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首頁> 區塊鏈> 資訊> SHIBonomics: The 4 trillion token shuffle by this huge SHIB whale

SHIBonomics: The 4 trillion token shuffle by this huge SHIB whale

發布時間:2024-08-27 作者:佚名 來源:互聯網

A huge Shiba Inu (SHIB) whale has been activating ‘whale alerts’ in the crypto community with a total of 13 transactions that follow the same pattern, which could indicate that they were done by the same entity. Finbold looked at the ‘4 trillion token shuffle’ tracks, and this is what was found.

SHIB Whale transactions study. Source: Etherscan (Vinicius Barbosa @ Finbold)

From Bitvavo to the most recent address holding the 4 trillion SHIB

On August 20, the Bitvavo account that received the 4.65 trillion SHIB, sent 4.66 trillion SHIB to another Bitvavo account. These internal transactions are usually ‘wallet management’ transactions, to/from cold wallets, in order to keep the digital assets safe.

And it was on August 21 that the ‘4 trillion token shuffle’ left Bitvavo’s wallet, which is usually recognized as a withdrawal transaction after a purchase. For this specific case, the whale could have used the CEX to trade the tokens from August 17 to August 21 — which corresponds to the period of constant updates by the Shibarium team, first assuring that “funds were SAFU [safe]”, and then, on August 22, publicly announcing that the Shibarium Network would be safely restarted soon.

SHIB/USDT 1-day candles, with the transactions’ dates. Source: Tradingview (Vinicius Barbosa @ Finbold)

The final transaction — by press time — was made from “0x344…5d3f6” to “0xA9d…76715”, following two other transactions after leaving the centralized exchange. This Shiba Inu whale now has 4.62 trillion SHIB sitting in this final address, but the ‘4 trillion tokens’ can be moved again soon.

Moving tokens from one address to another, sometimes using CEX’s addresses, are often used as part of a ‘mixing strategy’, where the cryptocurrency owner tries to make it harder for their balances to be tracked — but a mixing strategy can only be effective if the holder uses different patterns of transactions.

By using the same pattern, and very similar amounts, trackers can make educated guesses, like supposing that all the registered transactions from July 14 to August 22 were made by the same entity (person or institution).

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