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Friends, I am glad to announce great news: in the upcoming updates of the MetaTrader 5 platform, every trader will have access to Artificial Intelligence and an AI agent — right inside the familiar trading terminal, without the need to install third-party programs or use external services. This means that now each of you will be able to harness the full power of artificial intelligence right in MetaTrader 5: quickly analyze market situations, get help with chart and indicator analysis, create trading plans, and make more informed decisions. The built-in AI agent will take your work to a new level — it can not only advise but also perform tasks autonomously: search for needed information, process data, and help automate routine processes, freeing up your time for what matters most — strategy and analysis. Thus, MetaTrader 5 transforms from a simple trading terminal into a full-fledged intelligent assistant for traders, and these tools will be available to absolutely everyone.
Artificial Intelligence (AI) — is a field of computer science concerned with building systems capable of imitating human cognitive functions: learning, reasoning, analyzing information, and making decisions. Unlike conventional programs that operate on rigidly predefined rules, modern AI uses machine learning technologies and neural networks. This allows it to independently discover hidden patterns in huge volumes of data, adapt to new conditions, and solve complex logical and creative tasks.
The essence of AI is not about creating a thinking living being, but about expanding human capabilities and automating intellectual labor. Today it acts as a powerful assistant in all areas — from generating texts and recognizing faces to controlling unmanned vehicles, precise medical diagnostics, and predicting climate change. AI processes gigantic amounts of information in seconds, freeing people from routine and helping find solutions where the human brain is powerless against the scale of data.
An AI agent (artificial intelligent agent) — is an autonomous system that is capable not only of answering questions but also of independently performing actions to achieve a set goal. Unlike ordinary chatbots, an agent has agency: it analyzes the surrounding environment, plans its steps, makes decisions, and uses external tools (e.g., searches for information on the internet, runs code, sends emails, or works with databases). The user simply formulates the final task (for example, «find and book the best hotel according to my criteria»), and the agent itself develops and executes a scenario to accomplish it.
The essence of an AI agent lies in the transition from the model of a simple «advisor» (co-pilot) to the model of an «executor» (agent). Its architecture combines three elements: a «brain» based on a large language model (LLM), memory for storing context, and a set of digital tools for interacting with the outside world. Due to this, agents are able to adapt to changing conditions, learn from their mistakes during task execution, and work practically without human involvement, making them the prototype of full-fledged digital employees of the future.
The main difference between artificial intelligence in the general sense and an AI agent lies in the transition from a reactive response to proactive goal-setting and action: classical AI (for example, a language model) — is a «brain» that learns from data and generates a result strictly in response to a user request, without affecting the environment outside its output, whereas an AI agent — is an autonomous system in which this «brain» is supplemented with a cycle of «planning — tools — memory — action», allowing it to independently break down a complex intention into steps, interact with external services, search for information, execute code, and adjust its plan until the task is solved. In other words, AI knows and answers, while an agent — sets itself subtasks and acts.
A prompt (from English «prompt» — a hint, a nudge) — is a text query, instruction, or script in natural language that a user feeds as input to a generative model (for example, a large language model) to set its direction, role, format, and boundaries of the desired response. In essence, a prompt acts as a programming interface for an intelligent system without code: it can include a task description, examples of desired behavior (few-shot), context, constraints, and explicit style guidelines, allowing you to turn the same universal model into a translator, programmer, editor, or analyst solely through well-crafted text.
A prompt in the context of trading — is a text instruction that a trader inputs into a neural network or AI agent to get analytics, a trading idea, or to automate part of their work. With a well-composed prompt, you can get AI to analyze charts and indicators, assess the news background and market sentiment, draw up trading plans, calculate risk management, or even write code for trading bots. For instance, instead of a vague query like «what will happen to Bitcoin?» an effective prompt looks like this: «analyze the current situation for BTC/USDT on the daily timeframe, taking into account support and resistance levels, RSI, and volumes, and suggest an entry scenario with a stop-loss and take-profit at a risk of 1% of the deposit». The more precisely the trader describes the context — the asset, timeframe, strategy, acceptable risk, and response format — the more useful the result will be, so prompt engineering in trading is effectively becoming the skill of properly setting tasks for an AI assistant.
