TL;DR Arrakis Pro offers bespoke liquidity management services for onchain liquidity. In this case study, we look at how a ∼$2 million liquidity top-up to an Arrakis Pro user’s Uniswap V3 pool impacted its performance across various metrics. The results show that this liquidity top-up strengthened the pool and encouraged healthy trading activity.

Arrakis Pro is the industry-standard liquidity management solution for token issuers. We’ve helped over 50 projects manage their Protocol Owned Liquidity (POL) from the TGE phase and beyond, guiding them through the complexities of concentrated liquidity, Hooks, and more.

Our offering can be thought of as a layer above liquidity solutions like Uniswap. By providing the first onchain market making solution, we help teams navigate the onchain liquidity landscape.

Onchain liquidity is complex and token issuers face risks as soon as they deploy their POL to pools. These include inventory risk, high price impact, IL, and MEV attacks. If the liquidity is deployed carelessly, users can also suffer from worse price execution.

Arrakis Pro uses several strategies to optimize pool performance. After a pool’s bootstrapping phase, we often inject liquidity into the pool when the conditions are favorable, which can help mitigate risks and promote healthy trading activity.

In this case study, we explore how a ∼$2 million liquidity injection into an Arrakis Pro user’s vault on May 1, 2024 stabilized the UniV3 pool. The pool in question had ∼$1 million in POL before the injection and this was Arrakis Pro’s first top-up to the vault. We deployed 700,000 tokens for the top-up and the figures quoted are based on the USD value at the time of our analysis (just over three months after the injection). This study was originally conducted for the token issuer and the results were sent to them to show the efficacy of the top-up.

Similar to other pools managed by Arrakis Pro, the project’s token was paired against wETH. Projects have less exposure to volatility risk when the paired token is wETH rather than a stablecoin, which is why we recommend this approach as standard.

We assess the impact of the liquidity injection by comparing the pool’s performance six months before and three months after the injection. Our results indicate that its performance improved across many key metrics.

This piece offers an insight into how we use liquidity injections to support token projects. It also demonstrates the various considerations token issuers must make when they deploy their liquidity onchain.

More users trading onchain vs. Binance and other CEXs

We looked at the pool’s volume share vs. the broader market before and after the injection. The pool was the most liquid DEX pool, with the majority of volume shared among CEXs like Binance and Coinbase. By comparing the volume share on our two timeframes, we can assess the pool’s competitiveness relative to the rest of the market.

The pool’s average daily volume share was 0.97% in the six months prior to the top-up and 2.06% in the three months after the top-up. The median daily volume was 0.35% in the six months prior to the injection and increased to 1.15% in the three months after.

Our results indicate that the pool became more competitive relative to the broader market, with the average volume share doubling. The sharp increase in the median figure also supports this point, as it is less impacted by data skews such as the spikes seen during periods of heightened volatility.

While this pool was the most liquid DEX pool before the top-up, the jump against CEXs suggests that the token became more appealing to trade onchain after it received the liquidity boost.

More Binance-like pricing onchain after 70% drop in CEX/DEX price discrepancy

We analyzed the price discrepancies between the pool and the CEX with the highest volumes for the token, Binance, to assess whether the onchain market became more efficient after the top-up. Users benefit from improved price execution when the discrepancy between DEX and CEX prices is low.

In this analysis, “price discrepancy” takes account of slippage, volatility, and arbitrage potential to give a view of the market’s stability.

The average price difference between Uniswap and Binance was 8.1% on the buy side in the six months prior to the top-up and it declined to 2.4% in the three months after. On the sell side, the average discrepancy was 6.8% before the top-up and 2.1% after.

The above table shows that the volume ($) and number (#) of trades with price discrepancies of over 5% and 10% significantly decreased following the top-up.

Our findings point to a clear conclusion: Onchain traders got better price execution after the liquidity injection.

For token issuers, eliminating price discrepancies between onchain pools and CEXs is key for promoting a healthy trading environment. Moreover, large price discrepancies can leave token issuers exposed to IL when arbitrageurs rebalance pools.

Sharp decline in arbitrage opportunities

We looked at the number of arbitrage opportunities in the pool before and after the top-up to determine the risk of LPing to the pool. For this analysis, “arbitrage potential” refers to the number of times when the price discrepancy between the pool and Binance was large enough to extract a profit.

We looked for price spreads of 5% or more due to the rough costs of executing an arbitrage trade across both Binance and Uniswap, factoring for slippage (∼2% on Uniswap, ∼0.5-1% on Binance), trading fees (∼1% on Uniswap), and gas/transaction fees (∼1% total).

In the six months prior to the top-up, the average number of monthly arbitrage opportunities above 5% was 18.9 and the number declined to 9.5 following the liquidity injection. The number of opportunities above 10%, meanwhile, was 13.4 before the top-up and 5 after.

The decline in the number of arbitrage opportunities points to a healthier, more efficient market. Token issuers and LPs carry the cost as IL when pools have a large price discrepancy against CEXs because MEV searchers arb away the difference. This means the pool had more favorable conditions for them following the top-up.

MEV awareness is one of Arrakis Pro’s key focuses. Read about how our first UniV4 Hook, the Arrakis Pro Private Hook, protects LPs from MEV and LVR to learn more.

Price impact and volatility reduced

We measured the pool’s price impact before and after the top-up to determine its stability. When liquidity is shallow, large trades have a bigger impact on the price. Minimizing price impact promotes stability (though this can create inventory risk if liquidity is spread too thin).

After the liquidity top-up, price impact on the buy and sell side temporarily spiked because we used the bootstrapping strategy to address an imbalance in the pool (the pool had too much governance token relative to WETH). Still, the table shows that price impact significantly declined outside of this period for trades at a $10,000 size.

As the table shows, the average buy impact saw a significant decline following the top-up (though the sell impact increased). Moreover, the price impact was lower in both the best and worst cases for buying and selling after the top-up. The decline was particularly sharp in the best case trades on both the buy and sell side.

The change in the price impact following the liquidity injection shows that liquidity depth improved in both directions, giving traders a better experience.

Minimizing price impact is of particular interest to token issuers because trades that have a large impact can eat into their inventory. If liquidity is overconcentrated on one side, a whale can buy or sell tokens at a favorable price. Conversely, if liquidity is underconcentrated, a whale’s trade can cause a significant price movement, opening space for arbitrageurs to rebalance the pool. Arrakis Pro strikes a constant balance between minimizing price impact and inventory risk.

Summing Up

Our results from this study indicate that the pool’s performance improved across many metrics following the liquidity top-up. While the nominal fees and trade size decreased in line with the token’s price decreasing, the pool gained a larger share of volume and more price parity with CEXs. Moreover, the number of arbitrage opportunities and price impact declined. These metrics are key indicators we look for when assessing the health of onchain pools.

Although this case study only looks at one pool, it’s based on a typical strategy we use to optimize performance across pools. For this reason, it should interest any token issuer looking to use their POL more effectively. Our analysis also shows us that onchain liquidity is complex and one must take account of many factors to assess a pool’s health.

But liquidity injections are only one measure we take to support token issuers at Arrakis Pro. Other strategies we use include liquidity bootstrapping, setting wide ranges to stay market-neutral for TGEs, and using atomicity and dynamic fees to prevent MEV attacks. Learn more in our deep dive on mastering POL.

For more information on how Arrakis Pro can support your project, read our introduction to the solution or fill out our contact form to reach out about onboarding.