Disclaimer: This is not financial advice. Anything stated in this article is for informational purposes only and should not be relied upon as a basis for investment decisions. Triton may maintain positions in any of the assets or projects discussed on this website.
This is a multi-part series on Triton’s investment process for liquid crypto:
Data Dashboards for Liquid Crypto
As discussed in a previous post, in order to identify which factors were most significant in predicting token returns, we segmented the crypto market into two high-level categories: ecosystem tokens and app tokens. We ran multiple models using both macro and project-specific factors and identified several significant variables that we can track over time and use as the basis for our bespoke data dashboards for each category.
Using Regression as a Starting Point
In the crypto space, most code is open source and everything that happens on the blockchain is transparent (barring zero knowledge and other technical obfuscation). This means that, with sophisticated data science capabilities, we can aggregate the most relevant metrics for each vertical we invest in and track these metrics over time to better understand how the use of different blockchains and decentralized applications is evolving.
At a high level, we have identified key metrics for two broad categories: ecosystem tokens and app tokens. We have further subdivided these categories into 24 specific verticals (DEXs, Derivatives, Lending, etc.). After successive rounds of modeling for each vertical, we identified the fundamental metrics that have historically been most significant for a given vertical. These variables then inform which data we track for each vertical. We use the dashboards we create from this process to monitor growth and competitive position within each vertical.
Building our Data Dashboard Suite
For each vertical, we create a suite of data dashboards, which provide a comprehensive view of activity for that vertical, and a fundamental source of truth upon which we can base our investment and trade management decisions. By way of example, if we subdivide ecosystem tokens, we get the following verticals - Layer 0s, Layer 1s, and Layer 2s. We built unique sets of internal dashboards for each of these verticals.
Below we provide an example of how we use these dashboards to make data-driven decisions around capital allocation and investment sizing for the “Layer 2” vertical.
*A note before continuing - The below data dashboard case study focuses on the Layer-2 vertical for Ethereum. After completing this dashboard build, we realized the data insights would be valuable to the broader Ethereum community. We applied for and received an Ethereum Foundation grant to build out the interface further and open-source this dashboard. The resulting interface can be found at growthepie.xyz.
You can find a screenshot of this dashboard below.
Layer 2 Case Study
With the proliferation of Layer 2 execution environments, decentralized applications that were formerly built on the Ethereum base chain have migrated to Layer 2 execution environments for faster settlement times and lower fees. One type of Layer 2 execution environment is “Optimistic Rollups”, the leaders of which are Arbitrum and Optimism. The first step in our analysis involves aggregating and cleaning data across Ethereum, Arbitrum, and Optimism to create a high-level view of how these different ecosystems are growing or contracting relative to one another.
Ethereum
Arbitrum and Optimism
Refining Our Analysis
As these different ecosystems begin to grow across the key metrics we identified during the modeling phase of our process, we further refine our analysis by understanding which activities are gravitating to certain environments over others.
To create this view, we built a set of dashboards that parse out specific activities for each chain. This process was extremely manual, as we had to go through all smart contracts on each of these chains, and categorize each project using specific tags, such as ‘Stablecoin’, ‘Native Transfer’, ‘Bridge’ and “Arbitrage, and MEV’. We then fed this data into our dashboard suite for each chain. As an example, a high-level view of select L2 blockspace usage is shown below:
Deriving Insights and Making Capital Allocation Decisions
Using the insights from these dashboards, we can determine which activity uses the most gas on each chain, which is one proxy for blockspace (i.e. in a blockspace auction, these activities bid higher for space, and are therefore more valued on that specific chain). For example, if we see that DeFi activity on Optimism is growing at a faster rate than DeFi activity on Ethereum and Arbitrum, we will monitor this closely.
If this trend persists, we then do a deep dive into DeFi projects built on Optimism. After writing a report on Optimism DeFi projects, we can compare them relative to one another and invest in the most promising projects in that vertical for that specific ecosystem.
Expanding Our Analysis Across All Ecosystems
Using the same process shown above for Layer 2s, we have built a set of high-level key metric views and fine-grained analysis dashboards for all of the verticals we track across the crypto space.
As the space grows and activity gravitates toward different ecosystems and execution environments, we believe we will be able to make more rigorous, data-driven capital allocation decisions in advance of price movements across multiple verticals.
In the next post, we discuss how we corroborate our data findings with a robust research process for each project.
MEV (Maximum Extractable Value) is the value captured by third parties on blockchain networks
