Recently, Notional launched the incentive program for V3 LP, offering an annualized interest rate exceeding 100%, reigniting my interest in this project.

Let’s start with the mining activity: In addition to the native NOTE incentives from the project itself, by the end of March, an additional 500,000 ARB will be allocated for LP. V3 allows users to leverage liquidity provision by automatically pledging nTokens to obtain tokens and provide more liquidity, resulting in an annualized interest rate of over 100%.

Notional is one of the innovators in fixed-rate lending protocols, having pioneered the creation of the fCash (zero-coupon bonds) trading market. It provides liquidity to both borrowers and lenders through an Automated Market Maker (AMM) mechanism.

Product Features:

1. Liquidity Providers: Liquidity providers inject funds into the nToken account, becoming counter-parties for participants in fixed-rate borrowing and lending. When LPs inject funds, they acquire a token account (such as the nUSDC account in the example) and receive prime cash (prime USDC), fCash (fUSDC), and -fCash (-fUSDC). In essence, as LPs provide liquidity, they simultaneously contribute liquidity funds and assets (acting as counter-parties for both borrowers and lenders) to the pool. The actual fixed-rate transactions between borrowers/lenders and LPs occur as follows: lenders buy fCash assets using prime cash; borrowers sell fCash (holding -fCash) in exchange for prime cash.

Once LP funds enter the “big account” — nToken account, they are allocated to various fixed-rate pools based on the proportions specified in the protocol. Funds in pools with different durations need periodic adjustments to maintain a balanced financial situation. Otherwise, some pools may deplete, while others accumulate excessive funds. The platform has devised a recognized parameter for the allocation of funds to different pools. This approach is relatively automated, ensuring efficient management of the capital distribution across various pools .

V2 nToken parameters

n order to incentivize users to participate as Liquidity Providers (LP), those providing liquidity can earn prime cash returns (from the current pool), fcash returns at different durations, transaction fees (paid by both parties in fixed-rate borrowing and lending, currently charged at 8% of the borrowing/lending rate), and token rewards.

The return on Prime ETH is from the V3 current pool (which follows a model similar to Compound/Aave). The returns on fETH, on the other hand, originate from the asset returns at various terms. It’s important to note that when a specific term lacks borrowers but has an excess of lenders, resulting in a decrease in fCash, LP holders may incur liabilities and need to pay interest. This scenario could potentially lead to a decrease in the LP’s Annual Percentage Yield (APY).

V3 allows for leveraged LP, with liquidity providers facing minimal risk of liquidation at certain leverage multiples. For instance, in the case of USDC depicted in the chart below, at a 5x leverage, the annualized yield exceeds 100%, and there is no risk of liquidation. This is due to the platform’s control over the upper and lower limits of interest rate fluctuations, effectively managing the potential decrease in the values of fCash and nToken.

The high returns and absence of liquidation risk for leveraged LP in V3, coupled with the recent activity subsidies, make the earnings exceptionally enticing for participants. There’s no need for constant monitoring or portfolio adjustments, adding to the overall appeal.

1. Fixed-term Lender: Fixed-term lenders purchase fcash from the pool, acquiring lending assets and consequently earning fixed-rate returns. Lenders have the option to redeem early, achieved by selling their fcash holdings to receive prime cash. This essentially involves the transfer of debt rights (selling fcash and obtaining cash).
2. Fixed-term Borrower: Fixed-term borrowers purchase cash from the pool while simultaneously incurring liabilities (recorded as -fcash). They are obligated to pay fixed-rate interest on their debt. Borrowers can repay in advance, but instead of a direct repayment, they provide a loan with the same maturity date, offsetting the outstanding liabilities.

