A prediction market orderbook works the same way as orderbooks in traditional financial markets: traders place limit orders to buy or sell outcome shares at specific prices. The orderbook displays these orders in real time, revealing where traders agree or disagree about the likelihood of an event. As orders match, trades occur and the market probability updates.
Platforms like Kalshi and Polymarket (in their orderbook-based markets) use orderbooks to set prices through supply and demand rather than automated curves. Myriad and Manifold also incorporate orderbook-style mechanics in certain contexts, allowing users to post bids or offers directly. Orderbooks provide a more granular picture of trader intent, showing not only the current probability but also how much liquidity exists at each price level. This structure produces detailed prediction markets data that reveals market sentiment with exceptional transparency.
Orderbooks are especially useful in high-volume or high-stakes events where traders want more control over price execution. They allow participants to see where large interest clusters form and how aggressively others are willing to trade.
Prediction market orderbooks show the underlying structure of price formation. They help traders understand liquidity, price pressure, and sentiment, creating clearer and more reliable prediction markets data.
Orderbooks are used because they provide precise, trader-driven pricing. Instead of relying on an automated function, prices reflect real supply and demand for outcome shares. This transparency helps traders place informed orders and interpret market behavior. It also generates prediction markets data that captures depth, liquidity, and sentiment in greater detail.
Orderbooks influence forecasting by showing where traders are concentrated and how strongly they believe in certain outcomes. Deep bids may signal confidence in upward movement, while heavy asks may indicate skepticism. These patterns help analysts understand market expectations beyond the headline probability. The resulting prediction markets data offers richer insight into conviction and crowd disagreement.
Analysts can examine spreads, depth, and order clustering to evaluate liquidity and market confidence. Tight spreads suggest agreement or high activity, while wide spreads reflect uncertainty or low participation. Large, layered orders may reveal informed traders positioning strategically. These patterns make prediction markets data more informative and help identify moments when prices may shift.
On Kalshi, a market forecasting whether an economic policy will be implemented shows a tight bid–ask spread and deep liquidity near the midpoint. Traders can see clear clusters of orders at specific price levels, helping them understand where sentiment is strongest and how much capital backs each side of the forecast.
Orderbook-based forecasting benefits from structured depth data, trade history, and continuous updates. FinFeed's Prediction Markets API provides high-quality prediction markets orderbooks data that developers can integrate into tools analyzing spreads, liquidity, and orderbook behavior.
