How to Design a Prediction Market with Recurring Events

A recurring prediction market is a series of markets that automatically resolve and redeploy for periodic events, such as weekly unemployment reports, monthly inflation data, or quarterly earnings. Unlike one-off markets, they require a factory contract pattern that spawns new market instances on a set schedule. The core design challenge is automating the lifecycle—creation, resolution, settlement, and archival—without manual intervention. This architecture is fundamental for creating persistent liquidity and engagement around time-series data.

The system design typically involves three key smart contracts. First, a factory contract holds the market template logic and parameters (like event cadence and oracle). Second, an active market contract manages the current event's trading, funded by a liquidity pool. Third, a controller or registry manages the lifecycle, calling the oracle at resolution time, settling the active market, and instructing the factory to deploy the next instance. Using a minimal proxy pattern (like EIP-1167) from the factory drastically reduces gas costs for deploying each new market.

Critical logic resides in the resolution and rollover mechanism. When the oracle reports the outcome (e.g., "US CPI > 3.0%"), the active market resolves, distributing collateral to winning shares. A portion of fees can be recycled to bootstrap liquidity in the next market. The system then must archive the resolved market's state and increment the event index. The new market's question must be deterministically generated (e.g., "Will the Feb 2025 CPI exceed 3.0%?") to ensure continuity. This automation is often triggered by a keeper network like Chainlink Automation or Gelato.

For developers, implementing this requires careful state management. Here's a simplified factory function outline:

solidityCopy
function createNewMarket(uint256 epoch) external returns (address) {
    require(msg.sender == controller, "Unauthorized");
    string memory question = string.concat("CPI > 3.0% for ", getMonth(epoch), "?");
    Market newMarket = Market(Clones.clone(template));
    newMarket.initialize(question, oracle, resolutionTime);
    emit MarketCreated(address(newMarket), epoch);
    return address(newMarket);
}

The controller would call this function after the previous market resolves.

Key design considerations include oracle selection (decentralized like Chainlink or UMA for robustness), liquidity migration strategies to reduce friction between epochs, and fee structure to sustain the system. Platforms like Polymarket use recurring markets for political events, while PlotX employs them for crypto price predictions. By designing for automation and low-cost deployment, you can create a prediction market protocol that provides continuous, non-stop trading on the world's recurring events.

To design a prediction market with recurring events, you must first understand the core components of a standard prediction market. This includes the market factory contract that creates new markets, the oracle that resolves outcomes, and the automated market maker (AMM) or order book that facilitates trading. Familiarity with a collateral token (like DAI or USDC) used for minting shares and a conditional token standard (like the ERC-1155-based system used by Polymarket or Omen) is essential. These contracts manage binary or scalar outcomes and the payout logic.

The key technical challenge for recurring events is automation. You'll need a reliable method to create, resolve, and settle markets on a fixed schedule without manual intervention. This typically involves an off-chain keeper or a smart contract automation service like Chainlink Automation or Gelato Network. These services can trigger contract functions based on time-based conditions, calling a createMarket() function weekly for sports matches or a resolveMarket() function after an event concludes. Understanding how to write upkeep-compatible smart contracts is crucial.

Your development environment must be configured for the target blockchain. For Ethereum or EVM-compatible chains (Polygon, Arbitrum, Base), you will need Node.js, Hardhat or Foundry for local development and testing, and a wallet like MetaMask. You should be proficient in Solidity for writing the core market logic and the automation-triggered functions. Knowledge of IPFS or a similar decentralized storage solution is also recommended for storing market metadata (event descriptions, images) in a censorship-resistant way, ensuring market integrity.

A recurring prediction market allows users to bet on the outcome of events that happen on a fixed schedule. The core architectural challenge is managing the lifecycle of these events—creation, resolution, and settlement—in an automated and trust-minimized way. Unlike one-off markets, the system must handle a continuous pipeline: a new market for "Will Team A win on Friday?" must be created, funded, resolved based on a verifiable result, and settled, all before the next identical market for the following Friday begins. This requires a robust oracle integration and a clear state machine for each market instance.

The smart contract architecture typically revolves around a factory pattern. A main factory contract deploys individual market contracts for each event instance. Each market contract has defined states: Open (accepting predictions), Locked (awaiting resolution), Resolved (oracle result reported), and Settled (payouts distributed). Key functions include createMarket(uint256 eventId, uint256 lockTime) to spawn a new instance, resolve(uint256 outcome) which can only be called by the designated oracle, and claimWinnings() for users. Storage must efficiently map user addresses to their positions across potentially hundreds of concurrent and historical markets.

