(Continued from above)
Liquidity Mining
Gauntlet sees liquidity mining as the more promising approach for driving Uniswap’s growth moving forward. Liquidity Mining (LM) is an incentive strategy in which rewards are paid out to liquidity providers. Typically, liquidity mining involves setting a fixed budget of tokens to be distributed to LPs proportional to the amount of liquidity they staked in the protocol over a set period of time.
How can Liquidity Mining drive long-term value?
There are 2 mechanisms Gauntlet has identified which would result in this long-term sustained growth due to a liquidity mining program.
Forgetful Liquidity Providers
One possible theory by which you would achieve a sustained lift from a liquidity mining program is through Forgetful LPs, whereby LPs transfer liquidity to pools in order to capture incentives and subsequently “forget” that their liquidity exists and fail to rebalance their portfolio after the incentives program ends.
This may be observable in some long-lasting liquidity mining programs but is unlikely to be observed in such a short-term (several weeks) program where the end date of the program was known from the start.
Liquidity Bootstrapping
Liquidity mining incentives can be useful in bootstrapping liquidity that sticks around after incentives are removed when the temporary boost in liquidity incentivizes more volume. Specifically, we hypothesize that the following chain of events can occur:
- Liquidity mining incentives introduced
- LPs add liquidity to the pool, which improves execution quality for traders
- Traders route more of their swaps through the pool and thus pay more fees to LPs
- Fees from traders further incentivizes liquidity in the pool until a stable equilibrium between liquidity and returns (fees + incentives) is reached.
- Liquidity mining incentives are removed, resulting in some liquidity being removed due to the lost incentives. * However, the new equilibrium between liquidity and fees is higher than before since the new fees keep some new liquidity in the pool, and that new liquidity keeps the trading volume flowing.
If this theory were true, we would expect to see increases in liquidity as well as trading volume (and by proxy trading fees) in pools where liquidity mining programs were run during the incentives period and a subsequent decrease in liquidity and trading volume after the incentives are removed (however a higher liquidity/fee equilibrium than the pool experienced before the incentives period). This theory requires 2 things to hold true:
- Liquidity must increase as a result of liquidity mining incentives
- Trading Volume (fees) must increase as a result of the increase in liquidity.
Liquidity Mining on Optimism Case Study
In late 2022 and again in early 2023, Uniswap conducted a liquidity mining experiment on Optimism in which OP incentives were distributed to LPs of specific pools through four liquidity management protocols. The results of the experiment were mixed: it was clearly observed that liquidity in all relevant pools appeared to increase throughout the duration of the incentives period and subsequently decrease after the incentives period was concluded. Prior analysis by community members focused on the trend of TVL significantly decreasing for most pools after the incentives period finished, concluding from this that the incentives program had been ineffective at creating sticky liquidity or any long-term flywheel effect.
From prior analysis, it would be natural to conclude that a liquidity mining rewards program is ineffective at generating a long-term “flywheel” of sustained lift in liquidity and volume for their protocol. However, Gauntlet re-examined the data for the Optimism liquidity mining experiment and arrived at a different conclusion for certain pools.
Our full analysis is posted here and summarized below.
Approach
We identified and attempted to correct 2 primary flaws in the existing research:
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The existing research did not account for or normalize overall market trends for the pairs relevant to the experiment
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The existing research did not analyze the dynamics of trading volume in relation to improvements in liquidity
In order to improve on the previous analysis and account for the noise of market fluctuations affecting liquidity and trading volume, we paired each pool that was part of the incentive program with a control pool that shares similar market characteristics which was not part of the incentive program. We can view the pools with incentives as being the “treatment group,” while pools without incentives were the “control group.” These pairs are shown below.
We computed a time series of each treatment pool’s relative share of TVL and volume given its control pool, which accounts for deviations in usage that affect the tokens at hand or Uniswap as a whole. We then performed a one-sided t-test to see if there was a meaningful increase in volume or TVL after the incentives started or if there was a meaningful increase in volume or TVL after the incentives ended (relative to levels before the incentives started). If liquidity mining was successful, then we expect to see that TVL increased during the experiment, leading to trading volume increasing during the experiment, leading to TVL and trading volume increasing after the experiment relative to before the experiment.
