Our goal with the Feature Generation module is to provide a knowledge-intensive and
computationally efficient coarse-grained analysis of historical prices which can be analyzed
further in a second layer of reasoning. The domain knowledge implemented in the module
is thus limited to methods and techniques in technical analysis. The technical analysis
literature includes a wealth of different stock analysis techniques, some of which involve
complicated and intricate price patterns subjective in both detection and interpretation.
These methods would be both computationally expensive to detect and evaluate, and have
consequently been disregarded. We thus apply Occam’s razor to the choice of methods
in technical analysis, focusing on the most popular indicators that can be efficiently
operationalized and are intuitive in interpretation.