Oracle design is critical for automation. For recurring events, you need a reliable data feed and a scheduled execution mechanism. Using a decentralized oracle like Chainlink, you can configure an Upkeep from Chainlink Automation to trigger the resolution function at the market's lock time. The upkeep calls an API (e.g., a sports data provider) and feeds the result into the resolve function. The contract must validate that the caller is the pre-defined oracle upkeep. Without this automation, the system requires manual intervention, breaking the promise of decentralization and creating a central point of failure.

Consider the user experience and liquidity. To facilitate easy trading, each market can be integrated with an Automated Market Maker (AMM) pool, like those on Uniswap V3, allowing users to buy and sell shares of outcomes dynamically before resolution. Alternatively, the contract can implement a simple order book or liquidity pool natively. The treasury model must also be defined: does the protocol take a fee on each market creation or settlement? A common model is a 2-5% fee on winnings, which is sent to a treasury contract to fund development and insurance pools.

Finally, audit and test the lifecycle exhaustively. Use a forked mainnet environment with tools like Foundry to simulate the passage of time and the oracle response. Write tests that cover edge cases: oracle failure, a market resolving in a draw, or a user trying to claim from an unsettled market. Security reviews should focus on the oracle integration and state transition logic, as these are the most vulnerable points. A well-architected recurring prediction market becomes a reliable, autonomous engine for decentralized forecasting.

The Factory Contract Pattern is a foundational design for creating scalable decentralized applications. It separates the logic for creating new contract instances from the logic of the instances themselves. In a prediction market context, this means a single, audited factory contract can spawn an unlimited number of individual market contracts, each representing a unique event like "Will ETH be above $4000 on June 1st?" This pattern is used by protocols like Polymarket to manage thousands of markets efficiently, reducing deployment costs and simplifying upgrades.

For a recurring event system—such as weekly sports matches or monthly inflation reports—the factory is essential. The core factory contract stores the immutable market logic and a registry of all created markets. When a user requests a new market for "NFL Game: Team A vs. Team B," the factory deploys a new instance using new Market(...), passing in the event-specific parameters (resolution time, outcomes, creator fee). This keeps the core market logic consistent and secure while allowing each instance to manage its own liquidity, bets, and resolution state independently.

Here is a simplified Solidity snippet for a prediction market factory core:

solidityCopy
contract PredictionMarketFactory {
    address[] public allMarkets;
    address public marketTemplate;

    event MarketCreated(address indexed market, string eventQuestion);

    constructor(address _template) {
        marketTemplate = _template;
    }

    function createMarket(string calldata _question, uint256 _resolutionTime) external returns (address) {
        bytes memory bytecode = type(Market).creationCode;
        bytes32 salt = keccak256(abi.encodePacked(_question, _resolutionTime));
        address market;
        assembly {
            market := create2(0, add(bytecode, 32), mload(bytecode), salt)
        }
        Market(market).initialize(_question, _resolutionTime, msg.sender);
        allMarkets.push(market);
        emit MarketCreated(market, _question);
        return market;
    }
}

The create2 opcode enables deterministic address calculation, allowing users to verify a market's address before it's funded.

Key design considerations include upgradeability and fee management. While the factory pattern simplifies logic upgrades (you deploy a new factory), migrating existing market data is complex. A common approach is to make markets proxy contracts that delegate logic calls to a central, upgradeable implementation contract. Furthermore, the factory can enforce a fee structure, collecting a small percentage from each resolved market to fund protocol development. This is more gas-efficient than each market contract holding its own fee logic.

To manage recurring events, your system needs an oracle integration. The factory or the individual market must specify a trusted data source, like Chainlink for sports data or a decentralized oracle network (DON) for custom events. The market's resolution function should be permissioned, often callable only by the designated oracle or after a timelock for community verification. This prevents market creators from manipulating outcomes and is critical for user trust.

In production, complement your on-chain factory with a subgraph (using The Graph) for efficient querying of all created markets, their states, and volumes. This off-chain indexing is necessary because iterating through the allMarkets array on-chain is prohibitively expensive. A well-designed factory pattern, combined with robust oracle feeds and off-chain indexing, creates a scalable, secure, and maintainable foundation for a prediction market platform handling thousands of concurrent events.

