Torch Litepaper
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  • Abstract
  • Introduction
    • Problem statement
  • Solution limitations
  • Vision & Objectives
  • How it works
  • Continuous market
  • Forecasting model
  • Probability map
  • Public goods
  • Betting
  • No exits
  • Prediction resolution
  • Hitting the range
  • Payout system
  • Payout formula
  • Reserve management
  • System mechanics
    • Key parameters
    • Lead time quality
    • Local confidence
    • Boldness quality
    • Sharpness quality
    • Bonus share
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Continuous market

Torch introduces a novel market structure designed for continuous forecasting of crypto token prices. Unlike traditional prediction markets (like Omen, Polymarket) that focus on discrete binary or categorical events, Torch allows traders to predict any price interval, at any time, for any future moment. The result is a unified dynamic market that builds a time–price probability surface – a collective signal layer of future price expectations.

Most prediction markets today are built around conditional tokens. These require a separate market to be created for each question and resolve based on an external oracle after a fixed date. While effective for event-based forecasting, these systems are limited in scope when it comes to price discovery, alpha detection, and continuous signal generation.

Torch replaces that model with range-based betting and deterministic resolution:

  • Traders (human or AI) can stake on price ranges at any time, no need to create a new market

  • Bets are evaluated at the specified future timestamp against the token price, fetched from price oracles

  • All activity contributes to a shared forecast surface, revealing directional consensus, volatility bands, and speculative imbalance in real time

Feature
Torch
Prediction markets

Market structure

Continuous (price and time intervals)

Discrete binary or categorical

Market structure

Unified, perpetual per token

One market per question

Betting format

Range-based (e.g. $3500–3700 in 3 days)

Fixed outcome tokens (Yes/No, A/B/C…)

Liquidity model

Fully parimutuel, trader-funded

AMM-backed, requires external LPs

Resolution mechanism

Oracle-based, objective token price at timestamp

Oracle-based, often slow or disputed

Use case

Token price forecasting, alpha discovery

Event resolution, binary outcomes

Forecast output

Public probability map (price × time)

Outcome probabilities per market

Participation mode

Open and permissionless

Requires market creation and LP funding

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Last updated 11 days ago