Introduction: The Underlying Logic of the Liquidity Game

In financial markets, retail investors are often seen as the “bag holders” when institutional investors exit their positions—when institutions need to sell off large amounts of assets, retail investors are typically the ones left to absorb the price drop. This asymmetry is further amplified in the cryptocurrency space, where the market-making mechanisms and dark pool trading on centralized exchanges (CEX) exacerbate this information imbalance. However, with the evolution of decentralized exchanges (DEXs), new order-book DEXs like dYdX and Antarctic are innovating mechanisms to reshape the distribution of liquidity power. This article will analyze how excellent DEXs physically separate retail and institutional liquidity by focusing on their technical architecture, incentive mechanisms, and governance models.

Liquidity Stratification: From Passive Absorption to Power Redistribution

The Liquidity Dilemma of Traditional DEXs

In the early Automated Market Maker (AMM) model, retail liquidity providers (LPs) faced significant adverse selection risks. For example, although Uniswap V3’s concentrated liquidity design improves capital efficiency, data shows that retail LPs typically have an average position of only $29,000, mostly concentrated in smaller pools with daily trading volumes under $100,000. In contrast, institutional players dominate large trading pools with average positions of $3.7 million, and in pools with daily trading volumes over $10 million, institutions account for 70-80% of the liquidity. When institutions execute large sell-offs in this structure, retail liquidity pools are the first to take the brunt of price declines, forming a typical “exit liquidity trap.”

The Need for Liquidity Stratification

The Bank for International Settlements (BIS) has highlighted a significant professional stratification in the DEX market: although retail investors account for 93% of total liquidity providers, 65-85% of the actual liquidity is provided by a small number of institutions. This stratification is not accidental but a necessary result of market efficiency optimization. A well-designed DEX needs to separate “long-tail liquidity” from retail investors and “core liquidity” from institutions. For example, the MegaVault mechanism introduced by dYdX Unlimited allocates USDC deposited by retail investors to sub-pools controlled by institutions, ensuring liquidity depth while protecting retail investors from large transaction impacts.

Technical Mechanisms: Building a Liquidity Firewall

The Innovation of Order Book Models

Order-book DEXs can build multi-layered liquidity protection mechanisms through technical innovation. The core goal is to physically separate retail liquidity needs from institutional large trades, ensuring that retail investors are not passively exposed to large market fluctuations. The design of a liquidity firewall must balance efficiency, transparency, and risk isolation capabilities. The key is to use a hybrid architecture that combines on-chain and off-chain coordination, ensuring both user autonomy over assets and protection from market volatility and malicious attacks on liquidity pools.

The hybrid model processes high-frequency operations like order matching off-chain, leveraging the low latency and high throughput characteristics of off-chain servers to significantly improve transaction execution speed, avoiding slippage caused by blockchain network congestion. Meanwhile, on-chain settlement ensures asset security and transparency. For instance, DEXs like dYdX v3, Aevo, and Antarctic utilize off-chain order book matching while conducting final settlement on-chain. This retains the core advantage of decentralization while achieving trading efficiency comparable to centralized exchanges (CEXs).

Additionally, the privacy of off-chain order books reduces the exposure of trade information, effectively curbing issues like front-running and sandwich attacks, which are common in miner extractable value (MEV) scenarios. Projects like Paradex use the hybrid model to mitigate market manipulation risks caused by the transparency of on-chain order books. The hybrid model also allows for the integration of professional algorithms from traditional market makers, offering tighter bid-ask spreads and deeper liquidity through flexible management of off-chain liquidity pools. For example, Perpetual Protocol employs a virtual Automated Market Maker (vAMM) model combined with an off-chain liquidity supplementation mechanism to alleviate the high slippage problems faced by purely on-chain AMMs.

