✨Introduction

• NEAR: NEAR is a blockchain platform designed for developing decentralized applications.
• Whales: In the context of blockchain and cryptocurrency, whales are individuals or entities that hold large amounts of a particular cryptocurrency or token.
• Staking: Staking is the process of holding and locking up a certain amount of cryptocurrency or tokens in order to support the network and receive rewards.
• Defi: Defi, or decentralized finance, refers to financial applications built on blockchain technology that are designed to be open and accessible to everyone, without the need for intermediaries such as banks.
• NFTs: NFTs, or non-fungible tokens, are unique digital assets that are stored on a blockchain and can be bought, sold, and traded like other cryptocurrencies.
• DEXs: DEXs, or decentralized exchanges, are peer-to-peer marketplaces that allow users to trade cryptocurrencies without the need for intermediaries such as centralized exchanges.
• Swaps: Swaps refer to the exchange of one cryptocurrency or token for another, often facilitated by decentralized exchanges or automated market makers.
• Astro-stakers: Astro-stakers is a staking pool on the NEAR protocol that offers staking services for NEAR tokens.
• Figment.poolv1.near validator: Figment.poolv1.near validator is a validator node on the NEAR protocol that offers staking services for NEAR tokens.

🛠️ Method

As the first step, I prepared a list of the top 1000 wallet addresses with the most NEAR token balance by tracking their transfer transactions in and out of their wallets and bridging from or to other assets, then compared their balance with NEAR Explorer to verify my method of finding balance. After verifying, I found the top 20 addresses with the most balance had not had enough transactions to track their activities. So I had to find the top NEAR holders based on the number of transactions who had more than 100,000 NEAR in their wallets and called them as whales.

I track their activities on swaps interactions to find their favorite assets, staking interactions and NFT marketplaces actions.

Identifying power players and whale behavior requires ongoing observation and analysis of activity within the NEAR ecosystem. By examining transaction history and other patterns of behavior, it's possible to gain insight into the activities of the top NEAR holders and their level of influence in the ecosystem.

🌡️ Analysis

✍🏻 Conclusion

In conclusion, the analysis of the top NEAR whales has revealed interesting patterns and behaviors among power players within the ecosystem. It's clear that staking and interactions with Defi protocols are popular among the whales, with the astro-stakers poolv1.near and figment.poolv1.near validator being the top staking destinations. The whales also seem to be buying more NEAR tokens, indicating a belief in the token's undervaluation. While diving was a popular activity among the whales at the end of last year, it has disappeared from their actions since the new year. Overall, this analysis provides valuable insights into the behavior of power players within the NEAR ecosystem and can be used to inform decision-making about activities within the ecosystem.

Hey there 👋!

Firstly, I appreciate you sticking with it until the conclusion.

I'm Hamed, a civil engineering Ph.D.

student interested in data analysis.

I've made many similar dashboards and visualizations since I started at Flipside in January.

Please take a look at my various contact details and let me know what you think.

About:

• Author: HaM☰d

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• Discord: 0xHaM☰d#8391

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• Twitter: @arjmandi_hamed

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• Email: h_arjmandi2012@yahoo.com

⏳ Last 10 Transactions of the Top 20 Whales.

In the chart to the left, I tracked the last 10 whales' transactions by ranking their block_timestamp by descending state. As can be seen, with the exception of the unknown transactions, the whales paid less attention to Defi and NFT and were drawn to staking over the last 10 transactions count.

⏳ Cumulative Top 20 Whales Interactions

The top 20 whales have had over 60.9K transactions on Defi, 27K on tokens (according to the above PIE chart, the NEAR token received the most attention), and 13.9K interactions with dapps.

🔎 Findings:

According to the chart on the left, diving was the most popular known activity among the top 20 whales over the previous 90 days until the end of last year, but it approximately disappeared from the vision of whales actions since new year.

NFT and Entertainment were not well-liked by the whales.

But wait, what about the other activities?

⏳ The following charts represent the swap metrics by top 20 whales.

🔥 The top 1000 wallets addresses with the most NEAR balance and the count of their transactions are provided in the table above.

As can be observed, among the top 20 addresses, only 4 addresses have had more than 200 transactions count, so I had to exclude them and distinguished the addresses with the most transactions count among top 1000 wallets by the most NEAR balance.

🔎 Findings:

According to the charts above, the whales tend to buy more NEAR, and it seems they believe the current price of NEAR is undervalued, so that 46% of their swapped volume and 33% of their transactions have been allocated to buying NEAR tokens.

The first few days of the new year were the most volatile in terms of transaction volume and trade counts.

⏳ Let's take a look at the top 20 whales' staking metrics.

As can be seen in the following graphs, the whales' staking volume increased over the past 90 days until January 19th, when it passed 90M NEAR per day, then decreased, but over 20M NEAR per day by the top 20 whales is so meaningful.

Their first staking destination was the astro-stakers. poolv1.near, followed by figment. poolv1.near validator.

⏳ Top 20 Whales Most Interactions

Although the DEFI protocols received the least amount of whale attention in the previous ten transitions, they were the most popular for whale interactions in the previous 90 days.

According to the charts below, 55% of whale interactions have been allocated to Defi protocols, while NFT and Dexs have had the lowest shares of interactions.