Comparing algorithmic trading strategies by analogies to machine learning

In technical analysis-based algorithmic trading strategies, we use historical price patterns to predict future prices and trade accordingly. This is analogous to machine learning where we use the existing data patterns to classify or predict new patterns. This paper uses this analogy and explains trading strategies as a machine learning classification problem. We derive simple approximations that relate the performance of trading strategies to machine learning statistics. We introduce a new performance measure of the Return Efficiency Index. This index provides a link between trading strategy return statistics and classification accuracy. It has a simple geometric interpretation, similar to the ROC index in machine learning, and can be used to compare strategies in terms of their ability to capture the potential returns possible with the underlying assets. We illustrate the proposed approach by a detailed comparison of daily trading strategies designed by analogies to nearest neighbor classification widely used in machine learning and to some strategies based on deep learning.