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One mechanism for improving capital efficiency in CFMM trading is borrowing against LP shares (e.g. @AaveAave, @AlphaFinanceLab, @MakerDAO, @SushiSwap)

How safe is it compared to normal lending?

New 📝💧 from @htkao, @GuilleAngeris, @alexhevans y moi

stanford.edu/~guillean/pape…

How safe is it compared to normal lending?

New 📝💧 from @htkao, @GuilleAngeris, @alexhevans y moi

stanford.edu/~guillean/pape…

We first show how to compare Loan-to-Value (LTV) / collateral factors between borrowing A with B as a collateral vs. borrowing A against an A/B LP pool share

Turns out, with dynamically adjusted LTVs you can make LP share lending *more* safe than lending of the underlying

Turns out, with dynamically adjusted LTVs you can make LP share lending *more* safe than lending of the underlying

👉🏾 Lending + CFMM protocols like @SushiSwap’s Kashi can provide way better efficiency if they dynamically adjust LTVs in response to price changes and fee accruals

I found this insanely counterintuitive until I wrote the equations — LP shares can be amazing collateral w/ care

I found this insanely counterintuitive until I wrote the equations — LP shares can be amazing collateral w/ care

This is a good thread about the MEV War of 2021™️

One thing I will say is that most of the fair methodologies have a downsides themselves:

1. Added latency

2. Lack of guarantees about economic price ordering

3. Extremely unproven in production (similar to ZKPs in 2012)

One thing I will say is that most of the fair methodologies have a downsides themselves:

1. Added latency

2. Lack of guarantees about economic price ordering

3. Extremely unproven in production (similar to ZKPs in 2012)

Why added latency?

Theoretical (@vegaprotocol’s Wendy) and practical protocols (@valardragon) add a >= 1 block commit-reveal from validators OR added rounds of BFT-style message passing. Griefing vectors (DDoS-esque) are abundant + provable models have weak synchrony guarantees

Theoretical (@vegaprotocol’s Wendy) and practical protocols (@valardragon) add a >= 1 block commit-reveal from validators OR added rounds of BFT-style message passing. Griefing vectors (DDoS-esque) are abundant + provable models have weak synchrony guarantees

Recent papers from @algo_class and Joachim Neu show lower bounds on these latencies and it is very unclear if the practical implementations even come close to saturating these bounds (Kelkar, et. al get to weaker bounds in their paper)

⚠️ Paper Alert ⚠️

Remember the Twitter argument between @danrobinson and @SBF_Alameda?

Recall how it hinged on logarithmic vs. linear utility functions?

Using optimal control, we show utilities are a red herring

joint w/ @alexhevans @GuilleAngeris

stanford.edu/~guillean/pape…

Remember the Twitter argument between @danrobinson and @SBF_Alameda?

Recall how it hinged on logarithmic vs. linear utility functions?

Using optimal control, we show utilities are a red herring

joint w/ @alexhevans @GuilleAngeris

stanford.edu/~guillean/pape…

Flip the problem upside down: LP returns are a function of how close the weights w (@BalancerLabs portfolio weights) are to the 'optimum' weight w*

Arbitrageurs can be viewed as a stochastic control mechanism that moves w around w*

Can you control |w-w*| as a function of fees?

Arbitrageurs can be viewed as a stochastic control mechanism that moves w around w*

Can you control |w-w*| as a function of fees?

⚠️ Γ Alert ⚠️

What does part of Paul Milgrom's 2020 Nobel Memorial Prize have to do with 🦍-ing into pool 2?

2nd part of our series on CFMM shape looks at:

💹 How do you compare LP return from different pools?

🤼 Quantifying adverse selection in CFMMs

medium.com/gauntlet-netwo…

What does part of Paul Milgrom's 2020 Nobel Memorial Prize have to do with 🦍-ing into pool 2?

2nd part of our series on CFMM shape looks at:

💹 How do you compare LP return from different pools?

🤼 Quantifying adverse selection in CFMMs

medium.com/gauntlet-netwo…

What does Gauss's Theorema Egregium (1827) have to do with getting rug pulled in Uniswap?

This post (1 out of 3) introduces (only w/ pictures!) new work on understanding constant function MMs (CFMMs) as the primary market for an asset

Part I: Curvature

link.medium.com/RVPG7R85Fbb

This post (1 out of 3) introduces (only w/ pictures!) new work on understanding constant function MMs (CFMMs) as the primary market for an asset

Part I: Curvature

link.medium.com/RVPG7R85Fbb

Why curvature?

@CurveFinance made it clear that some assets perform better on 'flatter' CFMMs and others on 'sharper' CFMMs

But what does it mean to be 'better'?

Our paper studies what happens when traders arbitrage btw. two CFMMs and look at the max their prices differ by

@CurveFinance made it clear that some assets perform better on 'flatter' CFMMs and others on 'sharper' CFMMs

But what does it mean to be 'better'?

Our paper studies what happens when traders arbitrage btw. two CFMMs and look at the max their prices differ by

When we dug into this a little more, it became clear that Gaussian curvature controls a lot of facets of CFMMs:

1. Price synchronization between two CFMMs

2. Adverse Selection for LP returns

3. Price stability

4. Optimal incentives for yield farming

1. Price synchronization between two CFMMs

2. Adverse Selection for LP returns

3. Price stability

4. Optimal incentives for yield farming

As much as I love the Penrose tiling and long-range order (I used to do glass research!), this seems like a terrible idea

1. Diverge correlation times/lengths means time to verify of a single transaction’s validity could take way longer than block propagation

😬😬😬

1. Diverge correlation times/lengths means time to verify of a single transaction’s validity could take way longer than block propagation

😬😬😬

2. If you want to anonymize a transaction graph by using a lattice with dense spectra (like the Penrose tiling) to define a DAG, note that you aren’t guaranteed that there isn’t *any* local structure that an adversary can find — only that no tx ordering will be unique

2. (cont.) It is possible that prefixes of tx ordering overlap an arbitrary amount, so there isn’t as much transaction ordering entropy as there is from cryptographic graph traversals (e.g. expander graph walks in supersingular isogeny signatures, lattice based crypto)