How to Implement a Fee Market Analysis for MEV Strategy

Maximal Extractable Value (MEV) is profit extracted by reordering, inserting, or censoring transactions within blocks. A fee market analysis is the foundational data layer for any MEV strategy, providing the real-time intelligence needed to estimate transaction inclusion costs and prioritize opportunities. This involves monitoring the mempool (pending transactions), analyzing gas prices, and understanding block space demand. Without this analysis, searchers risk overpaying for failed transactions or missing profitable arbitrage and liquidations due to slow execution.

The core of fee market analysis is tracking the base fee and priority fee (tip). The base fee is algorithmically adjusted per block by the protocol, while the priority fee is a bid to validators for faster inclusion. To implement this, you need to subscribe to a node's JSON-RPC endpoint for new pending transactions (eth_subscribe, newPendingTransactions) and new blocks (eth_subscribe, newHeads). Tools like Erigon or Geth in archive mode are commonly used. For scalable analysis, services like Flashbots Protect, Blocknative, or Alchemy's Mempool API provide enriched data feeds.

A basic Python implementation using Web3.py involves creating a mempool listener and a gas price analyzer. The following code snippet sets up a subscription to the pending transaction stream and calculates the current gas price percentiles, which are more reliable than a simple average for strategy decisions.

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from web3 import Web3
import asyncio

w3 = Web3(Web3.WebsocketProvider('wss://mainnet.infura.io/ws/v3/YOUR_KEY'))

async def log_new_transaction(tx_hash):
    try:
        tx = w3.eth.get_transaction(tx_hash)
        print(f"Tx: {tx_hash.hex()}, Gas Price: {tx['gasPrice']}, From: {tx['from']}")
    except Exception as e:
        print(f"Error fetching tx {tx_hash}: {e}")

def handle_new_block(block_hash):
    block = w3.eth.get_block(block_hash, full_transactions=True)
    gas_prices = [tx['gasPrice'] for tx in block.transactions]
    if gas_prices:
        gas_prices.sort()
        print(f"Block #{block['number']} - 50th pct: {gas_prices[int(len(gas_prices)*0.5)]}, 90th pct: {gas_prices[int(len(gas_prices)*0.9)]}")

For advanced MEV strategies, simple gas price tracking is insufficient. You must analyze transaction bundles and their dependencies. This involves building a graph of pending transactions to identify frontrunning or backrunning opportunities. Key metrics to compute include: inclusion time delay (how long a tx sits in the mempool), gas price volatility, and block space saturation (percentage of gas limit used). Monitoring the gasUsed versus gasLimit in recent blocks provides a leading indicator of network congestion and impending base fee increases.

Integrate your fee market analysis with a searcher system. The output—a recommended max priority fee per gas (maxPriorityFeePerGas) and max fee per gas (maxFeePerGas)—should be fed into your transaction simulation and bundling engine. For Ethereum post-EIP-1559, a robust strategy often sets the maxFeePerGas at the 90th percentile of recent included transactions and the maxPriorityFeePerGas based on the current bid required to be in the next 1-2 blocks. Always simulate transactions using a tool like Tenderly or Ganache before broadcasting to a private relay like Flashbots to avoid wasting gas on reverts.

Finally, backtest your fee market model against historical data. Use datasets from Google BigQuery's public Ethereum dataset or Dune Analytics to correlate your predicted optimal gas prices with actual inclusion rates and costs. Continuously refine your model by incorporating new variables like time-of-day patterns, major NFT mint events, or Layer 2 withdrawal batches that cause mainnet congestion. The most successful MEV strategies treat fee market analysis not as a static component but as a continuously learning system.

Analyzing the Ethereum fee market requires a foundational understanding of its core components. You must be familiar with EIP-1559, which introduced the base fee and priority fee (tip) model. The base fee is algorithmically adjusted per block and burned, while the tip is paid directly to validators to incentivize inclusion. Your analysis will focus on predicting fluctuations in these values to optimize transaction ordering and timing. A working knowledge of block building, mempool dynamics, and the role of searchers and builders in the PBS (Proposer-Builder Separation) landscape is essential.

