Clearmatics Unveils Forecast Markets on Autonity Layer‑1

Clearmatics announced new forecast markets that will run on its soon-to-launch Autonity blockchain and Autonomous Futures Protocol (AFP). The platform allows traders to create and trade dated futures contracts tied to any public time series data.

The forecast markets track underlying metrics including crypto indexes, inflation rates, global temperatures, and GDP figures. Autonity is an Ethereum-compatible layer-1 blockchain. The company will host a “Forecastathon” next month for quants, engineers, and DeFi users to build products on the network.

Forecast markets operate differently from prediction markets. They offer symmetrical payoffs that move one-to-one with underlying data rather than delivering one-time payouts to winners. Forecast markets can run indefinitely, allowing liquidity to build up and enabling ongoing trading on long-term risks. Prediction markets settle and close after events conclude.

AFP supports the permissionless creation of dated futures contracts that can track any underlying time series of interest, not just market time series, but non-market time series, like GDP, inflation, global temperature, blockchain metrics,

said Robert Sams, CEO of Clearmatics.

Sams stated that forecast markets complement rather than replace prediction markets. While prediction markets focus on individual events and topical narratives, forecast markets provide continuous contract series for hedging persistent risks.

The AFP separates exchange and clearing functions. Trades execute on any non-custodial venue, while collateral, margin requirements, and auctions of under-collateralized positions happen on-chain. The architecture addresses open interest fragmentation across multiple trading venues in existing DeFi derivatives protocols.

DeFi derivatives markets have been dominated by perpetual futures on large exchanges. Clearmatics believes on-chain dated futures tracking real-world data can deliver greater value and social utility. Sams suggested quants and machine-learning researchers could test predictive models on the new instruments.