Crypto Without Traders: What Happens When Bots Dominate Market Microstructure

A trader hits sell and expects the usual - a wall of bids that will absorb their order. What they find instead is that the bids are simply not there. The reason is the crypto bots market structure, where your trading counterparty is now code, rather than another human. Instead of trading with people, you are now up against market-making algorithms and execution engines.

They make calculations and decisions in a fraction of a second, and their decisions are based on data, not feelings or FOMO. They don’t chase after opportunities that aren’t really there, and they act on preset risk limits. This changes the market itself, since bots control execution, liquidity provision, short-term price discovery, and more. The entire market behavior shifts according to models that can make a move as soon as the risk changes.

On speed, humans can’t compete with this AI-driven market microstructure. What you can do is understand that this shift means looking at more than charts. You need to understand where the liquidity comes from, and how these systems react under stress. Above all, you should understand what will happen when all of these different trading systems make the same decision at once.

Who Actually Trades Crypto Now?

The best way to define market microstructure is as the machinery operating underneath a price chart. It has its rules, orders, liquidity, and participants, all of which determine how buying and selling translates into an actual market price.

In the context of crypto, this machinery now increasingly runs via automated systems. It’s not the people who decide when to trade anymore; it’s a system of trading bots reacting instantly to market changes.

The Main Players

So, who actually participates among the crypto market-making bots today? The answer is: several groups, including:

• Market makers: These crypto market-making bots are algorithms that constantly post buy and sell quotes, thus earning the spread and managing inventory and exposure at the same time. They optimise for providing liquidity while avoiding exposure to too much risk at any given time.
• Arbitrage bots: These are systems that monitor different crypto markets and exchanges, trying to detect price differences. They buy assets where they are at their cheapest, and sell where they are most expensive. In short, they make money by comparing and using these price differences.
• HFT and execution algorithms: High-frequency trading systems and execution algorithms optimise for speed and execution quality. They aim to minimize market impact by breaking large orders into smaller trades.
• Liquidation and risk engines: Automated systems that monitor leveraged positions. When collateral or risk limits get breached, they immediately react by closing the positions. They optimise for limiting losses and protecting the exchange; their priority is not to get the best price for the trader.
• Human traders: Human traders are still around, including retail and professional traders who rely on news, charts, long-term fundamentals, and various narratives to try and predict market behavior. Their goal is to make the highest possible returns using a mix of information, experience, and their own judgment. They cannot beat bots in raw reaction speed, so they don’t even try it; they try to predict the market, rather than react to it.

How Much Trading Is Actually Done by Bots?

There is no precise figure that reveals how much of the trading is done by bots as opposed to humans, but there are estimates. Most of the estimates coming from what is considered reliable sources commonly put automated trading between 65% and 90% of total crypto volume.

Chainalysis pointed out one reason, saying that MEV bots and arbitrageurs can look similar to wash-trading activity when researchers try to analyze on-chain transactions. This happens even though their purposes are completely different, but the result is that it is pretty much impossible to separate legitimate automated trading from other forms of volume that machine trading generates.

This introduces a different point, however - that the exact percentage is not as important as the structural shift. Humans still decide what assets to trade and what strategies to use, but more and more, it’s the software that determines how these decisions play out in the market.

What is a crypto market microstructure?

Crypto market microstructure is a set of rules and mechanics that determine how buy and sell orders transform into actual prices. It covers things like order books, spreads, liquidity, market makers, trading algorithms, and how different participants interact when executing trades.

How Bot-Native Markets Behave Differently

When algorithms dominate the market, it tends to look more efficient, at least on the surface. With a bot trading crypto, spreads tighten and prices get updated immediately, while arbitrage keeps different platforms closely aligned.

The problem is that this same automated response can often make liquidity that you see less reliable. This happens because the algorithms that provide it are reassessing risk all the time, and if they decide the market is too risky, they withdraw without warning.

Spreads are tighter, but depth behind them is thin

With tight spreads, you end up with the best bid and ask sitting very close together. This creates the impression that the market is deep, but underneath the surface, there is a lot less capital available than the spread suggests.

This means that a single larger market order can ruin the system and move the price much further than you might expect judging by the data. This makes it risky and subject to large sudden changes, and failure to react in time can lead to large losses.

Liquidity is conditional

Liquidity being conditional means that it can easily vanish. When market makers detect unusual market behavior, such as pricing changes, they can widen spreads or choose to pull their orders.

From the human trader’s perspective, this can look like the previously plentiful liquidity has suddenly disappeared, instead of slowly becoming more expensive, as the trader might have expected.

News is priced in before a human can read it

Algorithms work at lightning speed and can collect information, process it, decide what it means, and react to it before a human trader can read a headline and decide what it means. In terms of speed, humans cannot compare to trading algorithms at all. By the time a human reacts, even to major announcements, automated systems will have already adjusted their positions.

Bots react to bots

With the majority of market participants now being bots (according to estimates), that means that bots react to bots all the time. This creates a feedback loop, which is especially obvious when the market moves fast.

One algorithm decides to sell because of volatility spikes, and another reacts. It detects the price change caused by the first algorithm’s reaction and decides to sell as well. Risk systems then reduce exposure automatically, and what happens next is likely an overshot, regardless of direction. It all happens very quickly, instead of the market adjusting slowly.

Human-Led vs. Bot-Native Markets

All of this doesn’t mean that the bots always make markets worse. It means that the liquidity and price are conditional, depending on what the bots currently believe about risk.

