Introduction

Firstly, I would like to address the problem I encountered while solving this Bounty. While writing the SQL query, I realised that there are many types of bots, making it difficult to track and filter what each bot is doing and when. In order to filter out my analysis, I have decided to focus on arbitrage bots, using the following proxy definition:

select date_trunc('minute',block_timestamp) time,TRADER,count(*) count

from terra.swaps

group by time,trader

having count(*)>10

order by count desc

limit 30

For this dashboard, I will also use the following definitions:

• Bot swappers: at least 10 or more transactions per minute.
• Average swappers: at least 2 or fewer transactions per minute.

Firstly, let's have a look at the transaction counts!

Note: In this dashboard, I used the word 'transaction' and 'swap' interchangeably. I know that it would probably be clearer to stick to swaps only, however, I hope that this is clear enough with this disclaimer! :)

Average user transactions vs Bot transactions

The two charts shown above suggest that bot transactions are not as common as one could think. The count of bot transactions is very small compared to standard transactions, we can see that on most days the count for bot transactions stands at 0. What is interesting, is the fact that bot transactions occur on a rather seasonal basis. In order to get a clearer picture of what this means, I will do some further analysis next.

Bot transactions vs LUNA price

The chart above shows the price of LUNA together with the count of bot transactions per each day. We can see almost no correlation, the only day when the marker colour changes significantly is May 23rd 2021. What we can grasp from this, is the fact that the bots are not more active when the price of LUNA changes, they are clearly not trading bots.

Here we can see that there is some correlation between the mid-May dip in LUNA's price and swap spamming on bot activity, as well as average swappers. But other than that, I would say it's not meaningful to conduct any more correlation analysis, as the count of average user transactions is huge compared to bot transactions count.

The final chart of this dashboard confirms the previous statement, that on almost all days, the % of bot transactions is 0. Note: this graph is normalized with the y-axis being logarithmic, in order to show any differences between counts more clearly.

Conclusion

This Bounty has shown that arbitrage bots are not as popular as I initially thought they would be. It is important to keep in mind that this analysis consisted only of that one type of bot swaps, in order to get a better insight into this topic, one would have to include different types of bots in the analysis as well as other types of transactions and not only swaps.