Setting Up a Real-Time Monitoring System for MEV Extraction

Maximal Extractable Value (MEV) refers to the profit that can be extracted by reordering, including, or censoring transactions within a block. Real-time monitoring is essential for understanding the MEV landscape, identifying new strategies, and detecting malicious activity like sandwich attacks or time-bandit forks. A monitoring system processes raw blockchain data—mempool transactions, block proposals, and final state changes—to surface these opportunities and risks as they happen.

To build an effective monitor, you need to capture data from several sources. First, connect to mempool providers like Flashbots Protect, BloxRoute, or a local Geth node to see pending transactions. Second, subscribe to a block builder API (e.g., from bloXroute or an MEV-relay) or run an execution client to observe proposed blocks. Finally, you'll need an RPC endpoint from providers like Alchemy, Infura, or a local node to query final on-chain state and event logs for confirmation.

The core of the system is an event-driven architecture. Using a library like Ethers.js or Viem, you can listen for events such as pendingTransactions and newHeads. When a new pending transaction arrives, your application should decode it, analyze its intent (e.g., a large DEX swap), and check for known MEV patterns. When a new block is proposed, compare its transactions against the mempool to identify which ones were included and in what order.

For practical analysis, you'll need to identify specific MEV strategies. Common patterns include: Sandwich attacks, where an attacker places transactions before and after a victim's large swap; Liquidations in lending protocols like Aave; and Arbitrage between DEXes like Uniswap and Sushiswap. Implementing detectors requires calculating potential profit by simulating trades using on-chain price data and factoring in gas costs.

Here is a basic code snippet using Ethers.js to listen for pending transactions and log large DEX swaps:

javascriptCopy
const { ethers } = require('ethers');
const provider = new ethers.JsonRpcProvider('YOUR_RPC_URL');
provider.on('pending', async (txHash) => {
  try {
    const tx = await provider.getTransaction(txHash);
    if (tx && tx.to === '0xUniswapRouterAddress') {
      console.log(`Large swap pending: ${txHash}`);
    }
  } catch (err) {}
});

This simple listener is the first step toward building more complex pattern recognition.

After detection, storing and visualizing data is crucial for analysis. Stream processed events to a database (like PostgreSQL or TimescaleDB) and use a dashboard framework (like Grafana) to create real-time charts of MEV volume, profit by strategy, and top extractors. For advanced research, consider contributing to or using open-source MEV inspection tools like EigenPhi's public datasets or the Flashbots MEV-Explore repository to validate your findings and understand broader ecosystem trends.

To monitor MEV extraction, you need reliable access to blockchain data. A dedicated Ethereum execution client (like Geth or Erigon) running an archive node is ideal for historical analysis. For real-time monitoring, you'll require a connection to a mempool data provider such as Flashbots Protect RPC, BloxRoute, or a local Geth node with transaction streaming enabled. This provides the raw, pending transaction data where MEV opportunities are first identified. You will also need access to a block builder API (e.g., Flashbots, bloXroute, Builder0x69) to observe the contents of proposed blocks before they are finalized on-chain.

Your development environment should be configured for data processing. We recommend using Python 3.10+ or Node.js 18+ for scripting and analysis. Essential libraries include web3.py or ethers.js for blockchain interaction, pandas for data manipulation, and a time-series database like TimescaleDB or InfluxDB for storing and querying high-frequency event data. A basic understanding of smart contract interactions, gas mechanics, and common DeFi protocols (Uniswap, Aave, Compound) is necessary to interpret the transactions you'll be monitoring.

Finally, you must establish a clear monitoring objective. Are you tracking general network activity, specific searcher addresses, or particular exploit patterns like sandwich attacks or liquidations? Define the key metrics: profit per bundle, inclusion rate, latency from mempool to block, and failed transaction analysis. Having these components and goals defined will structure the data pipeline you build in the following sections, turning raw blockchain streams into actionable MEV intelligence.