In order for a trading prompt to yield an accurate and useful result, it must work like a precise technical specification for an analyst. The universal formula is as follows: Role + Market Context + Specific Task + Constraints and Criteria + Output Format.
The key rule — a prompt must contain actual market data and not rely on the model's «knowledge», otherwise you will get hallucinations. The stricter the structure you impose, the closer the response will be to an algorithmic report.
«You are a professional technical analyst. Analyze the EUR/USD pair on the daily timeframe. Identify key support and resistance levels, the current trend, and possible price movement scenarios for the next week. Format the answer as: 1) overall picture, 2) levels in a table, 3) bullish and bearish scenarios with probabilities. Specify what data you need from me for a more accurate analysis.»
«You are a risk manager. My deposit is $5,000, the maximum allowable risk per trade is 1%. I want to open a position in gold (XAU/USD) with an entry at 2650, a stop-loss at 2630, and a take-profit at 2700. Calculate the position size in lots, the risk/reward ratio, and evaluate whether it is worth entering this trade from a risk management perspective. Provide the answer with a step-by-step calculation.»
«You are an MQL5 developer. Write the code for an Expert Advisor for MetaTrader 5 using the strategy: enter a buy when the fast EMA(9) crosses the slow EMA(21) upwards and RSI(14) is above 50; exit on a reverse crossover or a 50-pip stop-loss. Add comments to the code and explain which parameters can be optimized.»
«You are an experienced mentor trader. My strategy: I trade breakouts of levels on the hourly timeframe for GBP/USD, entering after the price consolidates beyond the level, with a stop behind the broken level, and a target at the nearest next level. Analyze the weak points of this strategy, suggest 3 specific improvements, and draw up a 7-point checklist for reviewing each trade before entry.»
«You are a trading statistics analyst. Analyze my last 30 trades. Calculate the win rate, average profit factor, and maximum losing streak. Identify patterns: on which days/setups do I lose most often? Give 3 specific recommendations to improve results. Present the answer in a table with conclusions.»
You are a [ROLE: technical analyst / macro analyst / risk manager / MQL developer / day trader].
Instrument: [TICKER, timeframe, analysis period — e.g., EURUSD H1 for the last 200 candles].
Market context (latest data): [INSERT OPEN/CLOSE PRICE, HIGH/LOW, INDICATOR VALUES (RSI, ATR, volume), KEY SUPPORT/RESISTANCE LEVELS, BRIEF NEWS BACKGROUND — everything that may affect].
Task: [SPECIFIC ACTION — "find a breakout setup with retest", "calculate risk and position size", "write a strategy script", "make a pre-trading plan for the first 2 hours of the session", "compile a macro event summary"].
Selection criteria and constraints:
- [ignore signals if ATR is below X];
- [use only RSI and moving averages];
- [do not suggest short positions];
- [account for commission X and spread Y];
- [reject setups with risk over 2% of the deposit];
- [any other strict conditions].
Response format: [EXACT STRUCTURE — "output a table: Date | Signal | Entry Point | Stop-Loss | Take-Profit | R/R Ratio", "return JSON with keys: signal, entry, stop, target, confidence", "give a bullet list of three blocks: Levels / Scenario 1 / Scenario 2", "output only clean MQL5 code without explanations"].
Additional requirements:
- Do not give guaranteed forecasts or unconditional buy/sell signals.
- Separate facts from assumptions.
- Specify what data was used and how up-to-date it is.
- Do not change specified trade parameters without explanation.
- If there is not enough information, first ask clarifying questions.
- Remind that the final decision and responsibility for the trade rest with the trader.
Friends, the hype around artificial intelligence is attracting more and more beginner and experienced traders to attempts at automating trading strategies. People come and ask AI to program a profitable system for them, sincerely believing that it can make them rich. As a result, such traders spend dozens of hours generating random pseudo-codes that compile without errors, but in real trading bring only bitter disappointment. At the moment, artificial intelligence is not yet capable of creating truly complex and consistently profitable trading strategies — it only delivers a «pig in a poke». Having realized this, traders start looking for programmers who can fulfill the technical task exactly as the customer needs.
