Layer 2 scaling solutions necessitate fundamental changes to AMM design, moving beyond simple EVM porting to address new constraints and opportunities.
LABS
AMM Design Considerations on Layer 2
Chainscore © 2025
Core Architectural Shifts
Sequencer-Centric Execution
Sequencer pre-confirmations enable new UX paradigms. AMMs can offer instant, zero-slippage swaps based on the sequencer's promise before the batch is settled on L1. This requires robust fraud-proof or validity-proof systems to secure the interim state. For users, this means near-instant finality and capital efficiency previously impossible on L1.
Cost-Optimized State Management
State growth gas costs shift from storage to calldata. AMM designs minimize on-chain state writes, using storage slots only for core liquidity reserves while moving position tracking and fee accounting off-chain or into cheaper data structures. This reduces the cost of frequent LP operations and high-frequency trading, making smaller positions viable.
Modular Fee & Incentive Design
Multi-dimensional fee tiers must account for L1 settlement costs and L2 execution costs separately. Protocols often implement dynamic fee models that adjust based on network congestion and batch submission costs. This matters for LPs who need predictable returns and traders seeking optimal routing across a fragmented L2 landscape.
Cross-Layer Liquidity Synchronization
Native bridging mechanics are integrated into pool design. AMMs can act as canonical bridges for their native assets, using pool reserves for instant liquidity on L2 while managing the slow L1 withdrawal process in the background. This eliminates the need for separate bridge liquidity and reduces capital lock-up for users moving assets between layers.
Proof-Aware Contract Logic
Validity proof verification (ZK-Rollups) or fraud proof challenges (Optimistic Rollups) influence contract design. AMM logic must be compatible with the L2's proof system, often requiring circuit-friendly operations for ZK or incorporating dispute time windows for Optimistic chains. This ensures the protocol's security is aligned with the underlying L2's guarantees.
Fee Model Analysis
Understanding AMM Fees on L2
Automated Market Maker (AMM) fees are the primary revenue source for liquidity providers. On Layer 2 networks like Arbitrum or Optimism, the fee model is a critical design choice that balances user cost, LP profitability, and protocol sustainability.
Key Components
- Swap Fee: The percentage charged on each trade, typically ranging from 0.01% to 1%. This fee is distributed to LPs.
- Gas Fee: The cost to process the transaction on the L2 network, which is significantly lower than Ethereum mainnet.
- Protocol Fee: An optional fee taken by the protocol itself (e.g., Uniswap's governance-controlled fee) from the swap fee.
Example Scenario
When swapping ETH for USDC on a 0.3% fee pool on Arbitrum, you pay the 0.3% swap fee (to LPs) plus a small gas fee in ETH. The total cost is dramatically lower than a similar swap on Ethereum mainnet, making small trades viable.
Optimizing for Capital Efficiency
Process overview for designing AMMs that maximize asset utilization and minimize idle liquidity on Layer 2.
1
Analyze Target Asset Correlations
Identify and categorize asset pairs based on their expected price stability to inform pool structure.
Detailed Instructions
Begin by researching the historical and expected price correlation between the assets you plan to pair. Highly correlated pairs, like stablecoin-stablecoin or wrapped versions of the same asset (e.g., WETH/ETH), are ideal for low-fee, low-slippage pools. For volatile pairs (e.g., ETH/altcoin), higher fees are necessary to compensate LPs for impermanent loss risk. Use on-chain data from sources like Dune Analytics or The Graph to model historical volatility. This analysis directly informs your fee tier strategy and whether to implement concentrated liquidity, as stable pairs benefit most from narrow, active price ranges.
- Sub-step 1: Query historical price data for candidate asset pairs from an oracle or DEX aggregator API.
- Sub-step 2: Calculate the 30-day and 90-day rolling correlation coefficient and standard deviation of returns.
- Sub-step 3: Categorize pairs as 'stable' (correlation >0.95), 'correlated' (0.7-0.95), or 'volatile' (<0.7).
typescript// Example: Calculating correlation using price data arrays function calculateCorrelation(pricesA: number[], pricesB: number[]) { // Implementation for Pearson correlation coefficient }
Tip: For nascent L2 ecosystems, proxy data from Layer 1 mainnet may be necessary, but adjust for potential differences in initial trading behavior.
2
Implement Concentrated Liquidity Mechanics
Deploy a V3-style AMM that allows liquidity providers to set custom price ranges.