You will need access to historical and real-time blockchain data. The primary tools for this are RPC endpoints and specialized data providers. For direct node access, services like Alchemy, Infura, or running your own Erigon or Geth archive node are necessary. For higher-level analytics, platforms like Dune Analytics, Flipside Crypto, or The Graph offer pre-indexed datasets on gas prices, block space utilization, and transaction flows. You'll be querying for metrics like average gas used, base fee trends over time, and tip distributions.

The core of your analysis will be built with code. Proficiency in Python or JavaScript/TypeScript is recommended due to rich ecosystem support. Essential libraries include web3.py or ethers.js for interacting with the blockchain, pandas for data manipulation, and plotting libraries like matplotlib or plotly for visualization. You should set up a development environment with these packages installed. For example, a basic Python setup might start with pip install web3 pandas matplotlib to fetch and chart historical base fee data.

Your analysis should target specific MEV opportunities. Common strategies include arbitrage, liquidations, and NFT minting, each with distinct gas price sensitivities. An arbitrage bot might require submicrosecond latency and a complex model to outbid competitors, while a liquidation strategy may prioritize reliability over speed, submitting transactions with a high tip when the base fee is predictably low. Defining your strategy's time-sensitivity and profit margin threshold is a prerequisite for building a meaningful fee model.

Finally, you need a framework for simulation and backtesting. Before deploying capital, you must test your fee market assumptions against historical data. This involves reconstructing mempool states and simulating your transaction's placement within historical blocks. Tools like Ethereum Execution Layer Specification (EELS) for local simulation or custom scripts using transaction traces from Etherscan or Blocknative can be used. Backtesting validates whether your fee prediction model would have captured profits or suffered from frontrunning or inclusion failures.

EIP-1559 fundamentally changed Ethereum's fee market by introducing a base fee and a priority fee (tip). The base fee, which is algorithmically adjusted per block and burned, represents the minimum cost to include a transaction. For a transaction to be prioritized, users add a priority fee for the block proposer. For MEV searchers, the goal is to get a transaction included in a specific position within a block, which requires outbidding competing transactions. Analyzing the fee market means tracking the relationship between the base fee, the total gas used in recent blocks, and the tips being paid for priority inclusion.

To implement a fee market analysis, you need real-time data. Key metrics to monitor include the base fee trend (is it rising or falling?), the block gas used relative to the target (15 million gas), and the distribution of priority fees in recent blocks. A simple Python script using the Web3.py library can fetch this data. For example, you can query the last 20 blocks to calculate the average and 90th percentile priority fee, giving you a target for your own transactions. Tools like the Etherscan Gas Tracker and Blocknative's Gas Platform provide aggregated views, but for automated strategies, direct RPC calls are essential.

Your MEV strategy must adapt to market conditions. In a calm market with low base fees and consistent block space, you might submit transactions with a modest tip. During periods of high demand—such as an NFT mint or a major DeFi liquidation—the base fee will spike, and you'll need to aggressively increase your priority fee to win the block space auction. Sophisticated bots often implement dynamic fee estimation, adjusting their bid based on the velocity of base fee changes and the gas consumption of their target transaction. A common tactic is to set a fee cap (maxFeePerGas) significantly higher than the current base fee to ensure inclusion across multiple blocks if necessary, while the priority fee drives immediate priority.

Understanding block builder behavior is crucial for advanced MEV. Since the implementation of Proposer-Builder Separation (PBS), validators outsource block construction to specialized builders. These builders optimize for maximum extractable value, which includes both the priority fees and any arbitrage or liquidation profits packaged into the block. Your transaction's effective tip must compete not only with other user tips but also with the value of the MEV opportunities a builder can capture by including other transactions. Analyzing successful blocks on a relay like Flashbots can reveal the going rate for inclusion in high-value blocks.

Finally, integrate your analysis into a transaction submission loop. Your bot should: 1) Poll for the latest block header and fee data, 2) Calculate an optimal maxPriorityFeePerGas based on your target inclusion speed (next block vs. within 5 blocks), 3) Construct and sign your transaction, and 4) Submit it via a reliable method, such as the eth_sendRawTransaction RPC call or a private transaction pool like Flashbots Protect. Always include robust error handling for replacement transactions (using the same nonce with a higher fee) if your initial bid is too low and gets stuck in the mempool.

