We provide an overview of Panoptic Research and our accomplishments in the 1 month we have been operational. Research has always been Panoptic’s raison d’être and continues to be at the forefront of our company.

Note: This post is co-posted at https://blog.panoptic.xyz/.

First, we added key hires to our team — learn more about these awesome folks here: https://www.panoptic.xyz/about.

Then, we initiated the Research Bites program at Panoptic Research. This is a publicly accessible, free-to-use collection of research analysis and corresponding code hosted on our GitHub repository at https://github.com/panoptic-labs/research.

Follow and star this repository to keep up with the latest Panoptic Research!

You may clone, modify, and build upon this work as you see fit. This is intended to advance DeFi and OpFi in particular.

Please get in touch with us if you have any questions or wish to contribute with your own research.

Here is a brief overview and history of our Research Bites effort, which was launched via our blog (first posted on our website here):

• January 5th: The 1st Research Bite is published under the tag #ResearchBites

• January 12th: Panoptic Research is officially launched

• January 16th: The 2nd Research Bite is published

• January 18th: The 3rd Research Bite is published

• January 20th: The 4th Research Bite is published

• January 23rd: The 5th Research Bite is published

• January 25th: The 6th Research Bite is published

• January 26th: The 7th Research Bite is published

• February 1st: The 8th Research Bite is published

• February 2nd: The 9th Research Bite is published

Next, we will classify and describe these findings. They are all oriented on providing liquidity in Uniswap (and more generally in concentrated liquidity AMMs).

Disclaimer: This is never financial advice, and you must do your own research at all times (DYOR). This content is provided free of charge and solely for educational reasons.

We launched two separate tracks under the Research Bites initiative. First, and to support the main track “Research Bites,” we have our Research Tutorials.

Let’s dive into that first:

Research Tutorials

Our goal in launching the Research Tutorials program was to make it easier for readers of our general Research Bites series to follow along and replicate our results. As for the latter, we’d like to stress that all research bites are available for download on GitHub. Our post from the 16th of January details the use of Google BigQuery to obtain and analyze blockchain data.

Next, we demonstrate the algorithm and mathematics underlying the deployment of liquidity for Uniswap positions represented in percentage ranges (deploying to a strike price p ±30%, for example) in our bite from January 18th — something that permeates many of our “Bites.”

Look for future tutorials to be posted to this repo, and let us know if you’d like to see a specific tutorial.

Read more here:

• https://twitter.com/Panoptic_xyz/status/1615373053705306112?s=20&t=_OOjlZUlTcfjOZVSdLfeMA

• https://twitter.com/Panoptic_xyz/status/1615816389490802689?s=20&t=pp6LefJX2MqBGvEe6C4NnQ

Next, in the following sections, we provide and overview of the main track Research Bites:

Does the price of the most traded asset in Uniswap v3 follow a Geometric Brownian Motion (GBM)?

We look at the ETH-USDC UniV3 pools and discover that the price fluctuates in a way that deviates significantly from a Gaussian distribution. In addition, it deviates from an exponential pattern. As a matter of fact, we discover that the distribution more closely resembles a power law.

The key insights are:

• The 5bps pool gets 90% of all USDC volume

• The 5bps pool likely follows the CEX price more closely due to its 0.05% arbitrage fee

• The distribution of price jumps does not follow a normal distribution

• The price jumps follow a power law distribution (!)

And we were left with a few open research questions:

• Can a GBM have a power law random process?

• What is the impact of a large kurtosis on price action?

Read more here:

• https://twitter.com/guil_lambert/status/1611151537039884290?s=20&t=_OOjlZUlTcfjOZVSdLfeMA

How long does the price remain within a given range?

We analyze the relationship between the range of deployed liquidity and the first exit time from that range. We find an interesting relationship following a square law. This seems to follow from the price *ticks* moving as a GBM. We find that we can go further into new territory and derive a “per tx” volatility based on the exit times.

Because significant occurrences have been flagged in this data, the volatility indicator, which is the average of several transactions, contains valuable information.

Key insights here are:

• The average time spent within a range ±h scales as (h/σ)²

• E[first exit time] can be used to develop resilient and efficient volatility estimators (when on-chain σFTE ?)

• The volatility calculation in the ETH-USDC pool can highlight volatile macro events

Open research questions:

• Do this still hold if price ticks are not a Brownian motion (see quoted thread)

• What about 30bps and 100bps pools?

• Should the range be adjusted for different volatility environments?

• What’s the optimal range for a given σ?