In practical terms, achieving early redemption or repayment for lenders and borrowers may involve costs. On one hand, the essence of lenders redeeming early is selling fCash. If the assets they hold do not offer favorable returns — for instance, if the asset’s interest rate is 5% while the current market rate is 10% — lenders may have to sell fcash at a discount. On the other hand, if a borrower wishes to repay early, the platform’s approach isn’t to have the borrower return the funds directly. Instead, the funds are used to invest in assets with the same maturity date. This results in a net position where the borrower’s debt becomes zero. Suppose the borrower’s borrowing rate was 10%, and currently, there are no willing borrowers in the market (no one willing to provide fCash liquidity), or the borrowing rate is extremely low, approaching 0%. In such scenarios, early repayment might lose its significance.

Certainly, whether it’s lenders selling fcash at a discount or borrowers making early repayments with higher “penalties,” although it may result in some losses for both parties, it can be beneficial from the perspective of gaining liquidity and flexibly reducing liabilities.

4. Floating Rate Borrower and Lender: V3 offers floating-rate borrowing and lending through the current pool, with a model similar to other lending projects.

Additionally, participants have the option to engage in leveraged vaults, tailored for specific strategies such as ETH mining, and leveraged LP, involving pledging nTokens to obtain funds for cyclic liquidity provision.

How are Interest Rates Determined

The trading price of fCash is equal to the present value of future cash flows.

Within Notional’s various liquidity pools, the trading price of fCash is influenced by the current Notional interest rate level and the time to maturity. The interest rate level is determined by the graph above, which is associated with the fund utilization rate.

Risk Management

The mechanism for standard collateralized borrowing involves several key parameters related to liquidation (V2):

1. Default liquidation portion: Default liquidation is set at a default ratio of 40%, which can be adjusted lower by users.
2. To mitigate the impact of price attacks, the time window for oracle price retrieval is set at 360 minutes, a relatively lengthy period.
3. Exchange rate haircuts are used to calculate the collateral value. Currently, stable-coins have a haircut of 92%, while non-stables have a haircut of 78%.
4. Exchange rate buffer is employed in calculating the value of the loan. Presently, stable-coins have a buffer of 1.09, and non-stables have a buffer of 1.29.

5. Liquidation discounts are currently set at 4% for stable-coins and 6–7% for non-stables, serving as incentives for liquidators.

Token Incentives

Currently, the main incentive program is on LPs. Token incentives in V2 primarily stem from tokens issued by the project itself, with their value significantly influenced by their market prices.

Recently, the activities in V3 have propelled the yield of leveraged LPs to over 100%. The project has not disclosed the overall details of the token economics.

Project Performance

For loan balances, V2 has surpassed $10 million, while V3 has exceeded $2 million (as of January 12). A few days after the start of V3 promotion campaign, the loan balance has already surpassed $8 million (as of January 19).

The total token supply is 100 million, currently traded on Balancer and CoinEx, with a daily trading volume of a few tens of thousands of dollars, priced at around $0.1. The Fully Diluted Market Cap (FDMC) is around $10 million (as of January 12).

How the Project Generates Profit

The project generates revenue through transaction fees, currently set at 8% of the current fcash interest rate and collected from both borrowers and lenders. While this fee may not be considered high, it could become more significant if the liquidity in the fcash trading market increases.

Of the transaction fees, 80% (for several major currencies) goes to the platform, and the remaining 20% is allocated to nToken holders (i.e., liquidity providers).

Project Evaluation

fCash itself represents a zero-interest bond, with its value approaching 1 cash as it approaches the maturity date. Notional has created a market for zero-interest bonds or, in other words, an interest rate trading market where lending assets can flow. Borrowers and lenders can flexibly invest/borrow or redeem/repay through trading fCash, providing great flexibility.

The main challenge for Notional lies inits difficulty to quickly scale its transaction volume. On one hand, the LP is backed by pools with different durations, leading to fund dispersion, making it challenging to scale operations initially. In the decentralized finance (DeFi) world, large players dominate, and meeting the demands of substantial borrowing clients is difficult. On the other hand, the current main source of LP income is token incentives. If prices decline or subsidies decrease, the attractiveness of the income may diminish. However, it’s essential to recognize that the fixed-rate market is a demand-driven market and remains a blue ocean area where competition in DeFi lending has not yet fully formed. Once the scale is achieved, Notional’s advantages in the fixed-rate market can come into play, offering promising prospects for the future. The recent efforts by the project team in operations have led to a rapid increase in Total Value Locked (TVL) and outstanding loan balance, which is commendable and worth continued attention.

