My name is Thomas Hepner. I am a self-taught data scientist and investor that has recently become captivated by the Index Cooperative.
I am hoping to contribute to the Index Coop’s long-term success by exploring and discovering ways that data and analytics can benefit the organization!
I am planning to dive deep into the existing queries in the Dune Analytics dashboard to better understand the data that the Coop has at its disposal and also to create a separate Treasury & Tokenomics Dashboard.
Here are some of the initial ideas I am planning to research:
Index market share of decentralized index products (i.e Index, PowerPool, PieDao, etc.)
What other metrics would the Coop like to see?
Please drop your suggestions in the comments!
Also please let me know if you are interested in getting involved! I am with my fellow data nerds in the #analytics channel in the Discord Server as well.
This is fantastic! Would be interesting if we could also see unincentivized product supply growth. This paints a clearer picture and avoids confounding with price growth. Might be a harder calculation though.
A calculation on the relative distribution of INDEX tokens might also be useful. It could answer the question: How many whales do we have compared to other protocols? Compared to ETH or BTC?
I’m keen if we can look more deeply into INDEX if/when the proposed intrinsic productivity experiment goes live. It would be interesting to see:
Holders LP’ing vs just holding INDEX
Does the balance shift away from Sushi/Uni/wallets to our own contract when we launch intrinsic prod. How does that tie in with rewards APY if we use a risk backstop mechanism, at what APY do we entice holders to move
Then if we can throw any more light on Dylan’s work as he mentions above
@Thomas_Hepner cool! More hard data like this will be great for decision making. Here’s a semi-self-focused data request - Historical data on INDEX liquidity and pool revenues would help gauge how a delegated loan might work re: Aave Credit Delegation and INDEX Liquidity Provisioning
Would love to stay in the loop here and help out where I can. Looks like some of the issues already identified in the analytics repo are mentioned here. Let me know what I can do to help!
Putting on my accounting hat, I find the first two points very interesting. Categorising and specifying treasury expenses lays out a basis for all sorts of things:
It would allow to better compare spending over time,
which could improve predictions or planning,
it would allow to establish KPIs,
or even to compare with other DAOs (if similar information exists)
and finally, comparing expenses to income is always useful. For instance, if a paid marketing activity can be directly connected to increased AuM, and thus increased income from fees (I think the GWG will use similar metrics)
Overall insights like that can certainly support the treasury committee and other community members in more effective decision making.
Obviously there are metrics around the performance of DPI, the composition and the composition vs time (Which may need the wrights from DeFi Pulse).
I would also be interested to see information on token age. I think Bitcoiners use a measure of how long it has been since a coin has moved.
I looked at the top 28 DPI addresses and found 14 that had purchased and never sold (Buried in this post) Which account for 13,000 DPI.
Knowing how many other holders are buying and holding would be good.
Also possibly looking at new buying and selling by the larger wallets.
@LemonadeAlpha did a review on large purchases on Uniswap to help us understand the liquidity reqiements. when I looked at it I reaslised most of the larger trades were the Alpha farm deploying, and arbitrage. I would like to see similar analysis to date but removing the farming accounts (who should know how to avoid slippage) and arbitrage (as “Slippage” is most likely the profit margin of the Arb bot…) I don;t think this is a real time dashboard data, but could be an interesting snapshot / blog post.
Some form of tracking of the more active whales (>1,000 DPI), to see if they are farmers who have just unwound there exposure to divergence loss.
The uniswap Liquidity pool is obviously the largest DPI holder at the moment, so I’m not sure how much we can dig into that (I need to repost my blog on the 1st liquidity mining programme)
Is there a way to look at wallets that have purchased / issued DPI and then oved it into the liquidity pool and remained invested?
I think this is quite valuable. I often see the equivalent data expressed for publicly listed companies. Annual reports normally contain a Top 20 shareholder table and in presentations, this is often shown as a pie chart categorising investors. This is an important metric when considering DAO governance. Data categorising how long token have been at one address would be a good way to see how active holders are.
I think a lot of the points here will fit nicely into the DPI Retention Analytics project. I will work on translating some of the key views you have mentioned into this project and hopefully we can get some of them built out there (tracking whales, exposure through LP vs buying and holding, token age, etc)
@LemonadeAlpha Shared an example of a consumable in the #analytics channel that could eventually come out of the Treasury & Tokenomics Dashboard work from the Yearn Ecosystem:
hey @Thomas_Hepner, can we track/measure the cost of gas that we spend on rebalances, reward distributions and other core activities of the Coop?
I’m thinking of this from the income statement perspective. Our income is the fees + intrinsic productivity in the future. Our operating expenses are gas costs + any overhead + contributor rewards. The balance is our net income.
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