At its core, MEV (Maximal Extractable Value) monitoring involves tracking specific on-chain patterns. The primary data sources are transaction mempools and block data. A real-time system must ingest pending transactions from public mempool endpoints (e.g., via WebSocket from providers like Alchemy or Infura) and compare them against the final state of newly mined blocks. The key signal is identifying transactions that were included, reordered, or sandwiched relative to their original position in the mempool. This requires maintaining a local state of pending transactions and mapping them to their final block inclusion.

Understanding the main MEV strategies is crucial for pattern detection. Arbitrage involves profiting from price differences of the same asset across DEXs within a single block. Liquidations occur when a position becomes undercollateralized, triggering a profitable liquidation transaction. The most predatory form, sandwich attacks, involve placing one transaction before and one after a target victim's trade to manipulate the price. Monitoring for these requires analyzing transaction sequences for known contract addresses (like DEX routers) and tracking sudden, correlated price impacts on decentralized oracles.

To detect these events programmatically, you need to parse transaction calldata and track internal traces. For example, a sandwich attack can be identified by finding a victim swap transaction tx_v where the preceding transaction tx_a and following transaction tx_b are both from the same sender and interact with the same pool, with tx_a raising the price and tx_b selling the asset back. Tools like the Ethereum Execution API's debug_traceTransaction or specialized MEV inspection libraries (e.g., mev-inspect-py) can reconstruct these flows by analyzing the execution traces and state changes within a block.

Setting up alerts requires defining thresholds and heuristics. You might configure your system to flag any transaction bundle where profit, measured in ETH or USD, exceeds a certain value (e.g., 0.5 ETH). It's also critical to monitor for failed extraction attempts, such as reverted transactions or out-of-gas errors from competing bots, as they indicate contested opportunities and network congestion. Integrating with a service like Flashbots Protect or monitoring the flashbots namespace in block headers can help identify transactions that were submitted via private channels to avoid frontrunning.

Finally, a robust monitoring dashboard should visualize metrics like extracted value per block, top extracting addresses, and most targeted protocols. By tracking these over time, you can identify trends, such as increasing sandwich activity on a new DEX or a rise in arbitrage profits following a major oracle price update. This data is essential for developers to assess protocol vulnerability, for traders to understand market dynamics, and for researchers to quantify the scale and impact of MEV on the network.

The mempool is a network node's holding area for transactions that have been broadcast but not yet confirmed. For MEV searchers, this is the primary data source. A mempool watcher connects to one or more Ethereum nodes (like Geth or Erigon) via their JSON-RPC or WebSocket endpoints to subscribe to new pending transactions. The goal is to receive, parse, and filter this stream with minimal latency to identify profitable opportunities, such as arbitrage or liquidations, before other network participants.

You can build a basic watcher using the web3.py or ethers.js libraries. The core function is subscribing to the pendingTransactions event. For example, in Python with web3.py, you would establish a WebSocket connection and create a filter: pending_tx_filter = w3.eth.filter('pending'). You then poll this filter in a loop to get new transaction hashes, which you can subsequently fetch for full details using w3.eth.get_transaction(tx_hash). This gives you access to critical fields like to, from, input data, and value.

For production systems, connecting to a single node is insufficient due to rate limits and network diversity. Professional searchers use a mempool distribution network like BloXroute, Blocknative, or a proprietary set of geographically distributed nodes. These services offer optimized feeds that reduce latency and increase transaction coverage. Your watcher should implement logic to de-duplicate transactions from multiple sources and handle reconnects to maintain a consistent feed, as missed transactions directly translate to lost opportunities.

Once you have the raw transaction data, the next step is transaction simulation. Simply observing a transaction is not enough; you must predict its effect. This involves using a tool like Ethereum Tracer (ethers.js's call) or a dedicated simulation service like Tenderly or Foundry's cast to locally execute the transaction against a recent state. This simulation checks if the transaction will succeed and, crucially, calculates the state changes, such as token balance differences in a DEX pool, which form the basis of your profitability calculation.

