Who Actually Generates Volume on Solana? Researchers Analyzed 44 Million Transactions
When you look at Solana’s statistics, it seems as though we’re dealing with one of the most active blockchains in the industry. It has millions of users, massive trading volumes, new tokens appearing almost daily, and decentralized exchanges operating around the clock.
But this raises a perfectly logical question.
Who exactly is behind all this activity?
To answer this question, a group of researchers conducted one of the most extensive studies of automated trading in Solana’s history. Instead of analyzing just the blockchain, they combined two worlds: open-source code on GitHub and real-world network data.
As a result, we were able to see what usually stays off-camera.
Scope of the Study
The authors analyzed:
| What Was Studied | Quantity |
|---|---|
| GitHub repositories with bots | 586 |
| Solana Wallet Bots | 200 |
| On-chain transactions | 44 118 825 |
For academic research, this amount of data is considered very large. The study is not limited to individual examples but seeks to present an overall picture of the ecosystem.
$250 million every day
The most talked-about finding of the study relates to trading activity.
According to the authors' estimates, in January 2026 alone, the categories of bots studied accounted for approximately $250 million in daily trading volume on Solana's decentralized exchanges.
It's important to understand one thing.
The study does not claim that all of this volume is artificial.
A bot is just a tool.
It can automatically execute the owner's strategy, search for arbitrage opportunities across exchanges, purchase new tokens immediately after their launch, or support the work of market makers.
But the facts themselves tell a different story.
Automated systems have become full-fledged players in the market.
What do bots actually do?
After analyzing 586 GitHub projects, the researchers divided them into 15 categories.
This is what it looked like.
| Category | Repositories |
|---|---|
| Automatic Execution of Transactions | 137 |
| Sniping New Tokens | 134 |
| Arbitration | 47 |
| MEV and Transaction Ordering Strategies | 41 |
| Telegram / Discord Notifications | 37 |
| Copy Trading | 33 |
| DEX Integration | 30 |
| Wallet Monitoring | 26 |
| Wash Trading | 21 |
| Portfolio Management | 14 |
| NFT bots | 14 |
| Liquidity Management and Farming | 12 |
| On-chain analytics | 10 |
| Token Management | 8 |
| Managing Multiple Wallets | 6 |
The most interesting finding is that MEV accounts for only a small portion of the entire bot ecosystem.
Most projects focus on automating traditional trading.
What projects did the researchers examine?
This work is not based on theory.
The authors analyzed real, publicly available projects in detail.
| Project | Purpose |
|---|---|
| Soltrade | Automated Trading Based on Technical Indicators |
| Solana Trading Bot | Sniping New Tokens |
| Solana Arbitrage Bot | Arbitrage Between DEXs |
| Jito MEV Bot | Working with MEV and Jito Bundles |
| Raydium Volume Bot | Creating Artificial Trading Volume |
The last point is the most interesting.
Yes, there are public projects designed to create the appearance of high trading activity.
However, the study does not claim that these are the ones that account for a significant portion of Solana's volume.
It simply shows that such tools exist and are being actively developed.
Where do these bots operate?
The researchers also examined integrations with various platforms.
| DEX | Number of integrations |
|---|---|
| Raydium | 195 |
| Orca | 45 |
| Jupiter | 40 |
| Pump.fun | 36 |
| Meteora | 30 |
Arbitration systems are particularly interesting.
Nearly a quarter of such projects use multiple DEXs simultaneously to detect price discrepancies in near real time.
What Happens Inside a Blockchain
After analyzing 44 million transactions, the researchers divided 200 wallets into several stable groups.
| Indicator | Meaning |
|---|---|
| Bot wallets studied | 200 |
| Addresses exhibiting pronounced MEV behavior | 56 |
| Trading bots | 102 |
| Trading Bot Activity on Pump.fun | 80,9% |
Just a few years ago, it was believed that most automation was related to MEV.
However, the study's findings suggest otherwise.
Today, a significant portion of automated trading activity involves meme coin trading, the launch of new tokens, and traditional trading strategies.
An Unexpected Discovery
The researchers decided to check the quality of the code as well.
The result turned out to be unexpected.
More than 30% of the libraries in use have not been updated for over a year.
For example:
| Library | Lag |
|---|---|
| python-dotenv | 642 days |
| soldiers | 611 days |
| solana | 572 days |
| requests | 446 days |
This creates a sort of paradox.
Bots compete in milliseconds, but a significant portion of their infrastructure runs on long-outdated dependencies.
What does this say about Solana?
The most important conclusion of the study has nothing to do with the numbers.
In recent years, trading bots have evolved beyond being simple amateur scripts.
Today, these are full-fledged software suites with their own architecture, data analysis capabilities, risk management, support for multiple trading platforms, and a high degree of automation.
In fact, a separate algorithmic trading industry has already emerged around Solana.
It is this that provides a significant portion of the liquidity, transaction execution speed, and competition within the ecosystem.
Conclusion by KLEMPUS
When we look at the impressive trading volume charts, it's easy to imagine thousands of traders buying and selling tokens all at once.
A new study reveals a much more complex picture.
Algorithms are behind a significant portion of this activity.
Some make the market more efficient by identifying price differences between exchanges.
Others help market makers maintain liquidity.
The third group hunts for new tokens faster than anyone else.
There are also those whose job is to generate artificial trading activity.
What's important is something else.
Bots are no longer the exception. They have become an integral part of the modern cryptoeconomy.
And perhaps the main question in the coming years will no longer be phrased this way:
How many users does the blockchain have?
But actually, it's quite the opposite:
What portion of its economy is driven by people today, and what portion by algorithms?
Source of the study:
Demystifying Solana Bots: From GitHub Blueprints to On-Chain Fingerprints (2026).
Bots aren't cheats; bots are tools… they're no match for human traders)))
Of course, you're doomed when it comes to scalping. But when it comes to analysis and long-term trading, a bot doesn't stand a chance against a human.
It’s not publicly available yet, but I think the right people have the tools for the long term and, most importantly, the ability to influence the movement on a global scale.
Why just watch the movement when you can be part of it?