According to Fisher, E. (2024, November 15), "AI and ML are revolutionizing the trading world. By embracing these technologies, you can gain a competitive edge, make smarter decisions, and unlock new possibilities" A practical example is forecasting stock prices with AI/ML models trained on historical price data. As stock prices form a time-series dataset, these models analyze patterns to predict future values, offering traders valuable insights to inform decision-making.

Trading automation began in the 1970s with the New York Stock Exchange's (NYSE) Designated Order Turnaround (DOT) system. This was followed by program trading in the 1980s, which allowed trades to execute automatically based on predefined conditions like price and time. (Jauhari, 2023, n.p.)

 Before digitization, trading was entirely manual, conducted on physical floors where brokers vocally negotiated deals and signed documents—a process prone to human error and fraud.

Today, trading has become fully digital, with artificial intelligence (AI) and machine learning (ML) playing a critical role. These technologies enable rapid data analysis, trend identification, and trade execution, often within seconds, surpassing human efficiency. By reducing emotional biases, minimizing fatigue, and enhancing decision-making, AI/ML has become indispensable for traders seeking to optimize their performance.