A prediction market for recurring events requires a system that manages the complete lifecycle of a market across multiple iterations. Unlike one-off markets, you must design for automated settlement, collateral rollover, and liquidity migration. The primary challenge is handling user positions and locked collateral when one event concludes and the next begins. Key components include a market factory to spawn new instances, a resolution oracle for automated outcomes, and a vault system to manage funds between epochs. Protocols like Polymarket and Augur handle recurring events by creating new, distinct market contracts for each instance, which simplifies logic but fragments liquidity.

The core of the rollover mechanism is the settlement and re-entry process. When a market epoch ends, the oracle reports the outcome (e.g., "Team A won"). All collateral in winning outcome tokens is made redeemable at a 1:1 ratio for the base currency, while losing tokens are worthless. To facilitate rollover, the system can offer a batch claim-and-stake function. This allows users to atomically redeem winnings from the concluded market and commit them as collateral to the newly created market for the next event. This is critical for retaining liquidity and user engagement without requiring manual withdrawals and deposits.

Implementing this requires careful smart contract architecture. A typical setup involves a parent MarketFactory contract that uses a template (like a Market clone) to deploy a new instance for each epoch. Each Market contract manages its own bonding curve or order book. A central CollateralVault holds user funds, with internal accounting separating balances per market epoch. Upon settlement, the vault updates user balances based on the outcome. For the rollover, a function like rolloverPosition(uint fromEpoch, uint toEpoch) would burn the user's old shares, credit their vault balance, and mint shares in the new market. This keeps funds non-custodial within the system's contracts.

Oracle design is paramount for automation. You need a verifiable and timely data source for the recurring outcome. For sports results, this could be a trusted data provider like SportMonks or Chainlink Sports Data. For economic data, Chainlink Oracles or Pyth Network can provide CPI or employment numbers. The oracle must trigger the settlement function on the market contract. To prevent stagnation, incorporate a scheduled rollover that creates the next market instance a fixed period (e.g., 24 hours) before the current one resolves, ensuring continuous trading availability.

Consider the user experience and economic incentives. Without a rollover system, liquidity providers must manually reclaim and redeploy capital, leading to liquidity gaps and missed opportunities. An integrated system offers continuous yield from trading fees. However, you must also allow users to exit the cycle and withdraw. Furthermore, the design should account for market parameters that may change between epochs, like fee structures or oracle addresses, which should be settable by governance or a configured manager. Testing this system thoroughly on a testnet is essential, focusing on edge cases like oracle failure during rollover.

A prediction market for recurring events, like weekly sports matches or monthly economic reports, requires a systematic scheduling mechanism. Unlike one-off markets, the core contract must be able to autonomously create new market instances based on a predefined cadence. This is typically managed by a factory contract pattern, where a master contract contains the market logic and a scheduler (often an off-chain keeper or a time-based trigger) calls a function like createNewMarketRound(uint256 eventId) to instantiate a new trading period. Each round needs a unique identifier, often derived from a timestamp or a sequential index, to track bets and payouts separately.

The resolution of these events depends entirely on oracle integration. For reliable, recurring data—such as the winner of a weekly eSports tournament or the closing price of an index on the last Friday of the month—you need a deterministic and trust-minimized data source. Decentralized oracle networks like Chainlink are ideal, as they provide verified data feeds on-chain. Your market contract would include a resolution function that checks the specified oracle's latest answer against the market's condition after the event's conclusion time. For example, a contract might store the roundId and query Chainlink's ETH/USD feed to resolve a "price above $3500" market at a set weekly expiry.

Critical design considerations include fund segregation and round lifecycle. User funds for active predictions must be kept in escrow per round, often in a dedicated vault or via internal accounting mappings like round => user => stake. A clear lifecycle—Open, Locked, Resolving, Settled—must be enforced. The Locked phase begins when the event starts, preventing new bets. After the event ends, the contract enters Resolving, awaiting the oracle update. Once the data is reported, the contract calculates outcomes, moves to Settled, and enables users to claim winnings. This prevents disputes and ensures non-overlapping rounds.

For developers, implementing this involves writing a factory contract with createRound, a market contract with state management, and an oracle adapter. A minimal resolution function might look like:

solidityCopy
function resolveRound(uint256 roundId) external {
    require(state[roundId] == MarketState.Resolving, "Not in resolving");
    int256 oracleAnswer = chainlinkFeed.latestAnswer();
    bool outcome = oracleAnswer > targetPrice;
    _setOutcome(roundId, outcome);
    state[roundId] = MarketState.Settled;
}

Automating the scheduling can be done with a keeper network like Chainlink Automation or Gelato, which calls createNewMarketRound() on a cron schedule, making the system fully autonomous.