How much crypto trading is done by bots?

According to estimates, bots are now responsible for 65% to 90% of all crypto trading. However, there is no single audited figure for the entire market, this is simply an estimate by industry experts.

What Happens When the Machines Step Back

Now, let’s take a look at what happens when the machines step back and liquidity disappears. This is exactly what happened on October 10, 2025, when the bids really did disappear. The market suffered a macro shock while already carrying heavy leverage, and its reaction shows just how quickly automated crypto liquidity can disappear when bots decide that it is no longer safe.

What happened?

On October 10, there was a sell-off that pushed Bitcoin and other major assets down, which placed leveraged positions under pressure. As the prices started spiraling, automated liquidation began closing positions with insufficient collateral.

Market makers’ models had to decide what to do in the middle of extreme volatility. Prices were changing rapidly, and the risk skyrocketed. Plenty of companies, including Wintermute, stopped trading after their predefined risk conditions were unexpectedly breached. Providing liquidity was no longer safe under such conditions, and when the quotes disappeared, the order books became thin.

In less than a day, over $19 billion in leveraged crypto positions got liquidated, which is still the largest single-day liquidation event in the sector’s history. Reports have noted that perpetual-futures open interest dropped by 43% to around $123 billion. Even Hyperliquid’s open interest went down by 57%.

The incident also exposed a mechanism known as auto-deleveraging (ADL), which many traditional market participants were not familiar with. Essentially, when an exchange cannot close a losing position because there is not enough liquidity, the system closes positions on the other side by force. That means that even a trader with a profitable position is closed automatically, because the exchange had to reduce exposure.

It is not difficult to understand why market makers choose to step away during volatile periods. They make money by constantly buying and selling while hedging exposure. When the market becomes more volatile than usual, prices can change very quickly, and those hedges can’t always be adjusted in time, which means that every additional quote represents the danger of suffering losses.

If the model decides that the risk is greater than the potential reward of continuing the trade, it won’t take chances. To the bot, a temporary withdrawal is the only rational choice.

This results in a relatively fragile combination of markets that trade 24/7, but often have no circuit breakers, and cross-collateral margin that lets losses spread. With this combination, a shock doesn’t always remain a single price move, but starts to spiral due to the previously mentioned feedback loop. As it keeps going, the prices continue to sink, the system triggers automated liquidations, and the disappearing liquidity speeds up the fall even further.

Where Bots Take Value From Human Traders

Maximal Extractable Value, or MEV, is the money that trading bots can capture by controlling the order in which transactions are processed. On DEXes, a single transaction is not necessarily executed in isolation, as bots can see the pending trades, and find ways to profit from how those trades influence the market.

This can open a way for sandwich attacks. Imagine that you have submitted a large market swap seeking to buy a specific token. A bot sees your pending swap and buys the same token just before it. Your swap then executes at the higher price and pushes it up further. The bot sells straight afterwards and keeps the difference. You receive fewer tokens than you were quoted.

You pay for that speed indirectly by having a worse execution price than it was when you made the trade. Bots can also pay a priority fee on platforms that allow it to have their transaction processed before yours, even if yours was first, or they can tip the validator for their transaction to get priority.

Sandwich attacks show how automated strategies can affect crypto markets. Last year, in October 2025, a third-party tracker known as Ghost reported that wide, multi-slot sandwiches accounted for roughly 93% of identified sandwich attacks.

In total, the attacks managed to extract around 529,000 SOL coins. However, it should be noted that the totals vary depending on the methodology. The way it worked was through transaction reordering across slots, instead of a simple human front-run, which shows that automated strategies can be sophisticated enough to operate around ordinary users’ trades.

What Humans Lose and What Remains

The most difficult part for a human trader is to match the bots’ speed. They can react almost instantly to practically any event. They respond to news, price changes, pending transactions, and more, all in real time. A human trader needs to see the news, slowly read it, realize how it may influence the market, get to it, make a transaction, and wait for it to get processed.

While this can only take a few minutes for experienced traders, bots can do it all in seconds. That’s why short-term price opportunities are dominated by automated systems.

This doesn’t mean that you cannot trade as a human anymore. Not every type of trading depends on speed. Humans can still choose a longer time horizon, control position size, wait for better conditions, and make judgments about a piece of news, whereas a bot only reacts to its first price impact.

Habits That Matter More Now

You can also try to develop some helpful habits - most of them are basic practices that can reduce the cost of trading in a bot-dominated market. For example:

• Use limit orders when you can control the execution price instead of automatically accepting any price the market order finds
• Use sensible slippage settings, as particularly wide slippage gives a volatile market more room to execute a trade at a worse price
• Avoid thin pairs and unusually quiet periods. This is when a small order can move the market more than you might expect
• Do not assume that the displayed depth is stable or permanent. As we have seen, quotes can disappear when market-making algorithms decide that the risk is too high

Use MEV protection if a DEX (or a wallet) provide it, especially during larger swaps

Note that the market dominated by bots is not exactly fair, but it is also not rigged against you. It is definitely faster than you could ever be, which is why you should change your habits, rather than try to beat it at the game of speed.

Conclusion

So, when that trader hits sell and finds the bids gone, it’s not just because of the speed at which the market moves, it’s about who decided there was no longer enough liquidity to absorb the order. That’s because liquidity is no longer a property of a market, but a decision made by models, and as such, it can be withdrawn at any moment.