Artificial intelligence promises traders "easy money," but in reality, not everything works. This article is for those who want to understand what AI really does in the market, which models make a profit, and how to build their AI bot without deep programming knowledge. At the end, you will receive a working set of tools and a checklist that can be applied tomorrow.

Chapter 1. AI in Trading 2025: What Really Works

Artificial intelligence has become the main hype of the market: every day there are new "smart bots", models that "promise 90% win rate", and services that sell signals under the guise of a neural network. That is why the first thing to understand is that AI does not work everywhere in trading. But where it is really useful, its benefits are noticeable after just a couple of weeks of work.

In 2025, all the really used AI tools in trading can be divided into three groups:

1) Analytical models (Data/ML analysis)

This is the main working layer. Such models do not trade themselves, but analyze the market faster and deeper than a trader: they find abnormal volumes, predict price accelerations, estimate the probability of momentum, and compare the current market condition with thousands of similar situations in the past.

In fact, it is the "second brain of the trader", which helps to filter out bad trades and strengthen the good ones.

2) AI filters for strategies

This category is an underrated tool. The model does not generate signals, but only helps you choose the best ones that already exist. For example, your strategy shows 10 setups per day, and the AI discards 7 with a low probability of success.

Paradoxically, it is these filters that increase the win rate the most, because they do not allow the trader to enter into junk trades.

3) Auto trading based on ML

These are not classic "robots". These are the models that make their own decisions — to buy, sell, close a deal.

They work only in liquid markets and only under strict restrictions: volatility, trend, depth of the glass, lack of news. Otherwise, the model simply does not have time to adapt.

In the real market, such bots produce results only if:

1. the model is trained on large amounts of data
2. Risk management is tough
3. there are no attempts to "catch the bottom" or "guess the reversal"
4. The algorithm does not use too many weak indicators.

What doesn't work, despite hype

— "AI bots that give 80-95% win rate."

It's always retraining. In the live market, people die in 1-2 weeks.

— Bots without a volatility filter.

Any report, news, gap, and algorithm merges a series of transactions in a row.

— Models on low-volume stocks.

AI cannot "predict" a thin market where the price moves with a single order.

— Signals based on ChatGPT-like models.

LLMs don't understand the market — they guess the text. They don't create math.

Where AI really gives a trader an advantage

Data processing speed

The AI sees changes in the glass and volumes in milliseconds — faster than a human.

Identifying patterns that cannot be seen with the eyes

The models find patterns in dozens of factors at once.

Reducing the number of bad trades

The biggest impact on profitability is not in increasing the win rate, but in reducing the number of losses.

Complete lack of emotion

AI does not over-trade or take revenge on the market.

Chapter 2. AI bots in trading: how they work, why they are needed, and why most people don't earn

When it comes to AI bots, the imagination immediately draws the perfect picture: the neural network sits at the terminal, instantly reacts to the market, carefully conducts its position and consistently makes a profit. Reality is not that romantic. Most bots can actually count, but almost none of them can understand the market. And it is understanding, not speed, that makes a trader alive and profitable.

To figure out why some bots help while others drain the account, you need to separate them by functionality.

Signaling AI bots are analysts, not Traders

This is the safest and most useful level of automation. Such bots do not make decisions for the trader — they only analyze the data and suggest places where the probability of a qualitative movement is higher than normal.

Their role is simple: to filter out the noise and highlight the moments of power.

For example, a bot sees a sharp increase in volumes, an acceleration of the feed, a deviation in price from VWAP, or a change in the behavior of market makers. The trader makes the decision himself.

A signal bot is not "effortless trading", it is an amplifier of your market vision.

AI Strategy Filters — The Hidden Superpower of 2025

Professionals use AI not to generate signals, but to filter them. You can have a working strategy that shows 10-15 entry points per day, but the market is only suitable for two of them.

The AI filter does just that.:

– determines the state of the market (trend, flat, momentum);

– evaluates the liquidity and depth of the glass;

– compares the current setup with thousands of similar situations;

– leaves only those transactions where the probability of success is higher.

This is not a replacement for a strategy — it is a "second opinion" that cuts off most unprofitable entries. Such AI really increases the win rate, because it reduces the number of errors, rather than increasing the number of transactions.

Auto-trading AI bots are a powerful tool that does not tolerate chaos.

