Cross-Chain MEV Dashboard

Tracks the flow of MEV opportunities and extracted value across interconnected blockchains like Ethereum, Arbitrum, Optimism, and Base. Visualizes how cross-chain arbitrage and liquidations propagate through bridges and layer-2 networks, showing the origin and destination of value extraction.

Monitors pending transactions and mempools across chains to identify profitable MEV strategies in real-time. Highlights:

• Cross-DEX Arbitrage: Price discrepancies between Uniswap (Ethereum) and PancakeSwap (BNB Chain).
• Liquidation Cascades: A liquidation on Aave (Polygon) creating arbitrage on Compound (Ethereum).
• Bridge Latency Exploits: MEV from delays in cross-chain message finalization.

Aggregates profitability metrics for MEV participants operating across chains. Tracks:

• Searcher wallets and their success rates on different networks.
• Validator/Sequencer revenue from block-building and proposer-builder separation (PBS).
• Comparative analysis of MEV rewards per chain (e.g., Ethereum vs. Solana).

Maps the relationships between transactions, searchers, and validators/sequencers across blockchains. This graph analysis reveals:

• Sybil clusters of addresses controlled by the same entity.
• MEV supply chains where opportunities are identified on one chain and executed on another.
• The network effects of generalized frontrunning across layers.

Quantifies the cost of MEV extraction across different blockchain environments. Compares:

• Gas fees on Ethereum L1 vs. L2 sequencer fees.
• Slippage incurred during cross-chain arbitrage loops.
• The net profitability of strategies after accounting for bridge costs and message latency.

Provides access to historical datasets of MEV events across multiple chains, enabling quantitative research and strategy backtesting. Users can analyze the frequency and profitability of past sandwich attacks, liquidations, and arbitrage to model future opportunities and network risks.

Dashboards track price discrepancies for the same asset (e.g., ETH, USDC) across DEXs on different chains like Ethereum, Arbitrum, and Polygon. They identify cross-chain arbitrage paths, calculating potential profits after factoring in bridge latency and gas costs. This allows searchers to construct profitable bundles that execute swaps and cross-chain transfers atomically.

For protocols with cross-chain collateral (e.g., a loan on Ethereum backed by assets on Avalanche), dashboards monitor health factors across networks. They alert to liquidation risks and can surface MEV opportunities for liquidators to trigger cross-chain liquidation transactions, often involving a flash loan on one chain and an asset sale on another.

Analysts use dashboards to monitor activity on major bridges (e.g., Wormhole, LayerZero) and cross-chain swap protocols. They track:

• Slippage and price impact for large cross-chain transfers.
• Sandwich attack risks on the destination chain's liquidity pools.
• Latency metrics to identify delays that could be exploited for time-bandit attacks.

Stakers and node operators analyze dashboard data to compare MEV revenue potential across chains. Metrics include:

• Average block MEV on Ethereum vs. rollups.
• Proposer/sequencer payment trends from bundles.
• Cross-chain MEV's contribution to overall staking yields, influencing chain selection for validation.

Security teams deploy dashboards to detect malicious cross-chain MEV patterns in real-time, such as:

• Reorg attempts on a destination chain following a bridge transaction.
• Flash loan attacks that span multiple networks to manipulate oracle prices.
• NFT floor price manipulation across chain-specific marketplaces to enable fraudulent collateralization.

DeFi protocol developers use dashboards to design MEV-resistant mechanisms and optimize fee structures. They analyze data to:

• Model the impact of cross-chain transaction ordering on user experience.
• Set appropriate network fee subsidies or priority fee auctions for cross-chain messages.
• Test the resilience of new cross-chain atomic protocols against frontrunning.

Professional arbitrageurs and automated trading entities use dashboards to identify profitable opportunities across chains in real-time. They monitor for price discrepancies on DEXs, pending liquidations in lending protocols, and NFT floor price arbitrage. Their primary goal is to discover and execute cross-chain bundles before competitors.

Teams building DeFi protocols, bridges, and cross-chain messaging layers use dashboards to analyze the MEV landscape affecting their systems. They study extractable value leakage, sandwich attack vectors on their DEX pools, and the security implications of cross-chain transaction ordering. This informs protocol design and risk parameter adjustments.

Entities operating proposer-builder separation (PBS) infrastructure or validating across multiple chains use dashboards to optimize block construction. They analyze cross-chain flow to maximize revenue from inclusion fees and MEV-Boost auctions, while ensuring compliance with censorship resistance and OFAC requirements.

Quantitative funds and crypto-native institutions leverage dashboards for macro-level MEV analysis. They track total value extracted, dominant strategies, and network congestion to inform trading strategies and asset allocation. Metrics like MEV revenue per chain and searcher concentration are key indicators of ecosystem maturity and liquidity efficiency.

Auditors and security researchers monitor dashboards to detect malicious MEV activity and protocol exploits as they propagate across chains. They identify patterns of generalized frontrunning, time-bandit attacks, and the use of privacy pools by attackers, providing alerts and forensic analysis for vulnerable protocols.

Decentralized Autonomous Organizations (DAOs) managing cross-chain treasuries use dashboards to assess the MEV risks and costs associated with their governance operations and treasury movements. They analyze the impact of vote-buying MEV and the cost of executing large, cross-chain asset rebalances to optimize protocol-owned liquidity.

Tracks the total value of assets involved in MEV opportunities across chains. This includes cross-chain arbitrage volume, bridge exploit attempts, and the settled value of successful MEV bundles. Metrics are often broken down by source chain (e.g., Ethereum), destination chain (e.g., Arbitrum, Polygon), and asset pair.

Monitors the entities creating and capturing MEV. Key metrics include:

• Number of active searchers (wallets submitting bundles)
• Builder market share (proportion of blocks built by entities like Flashbots, beaverbuild)
• Success rate of MEV bundles across different chains
• Dominant strategies observed (e.g., arbitrage vs. liquidations)

Measures the temporal aspects of cross-chain MEV, which is highly sensitive to delays. Tracks bridge confirmation times, cross-chain message relay latency, and destination chain block finality. High latency can turn a profitable arbitrage into a loss, making this a critical risk indicator.

Quantifies the economics of cross-chain MEV extraction. Dashboards display:

• Estimated profit extracted by searchers (in USD/native gas token)
• Gas costs incurred on source and destination chains
• Bridge fees or messaging layer fees
• Net profit margins after all costs, highlighting the most lucrative corridors.

Identifies malicious or risky MEV activity. Tracks metrics like:

• Sandwich attack frequency and victim count
• Failed exploit attempts on bridges or DeFi protocols
• Unusual gas price spikes indicative of bidding wars
• Reorg attempts or other consensus-layer manipulations.

Shows MEV activity related to specific cross-chain infrastructure. This includes volume flowing through bridges (e.g., Across, LayerZero), messaging protocols, and interoperability hubs (e.g., Chainlink CCIP). Helps assess which infrastructure is most utilized for MEV and its associated reliability.