The crypto space moves fast; new tokens launch every hour, promising innovation, utility, and massive returns. But behind the hype, many projects are designed to deceive, with rug pulls, honeypots, and wash trading still among the most common forms of manipulation.
At Blokiments, we’re building tools to bring transparency to this chaos. Our Suspicious Patterns module analyzes on-chain trading behavior to uncover the hidden signs of manipulation before it’s too late. By combining wallet activity, trade volume analysis, and behavioral metrics, we can detect patterns that often appear in scam or non-organic tokens, helping traders make more informed, safer decisions.
In this post, we’ll walk through how suspicious pattern detection works, the key metrics to watch, and how these insights can help protect you from becoming the next victim of a rug pull.
If all metrics show no negative signals, that doesn’t mean the token is safe. It only means the scammer didn’t use any of the suspicious methods we track to manipulate the token. When a suspicious pattern is detected, however, we can be fairly certain that some form of manipulation, or at least unusual, non-organic behavior, is occurring.
Key Metrics for Scam Detection
🔴 Scam Makers Ratio
In the background, we maintain a database of wallets that interact exclusively with known scam tokens. For any token being analyzed, this metric represents the percentage of trading wallets that also appear in our scammer database.
This metric works particularly well on EVM chains, where many honeypots are deployed, and it produces very few false positives.
If the percentage is above 5%, that’s a strong red flag. If it exceeds 30%, avoid interacting with the token entirely. It’s most likely a honeypot, and you probably won’t be able to sell once you buy.
🔴 New Wallets Ratio, New Wallets Volume Ratio
These two metrics measure the proportion of wallets trading a token that are newly created.
Why is a high number of new wallets bad?
- •It often indicates the use of bots or artificially generated traders. Most organic traders have previously interacted with other tokens, so genuine trading activity usually involves established wallets.
- •Ideally, these two metrics should be as low as possible. If you see values above 50%, it likely means that most of the trading volume or transactions come from bots, and the token does not appear to be organic.
Why Do We Need Volume and Wallet Ratios? Projects use different methods to manipulate tokens. One common tactic is using a few new wallets to generate most of the token’s volume, artificially inflating trading activity. In such cases, the New Wallets Volume Ratio is crucial for detection. In other scenarios, scammers use many new wallets with smaller volumes to increase the number of unique trades and make the token appear more active or appealing. Here, the New Wallets Ratio becomes the key metric. In either case, if any of these two metrics show unusually high values, it’s a red flag.
Many projects, especially on chains like Solana or BSC, where transaction fees are low, use bots during launch to push their token into trending sections on analytics platforms. This doesn’t necessarily mean the token is an outright scam, but it often indicates non-organic trading activity. Ultimately, it’s up to each trader and their risk tolerance to decide how to interpret and act on this information.
What If the Project Uses Old Wallets? Sometimes, projects use bots that have already interacted with many tokens. In such cases, the “new wallets” metrics may not detect suspicious activity. We are currently working on a module update to improve bot volume detection and introduce new metrics designed to identify emerging scams more effectively. This update will allow us to detect and categorize market maker activity, MEV bots, and volume bots, providing a complete breakdown of the token’s volume structure for a clearer and more accurate analysis.
🔴 Buy/Sell Volume Breakdown Chart
The chart displays the token’s buy and sell volume over equal time intervals. The interval length depends on the token’s age, and you can view the exact intervals by hovering your mouse over the chart. This visualization helps identify trends such as increasing or decreasing volume, as well as other suspicious trading behaviors. Check the practical examples below to better understand how to interpret and use the chart.
These pattern detection examples work best when the token is at least a day old, as clearer trends can be observed in the charts.
- •🔴 Example 1: The token’s trading volume is steadily decreasing over time. While there are no suspicious patterns in this case, the chart indicates a declining level of interest in the token.
- •🟢 Example 2: We can see a significant increase, which may indicate growing interest in the token. This data should be analyzed alongside other metrics, but it still provides a good overview, suggesting that overall interest might be rising.
🔴 Trade Size Distribution Chart
This chart similarly breaks down the metrics into equal intervals and displays the number of trades by trade size within those intervals. It’s particularly useful for detecting unusual or bot-driven activity. For example, tokens that show thousands of micro-trades (under $2) often indicate the use of bots to artificially inflate transaction counts. The best way to learn how to interpret this chart is by reviewing a few practical examples.
[BAD] Example 1: A pattern dominated by large trades is highly suspicious for organic tokens, where trade sizes are typically more naturally diversified.
[BAD] Example 2: We can see that the number of small trades ($2–$100) and medium trades ($100–$1,000) is roughly the same. Also, there are a few spikes of over 1,000 micro-trades (below $2) during the first, sixth, and seventh intervals, which indicate highly non-organic behavior. These spikes were most likely caused by bots actively trading during those periods.
[GOOD] Example 3: An example of a more organic token is one where the number of micro-trades is low, and most trades fall within the medium trade range, showing no unusual patterns in the trade distribution.
[BAD] Example 4: In this case, we can see that on November 6th, the team likely ran a bot that generated over 2,000 micro and small trades. Looking at the overall chart, there is also a high number of small trades, which indicates poor and potentially manipulated trading activity.
Review the Holders vs. Makers Ratio
Another way to detect unusual patterns is by comparing the total number of token holders with the number of unique makers (active traders) in the last 24 hours. If the number of makers is higher than the number of holders, it likely indicates bot activity. This often happens when bots continuously buy and sell the token almost immediately, causing the maker count to rise while the number of holders remains unchanged. Some projects intentionally use this method to boost their visibility on analytics platforms such as Dexscreener. We issue a warning whenever we detect an anomaly in this ratio.
🔴 Review the Holders vs. Market Cap Ratio
Another important ratio to review is the holders-to-market-cap ratio. In some cases, the number of holders is relatively low while the market capitalization is very high. This often indicates that the market cap has been artificially inflated, likely through tight supply control to make the project appear more valuable or appealing than it actually is. A warning is also generated when such anomalies are detected.
Conclusion
Detecting scams in crypto isn’t about guessing; it’s about understanding behavioral patterns on-chain. By analyzing wallet activity, trade sizes, and volume distribution, our Suspicious Patterns module provides deep insights into how tokens are actually traded, helping you separate organic projects from manipulated or risky ones.
While no single metric can guarantee safety, combining these indicators paints a much clearer picture of a token’s health and legitimacy. Whether you’re a trader, researcher, or developer, using these insights early can save both your capital and your trust.
At Blokiments, we’re continuously improving our detection models and expanding coverage to better identify bot-driven volume, market maker activity, and emerging scam patterns across multiple chains.
💡 Want to integrate these insights directly into your product, bot, or service? We offer a Token Analyzer API that provides real-time suspicious pattern metrics, so you can automate scam detection and improve safety for your users.