To understand the limitations, it helps to understand the basic nature of an LLM. Models like ChatGPT are trained to predict words in sentences. They learn from articles, books, documentation, forum posts, and code samples. They recognize relationships between concepts, but they do not operate on numerical time series the way machine learning models do.

As a result, ChatGPT has no built-in ability to:

compute time-series correlations

evaluate returns or probabilities

check drawdowns or robustness

run backtests

optimize strategies

identify hidden structures in price data

It does not have historical price data. It does not run mathematical tests. It does not understand quantitative truths about the market. It understands the text that humans have written about the market.

This means that whenever you ask ChatGPT to create a strategy, it pulls from common trading ideas it has seen in text form. These include moving average crossovers, RSI signals, MACD patterns, Bollinger entries, breakouts, and similar structures. These are popular, not necessarily profitable.

There are several technical reasons for this.

A. It has no access to raw price data

The model cannot load OHLC data, compute indicators, or test the results of a strategy. It does not evaluate whether an idea works in the real world.

B. It cannot perform statistical testing

ChatGPT does not run regressions, optimizations, or probability models. It cannot validate an idea through objective evidence. It can only reference descriptions of what people have claimed works.

C. It cannot measure performance

There is no way for the model to compute returns, Sharpe ratios, equity curves, slippage, spreads, or trade frequency. Without these, it cannot judge the quality of a strategy.

D. It relies on common trading language

If thousands of traders describe something as effective, the model notices those patterns in text. That does not make the strategy itself profitable.

This is why an EA generated by ChatGPT is only as good as the logic you provide.

Although ChatGPT cannot discover an edge, it is extremely useful in other parts of the development pipeline. Understanding these strengths allows you to speed up EA creation and reduce human error.

Here are the areas where LLMs truly shine.

A. Converting Your Ideas Into Clean MQL Code

If you provide clear logic, ChatGPT produces clean, structured, ready-to-test code. It is especially good at:

generating full EA templates

writing indicator wrappers

building risk management modules

creating multi-symbol scanners

converting MT4 code to MT5 and vice versa

adding entry and exit conditions

implementing trailing stops and partial closes

creating configuration panels

handling order execution logic

This turns written trading rules into working software very quickly.

B. Refactoring and Debugging

ChatGPT is excellent at reading complex MQL code and improving it. It can:

spot logical errors

fix order handling issues

correct indicator buffer mistakes

clean up repeated blocks of code

improve structure

reorganize functions

rewrite procedural code into OOP modules

These improvements save significant development time, especially on large EAs.

C. Text-Based Intelligence That Traditional Models Cannot Provide

This is one of the most powerful uses of LLMs in trading. Unlike numerical ML models, LLMs excel at understanding text, narrative, and sentiment. This includes:

economic news summaries

sentiment analysis from headlines

central bank commentary interpretation

tone analysis for earnings calls

market narrative classification

detection of risk-off vs risk-on language

filtering misleading or promotional news

You can use this information as a filter or risk control layer in your technical strategy. For example, an EA may pause trading during uncertain macro conditions, or reduce position size if news sentiment turns sharply negative.

This combination of technical signals and textual intelligence is far more powerful than a pure indicator-based approach.

D. Documentation, Explanations, and Workflow Support

ChatGPT is also well suited for:

generating user manuals

writing product descriptions

documenting EA parameters

explaining strategy logic

creating onboarding guides

generating test plans

writing release notes

This level of clarity is valuable when shipping tools or selling EAs.

Once you know what the model can and cannot do, you can follow a workflow that produces consistent results.

Step 1: Define Your Strategy Clearly

Write out your logic in plain language. This includes:

entry rules

exit rules

filters

risk settings

trade management

session rules

timeframes

symbols

The quality of your EA depends entirely on the precision of your instructions.

Step 2: Use ChatGPT to Generate the Code

Ask the model to:

build the EA

structure it cleanly

include error handling

include risk and money management

ensure it compiles without warnings

You can request multiple versions if you want alternative structures.

Step 3: Test the Strategy Yourself

Use MT4 or MT5 to run:

backtests

stress tests

different spreads

Monte Carlo analysis

walk-forward tests

multi-symbol tests

This is the only way to verify actual performance.

Step 4: Adjust the Logic Based on Data

Based on your results, refine:

filters

volatility controls

timing rules

position sizing

entry precision

exit pacing

protections

Then ask ChatGPT to incorporate these adjustments.

Step 5: Add LLM-Driven Enhancements

Once the EA is stable, you can integrate:

sentiment signals

real-time news summaries

macro context filters

volatility warnings

fundamental sentiment overlays

event-driven position adjustments

These enhancements improve reliability during uncertain conditions.

The most powerful systems in the future will combine two worlds:

Data-driven models handle:

probability

classification

regime detection

feature analysis

numerical prediction

volatility modeling

LLM models handle:

narrative interpretation

news filtering

sentiment classification

documentation

automation of rules into code

dynamic logic generation

This is the direction institutional trading is moving. Retail traders who take this approach will stay well ahead of the curve.

ChatGPT does not generate profitable strategies on its own. It does not discover alpha, and it does not test ideas. What it does exceptionally well is help you build, scale, and refine your trading infrastructure. It turns your ideas into well-structured code, and it adds the kind of text-based intelligence that traditional models cannot provide.

Use ChatGPT for what it is good at, and you will build reliable EAs much faster than working alone. Expect it to invent profitable setups, and you will waste your time.

The edge comes from your insight and data. ChatGPT amplifies that edge by converting your thinking into efficient, maintainable, production-ready software.