The Ethereum base fee is the minimum price per unit of gas required for a transaction to be included in a block, algorithmically adjusted by the EIP-1559 mechanism. For MEV searchers, forecasting this fee is critical for two reasons: it determines the absolute minimum cost to execute a strategy, and its volatility directly impacts profit margins. A successful forecast model analyzes the fee market—the interplay between block space demand (user transactions) and supply (network capacity).

To build a forecast, you must first collect and structure historical data. Essential data points include: the base fee for each block, total gas used per block, and the gas target (currently 15 million gas). This data is readily available via node RPC calls (eth_getBlockByNumber) or APIs from services like Etherscan and Blocknative. The core relationship to model is how the base fee for block N+1 is calculated from block N: it increases if gas used exceeds the target, and decreases if it falls below, with a maximum change of ±12.5%.

Simple forecasting can use a moving average of recent base fees, but this lacks context. A more robust approach incorporates leading indicators of demand. Monitor pending transaction pools (mempools) via services like Flashbots to gauge immediate pressure. Correlate base fee spikes with on-chain events like major NFT mints, token launches, or liquidations in lending protocols. For example, a sudden influx of transactions to a new DeFi pool will consume gas and drive up the base fee for subsequent blocks.

For programmatic analysis, implement a model that weights recent trends. Here's a Python snippet using web3.py to fetch recent blocks and calculate a simple weighted average forecast:

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from web3 import Web3
w3 = Web3(Web3.HTTPProvider('YOUR_RPC_URL'))

recent_blocks = 50
gas_used_list = []
base_fee_list = []

for i in range(recent_blocks):
    block = w3.eth.get_block('latest' - i)
    gas_used_list.append(block.gasUsed)
    base_fee_list.append(block.baseFeePerGas)

# Calculate a simple forecast weighting the last 5 blocks more heavily
weights = list(range(1, 6)) + [1] * (recent_blocks - 5)
total_weight = sum(weights)
weighted_avg_base_fee = sum(b * w for b, w in zip(base_fee_list, weights)) / total_weight
print(f"Weighted average base fee forecast: {weighted_avg_base_fee}")

Advanced models integrate external data feeds. The Ethereum Average Gas Price chart from Ultrasound.money provides a macro view. Correlate fee trends with overall network activity metrics like total daily transactions (from Dune Analytics) and periods of high MEV activity. Remember, the base fee is burned, so it represents pure demand for block space, decoupled from miner/validator priority fees. Your forecast should output a range (e.g., 45-60 Gwei) for the target block, which then informs the maxPriorityFeePerGas you need to add to win the auction.

Continuously validate and refine your model. Backtest predictions against actual outcomes. A discrepancy often reveals unmodeled events, like a popular bridge processing withdrawals or a sudden market crash triggering widespread liquidations. Integrate this forecasting as the first module in your MEV pipeline; the output sets the economic guardrails for all subsequent strategy simulation and bundle construction.

The priority fee (or tip) is the portion of the transaction fee paid directly to validators to incentivize them to include your transaction in the next block. Unlike the base fee, which is burned, the tip is the validator's reward. In a competitive environment like a block space auction, setting this fee too low means your transaction may be delayed or ignored. Setting it too high wastes capital. The goal is to analyze the fee market—the real-time supply and demand for block space—to find the minimum effective tip.

To calculate an optimal tip, you must analyze historical data. This involves querying a node or block explorer API for recent blocks. For each block, you examine the transactions and extract their priority fees. A common approach is to look at the percentile distribution of tips from the last 50-100 blocks. For example, you might calculate the 75th percentile tip to target inclusion in the next few blocks with high probability. Tools like the eth_feeHistory RPC method provide this data directly, returning arrays of priority fees per block for specified percentile ranges.