先说挖矿活动：除了项目方自己的NOTE激励外，截止3月底，还将有500,000ARB也用于LP激励，V3允许用户通过加杠杆贡献LP，也就是自动质押nToken获得借款币种再提供流动性，如此循环，年化利率高达100%+。

Notional是固定利率借贷协议的创新者之一，它创造了fcash（0息债券）的交易市场，通过AMM的方式来给借贷双方提供流动性。

一、产品特点

1. 流动性提供方：流动性提供方向nToken账户注入资金，成为固定利率借和贷参与者的对手方。当LP注入资金，LP将拥有一个token account （比如例子中的nUSDC account)，获得prime cash (prime usdc) , fcash (fUSDC) 以及-fcash (-fUSDC)。也就是说LP在提供流动性时，同时为池子提供了流动性资金以及资产（同时是借贷双方），然后真正的固定利率borrower/lender就跟LP发生交易：如果是lender，就是用prime cash获得fCash资产，如果是borrower就是卖fCash（持有-fCash)来换prime cash。

LP资金进入到大资金账户后，将根据协议约定的比例分配到各个固定利率池子中。不同期限的资金池需要经常调整资金情况，不然就会有的池子干掉了，有的有资金过多。平台设计了一个认可的资金池分配币种参数。这个是个比较自动化的办法。

V2的做法：

为了激励用户来做LP，LP提供方可以获得prime cash收益（来自活期池），各期限的fcash收益、交易手续费（固定利率的借贷双方支付，当前收费是按照8%乘以借贷利率）、代币奖励。举例：

其中Prime ETH的收益来自V3的活期池子（这个活期池子的模型和Compoud/Aave类似），fETH的收益来自各个期限的资产收益。值得注意的是，当某个期限缺乏borrower但lender又过多，导致fcash减少的时候，LP持有者就会产生负债，需要支付利息，这可能导致LP的APY降低。

V3允许杠杆LP，在一定的杠杆倍数下，流动性提供方几乎没有被平仓的风险。比如下图的USDC，在5倍杠杆下，年化收益超过100%，并且不会被平仓：因为平台控制了利率浮动的上下限，所以fCach以及nToken的价值下跌也就得到了控制。

V3的杠杆LP收益高且不会有被平仓的风险，参与者无需盯盘调仓，再加上最近的活动补贴加成，收益非常具有吸引力。

2. fixed-term lender:从池子里买入fcash，获得借贷资产，从而获得固定利率收益。lender可以提前赎回，方式就卖掉手里的fcash，获得prime cash，本质是债权转让（sell fcash and get cash)。

3. fixed-term borrower:从池子里买入cash，同时背负负债（以-fcash记），需要付出固定利率的利息。borrower可以提前还款，但方式并不是直接还款，而是提供一笔同样到期时间的借贷资金，这样就抵消了负债。

*实操中lender和brrower想要实现提前赎回/还款是可能要付出成本的。一方面，lender提前赎回本质是卖出fCash，如果他们持有的资产回报不具有优势，比如持有的资产利率是5%，而当前市场利率是10%，那lender此时只能折价卖出fcash。而另一方面，如borrower想要提前还款，平台的处理并不是让borrower归还资金，而是让这笔资金用来投资相同到期日的资产，这样net之后borrower债务就是0。假设borrower当时借贷利率是10%，而现在市场上没有人愿意借款（没有人愿意提供fcash流动性），或者说借款利率极低接近0，那提前还款也变得没有什么意义。

当然，不论是lender折价卖出fCash还是borrower提前还款但是支付较高的“罚息”，虽然会导致借贷双方的一些亏损，但是从获取流动性、灵活减轻负债的角度也是挺好的。

