KalshiClosedWeather

Will it rain in St. Louis on Oct 31, 2026?

Where will it rain on Oct 31, 2026?

View on Kalshi ↗
Trading is closed on this market and it is awaiting settlement. Quotes are frozen at their last observed values.
Implied probability
99.0%
99.0¢ per contract
1 hour
0.0 pts unchanged
24 hours
+74.0 pts up
7 days
—
Kalshi
Will it rain in St. Louis on Oct 31, 2026? implied probability. Kalshi moved from 99% to 99%. Times shown in EDT.0%20%40%60%80%100%00:35 ET00:48 ET01:02 ET01:16 ET01:30 ET

Market statistics

24h volume
$96,675
Total volume
$98,828
Liquidity
—
Open interest
21,464
Spread
—
Best bid / ask
—
Closes
Nov 1, 01:00 ET
Status
closed
Hourly volume

Order book

Executable asks for both outcomes, deepest first

Order book unavailable

The venue did not return resting depth for this contract. Quotes and history above are unaffected.

Why is this moving?

Evidence-grounded explanation

St. Louis rain market surges to 99% on heavy volume

Weak evidence
Observed
Established from data

The Kalshi implied probability for rain in St. Louis on Oct 31, 2026, is currently priced at 99.0%.

Established from data

Over the last 24 hours, the price moved +74.0 points, rising from 25.0% to 99.0%.

Established from data

Trading volume over the past 24 hours was $96,400.

Established from data

Estimated resting liquidity stands at approximately $140K.

Established from data

There was no price movement (0.0 pts) during the most recent hour.

Plausible explanation
Consistent with the evidence, not proven

The significant price increase coincided with substantial trading activity ($96,400 volume), suggesting that market participants aggressively repriced the likelihood of rain based on new information or consensus shifts available to them.

Consistent with the evidence, not proven

The stabilization at 99.0% with zero movement in the last hour indicates that current pricing may reflect a near-certainty among traders, leaving little room for further upward adjustment.

Caveats
  • The evidence does not specify what external factors (such as weather forecasts) drove the repricing.

Evidence score 9/100. Ordering in time establishes correlation, not causation. Narrative written by qwen/qwen3.8-flash from a fixed evidence package and validated against it before publication; every figure is computed by ProudGlobeHead Markets.

Event timeline

Reconstructed from snapshots and trades

No timeline events yet

Timeline entries appear once price or trade activity crosses a reporting threshold.

Largest recent trades

By notional value

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Most price-impactful trades

Ranked by realised movement adjusted for liquidity — not the same as the largest

No impact measured yet

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