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Nastya
@nastya
So I'm trying to figure out who the best traders on Farcaster are - out of hundreds of thousands of users. What are the main challenges? First - the data. I mostly use Dune since it's the biggest source of blockchain data with rich context - but it's far from plug-and-play. Blockchain data is messy, and Dune queries often need cleanup or deeper knowledge to make sense. Even something that sounds simple - like getting DEX swaps for a wallet - turns into a whole project if you want to be accurate. Second - figuring out what metrics actually matter. How do you calculate PnL, and over what timeline, tokens, chains etc. A high total PnL might come from a one-time airdrop - not consistent skill. From what I've seen, past big wins don’t mean much - what really matters is whether the trader's still active and knows when to take profits. I've gone through many iterations of the algo, and I'm still refining it. But so far, the most useful approach is looking on the freshest metrics. Anyone cracked this already?
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Apurv
@apurvkaushal
tried this for a crypto rewind concept (similar to Spotify wrapped etc.) - custom indexing pipelines with support from OP for contextualisation (tysm @binji.eth ) we were able to do PnL etc. for a lot of smart contracts doing it multi chain is extremely difficult - often end up using Debank APIs for this (a lil expensive than we'd like)
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Nastya
@nastya
Thanks for sharing, I didn't know Debank has an API! Their data usually looks really good and covers a lot of chains. What did you mean by "support from OP for contextualisation"? I’m also working on a fully open-source library for transactions interpretation, so it's really interesting to learn more how others are approaching this problem.
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Apurv
@apurvkaushal
so the OP team shared w us labelled data of smart contracts that they themselves curated I think. cut our dev time by a lot..
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