A prediction markets historical dataset captures the full lifecycle of past markets—from the moment they open, through every probability update, to their final resolution. It includes time-stamped price paths, trading activity, liquidity changes, volatility patterns, and settlement details. This makes it possible to study how forecasts formed, how traders reacted to information, and how accurately markets anticipated real outcomes.
Platforms like Polymarket, Kalshi, Myriad, and Manifold all generate deep historical datasets that researchers and developers rely on to analyze market behavior. These datasets reveal information shocks, belief drift, consensus formation, and early signals that might not be visible in static snapshots. Over time, this historical data becomes a foundation for backtesting, model development, academic research, and performance benchmarking.
A strong historical dataset helps analysts understand long-term patterns, forecast reliability across event categories, and how prediction markets process information in different conditions.
Prediction markets historical datasets make forecasting research measurable. They turn years of prediction activity into structured prediction markets data that supports modeling, accuracy analysis, and decision-making.
They allow analysts to reconstruct how markets behaved around real events and evaluate whether probabilities were well-calibrated. Without historical data, it would be impossible to measure performance, detect long-term trends, or compare forecasting accuracy across topics. These datasets turn prediction markets data into a research-quality resource.
Historical datasets allow researchers to model market efficiency, evaluate information flow, detect mispricing patterns, and study how probabilities converge before resolution. Developers can backtest trading strategies or forecasting systems using past data. The richness of prediction markets data—complete with timestamps and event outcomes—makes deep behavioral modeling possible.
Analysts can identify which event categories are most predictable, how quickly markets respond to new information, how often markets overreact, and how well crowds handle uncertainty. Historical data also helps reveal structural patterns—such as recurring information latency or volatility cycles. These insights guide better interpretation of prediction markets data in real time.
A research group studying Polymarket and Kalshi downloads several months of historical markets on elections, economic indicators, and crypto milestones. By examining how probabilities behaved before key announcements, they uncover patterns in information absorption and identify event types that consistently produce accurate forecasts.
Accessing and analyzing historical data requires structured, high-resolution datasets. FinFeed's Prediction Markets API provides historical prediction markets data so developers can run backtests, build research tools, and study long-term forecasting performance.
