The torrid DeFi summer of 2020 was fueled with the launch of Compound’s governance token and was driven entirely by the concept of liquidity mining (aka yield farming). Without a doubt, liquidity mining has put DeFi in the spotlight so more people can see the power of DeFi over CeFi and TradFi. On the other hand, the abusive use of liquidity mining and its harm to many clumsy token buyers is absolutely damaging DeFi’s reputation. The net benefit of liquidity removal for DeFi as a whole is still up in the air.
There have been a few novel applications of liquidity mining, like YAM’s Pool1 design to bootstrap a crypto community, Sushiswap’s vampire attack to bootstrap AMM’s liquidity. But overall, most projects were simple copycats or straight forks. Unfortunately, there are also many scams that ride the market narrative.
The current design of the liquidity reduction is clearly sub-optimal and is one of the main causes that make the liquidity reduction programs of almost all projects unsustainable. To make matters worse, there hasn’t been any effort in the last 2 years to even try to fix the flaws in the liquidity reduction design.
Liquidity mining, more closely related to liquidity to support trading on Automated Market Makers (AMMs), is a token incentive scheme designed to attract Liquidity Providers (LPs) to provide liquidity for specific trading pairs/pools on AMMs.
Synthetix pioneered the on-chain implementation of reward token distribution to LPs for its sETH Uniswap pool. At a high level, in order to earn rewards, LPs must first provide liquidity to the sETH pool on Uniswap and then stake their Uniswap liquidity tokens into the staking rewards contract created in 2019. (This smart contract might be the most widely used and fork contract ever due to the DeFi and yield farming mania.) Reward tokens are distributed fairly to LPs based on their percentage of liquidity tokens wagered versus all tokens wagered by all LPs will.
From the perspective of tokenomics incentive design, the liquidity mining approach developed by Synthetix distributes reward tokens based on size of liquidity positions and let’s define this approach as Liquidity Mining 1.0 (LM1). Based on the results, such an incentive program worked for Synthetix to achieve its goal of getting more users to mint more sETH.
LM1 becomes the de facto liquidity mining design and implementation. It allows many projects to solve the liquidity challenge to some degree, at least initially. However, LM1 has many issues that contribute to its unsustainability.
First, reward tokens are distributed to LPs even though there may be little or no trades, meaning liquidity isn’t really being used. From a tokenomics perspective, using project tokens as an incentive for liquidity is expensive for most projects using them, as the incentive will not do much to help the log economy grow. And if the liquidity is not used, the incentive program deteriorates.
Second, in many cases, multiple pools need to be incentivized. The existing approach is to allocate a specific amount of reward tokens to each pool without considering each pool’s contribution, e.g. B. How many trades and how much trading volume are executed in each pool. The reward allocation decisions are made either by governance voting as in Curve, Balancer, or by the team as in Sushiswap, which are sometimes political or arbitrary.
LM1 can be improved and a much better liquidity mining incentive design is to distribute reward tokens based on AMM trading fees earned through liquidity positions. This design is fundamentally different from allocating tokens based on the size of liquidity positions, and let’s define this approach as Liquidity Mining 2.0 (LM2). LM2 clearly fixes the two major shortcomings in LM1 that were addressed in the section above.
First, LPs do not earn AMM trading fees during the fixed token distribution intervals when no trading occurs. No fee, no distribution of reward tokens. Additionally, it also discourages LPs from providing more liquidity than a project needs. With LM2, projects will not waste their valuable tokens on unused liquidity, thus reducing token inflation and downward pressure on token prices due to liquidity depletion.
Second, there is no need to manually allocate reward tokens to multiple pools, either through governance token votes or team decisions. These manual approaches create false incentives for LPs and unfairly treat liquidity across pools. When an LP position in a pool with LM2 earns more AMM trading fees, more reward tokens are distributed to that LP position, so simple and fair!
Typically, projects issue an ERC20 token (mostly used as a governance token) with a token amount cap and allocate a portion of these ERC20 tokens for liquidity reduction programs. In liquidity mining programs, a fixed amount of tokens is distributed at a fixed time interval, for example per block.
In LM1 implementations, the fixed amount of tokens per time interval is distributed evenly across the total amount of all LP tokens used for liquidity mining. Each staking LP earns the amount of reward tokens based on the amount of LP tokens they have staked. Each time the amount of LP tokens wagered during the time interval changes, the ratios are updated accordingly and the rewards are also updated accordingly. This implementation ensures a just Distribution of reward tokens to all LPs participating in liquidity reduction programs.
Unfortunately, it is very difficult to distribute the fixed amount of tokens per time interval based on trading fees collected from LP positions. Trading fees are determined by two dynamic, unpredictable parameters during the fixed time interval: 1) When trading fees are generated and earned from LP positions, it is dynamic and unpredictable as nobody can predict when traders will trade; 2) How much trading fees are generated and earned by LP positions is also dynamic and unpredictable as the size of trades is also dynamic and unpredictable. Therefore, distributing a fixed amount of tokens based on two dynamic and unpredictable parameters will create one unfair Distribution of reward tokens to all LPs participating in liquidity reduction programs.
A possible solution is to adjust the relevant data distribution models for the two dynamic parameters – trading time and trade size. And then you develop an on-chain implementation that dynamically updates the model based on each new trade and distributes reward tokens accordingly. The solution will be much closer to a fair distribution of reward tokens among all LPs participating in liquidity-reduction programs. It’s not rocket science, but may require a Ph.D. level big brains.
There are definitely other approaches to implement LM2. A better approach is to introduce a new token model that not only has better tokenomics but also simplifies the implementation of LM2. Innovative solutions are coming and please stay tuned! Follow Double on Twitter.
Liquidity mining has put DeFi in the spotlight. The current design and implementation of Liquidity Mining has some shortcomings and contributes to the unsustainability of Liquidity Mining programs. Improvements can be made and reward tokens should be distributed based on the trading fees earned from LP positions instead of the size of the LP positions. Due to the current token model and rewards distribution plan, it is a difficult problem to distribute reward tokens based on trading fees. Innovative solutions are coming. Please hold the line.
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