Off-chain processing of complex calculations, such as dynamic funding rate adjustments and high-frequency trading matching, reduces on-chain gas consumption, with only the key settlement steps remaining on-chain. Uniswap V4’s singleton contract architecture merges multiple pool operations into a single contract, reducing gas costs by 99%, providing a technical foundation for the scalability of hybrid models. Hybrid models also support deep integration with other DeFi components such as oracles and lending protocols. For example, GMX uses Chainlink oracles to obtain off-chain price data and combines it with on-chain liquidation mechanisms to enable complex derivative trading functions.

Building Liquidity Firewall Strategies that Meet Market Needs

A liquidity firewall aims to maintain the stability of liquidity pools through technological means, preventing systemic risks caused by malicious operations or market fluctuations. Common methods include introducing time locks when LPs exit (e.g., a 24-hour delay, extendable up to 7 days) to prevent sudden liquidity depletion due to high-frequency withdrawals. When the market experiences volatility, time locks can buffer panic withdrawals, protecting the returns of long-term LPs while ensuring fairness by transparently recording the lock-in period in smart contracts.

Based on real-time oracle monitoring of the asset ratio in liquidity pools, exchanges can also set dynamic thresholds to trigger risk control mechanisms. When the proportion of any asset in the pool exceeds a preset limit, relevant trading can be paused, or automatic rebalancing algorithms can be triggered to prevent impermanent loss from widening. LPs can also be rewarded in tiers based on their lock-up duration and contribution. Long-term LPs who lock assets will receive higher fee-sharing or governance token incentives, encouraging stability. Uniswap V4’s Hooks feature allows developers to customize LP incentive rules (such as automatic fee reinvestment), increasing loyalty.

A real-time monitoring system can be deployed off-chain to identify abnormal trading patterns (such as large arbitrage attacks) and trigger on-chain circuit-breaker mechanisms. These may pause trading on specific pairs or restrict large orders, akin to traditional financial “circuit breakers.” Formal verification and third-party audits ensure the security of liquidity pool contracts, while modular designs support emergency upgrades. The introduction of proxy contracts allows vulnerabilities to be fixed without migrating liquidity, avoiding a recurrence of issues like the DAO hack.

Case Studies

dYdX v4 - Full Decentralization of Order Book Model

dYdX v4 maintains the order book off-chain, forming a hybrid architecture with off-chain order matching and on-chain settlement. A decentralized network of 60 validators matches trades in real-time, with final settlement occurring only after a trade through an application chain built on the Cosmos SDK. This design isolates the impact of high-frequency trading on retail liquidity off-chain, with the on-chain system processing only the results, preventing retail LPs from being directly exposed to price fluctuations caused by large cancellations. The gas-free transaction model charges fees only after successful trades, preventing retail users from bearing high gas costs due to frequent cancellations, thus reducing the risk of becoming “exit liquidity.”

Retail users staking DYDX tokens can earn 15% APR in USDC stablecoin rewards (from transaction fee sharing), while institutions must stake tokens to become validators and participate in maintaining the off-chain order book, earning higher rewards. This layered design separates retail rewards from institutional node functions, reducing interest conflicts. Permissionless token listing and liquidity isolation algorithms allocate retail-provided USDC into different sub-pools to avoid large trades penetrating single-asset pools. Token holders vote on key parameters like fee distribution and new pair listings, preventing institutions from unilaterally modifying rules to harm retail interests.

Ethena - Stablecoin Liquidity Moat

When users stake ETH to generate the Delta-neutral stablecoin USDe, the Ethena protocol automatically opens an equivalent ETH perpetual contract short position on a CEX for hedging. Retail users holding USDe are exposed only to ETH staking yields and funding fee differentials, avoiding direct exposure to spot price fluctuations. When the price of USDe deviates from $1, arbitrageurs must redeem collateral through an on-chain contract, triggering a dynamic adjustment mechanism to prevent institutions from manipulating prices through concentrated sell-offs.