Here is a simplified Python example using the Web3.py library to fetch and analyze fee history:

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from web3 import Web3
w3 = Web3(Web3.HTTPProvider('YOUR_RPC_URL'))

# Fetch fee history for the last 50 blocks, for the 25th, 50th, and 75th percentiles
fee_history = w3.eth.fee_history(50, 'latest', [25, 50, 75])

# Extract the 75th percentile tips from the response
tips_75th = fee_history['reward']

# Calculate a recommended tip (e.g., the average of the recent 75th percentile values)
recommended_tip_wei = int(sum([t[2] for t in tips_75th]) / len(tips_75th))
recommended_tip_gwei = w3.from_wei(recommended_tip_wei, 'gwei')
print(f"Recommended Priority Fee: {recommended_tip_gwei} Gwei")

This code retrieves the historical reward data and computes a suggested tip based on recent market activity.

Your analysis must account for network volatility. During periods of high congestion (e.g., a popular NFT mint or a major DeFi event), the fee market can spike unpredictably. A static tip based on a slow period will fail. Therefore, robust MEV strategies implement dynamic fee estimation. This often involves setting a multiplier on the calculated percentile (e.g., 1.2x the 75th percentile during high gas times) or using a smoothing function like an exponential moving average (EMA) to filter out short-lived spikes and identify the underlying trend.

Finally, integrate this calculated tip with the base fee from Step 1 to form the total maxFeePerGas. The transaction's final cost will be min(baseFee + priorityFee, maxFeePerGas). By programmatically determining both components based on real-time chain data, your MEV bots or strategies can consistently secure timely transaction inclusion while optimizing cost efficiency, directly impacting profitability.

The total transaction cost for an MEV strategy is not just the base gas fee. It's the sum of several components: the base fee set by the network, a priority fee (tip) to incentivize validators, the gas cost of your bundle's execution, and any MEV-specific payments like backrunning fees or payments to searcher marketplaces like Flashbots. For strategies like arbitrage or liquidations, the gas cost can be substantial, as they often involve multiple contract calls across several transactions bundled together. A precise simulation must account for all these elements to avoid scenarios where the cost of execution exceeds the extracted value.

To simulate these costs, you need historical and real-time on-chain data. Use an RPC provider with access to archive data (e.g., Alchemy, Infura) and a block explorer API. The key data points include: the historical base fee per block, average priority fees for different confirmation speeds, and the exact gas usage of your target smart contract interactions. You can estimate gas usage by simulating the transaction locally using tools like eth_call or by deploying your strategy on a testnet and measuring consumption. Remember that gas costs can spike during network congestion, so your model should incorporate volatility.

Here is a basic Python pseudocode structure for a cost simulation function using the web3.py library. This example focuses on calculating the cost for a single target transaction.

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from web3 import Web3

def simulate_transaction_cost(w3, target_tx, base_fee_wei, priority_fee_wei):
    """
    Simulates the cost of a transaction.
    w3: Web3 connection
    target_tx: dict with 'to', 'data', 'value' keys
    base_fee_wei: current base fee in wei (from w3.eth.get_block('latest')['baseFeePerGas'])
    priority_fee_wei: your chosen tip in wei
    """
    # Estimate gas usage
    try:
        gas_estimate = w3.eth.estimate_gas(target_tx)
    except Exception as e:
        print(f"Gas estimation failed: {e}")
        return None
    
    # Calculate total fee per gas (Base Fee + Priority Fee)
    max_fee_per_gas = base_fee_wei + priority_fee_wei
    
    # Calculate total cost in wei and ether
    total_cost_wei = gas_estimate * max_fee_per_gas
    total_cost_eth = Web3.from_wei(total_cost_wei, 'ether')
    
    return {
        'gas_estimate': gas_estimate,
        'total_cost_wei': total_cost_wei,
        'total_cost_eth': total_cost_eth,
        'max_fee_per_gas_gwei': Web3.from_wei(max_fee_per_gas, 'gwei')
    }

This function provides a core building block. For a complete MEV bundle, you would sum the cost of all transactions in the sequence.

Your simulation must be integrated with your opportunity identification logic from Step 2. For every potential opportunity detected, run the cost simulation. The net profit is then: Opportunity Value (Δ) - Total Simulated Cost. Only proceed with constructing and submitting a bundle if this net profit exceeds your minimum threshold. This threshold should account for your operational overhead and the risk of the bundle failing or being outbid. Advanced simulations will run Monte Carlo analyses, varying base fee and priority fee inputs based on historical distributions to model a range of possible costs and success probabilities.