Results
Out of the 5 liquidity pools in the experiment, 2 of them (wstETH/WETH .05% and OP/USDC 0.3%) experienced a statistically significant sustained lift in both TVL and volume market share as compared to economically similar pools.
Three pools did not experience sustained lift (USDC/DAI 0.05%, WETH/DAI 0.05%, and OP/USDC 0.01%) for reasons that cannot be fully explained by our analysis. It is possible that one of several possible factors played a role either in confounding our analysis or preventing the effect from taking root. Some possible culprits may be a poor control pool analog, external incentives programs, cannibalization of liquidity or volume due to the incentives program, a lack of natural trading volume to route to these pools, or some other exogenous cause not included in this analysis. Future analysis efforts will be necessary to conclusively decide whether liquidity mining was a success or failure for these pools.
It is encouraging, however, that in this somewhat randomly selected experiment, we had a 40% success rate out of 5 pools in generating sustained impact through a liquidity mining incentives program. While a study of 5 pools cannot be conclusive, it supports the case that liquidity mining can be effective when pools are optimally selected through relevant analysis. We can the consider how to pre-identify pools that may be good candidates for liquidity mining and how to approach incentivizing them.
Pool Selection Methodology
The goal of liquidity mining is to maximize the Uniswap objective function as defined in our forum post. To do so, we need to maximize the gain in discounted protocol fee revenue (assuming the fee switch is on) while minimizing the spend of UNI tokens. This optimization needs to be done across the following parameters:
- Which pools do we incentivize?
- How much do we incentivize each pool?
- How long do we run the experiment for?
Our Approach
In order to bring in additional fees for the protocol, we need to increase Uniswap’s future trading volume. Additional trading volume can come from the following sources:
- Competing DEXes (Curve, Sushi, Balancer, etc)
- Competing CEXes
- Uniswap V2 and V1 pools
- New trading demand
Of these sources, competing DEXes provides the greatest opportunity: our trader elasticity analysis showed that on-chain traders tend to be efficient, so by improving trade execution quality, we can increase volume. Execution quality would be improved with more liquidity, and providing liquidity mining incentives, we can provide higher yield to LPs which would increase Uniswap’s liquidity.
The goal of candidate pool selection is to find ways to redirect trading volume from competing DEXes to Uniswap. Therefore, we are targeting Uniswap pools corresponding to pairs that have a high market-wide trading volume, but a low Uniswap v3 marketshare of trading volume.
Simulation
To help us evaluate the choice of incentive spend, we built a simulation that predicts the net benefit of incentivizing pool P given a daily spend amount S. The simulation works as follows:
- Initialization: for P and all competing DEXes that involve the same trading pair, get current liquidity (TVL) and volume (average daily volume, or ADV)
- Simulate the N days of the incentive period, doing the following for each day:
- Use the Liquidity Model to update the TVL of P given the pool ADV and incentive spend S
- Use the Volume Model to update the ADV of P given the pool TVL
- Simulate M days of the pool after the incentive period ends, doing the following for each day:
- Use the Liquidity Model to update the TVL of P given the pool ADV and 0 incentive spend
- Use the Volume Model to update the ADV of P given the pool TVL
- Compute the net benefit to the protocol via the Uniswap objective function using the timeseries of simulated ADV
Liquidity Model
The Liquidity Model determines pool TVL given ADV and incentive spend, and it works by assuming perfect elasticity for LPs, meaning that LPs would increase liquidity in a pool in order to maintain the same level of yield that is currently being earned. Yield is the sum of token incentives and trading fees. We assume that LPs use a rolling window of 14 days to compute trading fees, as this was the average update time we saw for whale LPs of major pools from our Mainnet LP Timing Analysis. After the first phase of the liquidity mining incentive is complete, we plan on building a more sophisticated Liquidity Model by fitting a statistical model to the data we collect on LP positions over the course of the experiment.
Volume Model
The Volume Model determines pool ADV given TVL, and it assumes that a 1% increase in liquidity market share leads to a 1% increase in volume marketshare. The market share for a pool is the percentage of liquidity or volume that the pool gets against all other pools that involve the same trading pair across all competing DEXes on the same chain. This linear relationship between the market shares of liquidity and volume is validated by the results of our Optimism LM analysis. In the future, we plan on building a more sophisticated Volume Model that reroutes historical swaps to the DEX that provides the best execution quality given its liquidity.
