🚨 $ETH Alert 🚨 A whale has just purchased 150,000 at a pr | Hua BNB on Binance Square

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🚨 $ETH Alert 🚨 A whale has just purchased 150,000 at a price exceeding 600 million dollars $ETH .

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The Power of Data: Modeling Token Holder Distribution With approximately 305,000 tokenholders, the project demonstrates a remarkably broad distribution of ownership. This is a key health metric for any decentralized network, as a widely held token base reduces the risk of centralization and manipulation, making the governance process more democratic and resilient. Understanding the concentration of tokens within this large holder base is crucial. This is where data analysis with Python becomes essential. By querying the blockchain, one can analyze the distribution of WCT tokens across all addresses. Using Python libraries, an analyst can calculate the Gini coefficient—a common measure of inequality—for the token's distribution. They can create charts visualizing the percentage of the total supply held by the top 10, 100, and 1000 addresses. Tracking how this distribution changes over time, especially after exchange listings and staking rewards distributions, provides a clear picture of whether the network is becoming more or less decentralized. This data-driven approach allows the community to objectively assess the health of its decentralized governance model. @WalletConnect #WalletConnect $WCT

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