4. floating rate borrower and lender。V3提供浮动利率借贷（活期池子），模型与其他借贷项目类似。

5. 另外，参与者还可以选择杠杆投资（针对特定策略的，比如ETH挖矿）和杠杆LP（质押ntoken获得资金循环提供流动性）。

二、利率如何确定

fcash交易价格等于未来现金流的现值

而其中interestRate与利用率有关

在Notional的各个liquidity pool里面，fCash的交易价格是受当前notional的利率水平和到期时间的影响的，而利率水平由上面与资金利用率相关的图决定。

三、风控

是普通质押借贷的逻辑，以下是一些与平仓相关的主要参数（V2）：

1. default liquidation portion，默认平仓比例40%（可调低）
2. 为了减少价格攻击的影响，oracle获取价格的时间窗口是360分钟，挺长的，所以
3. exchange rate haircuts 计算质押物价值，当前数据稳定币92%，非稳定币78%
4. exchange rate buffer 计算loan的价值，当前数据稳定币1.09，非稳定币1.29

5. liquidation discounts 目前稳定币4%，非稳定币6–7%，用于激励平仓者

四、token激励情况

目前主要是给LPtoken激励，V2的token激励主要来源是项目方自己发行的token，受其价格影响很大。

最近V3的活动，让杠杆LP的收益率超过了100%。项目没有披露代币经济学的整体情况。

五、数据表现

在贷余额，V2 $10mm+， V3 $2mm + (截至1月12日），V3活动开始后几天，在贷余额已经超过$8mm(截至1月19日）

token 总供应量100mm, 当前只在Balancer和CoinEx交易，每天几万美金的交易额，价格0.1美金，FDMC（fully diluted maket cap) 10mm左右（1月12日）。

六、项目如何盈利

交易手续费，目前是8%乘以当前的fcash利率，即使向借贷双方收取，这个手续费也不算高。但考虑如果fcash的交易市场流动性能起来的话，就还是不错的。

交易手续费的80%（几个主流币种）归平台，剩下的给nToken持有者（也就是流动性提供者）

七、项目评价

fcash本身是0息债券，fcash本质是0息债券，越接近到期日，1fcash价值越接近1cash。Notional创造了一个0息债券或者说利率的交易市场，借贷资产可以流动起来，借贷双方通过交易fcash可以实现投资/借款或者赎回/还款，非常灵活。

Notional的主要的问题在于难以起量。一方面，LP背后是不同期限的池子，导致资金分散，一开始很难规模化运作，而defi世界又是一个大户为主的世界，大的借贷客户需求难以满足。另一方面，LP目前收益主要是token激励，如果价格下跌，或者补贴降低，收益将不再有吸引力。但要知道固定利率市场是刚需市场，也是defi借贷尚未形成竞争格局的蓝海领域，一旦规模起来，notional的上述优势就将得以发挥，未来可期。最近项目方在运营上的发力，让TVL和借贷量迅速上升，可喜可贺，值得持续关注。

In the past year, Morpho, a DeFi lending market project, performed extremely well. The project raised $18 million in July 2022. Its volume (total supply plus total borrowing) has grown from tens of millions in July last year, to more than 1.1 billion, and its matched loans reached 265 million. Its innovative P2P matching engine improves the rate of both borrowers and lenders has been recognized by the market.

(1) Innovative matching engine

Traditional lending models such as Aave and Compound allow borrowers and lenders to be matched efficiently, but the utilization rate is usually at a low level, which leads to a large spread between the rates of borrowers and lenders (especially when there is no token reward). Morpho makes the best use of Aave, Compound and other underlying lending protocols, based on which designs a P2P matching engine, so that both borrowers and lenders can obtain better interest rates without losing liquidity.

As shown in the diagram below, Pool Borrow and Supply APY are the interest rates of users on Aave/Compound, and there is a large spread. Morpho allows both borrowers and lenders to match by P2P, which greatly reduces the rate spread so that users get better rate of P2P APY.