But fully autonomous bots are already aerobatics. This is not an indicator box purchased from Telegram. This is a model that:

1. analyzes the data flow in real time,
2. predicts the probabilities of short-term movements,
3. opens it independently/closes positions,
4. controls the risk in each transaction.

Yes, such systems exist. But they only work in structured markets — SPY, QQQ, liquid futures, top crypt. Where the glass is tight, the price behavior is logical, and the drivers are more or less predictable.

And where do they break?

It's simple: in chaos.

A gap, a news story, a report, a thin premarket, a sudden spike in volatility — and any model starts to make mistakes more often than a person.

By the way, a clarification on infrastructure risks is ideally inserted here — brokerage fees and hidden costs, which bots especially feel.:

For more information, see the material on swaps and commissions:

Chapter 3. How AI Bots Work: Internal Mechanics, Decision-making, and Real Limitations

There is a lot of talk about AI in the market, but there is very little understanding of how neural networks turn into trading solutions. Most people see only the “tip" — the “turn on the bot" button. But inside there is a rather complex system where each link affects the final result.

An AI bot is not just one model. This is a chain of four levels, and each one is responsible for its own piece of reality.

Level 1. Data flow is the raw material without which AI is blind

The bot does not start with the algorithm, but with the market: tick data, glass, volumes, deviations from VWAP, index context.

If this layer is dirty or incomplete, no neural network can save the strategy.

For a good bot, the data flow goes through three stages:

– cleaning →

– normalization →

– aggregation into features that the model understands.

This is something that is completely missing from the “$50 bots in Telegram”.

Level 2. Decision-making model (ML/AI core)

This is where the real work of AI begins, and it's not at all like “guessing the price direction.”

Good bots don't try to predict where the market will go.

They estimate the probability of a particular event:

1. will there be an impulse,
2. will the volume accelerate,
3. Will the level break,
4. whether the movement will start above the average.

The AI does not predict the price.

AI predicts the probability of crowd behavior.

This is a fundamental difference that is not present in the first chapter, and which is important to convey here.

Level 3. Strategy logic is the layer that keeps the bot from going crazy

Even the most accurate ML model is just an “advisor".

The decision to open a deal is made by the strategic layer: a set of conditions, filters, and rules that turn the model's output into action.

In a good system, it looks like this:

1. the probability of an impulse above the threshold →
2. volume confirmation is available →
3. the feed does not contradict →
4. volatility is normal →
5. only after that the deal is opened.

Without this layer, the bot turns into a chaos machine that trades every deviation.

Level 4. The execution environment is an infrastructure that can kill any model.

Even a perfect bot can drain an account if:

1. spreads are widening,
2. The commission eats up micro movements,
3. broker gives poor execution,
4. the server is lagging by 200-300 ms,
5. the instrument is low-liquid.

That is why infrastructure costs are doubly critical for AI bots.

Forwarding is appropriate here:

, learn more about commissions and hidden costs of the trader.

What seems like a small thing to a human kills the algorithm completely.

The bot works in mathematics, and any “market friction" breaks its accuracy.

Why AI bots merge

AI bots fail not because they don't analyze the market well, but because the infrastructure around them is poor.

Three major professional failures:

1. The model is faster than the market, but slower than the broker.
2. She makes the right decision, but gets the worst execution.
3. The market type is incorrectly selected.
4. The model is trained on SPY, and the trader runs it on TGLP or BMR.
5. She literally doesn't understand where she got to.
6. A person disables the strategic layer.
7. In order for the “bot to make more trades,” the trader simplifies the filters and loses protection.

These are technical reasons, not “AI is dumb.”

This chapter should explain them, not a repetition of the AI capabilities from the first one.

Where AI Bots unlock their Potential

The full potential of AI is revealed not in analytics, but in a multi-layered decision-making structure.

A person can analyze four parameters simultaneously.

The bot is forty.

And he also manages to compare them with 50,000 historical examples.

He wins not by speed, but by scale of thinking.

This is what makes it indispensable in:

1. impulse strategies,
2. HFT logic,
3. signal filtering,
4. risk control.

Chapter 4. How to build a simple ML bot for trading: the path from the idea to the first model

Many people imagine the process of creating an AI bot as something almost impossible: it takes years of experience, deep mathematics, and knowledge of programming languages. In fact, everything is simpler. To build a working ML model, a trader does not need to become an engineer — he needs a structure. And it is the lack of structure that makes most attempts fail.

