A forecast data feed delivers live probability updates from active prediction markets, allowing analysts, developers, or automated systems to track how expectations change second by second. Instead of checking individual markets manually, the feed aggregates probabilities into a structured, always-current stream. This makes it easier to monitor events, detect shifts, and understand how the crowd interprets new information.
Platforms such as Polymarket, Kalshi, Myriad, and Manifold generate rich streams of forecast data as traders buy and sell outcome shares. These streams capture belief updates, information shocks, liquidity changes, and overall market sentiment. When fed into tools or dashboards, forecast data feeds provide valuable insight into how forecasts evolve and how quickly markets absorb news.
Forecast data feeds are essential for analysts who want to model behavior, detect mispricing signals, track real-time sentiment, or integrate prediction market probabilities into larger forecasting systems.
Forecast data feeds turn raw prediction market activity into usable, continuous insight. They enable real-time analysis, faster reactions to information, and clearer interpretation of prediction markets data.
They are valuable because prediction markets change rapidly. A forecast data feed captures every movement—big or small—revealing how crowds process information in real time. This helps analysts detect sentiment shifts, identify early signals, and distinguish meaningful updates from noise. The resulting prediction markets data becomes more actionable and easier to integrate into forecasting workflows.
Forecast data feeds allow dashboards, models, and automated systems to stay updated without manual checks. Tools can visualize probability curves, react to sharp movements, or alert users when expectations change. This enhances situational awareness and makes prediction markets data far more accessible. Developers can also analyze historical feed patterns to improve model accuracy and behavior analysis.
Analysts can see how quickly markets respond to news, how volatility evolves, and where traders disagree or converge. They can identify moments of information latency, detect mispricing signals, or measure the impact of liquidity. Feed-based data offers a granular view of belief formation, helping improve both forecasting interpretation and market design.
A research team tracking Polymarket markets on geopolitical events uses a live forecast data feed to monitor sudden probability jumps during major announcements. When the feed shows a sharp shift after a press briefing, analysts immediately flag it as an information shock and review the underlying news.
A forecast data feed requires structured, real-time or latest probability updates. FinFeed's Prediction Markets API provides high-frequency prediction markets data that developers can integrate into dashboards, models, alert systems, and analytics tools for continuous forecast monitoring.