Detailed Instructions
Adopt a concentrated liquidity model, such as the Uniswap V3 design, where LPs deposit assets within a specific min/max price range. This dramatically increases capital efficiency for known trading ranges. The core smart contract must track individual LP positions as discrete NFTs. Key functions include mint, which creates a position given tick boundaries, and swap, which computes the swap amount based on the virtual reserves within the active tick. Ensure the contract uses fixed-point arithmetic (e.g., Q64.96 or Q128.128) for precision and gas optimization on L2.
- Sub-step 1: Define the tick spacing (e.g., 1 bps for stable pairs, 60 bps for volatile pairs) in your factory contract.
- Sub-step 2: Implement the
TickandPositionstructs to store liquidity net and gross values. - Sub-step 3: Code the swap function to iterate through initialized ticks, updating the current sqrtPrice and executing the trade.
solidity// Core structs for a concentrated liquidity position struct Position { uint128 liquidity; uint256 feeGrowthInside0LastX128; uint256 feeGrowthInside1LastX128; } // Mint function signature function mint( address recipient, int24 tickLower, int24 tickUpper, uint128 amount ) external returns (uint256 tokenId);
Tip: Use a tick bitmap to efficiently find the next initialized tick during a swap, which is critical for maintaining low gas costs on L2.
3
Calibrate Dynamic Fee Tiers
Set and optionally automate fee adjustments based on pool volatility and utilization.
Detailed Instructions
Static fees can lead to suboptimal LP returns or excessive trader slippage. Implement dynamic fee tiers that adjust based on real-time metrics. A common model uses the volatility oracle from the pool's own time-weighted average price (TWAP) to suggest fee updates. For instance, a governance process or automated function can increase the fee from 5 bps to 30 bps if 24h volatility exceeds a threshold (e.g., 5%). Alternatively, base fees on pool utilization—the ratio of traded volume to total liquidity—to incentivize more liquidity during high-demand periods.
- Sub-step 1: Deploy an oracle that continuously calculates the 1-hour and 24-hour TWAP and its standard deviation.
- Sub-step 2: Create a
suggestFeeUpdateview function that returns a recommended fee based on oracle data. - Sub-step 3: Implement a timelock-controlled
updateFeefunction or a decentralized keeper network to execute changes.
solidity// Example logic snippet for dynamic fee suggestion function getSuggestedFee(uint32 volatilityBps) public pure returns (uint24 fee) { if (volatilityBps < 100) return 5; // 0.05% for stable else if (volatilityBps < 500) return 30; // 0.30% for moderate else return 100; // 1.00% for highly volatile }
Tip: On L2, you can afford more frequent oracle updates and fee recalibrations due to lower gas costs, making the system more responsive.
4
Integrate with L2-Specific Yield Aggregators
Deploy vault strategies that recycle idle pool liquidity into other yield-bearing protocols.
Detailed Instructions
Capital outside the active price range in a concentrated pool is idle. Use L2-native yield aggregators (like Aave or Compound forks on the L2) to lend out this idle capital. Design a vault contract that manages LP positions and automatically deposits the idle portion of each token into a lending market. The contract must periodically rebalance by withdrawing loans to support liquidity if the price moves into a new range. This creates a composite yield from trading fees and lending interest. Ensure the vault uses secure, audited interfaces for the lending protocols deployed on your specific L2.
- Sub-step 1: Develop a
Vaultcontract that holds the LP NFT and interacts with the lending pool'ssupplyandwithdrawfunctions. - Sub-step 2: Implement a
rebalancefunction triggered by price movement or time, which adjusts lending positions based on the current tick. - Sub-step 3: Add a keeper incentive mechanism, paying a small reward in vault shares for calling rebalance.
solidity// Simplified vault function to deposit idle USDC function _depositIdleToAave(uint256 amount) internal { IERC20(USDC).approve(address(aaveLendingPool), amount); aaveLendingPool.deposit(USDC, amount, address(this), 0); }
Tip: Factor in L2-specific bridge timings and lending pool withdrawal delays when designing rebalance logic to ensure liquidity is always available for swaps.
5
Optimize Gas for Frequent Rebalancing
Reduce the cost of position management operations to make tight ranges and active strategies viable.
Detailed Instructions
Gas optimization is paramount on L2, as high-frequency actions like adding liquidity or compounding fees must remain economical. Use L2-specific opcodes and storage tricks. Batch multiple operations into a single transaction using a helper contract. For fee compounding, implement a fee auto-compound mechanism that reinvests accrued fees into the LP position without requiring the LP to claim and re-deposit manually. Store frequently accessed state variables in transient storage (tstore/tload if available) or use tightly packed structs. Prefer uint256 for math and store smaller values in lower uint types to save on SSTORE costs.