An AI bot is not a "smart program". This is a sequence of decisions. And the cleaner it is, the better the model works. It all starts not with the code, but with a very simple thing — the definition of the task.

1. Start not with the model, but with the question

Any ML system lives around a single phrase:

"What exactly do I want the AI to determine?"

Most beginners try to train the model to "predict the price". This is a guaranteed failure: the market is too noisy for direction prediction to be stable.

A working query sounds different:

1. "Determine if the pulse probability will increase in the next N seconds?"
2. "Will the price be able to stay above the VWAP?"
3. "Is volume acceleration likely in the next candle?"
4. "Is the market trending enough for the strategy not to crumble?"

The model should predict not the price, but the state of the market.

This is the foundation of ML in trading.

2. Data is the fuel for the model, but not in the way they think.

When a trader first comes across ML, he starts collecting tons of data: ticks, candles, volumes, glass, reports, news. The result is chaos that the model cannot handle.

Only three types of data are sufficient for a basic AI bot.:

1. Price (candles for 1-5 seconds, not minute candles)
2. Volume (in absolute terms and changes relative to previous ones)
3. Deviation from the baseline, such as VWAP or EMA

These parameters give the model 80% of the information about the current state of the market. Everything else is already an improvement.

Important: the data must be clean and consistent. Omissions, gaps, and different frequencies all reduce the quality of the model more than any errors in the code.

3. How the model learns: the task is easier than it seems

When the model has the data and the task, the training begins. And at this stage, it's important to keep things simple. An ML bot should not become a monster for 500 characters. His strength lies in minimalism.

The learning process in the correct form looks like this:

1. the model receives historical data;
2. analyzes price and volume behavior before successful impulses;
3. searches for repeated combinations of events;
4. turns them into a probability formula.

This is not magic, but the mathematics of patterns.

AI does not "understand the market" — it notices repetitions that we do not see with our eyes.

4. The strategic layer is what makes an ML bot a trader.

The model is just a "probability estimator". In order for a bot to trade, it needs a layer of rules that turns the "probability is high" statement into action.

But here it is important to avoid repeating chapter 2: now we are not talking about the structure of the system, but about the minimum set of rules that is needed specifically for an ML bot.

A good strategic layer contains:

1. entry condition (probability > set threshold),
2. checking volumes (an impulse without volume is not an impulse),
3. volatility check (too low — there is no point in trading),
4. cancellation rule (model made a mistake → exit immediately),
5. the pause rule after stops (the first protection against a series of losses).

This layer turns the model into a tool, not a guessing game.

5. How to test an ML bot so as not to get a fake result

Bots are being merged not because of mathematics, but because traders are testing them on the same dataset they are being trained on.

This is the No. 1 mistake in DIY ML.

Testing should look like this:

1. the model is trained on one part of the story;
2. then it starts in a completely different period, where the market behaved differently.;
3. then it is checked how it reacts to stressful events (gaps, reports, news).

If the bot behaves stably even on data that the model has never seen, this is a real result. If not, we have another "showcase bot" that is only good at presentation.

6. Practical example: basic ML bot for impulse movements

In order not to create a sense of abstraction, I will provide a real working framework.

The bot gets:

1. the last 150-300 ticks,
2. the last 5-10 seconds of volumes,
3. price distance from VWAP,
4. the rate of change of candles.

The task of the models is to determine whether the probability of an impulse will increase in the next 2-4 seconds.

When the probability exceeds the threshold, the strategic layer is activated.:

1. checking the volume →
2. checking the speed of the tape →
3. we enter with a minimum volume →
4. setting a fixed-size stop →
5. if there is no momentum, we close the deal.

This framework works better than 90% of telegram bots because it takes into account the real market behavior rather than indicator images.

By the way, the logic of impulses perfectly intersects with the concept of arbitration discrepancies — read more here.

7. What is important to understand before launching your first ML bot

AI is not a substitute for a trader. It does two things:

1. takes over the routine analysis,
2. it helps to filter out bad deals.

If the model is assembled correctly, it will not work wonders, but it will remove the chaos. And for most traders, it is chaos that is the main problem, not the lack of a "secret strategy".

Chapter 5. AI vs. Trader: Who Trades better and why the comparison is almost always wrong

Every time someone says: "AI will soon replace traders," he forgets one simple detail: a human and a neural network see the market in completely different ways. These are not two versions of the same tool — they are two different mechanisms of thinking. And trying to compare them directly often resembles an argument about which is more important — a calculator or a surgeon's intuition.