Finally, your watcher must be integrated with a decision engine. After simulating a pending transaction and identifying a potential MEV opportunity (e.g., a large swap that moves a pool's price), your system needs to construct and broadcast a bundle or backrun transaction. This requires connecting to a block builder or relay like Flashbots Protect, BloXroute's MEV-Blox, or a private transaction pool to submit your competing transaction in a way that it is ordered favorably in the upcoming block.

A sandwich attack is a specific form of Maximal Extractable Value (MEV) where an attacker exploits a pending user transaction. The attack consists of three core transactions executed in a single block: 1) a front-run to buy the target asset before the victim, 2) the victim's original transaction, and 3) a back-run to sell the asset after the victim's trade has moved the price. Detecting this pattern requires monitoring the public mempool for pending transactions and analyzing their intent and potential interactions.

To build a detector, you need to subscribe to a real-time mempool feed. Services like Alchemy's Mempool Streaming, Blocknative, or running your own Geth or Erigon node with WebSocket subscriptions provide this data. The key is to listen for new pending transactions and parse their calldata to decode the function call, typically a swap on a DEX like Uniswap V2/V3. You must identify the target token, the swap direction (e.g., ETH for USDC), and the minimum output amount specified by the user.

Once you identify a potential victim swap, you scan for surrounding transactions targeting the same liquidity pool. A classic signature is a pair of transactions from the same EOA (Externally Owned Account) or a smart contract deployed just for the attack, placed immediately before and after the victim's transaction. These will have nearly identical swap paths but reversed directions. For example, if the victim swaps ETH for USDC, the attacker's front-run will also swap ETH for USDC, and the back-run will swap USDC for ETH.

Implementing detection logic involves calculating the price impact of the victim's trade and checking if the surrounding trades profit from it. You can simulate the state changes using libraries like ethers.js or viem. A reliable heuristic is to check if the attacker's profit, after accounting for gas fees, exceeds a threshold (e.g., 0.1 ETH) and if the three transactions are contiguous in position within a block. This logic can be written in TypeScript using a Node.js or Python script that processes the stream.

Here is a simplified code snippet outlining the detection logic structure using ethers.js and a generic mempool watcher:

javascriptCopy
async function analyzeForSandwich(victimTx, pendingTxs) {
  // 1. Decode victim's swap parameters (tokenIn, tokenOut, amount)
  // 2. Filter pendingTxs for swaps on same pool with same sender
  // 3. Check if one swap is same direction (front-run) and one is reverse (back-run)
  // 4. Simulate price impact and profit for the suspected attacker
  // 5. If profit > threshold, flag as potential sandwich attack
}

False positives can occur from arbitrage bots or liquidations, so incorporating confidence scores based on transaction ordering, gas pricing (attacks often use high priority fees), and sender reputation improves accuracy.

After detection, the data should be logged to a database or alerting system. Tools like Prometheus for metrics and Grafana for dashboards can visualize attack frequency and volume. Continuously tuning your detector against known attack transactions from sources like the EigenPhi MEV dashboard is crucial for maintaining effectiveness as attackers evolve their strategies to avoid pattern-based detection.

Flashbots Protect is a free RPC endpoint (https://rpc.flashbots.net) that acts as a sentry for your transactions. When you submit a transaction through it, the Flashbots network analyzes it for common MEV extraction patterns—like frontrunning, sandwich attacks, and arbitrage—before it reaches the public mempool. This provides a crucial layer of visibility and protection by simulating transaction outcomes in a private environment. You integrate it by simply changing your wallet or application's RPC provider URL, requiring no smart contract modifications.

The core protection mechanism is the eth_sendPrivateTransaction RPC method. Unlike a standard eth_sendRawTransaction, this method submits your transaction directly to the Flashbots relay. The relay then privately shares it with a network of searchers and builders for inclusion in a block, bypassing the public mempool entirely. For monitoring, you can use the flashbots npm package to programmatically check the status of your bundled transactions and inspect simulation results, which show potential state changes and identify if your transaction was a target.