Security and user experience are paramount. Use pull-over-push patterns for payouts to avoid gas-intensive mass transfers and mitigate reentrancy risks. Implement emergency pause functions controlled by a multisig for the factory in case of oracle failure. Furthermore, clearly expose round schedules, oracle sources, and resolution times to users via your front-end. Transparent design, combined with robust oracle reliance, creates a prediction market that is both useful for recurring speculation and resilient enough to operate continuously with minimal manual intervention.

The core challenge for recurring events is liquidity fragmentation. A single market for "Team A to win the 2025 season" locks capital for months. A better model uses a series of short-dated markets for individual games or quarters, funded by a reusable liquidity pool. Protocols like Polymarket use conditional tokens (e.g., ERC-1155) where liquidity providers deposit collateral once, which is then reallocated as outcomes resolve. This design prevents capital from being idle and improves capital efficiency, which is critical for market depth and accurate pricing.

Economic security depends on a robust oracle resolution system. For each recurring round, you must define clear, objective resolution criteria and a trusted data source (e.g., a decentralized oracle like Chainlink or a designated committee with bonded stakes). Implement a dispute period where users can challenge incorrect resolutions by staking a bond. The design must also account for market exhaustion—ensuring there is sufficient liquidity and participant interest for the final events in a series, which may be less salient. Mechanisms like decreasing fee rates for later rounds or loyalty rewards can help sustain engagement.

From a smart contract perspective, use a factory pattern to deploy a new market instance for each event in the series from a single, audited template. This isolates risk; a bug in one market contract doesn't compromise the entire season. Each contract should handle its own lifecycle: creation, trading, resolution, and payout. Implement a fee structure that supports the protocol's economics; a common model is a small percentage fee on each trade and a larger fee on resolved profits, which can be directed to a treasury or liquidity provider rewards.

User experience must simplify interaction with the series. A dashboard should clearly show the user's position across all active and resolved markets. Implement batch resolution functions so oracle reports can settle multiple concluded markets in one transaction, saving gas. For liquidity providers, design an interface that shows aggregate yield across the entire event series and allows for partial withdrawal of unlocked funds from resolved markets, without requiring them to manually claim from each contract.

Finally, consider composability with other DeFi primitives. Can prediction market shares for recurring events be used as collateral in lending protocols? Can AMM liquidity pool tokens be staked elsewhere? Designs that enable this, like Gnosis Conditional Tokens, create additional utility and can attract more capital. Always audit the integration points, as cross-protocol interactions introduce complex dependency risks. The goal is a system that is secure, capital-efficient, and seamlessly integrated into the broader on-chain ecosystem.

You now have a functional blueprint for a recurring prediction market. The key components are in place: a factory contract (like EventFactory.sol) to spawn new markets, an event contract template with a configurable resolutionTime and eventWindow, and a mechanism to handle the lifecycle of resolution and fund distribution. The next critical step is rigorous testing. Deploy your contracts to a testnet (like Sepolia or Goerli) and simulate full event cycles. Test edge cases such as late resolutions, oracle failures, and high-volume trading to ensure the claimWinnings and resolveEvent functions are robust under all conditions.

To enhance your market's utility and security, consider integrating with a decentralized oracle. While our examples used a mock oracle, a production system requires a service like Chainlink Functions or Pyth Network for reliable, tamper-proof data feeds. For recurring sports events, you could trigger resolution via an API call to a verified stats provider. Additionally, implementing a liquidity pool or automated market maker (AMM) module, rather than simple peer-to-peer trading, can dramatically improve user experience by providing continuous pricing and instant trades. Reference projects like Gnosis' Conditional Tokens Framework can offer insights into advanced market structures.

Finally, plan for the frontend and user lifecycle. Developers should build an interface that clearly displays the schedule of recurring events, live odds, and users' active positions. Incorporate wallet connection via libraries like wagmi and viem. For further learning, explore the complete codebase of established prediction platforms like Polymarket or Augur v2, and review auditing reports from firms like OpenZeppelin to understand common vulnerability patterns. Your next step is to iterate on this foundation, prioritize security audits, and engage with the developer community on forums like the Ethereum Magicians to refine your design for mainnet deployment.