AI processes data perfectly. The person understands the context perfectly.

And both of these strengths can turn into weaknesses if they are misused.

How AI “sees” the market in reality

AI doesn't sense the market — it reads it as a sequence of numbers.

He doesn't understand that NVIDIA's report came out, that the FOMC is today, that the premarket is thin, that SPY has started to pull the entire sector.

There are only statistics for him:

1. how much volume has accelerated,
2. how has the speed of candles changed?,
3. how much has the price deviated from the baseline,
4. whether the current state coincides with historical patterns.

The AI doesn't know what's going on. The AI knows what it looked like before.

And this is what makes it incredibly powerful in situations where the market repeats itself, but absolutely useless in those where the context is changing before our eyes.

How does a trader perceive the same thing

Unlike AI, humans do not work through mathematics, but through causal relationships. He can trade quickly not because he analyzes tick changes, but because he instantly understands what caused the movement and whether it is organic or artificial in nature.

A trader has something that cannot be programmed.:

1. understanding the news,
2. feeling the pace of the market,
3. The feeling of dangerous moments,
4. the ability to change the plan in real time.

AI doesn't know how to do this, and probably never will.

A person loses in speed, but wins in flexibility.

AI wins in precision, but loses in meaning.

Where AI is objectively stronger than humans

A small structural block is appropriate here — it helps not to turn the text into an abstraction.

AI surpasses the trader where:

1. it is necessary to analyze many factors at the same time
2. A person does not keep 20 variables in his head → The AI holds 200.
3. a fine microstructure occurs
4. Changes in the flow of transactions in milliseconds are the territory of machines.
5. Cold-blooded discipline is needed
6. AI does not over-trade, does not take revenge, does not doubt, and does not get tired.
7. the strategy is repeatable and narrow
8. Impulses, scalping, micro-patterns — here the machine almost always blames the person.

Where humans remain stronger than AI

But what a neural network can't do is not want to.:

1. reading the news context;
2. understanding strange movements that are “not according to the textbook”;
3. recognition of fake impulses;
4. reaction to an unexpected risk (like political news);
5. adjusting the plan to the situation.

AI is good as long as the market remains familiar.

A person is good when the market becomes unknown.

That's why even the best AI systems in hedge funds work under human control, not the other way around.

Why is the question “who is better” generally incorrect?

AI and trader are not competitors.

AI is a tool that strengthens the trader.

And a trader is a context without which AI turns into a helpless probabilistic model.

If we compare them directly, the picture will look like this:

1. AI sees better,
2. a person understands better,
3. The AI keeps better track,
4. a person decides better,
5. AI is ideal in repeatable conditions.,
6. a person is ideal in unstable conditions.

Any attempt to pit them against each other ends with one conclusion: the bunch wins.

The main paradox: AI makes a trader better, but only if the trader remains a trader.

If a person tries to completely transfer control to AI, they lose their strongest point — the ability to adapt.

If he uses AI as a filter, as an assistant in analysis, or as a control tool, his win rate increases, risk decreases, and trading stability increases.

This is the essence of 2025.:

AI is not a substitute for a trader.

AI allows the trader to stay in the places where he broke before.

Chapter 6. How to train your AI model for trading signals: from a raw dataset to a working tool

Learning an AI model in trading is not like learning a model in classical tasks.

We're not trying to teach her how to understand text, recognize people in photos, or write music.

The model works with the market, an environment that lives, changes, and breaks any rules if you believe in them for too long.

Therefore, training a model for signals is not a process of “setting a neural network on graphs and that's it.”

This is a neat adjustment of which market conditions it should recognize and how to distinguish momentum from noise.

What should the model really predict?

The first mistake of everyone starting out in AI trading is the desire to train a model to “predict price movements.”

But that's not how the market works, and neither does the model.

A well-trained system answers only one question.:

“Are there conditions now for the appearance of a movement with a measurable probability?”

That is, she is not trying to solve the market.

She estimates the likelihood that other market participants will start acting in a certain way.

This is a key point that defines the entire learning architecture.

What kind of data is needed specifically for training the model, and not for beautiful statistics

If in chapter 3 we talked about the data for the bot to work, then another layer is important here — the data from which the model learns to recognize useful patterns.

A mature trader can trade with his eyes, but a model cannot.