The logic of the Morpho model is a new matching engine based on the underlying lending protocol. It defines a fair price, and allows borrowers and lenders to be matched by P2P. If no counter-party is found or when the gas fee exceeds the limit, the demand falls to the underlying lending protocols such as Aave or Compound (as shown in the diagram below).

Of course, Morpho’s P2P matching is not strictly a one-to-one user matching. The project’s yellow paper also explains that the essence of P2P matching is a state that makes the interest rate better.

(2) An agreed APY of each P2P lending pool by DAO

Based on the interest rate of the underlying lending pool, the parameter α is used to calculate the P2P APY. The parameter α is determined by the Morpho DAO community.

For example, in the USDC lending pool (on Aave V2), if the underlying Aave V2 borrow rate is 5%, the supply rate is 2%, and when α=0.3, then P2P APY=0.3*5%+(1–0.3)*2%=2.9%. For both borrowers and lenders interest rates are improved.

(3) The matching engine of large funds first

Considering the economic cost and operational feasibility of the Ethereum network, the logic of P2P matching or unmatching on the current platform mainly prioritizes large funds (for Aave V2 and Compound).

See the main parameters of matching engine below from its website:

For the Morpho-Aave V3-ETH lending pool, the matching engine also considers “Logarithmic Buckets”, so that similar loan sizes can be matched, which is more efficient.

(4) Fallback mechanism for the situation when demands cannot be matched by P2P

Due to the lack of sufficient liquidity or cost constraints of the P2P matching engine, there will be situations where the loan demand cannot be matched. At this time, Morpho will move the demand to the underlying lending protocol(such as Compound/Aave).

If a user’s loan demand is only partially matched in P2P, and the remaining demand is transferred to the underlying protocol pool, his comprehensive interest rate will be slightly worse than the P2P interest rate, but it is also better than direct lending in Compound/Aave .

(5) Delta mechanism to deals with the cost after incomplete unmatching

If suppliers/borrowers want to redeem/repay for the existing matched loans, Morpho may need to obtain/provide liquidity from the underlying pool. In theory, the originally matched users need to be unmatched. But if there are too many users of the matched loan, this unmatching may lead to high cost, so the system only allows some users unmatched. However, in fact, the matching structure has already been changed, and the actual interest rate of loans will become worse. Delta mechanism is to deal with this situation by adding a spread to P2P APY for both borrowers and lenders.

As shown in the Table below, user1’s fund 60 was first lent to user 2–7 (Table1) through P2P.

Now, user1 wants to redeem, and Morpho has obtained funds from the underlying protocols to meet user1’’s redemption demand, and so Morpho has a position of 60 in the underlying pool. At the same time, Morpho needs to update the loan matching orders details. However, due to system limitations (cost constraints), assuming that only 3 users are allowed to be unmatched (user 2\3\4), users 5/6/7 are still recorded as p2p matching (Table3). P2P should be 0, but now there are still 30 recorded, so that a delta describes this difference of 30, which can be used to calculate the spread added to the P2P rate to reflect the actual interest rate.

(6) To mirror of the risk control logic of the underlying protocol

For now, whether the demand is matched by P2P or falls to the underlying pool, Morpho mirrors the risk control of the underlying pool, and so there is a liquidation mechanism. While it is worth noting that, unlike Aave/Compound, Morpho’s model has an additional “funding party” — the underlying lending pool, so when liquidation occurs, sometimes Morpho needs to retrieve the customer’s collateral from the “funding party” , or unmatch the matched orders, and obtain liquidity from the “funding party”, so that the liquidation can be execute smoothly.

For example, in a P2P matching order, borrower A pledges ETH to borrows USDC. Suppose the price of ETH falls and borrower A needs to be liquidated, but his collateral ETH has been lent to another borrower B (who pledges WBTC to borrow ETH). At this time, Morpho needs to move the demand of borrower B to the underlying lending pool for the fund, i.e. the WBTC is transferred to the underlying agreement pool to borrow ETH, which will be used for the liquidation of borrower A’s position.

