Forward the Original Title: What are some ways to track the wallet address of a crypto KOL?

KOLs, due to their influence, can significantly impact projects through their actions. Typically, a project sees KOLs buying in first, then sharing within private communities, and only later making public announcements, such as on Twitter. If we have access to a KOL’s wallet address, we can immediately track what they are buying, when they are adding to their positions, and when they are taking profits or cutting losses.

This article shares some methods of finding KOL addresses and how to use these addresses.

1. Use gmgn

gmgn is a comprehensive platform offering trading, data analysis, and monitoring features. It is run by @haze0x. website：https://gmgn.ai/?ref=sxsy7oyJ

On gmgn, users can link their Twitter accounts to their wallet addresses, a feature we can use to find KOL addresses.

For example, if we want to find the wallet address of 0xsun, we can type “0xsun” into the search bar. The search results will display 0xsun’s address at the bottom, as shown in the image.

Clicking on the address in the search results provides detailed information, such as 0xsun’s Twitter follower count (with a link to their profile), win rate, and 7-day profit/loss summary, which in this case is $2.3 million.

Thus, when searching for a KOL’s address, we can check gmgn to see if they have linked their wallet. If they have, we can easily find it.

2. Use chain.fm

chain.fm is an on-chain monitoring platform that contains many “smart money” addresses, including those of numerous KOLs. It is managed by @zen913.

Earlier this year, chain.fm introduced a search function that allows users to look up addresses using keywords, making it possible to find KOL addresses using this feature.

I mentioned this method in my tweet here: https://x.com/0x_zibu/status/1882345639218118884.

There are two ways to find KOL addresses using chain.fm:

1. Directly searching for a specific KOL’s name

Using 0xsun as an example, we can type “0xsun” into the search bar. Several addresses will appear in the results, as shown in the image.

The first address in the search results is included in 426 channels, which strongly suggests that it belongs to 0xsun. To verify, we can cross-check it on gmgn to see if 0xsun has confirmed this address.

2. Search with the keyword “KOL”

We can also enter the keyword “KOL” to find all relevant data on chain.fm, as shown in the image.

When searching for “KOL,” chain.fm will list all channels containing categorized KOL addresses. We only need to verify their authenticity.

Under the wallet section, searching for “KOL” will also bring up addresses tagged with this keyword, as shown in the image.

Since addresses found on chain.fm are based on user submissions, they are not guaranteed to be 100% accurate. Therefore, cross-verification through gmgn, Twitter, or other sources is necessary.

3. Clues analysis based on Twitter

The above two methods are relatively simple, and you can find them as long as you are willing to spend time sorting them out. However, some KOLs do not take the initiative to disclose their addresses. In this case, you have to find them through other methods.

1. Clear Transaction Screenshots

Some KOLs share transaction screenshots in their tweets, displaying specific trade details such as transaction time, whether it was a buy or sell, transaction amount, and the quantity of tokens. These cases are particularly easy to track because we can filter and search based on the provided details.

For filtering transactions from screenshots, I recommend using Dexscreener (https://dexscreener.com/). You can apply detailed filters based on transaction time, type (buy or sell), amount, and token quantity, as shown in the image.

Dexscreener is the most comprehensive manual filtering tool I’ve used. Unlike other tools with basic filtering functions that seem more like placeholders (probably even the product manager doesn’t use them), Dexscreener provides in-depth transaction insights.

However, one limitation of Dexscreener is that it does not support transactions using aggregated routing on OKX. For those transactions, you will need to check the OKX Web3 platform (https://www.okx.com/zh-hans/web3) manually.

2. Indirect or Vague Mentions

Sometimes, KOLs don’t explicitly share transaction details but may mention entering a certain token at a specific market cap. In these cases, the available information is limited, and a single tweet may only provide a rough timeframe. To pinpoint the exact wallet address, you may need to cross-reference multiple tweets.

For example, yesterday, @niuniu255431 (zhou qi shu niuniu) tweeted about Burder’s wallet activity, as shown in the image. How can we identify the wallet in question?

Let’s collect all the valid information first:

(1) The tweet time is 2.5 9:53

(2) According to the tweet, Burder first bought $Ncat and $Noland, and then bought $Calicoin.

(3) The pictures in the tweets provide a lot of information, as shown in the figure. You can know:

a. The holding amount of $Calicoin is 4M

b. The holding amount of $Noland is 37.6M

c. The number of $Ncat positions is 30M, but it only accounts for 57% of the positions.

d. All holdings had only been held for 2 minutes at the time of the screenshot

(4) The launch time of $Calicoin is 2.5 8:23

Based on this information, we can deduce that Burder’s transactions occurred between 8:23 AM and 9:53 AM. Given that the holdings were only 2 minutes old when the screenshot was taken, the latest transaction likely happened at 9:51 AM. The tweet also mentions that Burder woke up late, meaning the earliest purchase would have occurred some time after the market opened—but without exact details, we can only start filtering from 8:23 AM.