She needs precise, numerical signs that reflect:

1. movement status (acceleration/deceleration of candles),
2. volume behavior (in absolute terms and in the rate of change),
3. the position of the price relative to the fair value level, for example VWAP,
4. Context: whether volatility is expanding or narrowing.

These signs are not needed for the sake of beauty — they form a “market feeling” for the model.

To give the model a real foothold, they first analyze the history manually: where was the momentum? Where was the deception? what unites these moments?

And only then do they turn these observations into formalized signs.

It is useful to refresh the basic indicators and their meaning here.

This is not “using indicators”, but “using the principles that lie beneath them".

How the training dataset is formed (and why it is more important than the model itself)

The most underrated part of learning is finding the right examples.

If a model gets thousands of situations that don't look like real market conditions at all, it will learn the wrong thing.

The training dataset must contain:

1. examples of real impulses,
2. examples of false impulses,
3. examples of complete stagnation,
4. periods of high and low volatility,
5. different market phases — trends, sideways movements, and pre-news moments.

This is the only way to make the model resilient to environmental change.

The important thing is that it was not mentioned above:

The model should not see the best examples, but typical ones.

Traders want to learn the “perfect setups" model, but the market is rarely perfect.

If you teach the ideal, you fail in the real market.

You teach the real thing, you survive.

How to understand that the model has really learned, and not just remembered

No training makes sense if the model fails the adequacy test.

To avoid repeating Chapter 3, here's a different line of thought.:

we are not evaluating the behavior of the bot, but the behavior of the model.

A good model:

1. confidently recognizes impulses, even if they are weaker than in the training dataset.;
2. It does not give signals in conditions that were not present in the training.;
3. she may make mistakes, but her mistakes are “reasonable” — she does not confuse flat with directional movement.;
4. it reacts correctly to sudden changes in volatility.

It's not about trading — it's about the mindset of the model.

A bad model does something else.:

1. responds to noise,
2. overestimates weak patterns,
3. it is lost at the slightest changes in conditions.,
4. gives signals “for the sake of the signal".

Such AI is not just useless, it is dangerous.

Final assembly: how the model turns into a signal source

When the model is trained, it outputs only a number — the probability of an event.

But a trader should have his own language of communication with AI.:

1. high probability → possible impulse;
2. average → observe;
3. a low → market does not provide an advantage.

This probability is then passed through filtering rules, which turn the “estimate” into a signal.

The AI function here is not to replace the trader —

but to give a clean, mathematical picture that does not depend on emotions.

The main result of model training is not accuracy, but stability.

High accuracy of the model during testing does not guarantee profit.

Sustainability, yes.

A sustainable model:

1. It works equally well in different periods.;
2. It doesn't take off in one market condition;
3. does not fall apart when conditions change;
4. it does not lose its quality under stress.

It is the resilience that makes AI a useful trading tool, not just another “bot that once showed 90% win rate.”

Chapter 7. Practice: how to implement AI in trading tomorrow and not break your strategy

Most of the materials about artificial intelligence end with inspiring statements: “AI is the future of trading,“ "AI increases the win rate,“ ”AI makes trading easier."

But when a trader closes an article and opens a terminal, the main question arises.:

What exactly should I do tomorrow morning?

This is where AI ceases to be a theory and turns into a tool.

Everything described below works regardless of whether a person uses a ready-made model or builds a bot on their own.

Step 1. Determine the role of AI in your trading

AI should not replace a trader.

He has to take one specific position.:

1. transaction filter,
2. The probability hint,
3. pulse detector,
4. an assistant in risk management.

Choosing a role is the starting point.

If the AI is responsible for everything, the strategy will fall apart.

If he is responsible for one thing, he strengthens the trader.

Your morning should start not with a search for signals, but with a question.:

Where exactly will AI help me make cleaner decisions?

Step 2. Configure the minimum set of parameters that the AI will monitor

In order for a model or bot to benefit, they must look at the market the same way a trader looks — only faster and more accurately.

Therefore, there is no need to immerse them in dozens of indicators.

Three market pillars are sufficient for the daily operation of AI:

1. The pace of the price,
2. volume rate,
3. position relative to the fair line (VWAP/EMA).

These three parameters are like pulse, respiration and pressure for the body.

If one of them "breaks", the AI will see it faster than a human.

Step 3. Turn the output of the model into a trading solution

The AI outputs numbers.

The trader provides solutions.

To link one to the other, you need a formula:

probability → action.

