Bridging the NLP Gap: How I Built a Thesis-to-Trade Translator for Prediction Markets

Bridging the NLP Gap: How I Built a Thesis-to-Trade Translator for Prediction Markets Prediction markets like Kalshi are genuinely interesting instruments. Unlike traditional financial markets, they price discrete binary outcomes — will the Fed cut rates in Q2? Will CPI come in above 3%? The contracts are clean, the payoffs are defined, and the information embedded in prices is surprisingly rich. But there's a UX problem that I kept bumping into as someone who actually trades on these platforms: the gap between forming a thesis and executing a structured trade is large, and it's filled with friction . I'd read a Fed statement, form a clear opinion — "the market is pricing in 60% odds of a May cut and that's too high given the language around persistent services inflation" — and then stare at Kalshi trying to figure out exactly how to express that as a trade. Which contract? What direction? How much? When do I exit? The Problem Is Translation, Not Intelligence This isn't really an intel