Retail users who stake USDe earn sUSDe (yield tokens), with rewards coming from ETH staking rewards and funding fees. Institutions provide on-chain liquidity through market-making for additional incentives. These roles are physically separated in terms of income sources. Reward tokens are injected into USDe pools on DEXs like Curve to ensure retail users can swap with minimal slippage, preventing them from bearing institutional selling pressure due to liquidity shortages. Future plans include using the governance token ETA to control the types and hedging ratios of USDe collateral, with the community voting to limit excessive institutional leverage.

ApeX Protocol - Elastic Market-Making and Protocol-Controlled Value

ApeX Protocol migrated from StarkEx to zkLink X, creating an efficient order book model with off-chain matching and on-chain settlement. User assets are self-custodied, stored in on-chain smart contracts, ensuring the platform cannot misappropriate funds. Even if the platform ceases operations, users can force withdrawals to ensure security. The ApeX Omni contract supports seamless deposits and withdrawals of multi-chain assets and operates without requiring KYC. Users can trade by simply connecting their wallet or social account, and they are exempt from gas fees, significantly lowering transaction costs. Additionally, ApeX innovatively supports one-click buying and selling of multi-chain assets like USDT, eliminating the hassle and extra fees associated with cross-chain bridging, especially useful for efficient Meme coin trading across multiple chains.

ApeX’s core competitiveness comes from the breakthrough design of its underlying infrastructure, zkLink X. zkLink X solves liquidity fragmentation, high transaction costs, and cross-chain complexity by using zero-knowledge proofs (ZKP) and aggregate rollups. Its multi-chain liquidity aggregation integrates assets across Layer 1 (L1) and Layer 2 (L2) networks such as Ethereum and Arbitrum into a unified liquidity pool, allowing users to access the best trade prices without cross-chain transfers. Meanwhile, zk-rollup technology enables off-chain batch processing of trades, optimizing validation efficiency through recursive proofs. As a result, ApeX Omni achieves throughput near that of centralized exchanges (CEXs), with transaction costs a fraction of competing platforms. Compared to single-chain optimized DEXs like Hyperliquid, ApeX offers users a more flexible and low-barrier trading experience with its cross-chain interoperability and unified asset listing mechanism.

Antarctic Exchange - Privacy and Efficiency Revolution Based on ZK Rollup

Antarctic Exchange uses Zero Knowledge (ZK) technology to combine the privacy properties of zk-SNARKs with order book liquidity depth. Users can anonymously verify transaction validity (such as margin sufficiency) without exposing position details, preventing MEV attacks and information leakage, effectively solving the industry’s problem of “transparency versus privacy.” Through Merkle Trees, the hashes of thousands of transactions are aggregated into a single root hash on-chain, drastically reducing on-chain storage costs and gas consumption. By coupling Merkle Trees with on-chain verification, Antarctic offers a “compromise-free solution” that combines the user experience of CEXs with the security of DEXs.

In designing LP pools, Antarctic adopts a hybrid LP model, seamlessly linking users’ stablecoins with LP tokens (AMLP/AHLP) through smart contracts, balancing the benefits of on-chain transparency and off-chain efficiency. When users attempt to exit liquidity pools, a delay is introduced to prevent market liquidity from becoming unstable due to frequent inflows and outflows. This mechanism reduces price slippage risks, enhances liquidity pool stability, and protects long-term liquidity providers from market manipulation and opportunistic trading.

In traditional CEXs, large capital clients exiting liquidity would rely on the liquidity of all order book users, often leading to a market crash. However, Antarctic’s hedging market-making mechanism effectively balances liquidity supply, ensuring that institutional investors’ exits don’t overly depend on retail liquidity, reducing the risk exposure for retail users. This system is more suitable for high-leverage, low-slippage, and market manipulation-averse professional traders.

Future Directions: The Possibility of Liquidity Democratization

Future DEX liquidity designs may evolve along two distinct paths:

Global Liquidity Network: Cross-chain interoperability technologies will break the isolation between chains, maximizing capital efficiency and allowing retail users to access the best trading experience through “seamless cross-chain” interactions.