To set up programmatic monitoring, install the Flashbots SDK: npm install @flashbots/ethers-provider-bundle. Then, instantiate a provider connected to the Flashbots relay. You can listen for bundle simulation results, which indicate if your transaction's execution path was altered by other transactions in the proposed bundle—a sign of a sandwich attack. The SDK also allows you to query the relay for your transaction's status and cancel it if it hasn't been mined after a certain number of blocks, preventing it from eventually leaking to the public mempool.

For a comprehensive alerting system, combine Flashbots Protect with on-chain monitoring tools. Use the Flashbots eth_getBundleStats API or the public Flashbots Explorer to track your transaction's journey. Set up alerts using services like Tenderly or OpenZeppelin Defender to notify you via webhook or email if a transaction exhibits suspicious patterns, such as a drastic gas price spike in the simulation or inclusion in a bundle with known arbitrage contracts. This creates a defense-in-depth strategy for MEV surveillance.

Remember that Flashbots Protect is primarily effective on Ethereum Mainnet. Its efficacy on Layer 2s or other EVM chains depends on those chains adopting a similar private mempool/relay architecture. Always use it in conjunction with other MEV mitigation strategies, such as setting rational gas limits, using deadline parameters on DEX swaps, and employing specialized smart contract designs like CowSwap's batch auctions or using the block.coinbase transfer trick to disincentivize extraction.

A real-time dashboard is critical for operationalizing MEV extraction. It provides visibility into key metrics like captured value, success rates, and gas costs across different strategies and blockchains. Without this centralized view, you're operating blind, unable to quickly identify profitable opportunities, detect failed transactions, or respond to changing network conditions. The goal is to move from manual, ad-hoc analysis to a continuous, automated monitoring system that informs decision-making.

The core of your dashboard is the data pipeline. You need to ingest and process events from multiple sources: transaction mempools (via services like Bloxroute or Blocknative), on-chain data (from nodes or indexers like The Graph), and your own bot execution logs. This data must be normalized, aggregated, and stored in a time-series database like InfluxDB or a data warehouse for efficient querying. Real-time stream processing, using frameworks like Apache Flink or Kafka Streams, is essential for calculating live metrics.

Key performance indicators (KPIs) to display include: Profit & Loss (PnL) per strategy and per chain, success/failure rate of bundle submissions, average gas price paid, and latency from opportunity detection to submission. You should also monitor system health metrics like RPC endpoint reliability, bot uptime, and wallet nonce status. Visualizing this data with tools like Grafana allows you to set up alerts for anomalies, such as a sudden drop in success rate or a spike in gas costs, enabling rapid intervention.

Here's a simplified example of a query to calculate hourly PnL from your database, assuming you have a transactions table with profit_eth and timestamp fields:

sqlCopy
SELECT
  DATE_TRUNC('hour', timestamp) as hour,
  SUM(profit_eth) as total_profit_eth,
  COUNT(*) as total_transactions
FROM transactions
WHERE timestamp > NOW() - INTERVAL '24 hours'
GROUP BY 1
ORDER BY hour DESC;

This data can be fed directly into a Grafana panel to create a profit-over-time chart, giving you an immediate view of your strategy's performance.

Beyond basic metrics, advanced dashboards incorporate competitive intelligence. This involves tracking the activity of other searchers, such as the frequency of sandwich attacks on specific pools or the prevailing gas bids for block space. By correlating your performance with network-level data—like base fee trends and validator set changes—you can refine your strategies. The dashboard becomes not just a reporting tool, but a strategic asset for optimizing your edge in the competitive MEV landscape.

Finally, ensure your dashboard is secure and access-controlled. MEV operations are high-value targets. Use API key authentication for data sources, read-only database users for your dashboard, and consider isolating the monitoring infrastructure from your core execution environment. Regularly backfill historical data to maintain long-term trend analysis. By implementing this system, you transition from reactive troubleshooting to proactive strategy management, a necessary step for sustainable MEV extraction.

