👀 Observation 3

In this section, the number of whales and the percentage of whales among all users, as well as the total number of nears in the wallets of whales and the percentage of this amount from the total number of nears in the wallets are shown. Then, the number and volume of whale transactions are shown and checked according to their type of activity.

• Total count of near whales was ==454== (==0.01445%== of all near users).
• Most of the activity of whales in terms of the number of interactions has been on dapps with ==over 25 M== transactions.
• Most of the activity of the whales in terms of the volume of interaction was on cex with a volume of ==over 500 M near==.

👀 Observation 4

In this part, the activity of whales is examined in terms of the number of transactions and the volume of transactions in CEXs.

• Maximum whales transaction count was okex with over ==18 K== transaction.
• Maximum whales transaction volume was okex with over ==300 M== Near.
• The growth of the cumulative graph of the number of whale transactions from Binance has recently decreased, and Okex is overtaking Binance in this regard.
• The growth of the cumulative graph of the volume of transactions of whales in relation to Binance continues to increase and its amount is much higher than other cex.

👀 Observation 5

In this part, the activity of whales is examined in terms of the number of transactions and the volume of transactions in defis.

• Maximum whales transaction count was Ref finance with over ==200 K== transaction.
• Maximum whales transaction volume was metapool with over ==3 M== Near.
• The growth of the cumulative graph of the count of transactions of whales in relation to Metapool continues to increase and its amount is much higher than other defi.
• The growth of the cumulative graph of the volume of transactions of whales in relation to Metapool continues to increase and its amount is much higher than other defi.

👀 Observation 6

In this part, the activity of whales is examined in terms of the number of transactions and the volume of transactions in dapps.

• Maximum whales transaction count was Nearcrowd with around ==25 M== transaction.
• Maximum whales transaction volume was Nearcrowd with over ==3 M== Near.
• The growth of the cumulative graph of the count of transactions of whales in relation to Nearcrowd continues to increase and its amount is much higher than other dapp.
• The growth of the cumulative graph of the volume of transactions of whales in relation to Nearcrowd continues to increase and its amount is much higher than other dapp.

🟡 Part(1). Overview of all near holders.

🟡 Part(3). Overview of near whales and their activity at each type.

👀 Observation 2

In this section, the top 20 addresses are shown according to the number of Nears in their wallets, as well as the total balance of these 20 wallets.

• Total balance of this 20 wallet was ==259 M Near==.
• Top wallet by balance :

:1st_place_medal: binancecold3.near = ==55.63 M

:2nd_place_medal: a778de7fc42034af1c85cac901fd40392dfc1cc69c3a688ea149112f57b0063e = ==21.44 M

:3rd_place_medal: c6d39aa078dbcba02800d1251194d33dbea2122d671435ac1c6ed5ffae383c03 = ==20.55 M

🟡 Part(2). Overview of top 20 near holders.

🟡 Part(4). Near whales interaction with cex projects.

🟡 Part(5). Near whales interaction with defi projects.

🟡 Part(6). Near whales interaction with dapp projects.

👀 Observation 1

In this section, an overview of the total number of Near network holders is given and then the holders are categorized.

• Total near holders was ==3.14 M== user.
• Total near holders balance was ==504 M Near==.
• Maximum ==3.016 M== (==96%==) of holders hold ==less than 5 Near==.
• In the secod place ==50.105 K== (==1.6%==) of holders hold beetwen ==5 - 20 Near==.

❓NEAR - 12. The Whales of NEAR

Where in the ocean of NEAR do the whales hang out? Examine the top 20 addresses by amount of NEAR held - excluding any custodial or exchange addresses that you can. Can you identify any “power players” or wallets of interest? What behaviors do these whales exhibit?

✍🏻 Methodology

In this dashboard, whales in the net and their activities are investigated. In this dashboard, first, 20 addresses with the most near were identified. Here, we selected addresses that had ==more than 100,000 near== in their wallet as whales. The balance of the wallet was obtained based on the difference between the sent and received near values. Also, the interaction of whales with cexs, defis and dapps has been investigated. The tables used in this dashboard are:

near.core.fact_transfers , near.core.dim_address_labels

📝 Introduction

NEAR is a Layer 1 blockchain protocol that uses its own sharding technology called Nightshade and is secured by staking-based consensus. For block production, NEAR is also using its own technology called Doomslug.

NEAR’s founders are Alexander Skidanov and Illia Polosukhin, who hail from Ukraine and were educated in computer science and mathematics. They have been working on NEAR Protocol since 2017 after contributing to Ethereum’s open-source code. NEAR mainnet came online in August 2020.

Polosukhin also worked on Tensor Flow, an open source machine learning library. They based the company in San Francisco, which is typical for most developers pushing the software envelope. Skidanov and Polosukhin aimed to create a better Ethereum, without relying on Layer 2 scalability.

In contrast, NEAR Protocol relies on making the mainchain (Layer 1) scalable. As such, it offers enterprise-grade performance from the get-go, offering up to 100,000 transactions per second once it’s fully upgraded. As a result, it has gained popularity across all continents to mainstream dApp/Web3 deployment, attracting 1,700 monthly active developers.

Venture capitalists backed NEAR to the tune of $566M so far, led by Andreessen Horowitz, DCG, Coinbase Ventures, Tiger Global, Accomplice, Pantera Capital, Electric Capital, Blockchange, Dragonfly Capital, Blockchain.com, and others. [source]

📃Contact Data

Discord : Abolfazl#2441

Email : Abolfazl.yeganeh77@gmail.com

Twitter : profile

Twite of this analyze : Link

thanks for MetricsDao and Flipsidecrypto team

Source

Introduction