Your next event prediction model may be powered by Inventzia SciTech

Prediction markets are evolving into one of the most interesting intersections of finance, data engineering, real-time information systems, and artificial intelligence. At Inventzia, we have been developing an automated application layer that connects directly to Polymarket and Kalshi APIs to support systematic market-making, signal discovery, and data-driven trading research across prediction market categories.

The result is a highly automated infrastructure stack designed to ingest market data, analyze participant behavior, identify pricing inefficiencies, and support execution workflows in fast-moving event markets.

Our application integrates directly with prediction market venues through their public APIs, continuously collecting and normalizing market-level, order-book, pricing, volume, and participant data. Rather than treating these markets as static betting boards, we model them as dynamic financial microstructures. Every market has its own liquidity profile, spread behavior, volatility regime, time-to-resolution curve, and trader ecosystem.

The trader ecosystem is among the most interesting to observe. One of the most interesting data pipelines we have built focuses on trader scouting. The objective is simple: identify participants who appear to have repeatable edge.

The implementation is more complex. The system tracks resolved markets, participant histories, category exposure, bet frequency, position timing, realized outcomes, and win/loss consistency. It then segments traders by category and evaluates performance over meaningful streams of bets rather than isolated one-off wins.

What we have found is striking. Across many prediction market categories, there are traders who show win rates above 95% over streams of tens of bets. These are not necessarily the most visible participants by volume. Some appear to specialize narrowly, focusing on specific domains where they may have superior information processing, better domain models. The highest-performing traders often appear highly category-specific. They are not universally good at everything. Instead, they seem to operate like specialized signal processors: strong in politics, sports, crypto, economic releases, entertainment, or other defined segments. One category stands out: weather.

In most categories, we observe pockets of extremely high win rates among top-performing traders. In weather markets, however, even the strongest predictors appear unable to sustain the same level of dominance. The best observed performers tend to top out closer to the 70% win-rate range, far below the 95%+ patterns visible elsewhere.

This is an interesting finding. Weather markets may be harder because they are linked to noisy physical systems, rapidly updating forecasts, model dispersion, local measurement details, and highly specific resolution criteria. Even when participants use sophisticated forecasting tools, the underlying uncertainty remains substantial.

Or, to put it less formally: if anyone had an unfair weather crystal ball, the data is doing a surprisingly good job of hiding it.