Discover and read the best of Twitter Threads about #researchbites
Most recents (11)
1/16 Did you know the "Father of Options" circumvented 19th century usury laws w/financial engineering to make $?
Let's dive into the fascinating story of how Russell Sage leveraged put-call parity to become a millionaire, and how put-call-LP parity revolutionizes DeFi optionsπ§΅
Let's dive into the fascinating story of how Russell Sage leveraged put-call parity to become a millionaire, and how put-call-LP parity revolutionizes DeFi optionsπ§΅
2/16 In this thread we'll:
β’ Show how Russell Sage made millions by subverting the law with options
β’ Use monkeys & bananas to explain put-call parity
β’ Explain why "put-call-LP parity" for DeFi options is as groundbreaking as put-call parity was for TradFi options
β’ Show how Russell Sage made millions by subverting the law with options
β’ Use monkeys & bananas to explain put-call parity
β’ Explain why "put-call-LP parity" for DeFi options is as groundbreaking as put-call parity was for TradFi options
3/16 What's usury?
Usury, aka predatory lending, is charging an excessive rate of interest on a loan.
Historically, usury was defined as charging *any* interest on a loan and was condemned by major religions & prominent philosophers (Moses, Buddha, Muhammad, Aristotle...)
Usury, aka predatory lending, is charging an excessive rate of interest on a loan.
Historically, usury was defined as charging *any* interest on a loan and was condemned by major religions & prominent philosophers (Moses, Buddha, Muhammad, Aristotle...)
1/11 HODL vs. LP vs. Calls β which one's best?
β¬οΈβHODL downside is substantial (can go to 0)
π§’ LP upside is capped on Uni V3 β token goes up, you now hold the other token
π«π§’ Call options have unlimited upside, capped downside β but pay premia
Incoming backtest π§΅π
β¬οΈβHODL downside is substantial (can go to 0)
π§’ LP upside is capped on Uni V3 β token goes up, you now hold the other token
π«π§’ Call options have unlimited upside, capped downside β but pay premia
Incoming backtest π§΅π
2/11 Let's backtest *hypothetical* DeFi call options (wen Panoptic? π)
ποΈ Jun '21 - Feb '23
βοΈ Periodic rebalancing (day, week, or month)
Strategy:
1. Buy at-the-money (ATM) call option
2. Exercise/close at end of period
3. Pay LP fees as premia
docs.panoptic.xyz/docs/panoptic-β¦
ποΈ Jun '21 - Feb '23
βοΈ Periodic rebalancing (day, week, or month)
Strategy:
1. Buy at-the-money (ATM) call option
2. Exercise/close at end of period
3. Pay LP fees as premia
docs.panoptic.xyz/docs/panoptic-β¦
3/11 πReturn breakdown on $ETH daily call options
Payoff: 519%
Premia: 397%
Profit: 122%
(All values are USDC)
πPayoff > Premia β Profitππ€π
That's pretty good! How does it compare to other rebalancing periods?π
Payoff: 519%
Premia: 397%
Profit: 122%
(All values are USDC)
πPayoff > Premia β Profitππ€π
That's pretty good! How does it compare to other rebalancing periods?π
1/13 π Want to understand the potential risk/reward of options trading?
Look no further than the Greeks!
We'll break down Delta, Gamma, Theta, Vega, & Rho and explain how they can help you make more informed decisions when trading Panoptions π
Look no further than the Greeks!
We'll break down Delta, Gamma, Theta, Vega, & Rho and explain how they can help you make more informed decisions when trading Panoptions π
2/13 The Greeks are a set of risk measures used in options trading to help investors understand the potential risks and rewards associated with their positions.
Continuing our #ResearchBites series on the Greeks, we'll discuss:
β’ Delta (Ξ)
β’ Gamma (Ξ)
β’ Theta (Ξ)
β’ Vega (Ξ½)
Continuing our #ResearchBites series on the Greeks, we'll discuss:
β’ Delta (Ξ)
β’ Gamma (Ξ)
β’ Theta (Ξ)
β’ Vega (Ξ½)
3/13 Delta (Ξ) measures the rate of change of an option's price in relation to changes in the price of the underlying asset.
Mathematically, Ξ is the partial derivative of the option value (V) w.r.t. underlying price (S)
Ex:
Ξ=1 β V increases by $1 for every $1 increase in S
Mathematically, Ξ is the partial derivative of the option value (V) w.r.t. underlying price (S)
Ex:
Ξ=1 β V increases by $1 for every $1 increase in S
1/25 DeFi Options Trading Is Powerful!
