AI in trading, has evolved from early rule-based systems AIQ, to the advanced machine-learning tools we see today. In the early days, AI’s role in financial trading was centered on mimicking human decision-making through structured systems that applied pre-defined rules. These rules were based on standardized interpretations of technical indicators, such as crossovers and divergences, which traders frequently use during technical analysis.

Unlike humans who might analyze these indicators in isolation, early expert systems combined them to generate a comprehensive view, providing weighted insights and reducing manual effort. Over the past 40-45 years, AI has advanced significantly in speed, precision, decision-making, and reducing human error. While initial explorations in neural networks showed promise, the focus shifted to rule-based systems, which laid the foundation for today’s machine learning and deep learning models. These modern systems are vastly more sophisticated, but their roots in expert systems demonstrate the steady progression of AI in finance.

However, rule-based systems have limitations. They depend on predefined rules and lack adaptability. While this ensures consistency, the systems do not learn or adjust as market conditions evolve. This raises questions about how much markets change versus what is already accounted for in technical indicators.

The rise of algorithmic trading brought advancements, with firms like Renaissance Technologies leveraging statistical models and high-speed trading techniques, including moving averages arbitrage. These developments mark a significant shift from the rule-based systems of the past to the dynamic, high-speed tools of today.

This session also covers a current market update using the AIQ market Timing tools.