It's not a list, it's a logic that sounds like this:

1. high probability + volume acceleration = trading chance;
2. average probability + counter-trend = observation;
3. low probability = the market does not provide an advantage.

The man stops guessing.

AI stops giving chaotic hints.

Both start working as one system.

If you need a basic guideline for risk/reward, it always remains the foundation, regardless of AI.

Step 4. Use AI as a defense, not as a trading engine

The biggest mistake is to think of AI as a deal accelerator.

On the contrary: AI should slow down inputs.

He has to talk:

1. "don't get involved, the volumes are weak",
2. "the market does not give an advantage",
3. "the probability is below normal",
4. "be careful — volatility is unstable."

Traders lose money not because they trade little, but because they trade a lot.

AI is an ideal filter against over—trading.

If AI causes fewer transactions in your trading, it works correctly.

If it causes more problems, you are using it for other purposes.

Step 5. Add a layer of self-control that makes a person worse than a machine

There are things where AI is objectively stronger.:

1. tracking loss series,
2. drawdown control,
3. automatic shutdown of trading in case of violation of limits,
4. analysis of the quality of transactions after the fact.

AI copes with these tasks better than humans, because it does not argue, does not seek excuses, and does not try to "win back."

One of the most powerful effects of AI —

he does not allow a trader to destroy a good idea with bad discipline.

Step 6. Gradual integration: Don't include everything at once

The correct way to implement AI consists of four steps:

1. The AI observes the market and compares its vision with a human.
2. The AI prompts, but the trader makes the decision.
3. The AI filters, and the person chooses the moment of entry.
4. Only then does the AI automate some of the routine actions.

This scheme protects the trader from two extremes.:

blind faith in AI and complete distrust of it.

What the trader will receive tomorrow

If you do even half of the above,

trading will change immediately — without complicated code and complex models.

You'll get:

1. fewer chaotic entrances,
2. a cleaner selection of deals,
3. early warning of a “bad market”,
4. risk control that doesn't break down due to emotions,
5. It feels like the market has become more logical and quieter.

AI is not a tool for brilliant ideas.

It's a tool for not destroying the good ones.

FAQ: the most common questions about AI in trading

When a trader first encounters artificial intelligence, questions appear faster than the first results. Below are short and direct answers that help you understand exactly what to expect from AI and what it can never give.

1. Can AI trade completely autonomously?

Not completely. The reason is not a lack of technology, but the nature of the market. AI perfectly senses repeatability: impulses, microstructure, and volume operation. But he doesn't understand the context — the reports, the news, the mood of the sector, the impact of the macro. Any situation where causal logic is important still belongs to a person. Therefore, the best form of interaction today is cooperation.: AI enhances the trader, not replaces him.

2. How to distinguish a working model from a guessing game?

The secret is simple: a model that really "sees" something behaves predictably and logically. She reacts confidently to the market conditions for which she was created, and remains silent where the market does not give an advantage. The guessing game gives out signals randomly — sometimes too late, sometimes too early, sometimes for no apparent reason. The working model is always explicable by the market, even if it is wrong. Never non—working.

3. Does AI increase its win rate?

Yes, but not at the expense of "brilliant signals". It makes trading cleaner because it removes weak trades. The trader starts to enter less frequently, but more accurately. This is especially noticeable in impulse strategies.: AI helps to cut off movements that look promising only visually, but are not confirmed by the volume or structure of the market. The increase in win rate comes as a result of discipline, not magic.

4. Is AI suitable for passing a prop challenge?

It is suitable, but only as an auxiliary tool. AI helps to keep limits, avoid questionable transactions, and maintain a stable work rhythm. But prop itself requires flexibility, which AI does not have: the market changes too quickly on the days of reports, CPI, FOMC or political news. The model does not have time to rebuild. Therefore, AI can be a good helper, but not an independent player.

5. What mistakes do traders make most often when using AI?

The most common mistake is expecting AI to take over trading. The second is the desire to complicate the model to a state in which it ceases to understand the market. The third is the abandonment of discipline: the trader begins to believe that the model will "pull out" any mistake. In reality, the opposite is true: AI makes only the system that was already clear and structured stronger. He doesn't cure chaos, he enhances it.

What remains unchanged

AI helps, but only if the trader remains in dialogue with the market. It makes trading cleaner, calmer, and more structured, but it doesn't create advantages out of thin air. And as soon as the trader stops thinking for himself, the AI stops working.