Finally, use this simulation to inform your bidding strategy. If the network is congested and base fees are high, you may need to increase your priority fee significantly to land your bundle in the next block, which erodes profits. Alternatively, you might decide to wait for a lower-fee environment. Tools like Flashbots Protect RPC or Blocknative's Mempool Explorer can provide real-time data on fee markets and pending transactions, allowing your model to make dynamic, informed decisions just before bundle submission.

After analyzing the fee market, the next step is to programmatically integrate this data into your MEV bundle construction logic. This requires connecting your analysis engine to a bundle creation library like mev-rs, mev-boost-relay, or ethers.js for direct RPC calls. Your system must ingest the analyzed data—such as optimal base fee predictions, priority fee percentiles, and block space congestion—and use it to set the maxPriorityFeePerGas and maxFeePerGas for each transaction in your bundle. This ensures your bundle is priced competitively for the target block.

A critical implementation detail is dynamic fee adjustment. Your code should not use static fee values. Instead, it should calculate fees based on the latest state. For example, if your analysis indicates the next block's base fee will be 15 Gwei and the 90th percentile priority fee is 2 Gwei, your maxFeePerGas should be set to at least 17 Gwei. Here's a simplified Python snippet using web3.py:

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# Pseudo-code for dynamic fee setting
def build_bundle_tx(analysis_result):
    base_fee_prediction = analysis_result['next_base_fee_gwei']
    priority_fee = analysis_result['p90_priority_fee_gwei']
    max_fee_per_gas = web3.to_wei(base_fee_prediction + priority_fee, 'gwei')
    transaction['maxFeePerGas'] = max_fee_per_gas
    transaction['maxPriorityFeePerGas'] = web3.to_wei(priority_fee, 'gwei')
    return transaction

You must also implement bundle simulation and validation before submission. Use a local Ethereum execution client (e.g., Geth, Nethermind) in a test mode or a service like Tenderly to simulate the bundle's execution against the latest state. This checks for revert risks and calculates the exact gas used, which is necessary for setting an accurate gasLimit. A failed simulation wastes no on-chain gas and protects your capital. The validation step should confirm that the bundle's profitability, after accounting for the newly calculated gas costs, still meets your minimum threshold.

Finally, integrate with a searcher-builder communication channel. For maximum effectiveness, submit your bundles directly to trusted builders via the MEV-Boost relay API or Flashbots Protect RPC. This requires signing bundles with your searcher key and formatting them according to the eth_sendBundle specification. Timing is crucial: your entire pipeline—from fee analysis and bundle building to simulation and submission—must complete within the 12-second window between Ethereum blocks to target the next slot.

To move from analysis to execution, you must integrate your fee market model with a live blockchain client or RPC provider. For Ethereum, tools like ethers.js or web3.py can subscribe to the pendingTransactions pool via a WebSocket connection to capture real-time gas prices and transaction types. Simultaneously, connect to a node's execution client (e.g., Geth, Erigon) to monitor the eth_feeHistory API, which provides historical base fee and priority fee percentiles essential for predicting future network congestion.

The core of a production system is a decision engine that consumes your real-time data stream. This engine should execute your chosen strategy—whether it's a simple threshold-based model (e.g., "submit bundle only if next block base fee is predicted to drop by >10%") or a more complex reinforcement learning agent. Code this logic to output actionable commands, such as constructing and signing a bundle of transactions for a searcher's private mempool or adjusting the gas parameters for a pending user transaction in a wallet application.

Finally, rigorous backtesting and simulation are non-negotiable before deploying capital. Use historical blockchain data from services like Google's BigQuery public datasets or Dune Analytics to replay your strategy against past chain states. Frameworks like foundry's forge allow you to simulate bundle execution in a local environment. Continuously monitor key performance indicators (KPIs) like capture rate, profit-and-loss (PnL), and gas efficiency in production, and be prepared to iterate on your model as network dynamics, like EIP-4844 blobs or new PBS developments, evolve the fee market landscape.