Quantitative#091

Bayesian Sequential-Update Probability Engine

Keep a running probability estimate for a market and formally update it (Bayesian updating — revise your belief by a set amount each time new evidence arrives) with every poll, stat, or on-chain signal, weighting each by how informative it is. When your updated estimate moves far enough from the market price, you trade. The edge is integrating many different kinds of evidence instantly and rigorously, while the market absorbs them piecemeal and slowly.

What you need to run it

Prior elicitation + likelihood models per evidence type
Streaming evidence feeds normalized to update events
Posterior engine with credible-interval-based triggers
Position sizing tied to posterior-vs-market divergence

Where this applies

Markets on Polymarket where bayesian sequential-update probability engine is the natural play:

Will the GOP win the 2026 Georgia Senate race?
Will the Fed cut rates at the next FOMC meeting?
Will Ethereum close above $5,000 on Dec 31, 2026?

Capabilities this demands

Model / quantData ingestionFeed ingestionDomain knowledge

At a glance

CategoryQuantitative

Requirements4

CapabilitiesModel / quant, Data ingestion, Feed ingestion, Domain knowledge

VenuePolymarket (CLOB, Polygon)

Build it

Related quantitative strategies

This is documentation, not advice. Poly Research & Robotics publishes how these strategies work because the method should be checkable — not as a recommendation to trade them. See the full strategy database (147 strategies) or the data resources directory.