There can be unlimited upsideβ¦π
But also unlimited downside π£
Every trader should know how to create these 18 options strategies in @Panoptic_xyz for any crypto asset, any strike, any size:
β€οΈ & rt π
There can be unlimited upsideβ¦π
But also unlimited downside π£
Every trader should know how to create these 18 options strategies in @Panoptic_xyz for any crypto asset, any strike, any size:
β€οΈ & rt π
2/25 In this thread we'll cover:
1. ‡οΈ
2. π
3. π€Έπ½ββοΈ
4. π ββοΈπ΅
5. ππ¦
6. π¦
7. π₯π¦
8. ππ§
9. π¦ΈββοΈπ
10. ‡οΈπ§
11. π¦ΈββοΈπ»
12. π₯π¦
13. π π§
14. βοΈπ§
15. βοΈπ§
16. π¦
17. π¦
18. π¦π¦π¦
1. ‡οΈ
2. π
3. π€Έπ½ββοΈ
4. π ββοΈπ΅
5. ππ¦
6. π¦
7. π₯π¦
8. ππ§
9. π¦ΈββοΈπ
10. ‡οΈπ§
11. π¦ΈββοΈπ»
12. π₯π¦
13. π π§
14. βοΈπ§
15. βοΈπ§
16. π¦
17. π¦
18. π¦π¦π¦
3/25 Think $HEX is worthless?
"Put" your money where your mouth is:
Buy a "put"β€΅οΈ Panoption!
Substantial upside π
Limited downside π
Bearish β¬οΈ
Short LP position
"Put" your money where your mouth is:
Buy a "put"β€΅οΈ Panoption!
Substantial upside π
Limited downside π
Bearish β¬οΈ
Short LP position
1/13 Why are some pools good πΆ and other pools bad π?
The answer comes from breaking down LP profits into:
1. Price changes π
2. Fees collected ποΈ
By comparing LPs to options, we discover parallel insights β let's explore! π§΅
The answer comes from breaking down LP profits into:
1. Price changes π
2. Fees collected ποΈ
By comparing LPs to options, we discover parallel insights β let's explore! π§΅
2/13 Price changes
β¬οΈ Price up: positive return
β¬οΈ Price down: negative return
β€΅οΈ Payoff determined by delta (Ξ) & gamma (Ξ) of LP position
Why use options terminology (Ξ & Ξ) for LPs?
Hint: that payoff looks awfully like a short put option!
β¬οΈ Price up: positive return
β¬οΈ Price down: negative return
β€΅οΈ Payoff determined by delta (Ξ) & gamma (Ξ) of LP position
Why use options terminology (Ξ & Ξ) for LPs?
Hint: that payoff looks awfully like a short put option!
3/13 Fees collected
β’ Determined by theta (Ξ) of LP position
π Ξ: Rate of time decay (dV/dΟ)
π° dV = fees collected
π§ dΟ = 1 block
β Ξ = fees per block π€―
β Near the money: Ξ > 0
β Far the money: Ξ = 0
β’ Determined by theta (Ξ) of LP position
π Ξ: Rate of time decay (dV/dΟ)
π° dV = fees collected
π§ dΟ = 1 block
β Ξ = fees per block π€―
β Near the money: Ξ > 0
β Far the money: Ξ = 0
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 π
Here's 8 reasons why @Panoptic_xyz is bullish on financial NFTsπ§΅
But...
$23 billion (yes, with a B) of value was traded on Uni V3 as financial NFTs π
Here's 8 reasons why @Panoptic_xyz is bullish on financial NFTsπ§΅
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
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
β
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.
β
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.
1/11 We simulated LP performance for 21 popular Uni V3 pools (high TVL & volume)
Results were surprising:
π’ LPs can be profitable!
π° Which pools made the most?
π Are narrow or wide ranges better?
Find out π§΅
Results were surprising:
π’ LPs can be profitable!
π° Which pools made the most?
π Are narrow or wide ranges better?
Find out π§΅
2/11 Previously, we explored the ETH-USDC 30bps pool.
For this study:
ποΈ Jun 2021 - Jan 2023 (20 months) for most pools
βοΈ Daily rebalancing
π Narrow (r = 1.05) & wide (r = 1.75) ranges
Here's how other ETH-stablecoin pools compareπ
For this study:
ποΈ Jun 2021 - Jan 2023 (20 months) for most pools
βοΈ Daily rebalancing
π Narrow (r = 1.05) & wide (r = 1.75) ranges
Here's how other ETH-stablecoin pools compareπ
3/11 Bad pools π (but can you spot the good pool πΆ?)
