Signal-Driven#130

Retrieval-Augmented Cross-Market Coherence Audit

A retrieval system (an AI that looks up and cites the actual market rules) builds a map of how markets logically relate: which ones are subsets, supersets, or can't both be true. An AI then checks whether their combined prices break the laws of probability, like P(A and B) priced higher than P(A) alone. You buy the underpriced leg and sell the overpriced one to capture the inconsistency.

What you need to run it

Embedding index over all market rule text
LLM logic-relation extractor (implication/exclusion)
Live multi-leg pricing + basket execution
Constraint-solver to size coherence trades

Where this applies

Markets on Polymarket where retrieval-augmented cross-market coherence audit is the natural play:

Will Democrats win both the House and the Senate in the 2028 elections?
Will Democrats win the House in the 2028 elections?
Will Bitcoin top $180k before the end of 2027?

Capabilities this demands

Model / quantData ingestionCustom code / APIMulti-venue

At a glance

CategorySignal-Driven

Requirements4

CapabilitiesModel / quant, Data ingestion, Custom code / API, Multi-venue

VenuePolymarket (CLOB, Polygon)

Build it

Related signal-driven 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.