Visualization
The figure below demonstrates a successful liquidity mining scenario according to our simulation. Specifically, this simulation run corresponds to the DAI/USDT 0.01% pool given an incentive spend of $6314. The top figures show raw ADV and TVL, while the bottom figures show the pool’s market share of ADV and TVL. The x-axis is time in days: t=0 corresponds to the period before incentives are introduced, t=1 corresponds to the first day of incentives, and the black dotted line shows the last day of the incentive period.
As expected, the introduction of incentives causes a big spike in TVL from LPs responding to the increase in net yield. This causes a proportional increase in ADV. During the experiment, there is a pinwheel effect of increasing ADV causing TVL to increase, and increasing TVL causing ADV to increase. Once the incentives end, TVL drops due to the decrease in net yield, which causes ADV to drop as well. But the equilibrium ADV and TVL is at a stable position well above where they were before the liquidity mining began.
The figure below demonstrates an unsuccessful liquidity mining scenario according to our simulation. Specifically, this simulation run corresponds to the METIS/WETH 0.3% pool given an incentive spend of $9170.
As before, the ADV and TVL increased in response to the incentives. However, due to the high ADV marketshare compared to the current TVL marketshare, the increase in trading volume was insufficient in incentivizing a high TVL in the longer term.
Parameter Selection
Choice of Pools: To determine which pools to incentivize, we started by identifying the trading pairs with the highest on chain 30 day trading volume. We then determined the top Uniswap V3 pool by volume for the top trading pairs, and computed its volume marketshare among competitors. We obtained a set of candidate pools by filtering this list down to the pools that compete with at least 1 non Uniswap pool and have a marketshare under 60%.
Incentive Spend: For each of these candidate pools, we optimized the incentive spend for the pool using our simulation. Specifically, we considered 100 multipliers against ADV from 0.01x to 100x on log scale, and used our simulation to estimate the net benefit. We dropped all candidate pools that were unable to provide positive benefit for any level of incentive spend. We used the best simulation result to determine an incentive spend for all pools we suggest moving forward with.
Incentive Period Duration: To determine the duration for running this incentive program, we look to the results of our Mainnet LP timing analysis, where we showed that the frequency at which whale LPs update their positions differs from pool to pool, ranging from once a day to once a month. In order to ensure that LPs have ample time to rebalance their positions during the incentive program, we conservatively recommend a duration of 4 weeks.
Example Pools
We are currently in the process of finalizing which pools to move forward with for the initial round of liquidity mining on mainnet, but we are able to share two examples of pools that look promising at this time: stETH/ETH 0.05% and SYN/ETH 0.3%.
Curve currently dominates the volume and liquidity marketshare of swaps between stETH and ETH. Preliminary data from our simulations suggests that with $153k of incentive spend, the protocol could receive a net $264k of benefit (assuming the fee switch is turned on), which represents an ROI of 173%.
Sushi currently dominates the volume and liquidity marketshare of swaps between SYN and ETH. Preliminary data from our simulations suggests that with $31k of incentive spend, the protocol could receive a net $34k of benefit (assuming the fee switch is on), which represents an ROI of 114%.
Conclusion
Between the options of Trade Mining, Payment for Order Flow (PFOF), and Liquidity Mining (LM), our research shows that Liquidity Mining is the most promising approach to growing the Uniswap protocol. Our analysis showed that trade mining does not make sense because there is very little sticky trade volume on alternative DEXes that could be rerouted to Uniswap through a trade mining incentive program. A PFOF program could be helpful in incentivizing external exchange aggregators to integrate with Uniswap or indirectly incentivizing LPs to add liquidity by increasing returns, but it is less efficient than one time payments to external exchange aggregators or liquidity mining for LPs. Liquidity mining is effective because when executed correctly, LM incentives can bootstrap liquidity that sticks after incentives are eventually removed and drive long lasting volume. Uniswap’s most recent liquidity mining program that targeted pools on Optimism has had mixed results, but it lacked a scientific approach to picking pools which led to suboptimal results.
Gauntlet has established a methodology for identifying promising pools to incentivize and to predict the impact of LM on their volume and liquidity, which we will describe in a future post. The next steps for our Uniswap Foundation engagement are to finalize which pools to go live with and to work with a launch partner to set up the incentive program. We are planning on going live with this incentive program in the next few weeks.