Rule-based Expert Advisors (EAs) and AI-powered trading robots are both automated trading systems, but they differ significantly in their design, functionality, and adaptability.

Rule-based EAs operate on predefined trading rules set by human developers. These rules are based on technical indicators, price action patterns, or other specific criteria. Once the conditions are met, the EA executes trades automatically. They are fast, reliable, and ideal for markets with consistent behaviour. However, they lack flexibility and cannot adapt to changing market dynamics without manual updates.

AI-powered trading robots, on the other hand, use machine learning algorithms to analyse large volumes of market data, identify patterns, and make predictions. Unlike rule-based systems, AI robots can learn and evolve, improving their performance through data-driven training. They are better at handling complex and non-linear market conditions, potentially offering higher adaptability and smarter decision-making.

In summary, rule-based EAs follow strict logic and are easier to control but require frequent updates. AI-powered robots are more autonomous, adaptive, and capable of processing unstructured data like news or sentiment, but they are more complex to develop, train, and monitor. Traders must choose based on their strategy, risk tolerance, and technical resources.