
In prediction markets, trading is allowed only up to a specific point in time. The closing date defines when participants can no longer open, close, or adjust positions.
This date is usually set before the event resolves. It helps prevent trading after outcomes are effectively known or highly predictable. After the closing date, probabilities stop updating through trades. The market remains open only for resolution and settlement processes. Closing dates vary by market design. Some close well before the event, while others close shortly before resolution depending on information flow and fairness concerns.
For analysts, the closing date is a critical boundary in prediction markets data. It separates live forecasting behavior from the final waiting period before resolution.
The closing date protects market fairness and data integrity. It ensures probabilities reflect genuine forecasting rather than late, unfair information.
In prediction markets, the closing date is the last moment when trading is allowed. After this point, participants cannot change their positions. This prevents last-minute trading based on near-certain information. It defines the end of active forecasting.
The closing date determines when probability streams stop reflecting new trades. After closing, prediction markets data becomes static until resolution. Analysts must account for this when measuring responsiveness and accuracy. Ignoring the closing date can distort analysis.
Prediction markets APIs expose closing dates as part of market metadata. Analysts use this information to filter live versus inactive markets. It is essential for backtesting, timing analysis, and forecast evaluation. APIs make closing dates explicit and machine-readable.
On Kalshi, a market predicting an economic release may close trading shortly before the official data is published. After that closing date, no new positions can be taken.
FinFeedAPI’s Prediction Markets API provides closing date information alongside prediction markets data. Analysts can identify when markets stop accepting trades and adjust models accordingly. This supports accurate lifecycle analysis, data filtering, and forecast evaluation. The API enables consistent handling of closing dates across prediction markets.
