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ICO analysis: technical approach. Part I - general conclusions



I thought for a long time how to start publishing in 2018: and I realized that the answer lies on the surface - with conclusions about what I did most in 2017, that is, with an analysis of ICO and forecasts for this industry. But I will say right away that the study turned out to be voluminous, so it was decided to split it into three, at least parts.

The first will be given the general analytics of ICO together with the Icofisher.com team - the project is in the beta version, but it copes well with the task (technical analysis of blockchain projects). In the second, I will try to dwell on one of the worst projects for the industry (in Russia) - ZrCoin, and in the third - already on the world scam of the MMM format - ... But first things first.
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On Forbes, there was already an article by Anti Danilevsky from Kickico , where he is looking at the portrait of a participant in the campaigns of the initial placement of tokens, but I will try to identify other trends in a larger sample.

2017 was remembered by the ICO boom in the crypto community: yet it is extremely difficult to find clear information on this phenomenon, and many still cannot understand what it was. For a start, I tried to answer the question: “who invested in 2017 and how much?”. The basis was taken from 40 ICO data: among them - EOS, Status, Bancor, Lexec, MatchPool, Tenx, IndaHash, Raiden ...

How much total?

So what sample do we have? 500K transactions. People invested from 0 (hereinafter accounting is only for whole units) to 150,000 ETH. 95% of all payments fall in the range from 0 to 20 ETH .

Let's try to divide all investors into groups depending on the amount invested. Applying the k-means method on the entire sample as well as on individual ranges, we identified the following groups (Figure 1):



Source: https://habr.com/ru/post/346796/


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