Read more here:

• https://twitter.com/Panoptic_xyz/status/1616518726282792960

How correlated are prices between stablecoin pools?

We looked at how different pools of stablecoins were linked to one another. This is because it will be easier to forwardtest and model stablecoin pools if their behavior is understood. Beta-trading methods, such as stat-arb, can be developed with the use of knowledge about inter-pool correlations.

We analyzed USDC, USDT, DAI, and FRAX in this context.

The key insights are:

• The per-tx price is less stable than one would think!

• There is, on average, ~1 tx every 5 minutes across all pools

• Tx amount ~log-normal; time between txs ~exponential

• Price across some pools are weakly correlated, with Dai being the odd one out

Read more here:

• https://twitter.com/Panoptic_xyz/status/1617628299542560769

How to hedge ANYTHING (including UniV3 LP positions) with Options

We published a bite on hedging strategies for all types of holdings and portfolios, including UniV3 LP positions. A straightforward illustration of hedge building is provided.

In a nutshell,

1. Be aware of the impact that price fluctuations have on your position

2. Find an opposite reaction

3. Determine which investments will produce the opposite effect (your hedge)

4. Buy the hedge. Hint: you can use Panoptic!

5. Rest easy knowing you’re protected.

Then, we looked into hedging impermanent loss (IL) with a strangle.

Note that shorting LP positions cannot be done when interfacing with AMMs. This is possible only with Panoptic. In Panoptic shorting LP positions is permitted for any asset and at any strike.

Key insights:

• LP positions look like short puts → LPers are selling options

• Only way to fully hedge an LP position is to short it

On top of that, hedging with traditional options can be costly in terms of time and money spent rolling positions. But these costs can be avoided through the use of Panoptic perpetual options (XPOs).

Read more here:

• https://twitter.com/Panoptic_xyz/status/1618353035138457600

How did The Merge impact Ethereum’s gas fee market?

Panoptic prioritizes gas-efficient contracts since they result in lower fees for the users. Optimizing gas usage in Solidity smart contracts is a complex problem.

Our goal with this bite was to provide a high-level overview of the gas fee market and the factors that go into setting gas pricing.

We see that gas distributions shifted following The Merge, when Ethereum became a more stable and cost-effective network.

It also made block time more predictable (almost always 12 seconds). Pre-merge, the block time was (roughly) exponentially distributed.

Read more here:

• https://twitter.com/Panoptic_xyz/status/1618777173061668867

How to Hedge using Beta

The beta (β) of an asset or portfolio is the ratio of its risk to that of a market index (M). Higher correlation and relative risk both increase beta (ratio of volatilities).

In this bite, we demonstrate how to calculate and evaluate beta for several different pools. Then, we demonstrate how to protect yourself against the market’s and economy’s long-term structural risks. Market risk can be eliminated from a portfolio S of value V by shorting V * β(S; M) units of the market index M.

Further, we compare the β of ETH with respect to SPY (just for kicks) and then to a number of different stablecoin pools with concrete instances (more “apples to apples” comparison). We obtained the beta values and showed them all in a table.

In summary, it’s important to know that:

• β measures risk w.r.t. a market index M. Different markets = different β

• There is no SPY equivalent in DeFi which means that we’d need to construct a M when β-hedging cryptos

• Can also use β to hedge options, but it’s more complicated (more on this later!)

• β(oSQTH; ETH) = 2 β(ETH; ETH), i.e., oSQTH is twice as volatile as ETH (easy to generalize to other power perpetuals)

Read more here:

• https://twitter.com/Panoptic_xyz/status/1620829599402971136

LP Performance Analysis

Here, we take a look at how the ETH-USDC 0.3% pool would have performed for a simple LP strategy.

We pose the following issues:

• How wide should your LP positions be for optimal returns?

• In this regard, is there a difference between a bear and a bull market?

We test out various range factors (widths of the LP positions). To maximize returns, we found that a narrower range was preferable in the bull market, but a wider range was better in the bear market.

We determined that cumulative returns to the backtested LP strategy were nearly 20 percent during the ten-month bull market period for most range factors.

In the bear market, the cumulative returns would be negative — but not as negative as HODLing ETH.


• Returns are overstated as they ignores gas & rebalancing fees

• Assumes fees/returns compound day-over-day

• Past performance is no guarantee of future results!

Open questions:

• How would returns on other pools like UNI, WBTC, and SHIB compare?

• How does the optimal range factor compare for stablecoin pools?

Read more here:

• https://twitter.com/Panoptic_xyz/status/1621252130815483904