- Sub-step 1: Audit all core AMM functions (mint, swap, collect) for redundant storage writes and replace with in-memory variables where possible.
- Sub-step 2: Create a
MulticallorRoutercontract that bundlescollectFeesandincreaseLiquidityin one tx. - Sub-step 3: Implement an optional keeper-based auto-compounder that triggers for positions with fees above a threshold (e.g., 0.1 ETH).
solidity// Example of a batched function for efficient fee reinvestment function collectAndCompound( uint256 tokenId, uint128 liquidityDelta ) external { (uint256 owed0, uint256 owed1) = nonfungiblePositionManager.collect(...); nonfungiblePositionManager.increaseLiquidity( tokenId, owed0, owed1 ); }
Tip: Leverage L2 rollup-specific features like Arbitrum's
arbgasinfo or Optimism's low-cost storage to further fine-tune transaction costs.
Network-Specific Implementation Factors
Comparison of key technical parameters affecting AMM design across major L2s.
| Technical Parameter | Arbitrum One | Optimism | zkSync Era | Base |
|---|---|---|---|---|
Avg. L1 Finality Time | ~12 minutes | ~12 minutes | ~12 minutes | ~12 minutes |
Avg. L2 Block Time | ~0.25 seconds | ~2 seconds | ~1 second | ~2 seconds |
Avg. Bridge Withdrawal Time | ~1 week (challenge period) | ~1 week (challenge period) | ~1-5 hours | ~1 week (challenge period) |
Typical L2 Tx Cost (Swap) | ~$0.10 - $0.30 | ~$0.05 - $0.15 | ~$0.01 - $0.05 | ~$0.01 - $0.10 |
State Update Finality to L1 | ~1-5 minutes (via batch) | ~1-5 minutes (via batch) | ~10-60 minutes (via validity proof) | ~1-5 minutes (via batch) |
Max Compute per Tx (Gas) | ~32M gas (ArbOS limit) | ~30M gas (EVM parity) | ~30M gas (EVM parity) | |
Native Token for Fees | ETH | ETH | ETH (paymaster abstraction common) | ETH |
Security and Trust Assumptions
Understanding the security models and trust dependencies of Layer 2 AMMs is critical for protocol design and user safety.
Sequencer Trust
Sequencer censorship is a primary concern. The central sequencer can reorder or censor transactions, impacting MEV and fair access.
- Users trust the sequencer to include their transactions promptly and honestly.
- Decentralized sequencer sets or forced inclusion via L1 mitigate this risk.
- This matters for users seeking guaranteed transaction execution and protection from front-running.
Data Availability
Data availability (DA) ensures transaction data is published so anyone can reconstruct state. Rollups use different models.
- Validiums use off-chain DA committees, introducing trust in data custodians.
- Optimistic and ZK rollups post data to Ethereum L1 for cryptographic guarantees.
- This is fundamental for users verifying asset ownership and challenging invalid state transitions.
Bridge and Escrow Security
Canonical bridges hold user funds locked on L1. Their security is paramount for the entire L2 ecosystem.
- A bridge hack compromises all bridged assets on the L2.
- Security depends on the bridge's multisig or smart contract implementation and audits.
- Users must trust the bridge's upgrade mechanisms and governance more than the L2's own sequencer.
Prover/Verifier Trust
Validity proofs in ZK-rollups shift trust from social consensus to cryptographic verification.
- Users trust the correctness of the zk-SNARK/STARK circuit and the prover's implementation.
- A bug in the verifier contract or proving system can lead to stolen funds.
- This matters as it replaces the need for a 7-day fraud proof window, enabling instant finality.
Upgrade Mechanisms
Protocol upgrades are often managed by a multisig or DAO, creating a centralization vector.
- A malicious or compromised upgrade can change AMM fee structures or drain liquidity.
- Timelocks and decentralized governance improve safety but add complexity.
- Users implicitly trust the entity controlling the upgrade keys, which is a critical long-term assumption.
Economic Security & Slashing
Economic security for fraud proofs in Optimistic Rollups relies on bonded validators being financially honest.
- Validators must post a bond that can be slashed for fraudulent behavior.
- The security level is tied to the bond size and the cost of corruption.
- For users, this means safety depends on sufficient economic incentives being in place to punish bad actors.
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Implementation FAQ
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