Polymarket Trading Bot Strategies: Bayesian Edge



A prediction-market price is not a price. It's a probability the crowd agreed on a moment ago. The second real news arrives, that number is wrong - not forever, just for the few minutes it takes everyone else to finish the same math a Polymarket trading bot can run in milliseconds. When a market says an outcome trades at 38 cents, it's telling you the crowd's probability for that outcome is about 38%. On Polymarket, the price and the probability are the same object. Every price on the order book is a snapshot of a belief - and unlike a stock chart, the correct way to update that belief when new information arrives isn't a matter of opinion. It's a matter of math. That math is Bayes' theorem, written down in 1763: P(H|E) = P(E|H) × P(H) / P(E) Your prior P(H) is the current market price. The likelihood P(E|H) is how likely the news would be if the outcome were actually going to happen. P(E) is how likely the news was in general. The posterior P(H|E) is the updated probability - the number your bot is trying to find. In practice, quant desks skip the clumsy denominator and work in odds instead: posterior odds = prior odds × likelihood ratio A market at 30¢ with prior odds ≈ 0.43 and a likelihood ratio of 3 (news three times more consistent with the outcome happening than not) updates to posterior odds of 1.29 - roughly 56% probability. That's a 26-point gap between market price and fair value, appearing the instant the news lands. A bot doesn't need the price to prove it right; it only needs to be positioned before the crowd finishes the same calculation. Turning this into a bot, in four steps: - Let the market price be your prior - never substitute a gut feeling for the crowd's number - Price the evidence, not the emotion - assign an honest likelihood ratio to each piece of news - Update, compare, and only trade a real gap - most news isn't worth trading after fees - Respect that the edge decays as the crowd catches up - the entire opportunity lives in the window between news landing and the crowd repricing Where this breaks: double-counting evidence already priced in, treating five outlets reporting the same leak as five separate confirmations instead of one, and feeding the formula miscalibrated likelihoods that produce confidently wrong posteriors. Full open-source implementation (five parallel strategies, risk controls, signal ranking): github.com/casatrick/polymarket-arbitrage-bot-python

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