(7) Marketing strategies for attracting large funds

The total circulation of Morpho tokens is 1 billion. At present, the platform tokens cannot be traded, but users can participate in incentive programs. The number of token distribution of the incentive program is determined by the community.

Since the platform does not charge spread or fees temporally, and the matching engine prioritizes large funds, it’s good for users with large funds, who can involve in borrowing and lending activities to obtain token incentives.

Assuming that the price of Morpho token is $0.5 and the market value of this project is $500 million, the token incentives on both sides of USDT borrowing and lending add up to an APY of about 2% (40 tokens are rewarded for every 1,000 US dollars), for the Aave V2 protocol; and the rate is about 13%, for the Compound protocol as the current supply/borrow volume level is relatively low.

(8) Discussion on Morpho’s model improvement

First of all, for now, Morpho’s P2P matching engine is built upon different underlying lending protocols. Morpho relies much on Compound and Aave and could be threatened if they build the same P2P matching engine. Morpho can explore the opportunity of aggregating the liquidity of different protocols, which will greatly improve efficiency and competitiveness.

Secondly, although the current risk control logic is mirrored from the underlying protocol, there are still some risks. For example, if a P2P matched user needs to be liquidated, Morpho needs to get back his/her collateral first, so there may be a time lag, resulting in the inability of liquidating or not being timely. So there is room for improvement in risk control engines.

Finally, the matching engine that prioritizes large funds is not good for users with small funds. This logic is mainly restricted by the high transaction costs of the Ethereum network. More public chains can be expanded to optimize the matching engine.

（1）创新的匹配机制

传统的Aave和Compound等借贷模型，让借贷双方高效获得匹配，但各资金池利用率长期处于较低水平，导致借贷双方存在较大息差，无法获得最好的利率（尤其在没有代币奖励的时候）。Morpho结合Aave和Compound等底层协议，设计P2P的匹配机制，让借贷双方获得更好利率的同时，不失去灵活的流动性。

如下图所示，Pool Borrow/Supply APY是客户在Aave/Compound获得的借贷利率，两者之间存在较高息差，而Morpho让借贷双方通过P2P撮合匹配，大大降低了借贷息差，获得更优的P2P APY利率。

Morpho本质是在底层协议上做了一层匹配逻辑，定义一个公允的价格，让有意愿的借贷双方实现匹配。如果没有找到对手方或者当匹配所耗费的gas费超过限制，则将借贷需求挪到底层借贷协议如Aave或Compound中（如下图）。

当然，Morpho的P2P匹配，并不是严格意义上用户纬度的一对一匹配，项目黄皮书也解释说，P2P匹配的本质是一个让利率变得更优的状态。

（2）通过DAO确定每个P2P借贷池子的利率

基于底层借贷池子的利率，带入参数α计算获得P2P APY，参数α由Morpho DAO社区决定。

比如，USDC借贷池子，底层Aave V2 borrow rate 为5%，supply rate 为2%，α=0.3时，P2P APY=0.3*5%+(1–0.3)*2%=2.9%，对借贷双方来说利率都更优了。

（3）优先大资金匹配的逻辑

考虑以太坊网络的经济成本和操作可行性，目前平台P2P的匹配或解除匹配的逻辑，主要优先大资金（对于Aave V2以及Compound）。匹配机制的考虑因素包括：

1. 提高经济效率，以最大化匹配的资金量
2. 提高Gas花费效率，以最小化Gas花费，避免过多小订单匹配导致的高额成本
3. 易用性，让用户或合约通过借贷，轻松在Morpho获益
4. 公平性，应尽可能让更多的流动性获益，而不考虑用户是谁

