Quantitative#094

Particle-Filter Nowcasting for Live Events

During a live event (a game, a vote count), use a particle filter — a method that keeps many guesses about the true state alive at once and reweights them as each new piece of noisy data arrives — to continuously estimate the real win probability, then trade the gap to the order book before it catches up. The edge is that this approach handles messy, partial live data better than the crowd's rough manual updates, and you act on it faster.

What you need to run it

Streaming partial-observation feed for the live event
Particle-filter/state-space model of the underlying process
Sub-minute repricing and order placement loop
Latency edge over manual traders on the same feed

Where this applies

Markets on Polymarket where particle-filter nowcasting for live events is the natural play:

Will [team] win tonight's NFL game? (in-game live market)
Will [candidate] win the 2026 Pennsylvania Senate seat? (live on election night)
Will [favorite] win the next Grand Slam tennis match? (live, point-by-point)

Capabilities this demands

Model / quantFeed ingestionLow latencyCustom code / API

At a glance

CategoryQuantitative

Requirements4

CapabilitiesModel / quant, Feed ingestion, Low latency, Custom code / API

VenuePolymarket (CLOB, Polygon)

Build it

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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 (297 strategies) or the data feeds directory.