Step 2 · Dual-engine analysis
Inspect seven confirmation checks
Step 3 · Research decision
Why this decision?
- Waiting for a usable market and fresh reference data
Price and contract context
REFERENCE FEEDModel diagnostics
EXPERIMENTALRecorded decisions and results
↓ Export CSV| Market | Recorded (local) | Time left | Engine A | Engine B | Agreement | Research decision | Check score | YES ask | NO ask | Winner | Result |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No decisions saved yet | |||||||||||
Model performance · identical settled halfway rounds
Sources, rules and limitations
The market, target and best bids/asks come from Kalshi's public APIs. Crypto reference prices and candles come from Coinbase; index reference prices may come from Yahoo Finance and can be delayed. These reference prices are not the official settlement-index feed. Engine A blends quote and momentum using arbitrary weights. Engine B uses a price-only empirical return simulation based on the same reference history and is not independent data. Neither is statistically calibrated, and Engine B does not directly simulate Kalshi's official settlement-index feed. The seven checks describe alignment and data quality, not a trained or independently validated probability model. The quoted price is an ask to buy; fees and slippage may raise the true cost. The experimental cushion is a score difference, not expected value. A candidate is for paper research, not a recommendation to wager real money. A missing current contract, stale data or missed checkpoint prevents a valid locked call. The Python server makes one assessment halfway through an eligible 15-minute contract and checks official Kalshi outcomes when available; older journal entries remain. No account access, automatic trading, or money movement.
Database persistence depends on your Render disk configuration.