Methodology

How Q forecasts, selects Signals, and scores every resolved outcome.

Q turns any question into a calibrated probability.

  1. 01Classify the questionResolution rule, horizon, and question type.
  2. 02Map the landscapeActors, assets, linked markets, and related questions.
  3. 03Research the evidenceCheck the sources behind each claim.
  4. 04Build scenariosBase rates, pathways, dependencies, and disconfirming evidence.
  5. 05Produce the forecastA calibrated probability with uncertainty and cited analysis.

Signal selection

Q generates many forecasts. The best ones become Signals. Q curates markets where it sees a large spread and short-term catalysts that it believes will move the market in its direction.

  1. 500forecasts / day
  2. 100spread > 10pp
  3. 50meaningful liquidity
  4. 10resolve within a week

Q gets better over time.

Learning loop

Score each outcome

Compare Q and the forecast-time market price with the resolved result.

Diagnose the error

Trace the evidence, expert weights, and missing inputs behind the miss.

Feed the next model

Turn repeatable findings into fine-tuning and data changes.

Resolve · score · fine-tune · forecast again

Resolved forecast accuracy

Q accuracy advantage+0.5%more accurate than Polymarket
Quotient Brier Index60.4
Polymarket Brier Index60.1

Last 60 days · 1,027 resolved forecasts across 199 markets

Methodology