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Feb 2 โ€ข 12 tweets โ€ข 6 min read
1/12 We analyzed simulated LP performance on ETH-USDC 0.3% pool.

Results were surprising:
๐Ÿ“ข The optimal width was wider than expected.

โ€ข What's the optimal width for max returns?
โ€ข How does that change for ๐Ÿ‚ vs ๐Ÿป markets?

Find out ๐Ÿ‘‡
2/12 The strategy is simple:

๐Ÿ’ฆ LP around the current ETH price with ยฑX% width
โš–๏ธ Rebalance your LP position after a day, week, or month (you pick)
๐Ÿ’ต Collect & compound your fees!
3/12 Our analysis includes >1.5 years of data (Jun 2021 - Jan 2023)

On 5 different range factors:
โ€ข ยฑ5% (r = 1.05)
โ€ข ยฑ20% (r = 1.2)
โ€ข ยฑ50% (r = 1.5)
โ€ข ยฑ75% (r = 1.75)
โ€ข โˆž (UniV2 full-range, r = 1000)

Which one did best?๐Ÿค”
4/12 For the FULL period (Jun 2021 - Jan 2023):
Daily rebalancing: r* = โˆž (full range)
Weekly rebalancing: r* = 1.5
Monthly rebalancing: r* = โˆž (full range)

LPs end up w/negative returns for this pool, but were raking in $ during the bull market (~40% returns in < 6 months) ImageImageImage
5/12 For the ๐Ÿ‚ market (Jun 2021 - Mar 2022):
Daily rebalancing: r* = 1.5 - โˆž
Weekly rebalancing: r* = 1.5
Monthly rebalancing: r* = 1.75

LPs end up positive! (~20% returns in 10 months) ImageImageImage
6/12 For the ๐Ÿป market (Apr 2022 - Jan 2023):
Daily rebalancing: r* = โˆž
Weekly rebalancing: r* = 1.75 - โˆž
Monthly rebalancing: r* = โˆž

LPs end up negative (-24% returns in 10 months, but compare that to -55% returns on ETH!) ImageImageImage
7/12 During ๐Ÿ‚ market, optimal range was tighter (r = 1.5 - 1.75)
But during ๐Ÿป market, optimal range was wider (r = 1.75 - โˆž)

Here's why ๐Ÿ‘‡
8/12 Tight range:
๐Ÿค‘ "Greedy"
๐Ÿš€ More fees! (~6,000% cumulative fees in 1.5 years, r = 1.05)
๐Ÿ’ฉ Buy losing asset faster, sell winning asset faster

Wide range:
๐Ÿฆบ "Safe"
๐Ÿš€ Buy losing asset slower, sell winning asset slower
๐Ÿ’ฉ Less fees (16% cumulative fees, r = โˆž) ImageImage
9/12 There's a tradeoff in choosing r:
โฌ†๏ธ fees โฌ‡๏ธ payoffs or โฌ‡๏ธ fees โฌ†๏ธ payoffs

@guil_lambert has a good "Guide For Choosing Optimal Uniswap V3 LP Positions" ๐Ÿ‘‡

โœ… Gives probability of landing ITM
โŒ Doesn't account for where we land ITM
โŒ Doesn't account for time spent ITM Image
10/12 Caveats:
โ›ฝ Ignores gas & rebalancing fees
๐Ÿ“ˆ Assumes fees/returns compound day-over-day
โš ๏ธ Past performance is no guarantee of future results!

Questions:
๐ŸŒŠ What about other pools? (๐Ÿฆ„, โ‚ฟ, SHIB, ...)
โš–๏ธ What about stablecoin pools?
11/12 Disclaimer:

๐Ÿ“ข None of this should be taken as financial advice.
โš ๏ธ Past performance is no guarantee of future results!
12/12 Comment below with questions.

Follow @Panoptic_xyz and @brandonly1000 for more #ResearchBites and other key updates!

Check out our blog ๐Ÿ‘‰ panoptic.xyz/blog
Star & follow our GitHub repo ๐Ÿ‘‰ github.com/panoptic-labs/โ€ฆ

๐Ÿค Like & Retweet if you found this thread helpful!

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More from @Panoptic_xyz

Feb 1
1/12 In this series, we will look at different (financial) Greeks.

Most know about alpha, but what about beta? How can we compute it? How can we use it to hedge our investments?

Let's discuss! ๐Ÿงต๐Ÿ‘‡
2/12 First things first:

Beta (ฮฒ) measures the risk of an asset or portfolio, S, against the risk of a reference market index, M.

See the mathematical definition below๐Ÿ‘‡๐Ÿค“

ฮฒ(S; M)= correlation(S; M) x volatility(S) / volatility(M) Image
3/12 Beta increases w/correlation & relative risk (ratio of volatilities).

How can we interpret this? If:

- ฮฒ = 1.5 โ‡’ The asset S incr. 1.5% for each 1% incr. in the index M

- ฮฒ = 0.5 โ‡’ S incr. 0.5% for each 1% incr. in M

- ฮฒ = -1.5 โ‡’S decr. 1.5% for each 1% incr. in M
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