With the above information, we can do this:

a. Write a script to obtain transaction data

Take out all the buying transactions of the three coins between 8:23 and 9:53, and see which address bought the three coins at the same time within this range, and the order and quantity match the information collected above.

b、Use Dune Analytics (https://dune.com/)

Compared with writing your own scripts to obtain transaction data, the data on dune is ready-made. At this time, you can use AI to write query statements for dune, filter out the addresses, and then verify them manually.

4. Analyzing Address Trading Style and Performance Metrics

Once we have identified a KOL’s wallet address, the next step is to conduct a detailed analysis of their trading behavior. This includes determining their trading style—whether they prefer internal market trades, early-stage external market trades, or later-stage external trades. Additionally, we examine whether they chase pumps or prefer pre-positioning, how they manage take-profits and stop-losses, their win rate, and their overall profit and loss performance.

The best platforms for analyzing wallet addresses include gmgn and debot.

1. gmgn

gmgn provides a comprehensive range of data analysis features and performance indicators.

For example, analyzing 0xsun’s wallet, we can see their profit and loss (P&L) over 1 day, 7 days, and 30 days, along with their win rate and total earnings. The latest profit and loss section displays a list of tokens traded over the past 30 days, which can be sorted using various metrics. Clicking on any token reveals detailed transaction records, as shown in the image.

2. debot

debot offers similar functionality to gmgn, with some differences in data processing algorithms and presentation formats. The analytics for 0xsun’s address on debot provide a slightly different perspective than gmgn, and users can compare both for a more comprehensive analysis.

As for which platform is better, gmgn or debot, the best approach is to use both simultaneously to cross-check data and avoid biases caused by missing or inaccurate information from a single tool.

5. Monitoring

After identifying a KOL’s wallet address and analyzing its data, the next step is tagging and categorizing these addresses and using tools to monitor their transactions in real time. This allows us to detect their trades before they are shared in private communities or on Twitter.

For monitoring, I highly recommend debot ( https://debot.ai?inviteCode=175623 ). It is the most feature-rich and granular monitoring tool I have used, with powerful AI signals and seamless transaction tracking.

Debot’s monitoring system is divided into two types: wallet behavior monitoring and wallet group behavior monitoring, as shown in the figure

1. Wallet Behavior Monitoring

This is a standard monitoring feature available in all tools, but while other tools push all addresses to a single Telegram group, debot’s advantage is that it can group addresses and push them to different Telegram groups based on these groupings, as shown in the image.

This allows us to create different Telegram groups for pushing notifications based on how we’ve tagged addresses - for example, spot traders, secondary market traders, priority watch addresses, and so on. A truly good monitoring tool should be able to categorize monitoring information. Previously, when using abot for monitoring, I would first tag addresses and then do secondary development after the Telegram group received monitoring messages, redistributing them based on tags. Now with debot, there’s no need for secondary development anymore.

2. Wallet Group Behavior Monitoring

If we are monitoring many addresses, there’s an unavoidable issue: too many push notifications. When there are too many messages, the human brain can’t process them all and tends to give up, which defeats the purpose of monitoring. To address this issue, my previous strategy was to do secondary development on the push notifications, setting them to only trigger when a certain number of different addresses performed actions within a specific timeframe - for example, pushing a notification when 5 different addresses made purchases within 30 minutes. I used this strategy for about a year with excellent results, significantly reducing message volume while still not missing any hot tokens.

debot‘s wallet group behavior monitoring perfectly implements my strategy above, and does it even more precisely with more granular metrics, as shown in the image. We can set up monitoring based on buys and sells, configure monitoring time periods, transaction amounts, notification frequency, and market cap size. It can fully meet all kinds of monitoring needs, and I highly recommend this feature.

6. File of 100+ KOL Wallet Addresses

Using the methods described above, I have compiled a list of nearly 100 KOL wallet addresses, as shown in the image.

You can analyze and mark each address according to the analysis method in Part 4, and find the addresses that suit your own habits for monitoring.

The file has been uploaded to my Telegram channel, and you can download it here.

If you share this file, please retain the original source!

7. Summary

This guide has introduced methods for finding KOL wallet addresses, as well as techniques for analyzing and monitoring them. If a particular KOL’s address is not included in the file, you can use these methods to locate it yourself. Additionally, the public wallet addresses used by KOLs are just one of many addresses they own. Most traders have multiple wallets, so once you have identified a primary address, you can search for related addresses to build a more comprehensive database.

Every KOL has a unique trading style—some may even engage in dumping on followers. Therefore, blindly following KOL trades is not advisable. Always analyze and select the addresses that best fit your own trading strategy.

Disclaimer:

1. This article is reproduced from [panews]. Forward the Original Title: What are some ways to track the wallet address of a crypto KOL?
   Copyright belongs to the original author [Zi Bu said]. If you have any objection to the reprint, please contact Gate Learn Team, the team will handle it as soon as possible according to relevant procedures.
2. Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.
3. The Gate Learn team translates other language versions of the article. Unless otherwise stated, the translated article may not be copied, distributed or plagiarized.