How does on-chain market making break through indicators and control funds silently?

Author: 0xLIZ

Taking the newly listed Binance alpha project $AGON as an example, let's briefly discuss how on-chain market making silently controls chip distribution and how it engages in offensive and defensive tactics against metrics on data platforms like gmgn (dev sell, bubble map, etc.).

Let's get closer to science.

I noticed this project last month because it didn't follow the market's fluctuations and exhibited strong whale behavior. I shared some thoughts on analyzing on-chain data with friends, and at that time, I found interesting points in the data in three areas (the screenshots were taken in February):

1. Several addresses belonging to the devs were continuously selling, initially selling a few hundred dollars, but later dumping heavily.

2. The top 100 holders are very strange; they basically withdrew funds from CEX on the same day, and all made small purchases around 200 tokens using USDT, with occasional small sell-offs.

3. It appears that there is a group continuously distributing small amounts of chips across different addresses, receiving them from the dev side.

Based on the above information, we have reason to suspect that this batch of addresses is actually controlled by one person who sells while switching addresses to buy, thus avoiding the aggregation function of the bubble map.

We can delve deeper into the on-chain traces to see if we can find clues linking these addresses. Unfortunately, these addresses extensively use exchange hot and cold wallets to evade association, making them appear as more mature players on-chain.

However, if we want to identify whether a batch of addresses is controlled by the same person, besides gas tracing, behavioral patterns are a good identification method, especially for humans.

We don't need many definitions; just a glance at the transaction records of each address can give us a rough estimate of their similarity.

For example, this batch of addresses has only actively traded this one token, and the transaction times and amounts are all within a certain range. Here’s a set of examples.

After reviewing a set of addresses, we found that these addresses can be roughly divided into two categories, represented by two typical addresses.

One category consists of addresses similar to the dev addresses, serving the purpose of hoarding and distributing chips:

After a large amount of buying at the opening, this batch of addresses distributes chips to smaller addresses through wash trading, thus distributing chips unnoticed while buying and selling.

The second category also plays a role in supporting the price; when the price falls below the target price, it provides a bottom support function.

On-chain trading incurs significant friction and may be subject to others seizing chips (such as through MEV), so how do these addresses handle this issue when transferring chips to smaller addresses?

There is an interesting feature on BSC, which was promoted by Binance teachers during discussions about MEV a few days ago, called Bundled Transaction.

In simple terms, this means packaging a bundle of transactions to be sent to the chain together; either all go through or none go through, preventing any other transactions from being interjected in between.

Following this feature, a clear characteristic of using this service is that a series of transactions will be linked together within the same block. We can look at the block at the opening, where a large number of addresses clearly took almost all the chips, reasonably inferring that this batch of addresses and the smaller addresses that exchanged chips in the same block belong to the whale group.

This function is used in two places: one is to ensure that enough chips are secured at the opening, and the other is to ensure that no friction occurs during the exchange.

At this point, we are basically familiar with how to achieve a nearly lossless transfer of chips from dev addresses to ordinary addresses through wash trading, which is how whales break through some commonly used metrics on platforms like gmgn (dev sell, bubble map, etc.).

The purpose of discussing this is to help everyone better understand the risks in their decision-making when chasing tokens and to improve risk control.

I have been dealing with data for a long time, and my connection with Crypto started here with data products. This version indeed poses many challenges to data products and analytical logic, with a rapid pace compressing decision-making time to minutes. After reviewing a few core metrics, one must decide whether to jump in, and I have also spent considerable time adapting to this version.

However, the offensive and defensive confrontation is ongoing; each metric will only be effective during a certain window period, for both individuals and institutions. Once a metric is thoroughly researched, it will become ineffective, and dynamically adjusting the metrics used is essential for sustaining success in this industry.