Co-Governance Ecosystem: Through innovative mechanism design, DAO governance will shift from “capital-based power” to “contribution-based rights,” creating a dynamic balance between retail and institutional participants in the ecosystem.

Cross-Chain Liquidity Aggregation: From Fragmentation to Global Liquidity Network

This path employs cross-chain communication protocols (such as IBC, LayerZero, and Wormhole) to build underlying infrastructure for real-time data synchronization and asset transfer across chains, eliminating reliance on centralized bridges. Zero-knowledge proofs (ZKP) or lightweight node verification technology ensures cross-chain transactions’ security and immediacy.

By combining AI predictive models and on-chain data analysis, intelligent routing will automatically select the best liquidity pool based on chain conditions. For instance, when Ethereum experiences an ETH sell-off that leads to higher slippage, the system can instantaneously draw liquidity from low-slippage pools on Polygon or Solana and perform atomic swaps to reduce retail pool impact costs.

Alternatively, unified liquidity layer designs will develop cross-chain liquidity aggregation protocols (such as Thorchain models), enabling users to access multiple-chain liquidity pools through a single point. Funds will be allocated to different chains as needed, and price discrepancies between chains will be automatically balanced using arbitrage bots to maximize capital efficiency.

DAO Governance Game Balance: From Whale Monopoly to Multi-party Checks

Unlike the previous path, DAO governance dynamically adjusts voting weights. The voting power of governance tokens increases with the holding period (similar to the veToken model), incentivizing long-term participation from community members while curbing short-term manipulation. By combining on-chain behaviours (such as liquidity provision duration and trading volume) with dynamic weight adjustments, the system prevents power concentration caused by large-scale token hoarding.

Incorporating the existing dual-track system, core decisions involving liquidity allocation must satisfy both the “majority of total votes” and the “majority of retail addresses” requirements, preventing whales from gaining unilateral control. Retail users can delegate their voting power to trusted “governance nodes,” which must stake tokens and undergo transparent audits. Any abuse of power will result in the slashing of staked tokens. Liquidity providers (LPs) who participate in governance will receive additional rewards, but their rewards will be proportionally reduced if their voting diverges from the community consensus.

NFTs, as a medium for transferring and trading labour relationships, can play an important role in DAO governance. For example, commission-sharing relationships, which are common on exchanges, can be directly linked to NFTs. When an NFT is traded, the associated commission relationship and corresponding client resources are also transferred, and the value of this NFT can be directly quantified based on the amount of resources. Some DEXs have already experimented with this approach, allowing NFTs to flow quickly to users who are truly willing to promote the DEX through transactions on OpenSea. Over 90% of the performance in the operations department has come from NFT-based commissions. The anonymity of NFTs can also help DAOs better manage their BD (business development) departments, preventing user retention from being impacted by the departure of any individual BD.

Conclusion: The Paradigm Shift in Liquidity Power

Outstanding DEXs essentially reconstruct the distribution of financial power through their technical architecture. Practices from dYdX, Antarctic, and other platforms show that when liquidity provision mechanisms shift from “passive absorption” to “active management,” and when order matching evolves from “price prioritization” to “risk isolation,” retail users no longer become victims of institutional exits but equal participants in ecosystem building. This transformation is not just about technical efficiency but embodies the core spirit of DeFi—bringing finance back to its essence of service, instead of being a zero-sum game.

Disclaimer:

1. This article is reproduced from [TechFlow]. The copyright belongs to the original author [Max.S]. If you have any objection to the reprint, please contact Gate Learn Team, and the team will handle it as soon as possible according to relevant procedures.

2. Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.

3. The Gate Learn team translates other language versions of the article, not mentioned in Gate.io, the translated article may not be reproduced, distributed or plagiarized.