β’ ETH-USDC (5bps): -18%
β’ ETH-DAI (30bps): -14%
β’ ETH-USDC (30bps): -12%
β’ ETH-USDT (30bps): -11%
β’ ETH-USDC (100bps): -9%
β’ ETH-USDC (1bp): -6%
β’ ETH-USDT (5bps): -3%
β’ ETH-DAI (5bps): +7%
(Returns in stablecoin)
β’ ETH-USDC (5bps): -18%
β’ ETH-DAI (30bps): -14%
β’ ETH-USDC (30bps): -12%
β’ ETH-USDT (30bps): -11%
β’ ETH-USDC (100bps): -9%
β’ ETH-USDC (1bp): -6%
β’ ETH-USDT (5bps): -3%
β’ ETH-DAI (5bps): +7%
(Returns in stablecoin)
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!
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.
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 (Ξ²)
- 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 π
- Beta (Ξ²)
- 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 π
1/12 We analyzed simulated LP performance on ETH-USDC 0.3% pool.
Results were surprising:
π’ The optimal width was wider than expected.
β’ What's the optimal width for max returns?
β’ How does that change for π vs π» markets?
Find out π
Results were surprising:
π’ The optimal width was wider than expected.
β’ What's the optimal width for max returns?
β’ How does that change for π vs π» markets?
Find out π
2/12 The strategy is simple:
π¦ LP around the current ETH price with Β±X% width
βοΈ Rebalance your LP position after a day, week, or month (you pick)
π΅ Collect & compound your fees!
π¦ LP around the current ETH price with Β±X% width
βοΈ Rebalance your LP position after a day, week, or month (you pick)
π΅ Collect & compound your fees!
3/12 Our analysis includes >1.5 years of data (Jun 2021 - Jan 2023)
On 5 different range factors:
β’ Β±5% (r = 1.05)
β’ Β±20% (r = 1.2)
β’ Β±50% (r = 1.5)
β’ Β±75% (r = 1.75)
β’ β (UniV2 full-range, r = 1000)
Which one did best?π€
On 5 different range factors:
β’ Β±5% (r = 1.05)
β’ Β±20% (r = 1.2)
β’ Β±50% (r = 1.5)
β’ Β±75% (r = 1.75)
β’ β (UniV2 full-range, r = 1000)
Which one did best?π€
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! π§΅π
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! π§΅π
2/12 First things first:
Beta (Ξ²) measures the risk of an asset or portfolio, S, against the risk of a reference market index, M.
See the mathematical definition belowππ€
Ξ²(S; M)= correlation(S; M) x volatility(S) / volatility(M)
Beta (Ξ²) measures the risk of an asset or portfolio, S, against the risk of a reference market index, M.
See the mathematical definition belowππ€
Ξ²(S; M)= correlation(S; M) x volatility(S) / volatility(M)
3/12 Beta increases w/correlation & relative risk (ratio of volatilities).
How can we interpret this? If:
- Ξ² = 1.5 β The asset S incr. 1.5% for each 1% incr. in the index M
- Ξ² = 0.5 β S incr. 0.5% for each 1% incr. in M
- Ξ² = -1.5 βS decr. 1.5% for each 1% incr. in M
How can we interpret this? If:
- Ξ² = 1.5 β The asset S incr. 1.5% for each 1% incr. in the index M
- Ξ² = 0.5 β S incr. 0.5% for each 1% incr. in M
- Ξ² = -1.5 βS decr. 1.5% for each 1% incr. in M
#ResearchBites from the @Panoptic_xyz team.
============
One of the core assumptions behind financial models is that prices follow a geometric brownian motion (GBM) --eg. Black-Scholes.
Does the price of the most traded asset in Uniswap v3 also follow a GBM?
Let's find out! π
============
One of the core assumptions behind financial models is that prices follow a geometric brownian motion (GBM) --eg. Black-Scholes.
Does the price of the most traded asset in Uniswap v3 also follow a GBM?
Let's find out! π
We will analyze the 5, 30, and 100bps ETH-USDC UniV3 pools.
The 5bps pool gets 90% of all volume and, zooming in on the price action, appear to have a smaller "per trade impact" than the 30 and 100bps pools
What is the size distribution of the price jumps for each pool?
The 5bps pool gets 90% of all volume and, zooming in on the price action, appear to have a smaller "per trade impact" than the 30 and 100bps pools
What is the size distribution of the price jumps for each pool?
If the price were a GBM, then the size of the price jumps would be normally distributed.
Instead, the price jumps are narrowly peaks around size~0.
They also have a kurtosis that's much larger than a normal distribution's (high kurtosis = long-tail event are more likely).
Instead, the price jumps are narrowly peaks around size~0.
They also have a kurtosis that's much larger than a normal distribution's (high kurtosis = long-tail event are more likely).
