Summary

This chapter explored the practical application of AI in financial trading, focusing on machine learning for pattern recognition. It highlighted effective techniques and using EasyLanguage within TradeStation to integrate advanced learning algorithms into trading strategies. Despite EasyLanguage’s limitations compared to Python, it remains a valuable tool for traders.

In this chapter, we learned that, unlike algorithmic trading, the primary goal of machine learning for pattern recognition is not to evaluate strategies from a financial perspective but, rather, to assess their predictive capabilities. Evaluating the financial performance of a strategy without verifying its predictive abilities first is a waste of time and often leads to overfitting.

This chapter drew inspiration from Sir R. Fisher’s pioneering pattern recognition experiments of the 1930s, which share many elements with trading. The key topics that were covered included defining machine learning...