What Is Actually Happening On The Blockchain? How On-Chain Data May Defend Your Portfolio On Top Of Increasing It

Joel Zhao
October 4, 2022
10 mins read

Beyond what we know on the price charts, there is a lot more going on being the scenes to keep the ~1T dollar industry running. Today, we go deeper and get insights with the CEO of Nansen, Alex Svanevik, Founder and CEO of CryptoQuant, Ki Young Ju, the CEO and Co-founder of Chainanalysis, Michael Gronager and Bobby Ong (Moderator), the Co-Founder of CoinGecko on what is really happening on the blockchain.

Insight into the blockchain with tools

Unlike tradeFi, on-chain tools provide layers of data that, when capitalized, could safeguard your assets/find you the latest “alpha” in the space.

“It is about staying ahead of large events…0n-chain data helps you defend your portolfio on top of increasing it.”
Alex Svanevik

One function the blockchain analytics platform Nansen has is its smart alerts. Prior to the UST diabolical, users got an alert on people pulling out liquidity from curve pools. This prompted some users to remove their UST and escape the de-peg.

However, with an abundance of data, one may get drowned in sifting out the noise and what is really important.

“A lot of on-chain data is noise, we need to build metrics/tools to account for all variables.”
Ki Young Ju

These metrics can go down right to the granular level such as internal movements of funds and miner flows among many others to provide insightful data. For example, when a 7-year-old Bitcoin suddenly moves, the tendency for it to be related to criminal activity is relatively high. Ki also added that these transactions usually go through “mixers” just like how TornadoCash works.

We have some "material non-public" information here.

This tweet is "non-material public" information. C'mon, just tell the truth. https://t.co/9UZOLDjZxw
— Ki Young Ju (@ki_young_ju) September 28, 2022

Taking a step back to look at things on a macro level, Michael talked about how on-chain data reflected certain behavioural shifts amongst countries.

“Philippines’s crypto activity went from 15th in the world to 2nd.”
Michael Gronager

Part of why the Philippines moved up the ranks was remittances, but mostly due to gaming. All of which can be assessed by on-chain data.

Popularizing “smart money”

So what is smart money? Is it still alpha if everyone follows it?

There are two high-level ways to think about smart money. Firstly, the social aspect of known entities. This smart money comes in the form of large funds, the lights of a12z, Panterra and Alameda are examples. Secondly, the behavioural aspect begs the question of “can you be smart money?” which can be decomposed to NFT flippers, yield farmers or token traders.

“A combination of a group of signals helps investors navigate the crpyto landscape.”
Alex Svanevik

While Ki mentioned before that there is a lot of noise on the blockchain, Nansen started smart money with the intention of surfacing signals.

There are millions of wallets on the chain, what people care about is a small subset of wallets that these wallets are transacting. Nansen comes into play by tagging and curating these selected addresses that people care about in investing and trading.

Tagging addresses even when entities attempt to hide them

“There is no silver bullet, there needs to be a combination of man and machine.”
Alex Svanevik

There is still a need for research analysts to monitor what is happening in space. “This can be through reading fundraising rounds and mapping token distribution back to the investors based on what happens on-chain.”

With that being said, more than 99% of the 100 million+ addresses tagged mostly come from an algorithm.

While the CryptoQuant team provides data to quantitative hedge funds and various tradfi institutions, it is evident that those big funds “don’t like the changing of data.” Ki focuses on building algorithms and explores the use of dusting algorithms and machine learning to help them figure out specific wallets.

Michael added how the use of machine learning has to be fed with “training data, finding the pattern and reuse it.”

However, if we want to do it at scale and look at billions of transactions across multiple years, “the only way it works is to have access to ground tools data.”

Once the above is accomplished, it could reveal insights into what big services wallets are and what it looks like and get data on personal wallets and how they perform cross-chain.