Real-time mispricing detection on Kalshi hourly and 15-minute range contracts. No hype. Just a model that's been right 93%+ of the time across 560 trades.
Paper trading results across all supported assets. Signal fires when Wang model detects statistically significant mispricing on Kalshi hourly range contracts.
| Asset | Win Rate | Trades (n) | Paper PnL | Bar |
|---|---|---|---|---|
| XRP | 97.7% | 86 | +$107.62 | |
| SOL | 96.0% | 76 | +$92.09 | |
| BNB | 95.2% | 84 | +$555.51 | |
| DOGE | 93.3% | 75 | +$49.04 | |
| BTC | 93.0% | 86 | +$25.00 | |
| HYPE | 77.6% | 67 | +$453.00 | |
| ETH | 75.6% | 86 | +$28.18 | |
| TOTAL | — | 560 | +$1,310.44 |
Higher frequency, lower conviction. Useful for scalping and establishing positions before hourly settlement.
| Asset | Win Rate | Trades (n) | vs. Baseline |
|---|---|---|---|
| SOL | 55.1% | 462 | +edge |
| BTC | 54.5% | 478 | +edge |
| HYPE | 54.3% | 434 | +edge |
| DOGE | 52.8% | 429 | +edge |
| ETH | 51.8% | 470 | +edge |
| TOTAL | — | 2,704 | $278 paper profit |
Results are paper trading performance. Past performance does not guarantee future results. Win rates calculated as percentage of trades that resolved in the predicted direction.
Three steps from model to trade. No discretion required.
The Wang Transform is an actuarial pricing model that measures how much a market price deviates from its fair value given implied volatility. When Kalshi's market price drifts outside the model's confidence band, a candidate signal is generated.
Candidate signals are filtered by edge threshold, liquidity, and time-to-settlement rules. Only high-conviction mispricings make the cut. You receive a clear direction (YES or NO) and target contract via email alert.
Place the trade on Kalshi at your own sizing. No automation, no API access required. The model tells you what to trade; you decide how much. Settlement is binary — it wins or it doesn't.
Early access pricing — rates will increase at launch.
All plans: email us at contact@quant-signals.ai to get started. Early access onboarding is manual.
The Wang Transform is a risk-distortion pricing model developed by Shaun Wang for actuarial use. Applied to binary prediction markets, it computes a "fair value" probability given an asset's implied volatility and market structure. When a Kalshi market price deviates significantly from this fair value, the model identifies a directional mispricing — that's the signal.
During early access, signals are delivered via email. Pro subscribers will also have access to webhook delivery for automated routing. We're keeping delivery simple until the model is fully validated at scale.
Currently: Kalshi hourly range and 15-minute contracts for BTC, ETH, SOL, XRP, BNB, DOGE, and HYPE. Coverage expands with plan tier. We focus on Kalshi because their contract structure is particularly well-suited to the Wang model's outputs.
No. Quant Signals provides quantitative model output for informational purposes only. Nothing here constitutes investment or financial advice. You are solely responsible for your own trading decisions and risk management. Past performance does not guarantee future results.
Win rate = (number of signals that resolved in the predicted direction) / (total signals fired). All results shown are paper trading — real market prices, real settlement outcomes, no real capital at risk. The A/B test result (z = −1.70) compares Wang-selected trades against a baseline of untargeted trades on the same contracts over the same period.