a 3 cent model edge is text rather than analysis.

How do you connect an LLM to prediction market data?

Two routes, depending on where the model lives. If it runs in an MCP capable client, point it at the Predictefy MCP server. Otherwise call the reads API over HTTP and put the response in the context yourself.

npx -y @predictefy/mcp

For a hand rolled pipeline, the reads API is a flat, predictable shape: /api/{venue}/{verb} against https://data.predictefy.com, with a bearer key.

curl -s -H "Authorization: Bearer pk_live_..." "https://data.predictefy.com/api/kalshi/{verb}"

Whichever route you take, the rule is the same. The model never types a price. It receives one, quotes it back, and reasons on top of it.

How do you stop an AI from hallucinating market prices?

You cannot talk a model out of hallucinating. You can make hallucination structurally obvious. Four rules do most of the work.

Require provenance on every number. Any price in the output must carry venue, ticker, side, size and the timestamp of the tool response that produced it. A number without those fields is a hallucination by definition, and a regular expression can catch it.

Make "no bid" a first class answer. Models fill silence. If a tool returns an empty side, the correct output is that the market is not tradable at any price shown, not a midpoint. With 18.1% of markets showing no bid, this branch fires on roughly one market in five.

Give it the spread and force a comparison. Ask for the estimated edge and the current spread side by side in cents, and require the model to say when the edge is smaller than the spread. On a median 5.1 cent Kalshi book, most model generated edges lose that comparison.

Refetch before any decision. Analysis written five minutes ago describes a book that may no longer exist. Put the last tool call immediately before the recommendation, not at the start of a long chain of reasoning.

Can an AI place trades on prediction markets?

Technically yes, which is why the boundary matters more than the analysis. With Predictefy that boundary is documented rather than assumed: no tool both builds and submits an order, so opening a position always takes a second, deliberate call.

Execution is also a separate, isolated, non-custodial service at its own origin, not the reads API. Its routes sit under /v1/exec/{venue}/orders/... for build, submit, cancel, modify and refresh. Orders are signed client side, and there is no generic server-side signing route.

curl -X POST -H "Authorization: Bearer $PREDICTEFY_KEY" -H "Idempotency-Key: 6f1c9a2e-..." "https://EXEC_ORIGIN/v1/exec/kalshi/orders/build"

The practical pattern: the model reads, qualifies and drafts, then a separate program or a person signs and submits. That split lets you run an aggressive analyst without running an aggressive trader.

How do you prompt an LLM to analyze a prediction market?

Ask for a filled form, not an opinion. Free prose invites narration; a fixed schema forces the model to admit what it does not have. Ask for, in order: venue and ticker, quote timestamp, YES bid and ask with sizes, spread in cents, 24 hour volume with its unit named, whether both sides are quoted, the model's own probability with its reasoning, the gap between that probability and the current ask, and whether the gap survives the spread.

Two guardrails belong in the same prompt. Name the unit every time, because Kalshi volume is contracts and Polymarket volume is US dollars, and an unstopped model will divide one by the other. And require the resolution rule to be answered before the probability, since most confident wrong analysis is a correct read of a question the market is not asking.

What do LLMs get wrong about prediction markets?

Live data fixes the price problem, not the judgment problem. Three failure modes survive.

It assumes short windows are liquid. Polymarket runs 5 minute, 15 minute and hourly Bitcoin windows, the 5 minute heading reading "BTC Up or Down 5m", with five minute assets covering BTC, ETH, SOL, XRP and HYPE. Five minute traded volumes seen were 18 USD, 78 USD and 229 USD, many at 0 USD, while hourly volumes were far larger at 8.4K, 10.1K, 12.5K and 46.6K USD. A model told to find fast crypto trades points at the emptiest part of the board.

It invents venue features. Ask for a 5 minute Kalshi crypto market and a model will describe one. None of Kalshi's 274 crypto series runs shorter than 15 minutes. So the venue field has to come from a tool response, not from memory.

It overstates the history it has. Stored history is proven for 11 venues, not all 17, so a backtest framed as covering every venue claims more than the data supports. Name the covered set in the prompt.

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