At the same time, artificial intelligence is an excellent aid for those who have at least a little understanding of programming trading robots. Friends, if you want to master the MQL language, learn to create expert advisors, and use AI as an assistant to develop your own strategies or fulfill orders from other traders, start with the MQL Master course. This is a course designed to teach programming in MQL4 and MQL5 — the most advanced languages for creating trading systems for MetaTrader.
An AI agent in MQL development — is an autonomous system based on a language model that not just generates code snippets on request, but carries out the full cycle of trading robot development: understands the task, makes a plan, writes the code, checks it for errors, corrects them, and brings the result to a working state. If a regular chat with AI is «asked got an answer check and refine it yourself», then an AI agent operates on the principle: «set the task the agent itself solves it and shows the finished result». It is important to note that a working state does not mean a profitable one.
Blind trust in AI agents for trading robot development is dangerous primarily because the agent does not understand the essence of the market and bears no responsibility for the result. A language model generates plausible but not guaranteed correct code: it can «hallucinate» — use non-existent functions, outdated MQL4 syntax instead of MQL5, incorrectly handle ticks, slippage, and requotes. Even if the code compiles successfully, it does not mean the advisor's logic is correct: the agent does not see the robot's real behavior in the market, does not account for spreads, swaps, liquidity, and the specifics of a particular broker. As a result, the trader gets beautifully formatted code with comments that can drain the deposit on a live account — and figuring out someone else's (essentially, machine) logic and finding an error in it without your own programming knowledge is practically impossible.
The second key problem — the loss of control and competence on the part of the trader. A trading robot is a tool to which you entrust real money, and completely handing over its creation to an autonomous system means not understanding exactly how it makes decisions, where its weak spots are, and when it needs to be stopped. Moreover, constantly delegating development to an agent does not allow the trader to grow professionally: they do not learn to translate a trading idea into an algorithm, do not master testing and optimization, and therefore cannot critically evaluate the result of the AI's work. The right approach — is to use the AI agent as an assistant and accelerator (generating templates, finding errors, hints on functions), while keeping the role of architect and controller: the trading idea, logic verification, historical testing, and the final decision to run the robot on a real account must remain in human hands.
An AI agent is a great assistant, but it is important to understand: it cannot predict the market. Its strength is in accelerating development, not in generating turnkey profitable ideas. The AI agent is your fast helper that you can assign routine and repetitive work. Ask it to write the first draft of an advisor, create a code template for calculating lots, or rewrite a confusing piece of code to make it clearer. It explains well what someone else's code does and can translate a simple trading idea («buy when the price closes above yesterday's high») into a piece of ready-made code. The agent is also useful for sketching out several options for entry rules — you can then check which ones are worth testing. The main thing is to treat its response not as a finished robot, but as a raw draft that definitely needs to be refined.
Everything the agent outputs must be carefully checked with your own head. First, simply read the code and look for silly mistakes: maybe it mixed up the trade direction, used prices that were not yet available at that moment, or overfitted everything too nicely to old data. Then run the robot in the tester over different periods, deliberately complicate the conditions (increase the spread, add slippage) and see if the strategy falls apart. Make sure that profitability does not disappear if you slightly shift the parameters. The agent can help here too — quickly calculate the return or compile a report — but the final decision to «run the robot with real money» always remains with you, because only you understand the cost of a mistake.
Friends, the hype around artificial intelligence is attracting more and more beginner and experienced traders to attempts at automating trading strategies. People come and ask AI to program a profitable system for them, sincerely believing that it can make them rich. As a result, such traders spend dozens of hours generating random pseudo-codes that compile without errors, but in real trading bring only bitter disappointment. At the moment, artificial intelligence is not yet capable of creating truly complex and consistently profitable trading strategies — it only delivers a «pig in a poke». Having realized this, traders start looking for programmers who can fulfill the technical task exactly as the customer needs.
At the same time, artificial intelligence is an excellent aid for those who have at least a little understanding of programming trading robots. Friends, if you want to master the MQL language, learn to create expert advisors, and use AI as an assistant to develop your own strategies or fulfill orders from other traders, start with the MQL Master course. This is a course designed to teach programming in MQL4 and MQL5 — the most advanced languages for creating trading systems.
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