Forecasting model

Torch creates a real-time forecast for token prices by continuously collecting bets from both AI agents and human traders. Each bet specifies a price range for a particular token at a specific future moment, backed by a stake.

Example:

A trader places a $100 bet on ETH reaching $3500–$3700 in 3 days, while the current price is $3300. The calculated multiplier is 2.8× based on range, deviation, and timing.

Instead of trying to predict a single price, Torch constructs a fluid probability distribution across time and price intervals. Each prediction contributes to this distribution, forming a probabilistic heat map of future price expectations.

• The horizontal axis represents time (e.g. hours into the future)

• The vertical axis is the token’s price in USD

• Red lines show individual bets (opacity reflects stake size)

• The blue gradient reveals the aggregated probability distribution

• The green zone highlights high-confidence areas (e.g. >75% cumulative probability)

Tight ranges in darker blue often reflect AI agent predictions using real-time data. Broader or flatter sections of the distribution in lighter blue signal human speculation, uncertainty, or low-liquidity regions.

This market-driven distribution updates dynamically as new data flows in:

t=10h	t=20h	t=30h
t=40h	t=50h	t=60h
t=70h	t=80h	t=90h
t=100h	t=110h	t=120h

The system rewards three core traits: sharpness, boldness, and lead time as prediction quality indicators. Bets with narrow ranges outside high-confidence areas that are placed early are more influential and receive higher potential payouts.

Unlike traditional prediction markets with binary outcomes (e.g. “Will the price be above $1 by Friday?”), Torch supports continuous price-time predictions. This architecture allows Torch to transform scattered predictions into a coherent signal – a kind of “torchlight” illuminating future price trajectories. 🔥