对于Morpho-Aave V3-ETH 借贷池子，匹配机制还考虑“Logarithmic Buckets”，即让借贷需求大小相近的资金获得匹配，更高效。

（4）Fallback mechanism解决P2P无法匹配问题

由于P2P本身没有足够流动性或者成本限制等原因，会出现借贷需求无法匹配的情况，此时Morpho会将需求转移到底层借贷协议池子中（如Compound/Aave)，满足用户流动性需求。如果一个用户的借贷需求在P2P中只获得的了部分匹配，剩下的需求转移到了底层协议池子中，他的综合利率将略差于P2P的利率，但也比直接在Compound/Aave借贷更优。

(5) Delta mechanism应对解除匹配利率偏差的问题

如果现有已经匹配的订单,supplier/borrower想要赎回/还款，此时Morpho可能需要到底层池子中获得/提供流动资金满足supplier/borrower的需求，理论上原来已经匹配的用户需要解除匹配。但如果已经匹配用户太多，这样操作可能导致昂贵的成本，所以真正被系统解除匹配的只有部分用户，然而实际上匹配结构已经发生了变化，借贷实际利率会变差。Delta mechanism指的是协议通过在借贷双方的P2P APY里增加一个差额来反应这个成本。

如下图,user1的资金60一开始通过P2P借给了user 2–7（Table1)。

此时user1想要赎回，Morpho从底层协议中获得了资金兑付user1的赎回需求，Morpho在底层池子中头寸为60。同时，Morpho需要更新借贷匹配关系，然而由于系统限制（成本限制），假设只允许解除3个用户的匹配（user 2\3\4)，用户5/6/7仍登记为p2p匹配关系（Table3)。本来P2P应该是0，但现在账户里登记的还有30，delta描述的就是这个差额30，借此可算出应该在P2P利率中增加多少差额得到实际利率。

（6）复制底层协议的风控逻辑

目前，不论是P2P匹配的还是转接到底层协议中的借贷需求，Morpho都按照底层协议的风控逻辑来做风控，也就是会有平仓机制。但值得注意的是，与Aave/Compound不同，Morpho的模式中多了“资金方” — 底层借贷池子，所以当平仓发生的时候，有时Morpho需要从“资金方”取回客户的质押物，或者将原来的匹配关系解除，从“资金方”获得流动性，使得平仓得以顺利完成。

比如一个P2P的匹配的订单，借款人A质押ETH，借USDC。如果ETH价格下跌，借款人A需要被平仓，但是他的质押物ETH已经借给了另一个借款人B（质押WBTC，借ETH），此时，Morpho需要将借款人B的需求转移到底层协议池子中获得满足，也就是将其WBTC拿到底层协议池子中借出ETH，这将用于借款人A的平仓。

（7）吸引大资金的运营策略

Morpho总发行量为10亿，目前平台代币暂时无法交易，但用户可参与交易挖矿激励，Token激励的数量和分配比重由社区决定。

由于目前平台不收取息差，而且匹配规则优先大额资金，对大资金用户是非常有好的，大客户可以到平台来参与借和贷获得token激励。

假设morpho价格为0.5，市值5亿美金，参考当前基于Aaave V2协议的池子USDT借贷两边token激励加起来年化收益率为2%左右（每1000美金奖励40个token)。

基于Compound的token激励为年化13%左右（目前其Compound池子中的USDT体量较低）

（8）Morpho的模式改进探讨

首先，目前Morpho的P2P匹配仍需要按照不同底层协池子来区分，较为依赖底层协议，如果Aave和Compoud复制其P2P匹配逻辑，将对Morpho形成竞争，Morpho可探索聚合不同协议流动性的可能性，这将大大提高运营效率，提高竞争力。

其次，当前风控逻辑虽然照搬底层协议，但仍存在一些风险，比如客户如果需要被平仓，Morpho需要先将其质押物取回，这样可能存在时间差，导致平仓无法进行或不及时，风控细节有可提升的空间。

最后，优先大资金的匹配逻辑，对小资金用户不利，该逻辑主要受限于以太坊网络的较高交易成本，Morpho可拓展更多公链，优化匹配逻辑。