Forecast Backtesting Dataset
In prediction markets, forecasts change continuously before an event resolves. A forecast backtesting dataset captures these historical probabilities along with resolution data for later analysis.
The dataset typically includes time-stamped probability snapshots, event metadata, and the final outcome. This allows analysts to replay how forecasts evolved and compare them against what actually happened.
Backtesting datasets make it possible to measure accuracy, bias, and calibration. Analysts can test whether probabilities were systematically too high, too low, or slow to update. They are also useful for behavioral analysis. Patterns like overreaction, persistence, or delayed response become visible only when forecasts are evaluated across many past events.
For analysts, forecast backtesting datasets turn prediction markets data into an evaluation tool. They support model validation, performance benchmarking, and continuous improvement of forecasting methods.
Why It Matters
Without backtesting, forecasts cannot be judged objectively. Forecast backtesting datasets help users understand how reliable prediction markets really are.
What is a forecast backtesting dataset in prediction markets?
In prediction markets, a forecast backtesting dataset is a historical record of forecasts matched with resolved outcomes. It allows analysts to test how well probabilities performed over time. This goes beyond anecdotal success or failure. It provides a systematic performance baseline.
How is a forecast backtesting dataset used with prediction markets data?
Analysts use forecast backtesting datasets to compare predicted probabilities with actual results. This supports accuracy measurement, calibration testing, and bias detection. Backtesting also helps refine forecasting rules and signal weighting. It is a core component of model development.
Why are forecast backtesting datasets important for prediction markets APIs?
Prediction markets APIs provide the raw historical data needed to build backtesting datasets. APIs deliver probability streams, timestamps, and resolution data in consistent formats. This enables automated and repeatable backtesting across many markets. APIs make large-scale evaluation practical.
Real-World Example
On Polymarket, an analyst may backtest how early election probabilities compared to final outcomes across multiple cycles. The backtesting dataset reveals which stages produced the most accurate forecasts.
Forecast Backtesting Dataset and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data required to construct forecast backtesting datasets. Analysts can retrieve historical probabilities, event metadata, and resolution results. This supports performance analysis, calibration studies, and forecasting research. The API enables consistent backtesting across prediction markets.