📝 Uniswap Arbitrum Liquidity Mining Full Program Retro
Today, we published an in-depth report and analysis outlining our Uniswap Arbitrum liquidity mining campaign results.
The campaign's goal was to explore the potential of a self-sustaining flywheel effect by incentivizing liquidity, improving price execution, and increasing volume market share on various Uniswap pools. To accomplish this, we strategically deployed 1.8M ARB tokens (~$1.7M USD at the time of commitment).
The high-level results were positive
• $15.5M in market share-adjusted TVL added to targeted pools during the program
• $10.6M of TVL maintained in targeted pools post-incentives
• $9.11 of TVL added per $1 of incentives during the program
• $5.99 of TVL added per $1 of incentives post-incentives
Volume and fees also increased significantly
• $823M in market share-adjusted volume added during the program, equating to $259 of volume added per $1 spent
• $318M in volume maintained post-incentives, reflecting $119 per $1 spent
• $725K in market share-adjusted LP revenue
• $114K in LP revenue maintained post-incentives, amounting to $1.37M in projected LP fees over the next 12 months.
• $187 in additional volume for every $1 spent on incentives
Takeaways and next steps
Alongside strategies like Protocol-Owned Liquidity (POL), Liquidity Mining (LM) remains a vital tool in our arsenal for enhancing onchain liquidity. LM provides significant leverage by bootstrapping a large amount of liquidity and TVL relative to the amount of dollars spent in the short term.
We will apply these learnings and strategies to future LM efforts. This combination of strategies enables us to effectively achieve these incentive programs' goals and maximize their impact. We are currently supporting the @arbitrum UADP/LTIPP incentivization program, where select pools will systematically be allocated incentives to encourage TVL growth.
Read our full report below 👇
Results and Analysis: Uniswap Arbitrum Liquidity Mining Program
1/ A few weeks ago, we published a thread on AVS allocation research we've been doing for @ether_fi. Today, we publish the full report!
The report covers:
1️⃣ AVS commitments
2️⃣ Impacts of adding AVSs
3️⃣ Optimal allocations to ensure $eETH maximizes risk-adjusted yield
👇🧵
@ether_fi 2/ Given payments and slashing are not live, this framework remains theoretical.
We start off by determining an initial restake per operator given @ether_fi's existing commitments and operator set.
@ether_fi 3/ We assume commitments will remain static, and aim to reduce the second-order downside of AVS slashing.
To accomplish this, we recommend minimizing allocation to a single operator by spreading allocations evenly while accounting for commitments of varying sizes.
1/ We added additional LRT and LST markets to the Gauntlet LRT Core MetaMorpho vault on @MorphoLabs to diversify collateral risk and optimize for yield.
Newly added markets are outlined below 🧵👇
@MorphoLabs 2/ $pufETH, a native LRT by @puffer_finance, generates restaking rewards through native restaking on @Eigenlayer.
Details: the pufETH/WETH market has an LLTV of 86%, an initial supply cap of 1,000 WETH, and uses the @redstone_defi market rate oracle.
@MorphoLabs @puffer_finance @eigenlayer @redstone_defi 3/ $rswETH, is a native liquid restaking token by @swellnetworkio.
Details: the rswETH/WETH market has an LLTV of 86%, an initial supply cap of 300 WETH, and uses the Redstone market rate oracle.
1/ With LRTs continuing to garner significant adoption, a framework for evaluating risks is paramount for users to understand the market risk landscape for each token.
Today, we published a blog on LRT market risk as part of our partnership with @EigenLayer.
Let's dive in👇🧵
2/ Our LRT Risk Framework is broken down into 4 attributes:
Gauntlet has now built the infrastructure to ingest market data and run simulations and economic stress tests on the @synthetix_io ecosystem.
Value at Risk, Liquidations at Risk, and Mint Usage are topline Risk and Capital Efficiency measures. VaR/LaR convey capital at risk due to insolvencies/liquidations when markets are under duress (i.e., Black Thursday).
"We are taking on the formidable engineering challenges associated with building predictive models of aggregate user behavior across DeFi" @ChiangRei, Co-Founder / CTO. "We leverage our Agent Based DSL to run thousands...
...of simulation scenarios parallelized across a cluster of machines, while taking into account the underlying market micro-structure such as market liquidity dynamics and network congestion trained on of the latest on/off-chain data"
We explored strategies that AMPL traders might build upon, particularly around rebase events.
From this analysis, we observed what you might expect: rebase events have in practice driven traders to trade around them (there is an increase in trading before & after the event).
Now just because people trade around these events, it doesn't mean they will be able to forever — these strategies *need* to be profitable or people will stop trading if they just lose money.
But don't take our word for it, look at the data and analysis!