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Feb 15 โ€ข 13 tweets โ€ข 5 min read
1/13 Why are some pools good ๐Ÿถ and other pools bad ๐Ÿ˜ˆ?

The answer comes from breaking down LP profits into:
1. Price changes ๐Ÿ“ˆ
2. Fees collected ๐ŸŽŸ๏ธ

By comparing LPs to options, we discover parallel insights โ€” let's explore! ๐Ÿงต
2/13 Price changes
โฌ†๏ธ Price up: positive return
โฌ‡๏ธ Price down: negative return
โคต๏ธ Payoff determined by delta (ฮ”) & gamma (ฮ“) of LP position

Why use options terminology (ฮ” & ฮ“) for LPs?
Hint: that payoff looks awfully like a short put option!
3/13 Fees collected
โ€ข Determined by theta (ฮ˜) of LP position

๐Ÿ•’ ฮ˜: Rate of time decay (dV/dฯ„)
๐Ÿ’ฐ dV = fees collected
๐ŸงŠ dฯ„ = 1 block

โ†’ ฮ˜ = fees per block ๐Ÿคฏ

โœ… Near the money: ฮ˜ > 0
โŒ Far the money: ฮ˜ = 0
4/13 In TradFi, options selling is more profitable when Implied Volatility (IV) > Realized Volatility (RV)

Can we compare IV-RV for LPs?

Yes! But let's use fees instead of IVs since:
โ€ข Easier calculation ๐Ÿงฎ
โ€ข Fees collected โ‡” options premia ๐Ÿ‘‡
โ€ข โฌ†๏ธ options premia โ‡” โฌ†๏ธ IV
5/13 Results match TradFi!

๐Ÿถ Good pools (green dots): lie below the line, compensated by high fees given volatility ("IV > RV")
๐Ÿ˜ˆ Bad pools (pink dots): lie above the line, not compensated enough ("IV < RV")

(Dot values are summed returns from LPing)
6/13 How do price changes and fees affect returns?

โฌ†๏ธ Price โ†’ โฌ†๏ธ LP returns (since fees are always positive)
โฌ‡๏ธ Price โ†’ โฌ†๏ธ LP returns if ฮ˜ dominates
โฌ‡๏ธ Price โ†’ โฌ‡๏ธ LP returns if ฮ” & ฮ“ dominate

Let's define "dominance" so we can analyze pool returns! ๐Ÿ‘‡
7/13 We define a metric to measure how much fees dominated LP returns

ฮ˜ dominance = fees / [ fees + |payoff| ]
(fees & payoff expressed as percentages)

Meaning:
๐Ÿ’ช100% ฮ˜ dominance โ†’ fees drove 100% of LP returns
๐Ÿค•0% ฮ˜ dominance โ†’ price movement drove 100% of LP returns
8/13 Previously, we found that LPing on $ENS was highly profitable (+124%), but $UNI was not (-28%)

By graphing ฮ˜ dominance next to cumulative returns, we find:
๐Ÿ˜” Bad days (negative returns) driven by price movement
๐Ÿฅณ Good days (positive returns) driven by fees
9/13 Breakdown of positive & negative returns confirms that
good pool ฮ˜ dominance > bad pool ฮ˜ dominance:

๐Ÿ˜”Bad days: 28% ($ENS) > 22% ($UNI)
๐Ÿ˜ŠGood days: 59% ($ENS) > 50% ($UNI)

The good pool also had a higher proportion of good days:
๐ŸคฉENS: 63% (272/433)
โ˜น๏ธUNI: 55% (335/608)
10/13 The good pool's fees made up for its bad payoffs ($ENS):
Fees: 466%
Payoff: -371%
Return: 95%

The bad pool's fees weren't enough to compensate ($UNI):
Fees: 309%
Payoff: -332%
Return: -23%

(All values are summed)
11/13
๐Ÿ“ฃ Key Insights:

1. LP = short option payoff
2. ฮ”, ฮ“, and ฮ˜ affect LP returns
3. LPs compensated when IV > RV
4. Good days/pools driven more by fees than by price changes
12/13 Disclaimer:

๐Ÿ“ข None of this should be taken as financial advice.
โš ๏ธ Past performance is no guarantee of future results!
13/13 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 10
1/12 The weekly volume on all NFT trading platforms was $120M last week. This includes BAYC, CryptoPunks, LOOT, Azuki, etc.

But...

$23 billion (yes, with a B) of value was traded on Uni V3 as financial NFTs ๐Ÿ“ˆ

Here's 8 reasons why @Panoptic_xyz is bullish on financial NFTs๐Ÿงต
2/12 First of all: why is Uni V3 a financial NFT platform?

Liquidity in Uni V3 is deployed under a price range, which means LP positions are non-fungible and can't be tracked using ERC20s

Instead, Uniswap issues an ERC721 to track the funds controlled by each LP position
3/12 Reason 1: Most derivatives in TradFi *are* NFTs

โ€”
Futures contracts expire at a set date, and each underlying has multiple tickers:
The Canadian dollar futures \6CH3 (exp. MAR-23) is different than the \6CM3 (JUN-23).

Options follow the OSI standard for exp, strikes, etc.
Read 12 tweets
Feb 8
1/11 We simulated LP performance for 21 popular Uni V3 pools (high TVL & volume)

Results were surprising:
๐Ÿ“ข LPs can be profitable!

๐Ÿ’ฐ Which pools made the most?
๐Ÿ“ Are narrow or wide ranges better?

Find out ๐Ÿงต
2/11 Previously, we explored the ETH-USDC 30bps pool.

For this study:
๐Ÿ—“๏ธ Jun 2021 - Jan 2023 (20 months) for most pools
โš–๏ธ Daily rebalancing
๐Ÿ“ Narrow (r = 1.05) & wide (r = 1.75) ranges

Here's how other ETH-stablecoin pools compare๐Ÿ‘‡
3/11 Bad pools ๐Ÿ˜” (but can you spot the good pool ๐Ÿถ?)

โ€ข ETH-USDC (5bps): -18%
โ€ข ETH-DAI (30bps): -14%
โ€ข ETH-USDC (30bps): -12%
โ€ข ETH-USDT (30bps): -11%
โ€ข ETH-USDC (100bps): -9%
โ€ข ETH-USDC (1bp): -6%
โ€ข ETH-USDT (5bps): -3%
โ€ข ETH-DAI (5bps): +7%

(Returns in stablecoin) Image
Read 11 tweets
Feb 7
1/13 How do you know if one LP position or portfolio is riskier than another?

Is LPing riskier than HODLing?

This is the first of a series of threads where we discuss different types of risk, how to interpret them, and how to hedge them.

Let's dive in!
2/13 Risk measures (RMs) are crucial in assessing the stability and performance of a portfolio, and they can be used to guide investment decisions.

When providing liquidity on Uni V3, there are several key risks to consider such as volatility, market risk, etc.
3/13 Some commonly used risk measures are:

- Beta (ฮฒ)
- Value-at-Risk (VaR)
- Conditional Value-at-Risk (CVaR)

All of these measures are related to the volatility of the portfolio. In particular, high volatility typically implies high risk.

Let's discuss them in more detail ๐Ÿ‘‡
Read 13 tweets
Feb 2
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”
Read 12 tweets
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
Read 12 tweets

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