A real-time alerting system is the core of an operational MEV extraction bot. Its purpose is to listen for on-chain events that signal a profitable opportunity and trigger your execution logic. This is typically built using a WebSocket connection to an Ethereum node or a service like Alchemy, Infura, or a dedicated MEV infrastructure provider. You subscribe to pending transactions in the mempool and new blocks, parsing them for the specific patterns your strategy targets, such as large DEX swaps, liquidations, or arbitrage openings.

The key to effective alerting is filtering and prioritization. The public mempool is a firehose of data; your system must discard noise and identify high-probability opportunities within milliseconds. Implement filters based on: contract addresses (e.g., Uniswap V3 routers), function signatures (like swapExactTokensForTokens), transaction value thresholds, and gas prices. More advanced setups use simulation in the alerting stage to pre-check an opportunity's profitability before committing to a bundle submission, saving resources on failed attempts.

Here is a simplified Node.js example using ethers.js and a WebSocket provider to listen for pending transactions and check for interactions with a specific Uniswap V3 router:

javascriptCopy
const { ethers } = require('ethers');
const UNISWAP_V3_ROUTER = '0xE592427A0AEce92De3Edee1F18E0157C05861564';

const provider = new ethers.providers.WebSocketProvider('YOUR_WS_URL');

provider.on('pending', async (txHash) => {
  try {
    const tx = await provider.getTransaction(txHash);
    if (tx && tx.to && tx.to.toLowerCase() === UNISWAP_V3_ROUTER.toLowerCase()) {
      console.log(`Potential MEV Tx: ${txHash}`);
      // Add your logic to analyze calldata, simulate, and build a bundle
    }
  } catch (err) {
    // Handle errors
  }
});

This basic listener is a starting point; production systems require connection resilience, queue management, and parallel processing.

After detecting a candidate transaction, your alerting pipeline must construct and route an actionable alert. This involves packaging key data—target transaction hash, estimated profit, required gas, deadline—into a message for your execution engine. Many searchers use a message queue (like RabbitMQ or Redis) to decouple detection from execution, ensuring alerts are processed reliably even under load. The execution engine then takes this alert, builds the competing bundle, and submits it via a relay like Flashbots Protect, Titan Builder, or a private mempool.

Finally, configure monitoring and logging for the alerting system itself. Track metrics such as: alerts per second, false positive rate, average processing latency, and connectivity status. Log all detected opportunities and their outcomes (ignored, simulated, submitted, won/lost). This data is critical for iterating on your strategy; analyzing why certain alerts were not profitable helps refine your filters and simulation parameters. Tools like Prometheus and Grafana can be used to visualize these metrics in real-time.

Your system is now operational, but it is a starting point. The next critical phase is active monitoring and iteration. Begin by establishing baseline metrics for your specific setup: track your average inclusion rate, the gas efficiency of your bundles, and the frequency of successful MEV opportunities captured. Use the alerting rules in Prometheus and Grafana to notify you of deviations, such as a sudden drop in block proposal success or spikes in transaction pool latency from tools like mev-inspect-py. Consistent monitoring will reveal the system's real-world performance and bottlenecks.

To enhance your system's effectiveness, consider these advanced integrations. First, connect to a private transaction relay like Flashbots Protect or the bloXroute Blinder to access a more competitive and opaque mempool. Second, implement more sophisticated opportunity identification by subscribing to specialized data streams. Services like EigenPhi provide APIs for real-time MEV opportunity alerts, while running your own mev-boost relay can give you direct insight into the builder market. For on-chain validation, regularly audit your contract interactions using tools like Slither or Foundry's forge inspect.

Finally, treat security and optimization as continuous processes. The MEV landscape evolves rapidly with new extraction techniques (e.g., PBS, SUAVE) and defensive measures like CowSwap's fairing protocol. Stay updated by monitoring Ethereum research forums, the Flashbots Discord, and repositories for key projects like mev-boost. Periodically review and update your alert thresholds, data retention policies, and the health checks for your Geth/Besu and MEV-Boost clients. A robust monitoring system is not a one-time setup but a dynamic component of your operational stack that requires ongoing maintenance to remain effective and profitable.