A common struct example in decentralized finance is the representation of a financial instrument, such as an OptionContract. This struct might contain fields like address underlyingAsset, uint256 strikePrice, uint256 expiryTimestamp, bool isCall, and address owner. Such a structure clearly defines the parameters and ownership of a specific option. This enables smart contracts to manage and execute complex derivative logic efficiently. It provides a standardized data model for on-chain options trading, facilitating interoperability.
User Position
Another practical struct example is a UserPosition within a lending or derivatives protocol. This struct could include uint256 collateralAmount, uint256 borrowedAmount, uint256 liquidationPrice, and uint256 lastUpdatedTimestamp. This structure aggregates all relevant data for a user’s specific financial standing. It allows the contract to quickly access and update a user’s entire position with a single data object. This simplifies risk management calculations and state updates, crucial for real-time market microstructure.
Market Order
Structs are also frequently used to define MarketOrder or LimitOrder types in decentralized exchanges. An Order struct might contain fields such as address maker, address taker, address assetIn, address assetOut, uint256 amountIn, uint256 amountOut, and uint256 expiration. This structured representation ensures all necessary details for a trade are bundled together. It facilitates efficient order matching and execution logic within automated market makers or order book protocols. These examples highlight the versatility of structs in modeling diverse financial operations.
Meaning ⎊ Order Book Feature Engineering Examples transform raw market depth into predictive signals for derivative pricing and systemic risk management.
Meaning ⎊ The Decentralized CLOB is a hybrid exchange design that separates high-speed order matching from final, trust-minimized settlement to enable institutional-grade options trading.
Meaning ⎊ Order Book Data Visualization converts raw market telemetry into spatial maps of liquidity, revealing the hidden intent and friction of global markets.
