mev-microstructure

A trading strategy that captures profit by buying low and selling high across markets where the same asset is priced differently. In DeFi, it primarily manifests as cross-pool price correction between DEXs and alignment between AMM spot prices and oracle quotes. While arbitrageurs extract profit, they also push prices toward consistency across platforms — making arbitrage the primary mechanism that keeps market pricing efficient.

An automated program that continuously monitors unconfirmed transactions and market price information on a blockchain, automatically detecting and executing <a href="/en/glossary/mev-microstructure/mev/">MEV</a> profit opportunities like arbitrage or sandwich attacks — the technical entity actually carrying out most real-world MEV extraction.

The extra profit block producers (miners or validators) or other participants who can influence transaction ordering can extract by reordering, inserting, or excluding specific transactions within a block — profit that comes from ordinary users' transactions themselves, not from the block reward.

A decentralized infrastructure made up of multiple independent nodes, each separately fetching the same piece of off-chain data (such as an asset's market price), then aggregating it into a single trustworthy value through a <a href="https://crypto-bible.com/en/glossary/blockchain-fundamentals/consensus-mechanism/" target="_blank" rel="noopener">Consensus Mechanism</a> to feed to smart contracts — avoiding the risk that manipulating a single data source alone could affect the entire system.

A service that feeds off-chain or on-chain asset price data into smart contracts; since smart contracts cannot independently fetch external price information, they rely on oracles to supply data needed for pricing-related logic.

An <a href="/en/glossary/mev-microstructure/mev/">MEV</a> attack technique where an attacker inserts one of their own transactions both before and after a victim's transaction, profiting from the price movement the victim's own trade causes — named because the victim's transaction ends up sandwiched between the attacker's two.

Calculating a single value by weighting and averaging multiple price data points across a time interval according to duration, rather than using only a single instantaneous quote — this requires an attacker to sustain manipulation across the entire time window, not just a single moment, substantially raising the actual cost of the <a href="/en/glossary/security-risk/oracle-manipulation-attack/">price manipulation attack</a> covered in an earlier article.