This dashboard focuses on detecting arbitrage opportunities between swaps executed on Thorchain and Ethereum. It identifies and counts the transactions that appear to be executed by arbitrage bots operating within a specific time window between these two blockchain networks.

Your query is structured well and appears to address the problem of identifying arbitrage bots that are operating between Thorchain and Ethereum by matching swaps within a 5-minute window.

We start by extracting and preprocessing relevant swap transactions from both Thorchain and Ethereum. By focusing on transactions from January 1, 2023, onwards, we ensure that we are considering a comprehensive dataset.

To accurately identify potential arbitrage opportunities, we introduce a time window of 5 minutes. This means we are looking for swaps where the same address performed transactions on both chains within a 5-minute interval. This interval is crucial as it allows us to capture the rapid sequence of trades typically characteristic of arbitrage activities.

The core logic of the query compares the timestamps and asset pairs involved in the swaps from both chains, ensuring that the conditions for arbitrage are met within this defined time frame. By doing so, we can isolate transactions likely executed by arbitrage bots and differentiate them from normal swaps.

Recommended Threshold Based on this analysis, setting the threshold around 100 transactions appears reasonable for the following reasons:

Capturing Active Bots:

A threshold of 100 will capture the top 7 wallets, which clearly engage in high-frequency arbitrage activity. These wallets are likely to be bots due to their high transaction counts.

Excluding Low Activity:

It excludes wallets with fewer than 100 transactions, which are likely less active and might include regular users or less aggressive bots.

Natural Break Point:

The gap between the 7th and 8th wallet (141 vs. 29 transactions) suggests a natural break, reinforcing 100 as a logical threshold.

The identification of arbitrage bots requires distinguishing them from regular users. A crucial step in this process is setting a transaction threshold that effectively separates high-frequency arbitrage bots from normal users. By analyzing the number of arbitrage transactions per wallet, we can determine a suitable threshold that helps in accurately identifying these bots.

In this analysis, we calculate the number of arbitrage transactions for each wallet and examine the distribution of these transactions. This approach allows us to identify a natural break point that differentiates high-frequency traders (likely bots) from less active users. Using this data-driven method, we establish a threshold that ensures the accurate identification of arbitrage bots while minimizing false positives.

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