In on-chain prediction markets, smart contracts are responsible for the entire market creation, trading, settlement, and fund distribution—in other words, the work traditionally completed by exchanges or platforms is automatically executed on-chain by programs.
A typical on-chain prediction market usually includes several basic steps: creating an event market, users purchasing outcome shares, settling results after the event ends, and winners receiving returns. The entire process is automatically executed by smart contracts without human intervention.
The main functions of smart contracts in prediction markets include:
Since all rules are written in the contract, the market operates transparently and cannot be arbitrarily changed, which is also an important characteristic of on-chain prediction markets.
One of the core challenges in prediction markets is: how does the blockchain know the event result. The blockchain itself cannot directly obtain information from the real world, therefore it needs oracles to transmit external data to the chain for confirming event results.
For example, if the prediction market event is “who will win a certain election,” then after the election ends, an oracle needs to submit the official result to the blockchain, and the smart contract can then settle based on the result. Without oracles, the prediction market cannot complete the final settlement step.
The roles that oracles undertake in prediction markets include:
Therefore, the reliability of oracles is extremely important. If an oracle provides incorrect data, the settlement results of the entire prediction market will have problems. In many systems, multiple oracles or decentralized oracle networks are used to improve reliability.
In prediction markets, liquidity determines whether trading is smooth. If the market lacks sufficient buyers and sellers, trading becomes difficult, therefore market-making mechanisms are needed to provide liquidity.
In traditional financial markets, market makers typically provide bid-ask quotes, while in on-chain prediction markets, many platforms use Automated Market Maker (AMM) mechanisms to provide liquidity. AMM uses algorithmic pricing rather than relying on human market makers, allowing the market to operate continuously.
Different prediction markets may adopt different liquidity mechanisms, such as AMM based on constant functions, or pricing models based on probability curves. The common goal of these mechanisms is to ensure that users can buy or sell outcome shares at any time, while prices continuously change with buying and selling activities.
From an operational logic perspective, on-chain prediction markets typically consist of three core modules:
These three components together constitute the basic operational structure of on-chain prediction markets.