Anchor Users

    This Analytics is done to visualize the number of new Anchor users for the past 3 months , that is since 1st Oct 2021. This analytics will also visualize their first activity on Anchor

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    2.0 Results

    Part 1: Number of New Anchor users

    The below chart shows the number of new address interacting with Anchors for the first time. Oct 1st yields the highest number of new Anchor users which is at whopping 4499 address. It then dropped to below 1000 new address per day and maintained relatively constant volume of new address per day till December 22nd.

    Part 2 : Cummulative new Anchor User

    The below chart shows the amount of new Anchor user for the past 3 months. Up till today, 24th Dec, about more than 90k new address has interacted with Anchors protocol. This is a good indication the rising umber of people interacting with Anchors and Terra ecosystem in general. This is because Anchor is one of the selling point of Terra ecosystem to the crypto community. This shows that Terra is becoming more mainstream among crypto communities.

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    Part 3: New User Activity

    The below shows the distribution of the number of user by their first activity on Anchor. Majority of new user firstly interacted with Anchor to Deposit their stablecoin, UST. The second largest chooses to borrow when their interacted with Anchor for the first time. The rest, claim ANC rewards and repay their borrowings .

    1.0 Methodology

    Part1 & 2 : Using Table Terra.msgs

          1) Extract msg_value:sender and min(block_timestamp)
    
          2) filter using Anchor contract,msg_value:contract='terra1sepfj7s0aeg5967uxnfk4thzlerrsktkpelm5s'
    
           3) Use inner join with another msg_value:sender and Block_timestamp  
    
          4) this will yield the distribution of new Anchor user per day
    

    Part 3: Using Table Terra.msgs

     1) Using Flatten Table on msg_value:execute_msg
    
     2) Extract lock_timestamp as t3,a.key as route,msg_value:sender
    
     3) Use inner join with table from Part 1 with date as common factor
    
     4) Thsi will yield the distribution of user correspond to their activity