I think most people probably haven't realized how much impact @blur_io has brought to the NFT industry and how serious the crisis is for @opensea and other exchanges. Today, I will use the data from @TransposeData to provide a simple analysis for everyone. 

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我想大多數人可能還沒有意識到@blur_io 給 NFT 行業帶來了多大的影響，以及@opensea 和其他交易所的危機有多嚴重。今天，我就利用@TransposeData的數據，為大家做一個簡單的分析。

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Starting from Feb 10th, the data for Blur began to show an upward trend, with a trading volume of 7984 ETH on Feb 10th, compared to 9244 ETH for OpenSea. However, in the following week, Blur achieved a significant increase in trading volume, doubling the volume of OpenSea on 15th 

從2月10日開始，Blur的數據開始呈現上升趨勢，2月10日交易量為7984 ETH ，而OpenSea為9244 ETH 。然而，在接下來的一周，Blur 實現了交易量的大幅增長，15 日的交易量是 OpenSea 的兩倍

Starting from mid-February, blur has dominated the market and almost all the data has been far ahead of opensea. However, on February 20th, opensea experienced a huge surge in trading volume. Excluding the impact of that day, the comparison between opensea and blur is shown in… https://twitter.com/i/web/status/1635789741064089600 

從2月中旬開始，blur佔據市場主導地位，幾乎所有數據都遙遙領先於opensea。然而，2月20日，opensea的交易量出現了巨大的暴漲。除去當天的影響，opensea和blur的對比如下圖……https://twitter.com/i/web/status/1635789741064089600

Taking yesterday's data as an example, opensea had a trading volume of 8295 ETH, blur had a trading volume of 23609 ETH, x2y2 had a trading volume of 3302 ETH, and sudoswap only had 31 ETH. Compared to opensea, other NFT exchanges are almost facing absolute challenges. 

以昨天的數據為例，opensea的交易量為8295 ETH，blur的交易量為23609 ETH，x2y2的交易量為3302 ETH，而sudoswap只有31 ETH。相比opensea，其他NFT交易所幾乎面臨著絕對的挑戰。

Last but not least, I'd like to introduce to everyone the online data analysis product that I have been using, @TransposeData. It provides real-time data. The Altas section is a collection of Transpose examples, where you can find various interesting online analysis examples. The… https://twitter.com/i/web/status/1635790392338812928 

最後給大家介紹一下我一直在用的在線數據分析產品@TransposeData。它提供實時數據。 Altas 部分是 Transpose 示例的集合，您可以在其中找到各種有趣的在線分析示例。 …https://twitter.com/i/web/status/1635790392338812928

If you want to do further analysis, here is the sql query that I used:
https://github.com/Daniel-Verilog/tranpose-sql-query
I also posted in the @TransposeData:
https://app.transpose.io/atlas/8OQmSYOAqLQd
thanks 🥳🥳🥳

如果你想做進一步的分析，這裡是我使用的 sql 查詢：
https://github.com/Daniel-Verilog/tranpose-sql-query
我還在@TransposeData 中發帖：
https://app.transpose.io/atlas/8OQmSYOAqLQd
謝謝🥳🥳🥳