Traditionally, retail traders have leveraged on price patterns discovered by applying graphical tools such as flags, fractals, pennants, heads, shoulders, etc.
However, while this method has been very profitable for many, it is often based on the individual's capability of empirically detecting/visualizing trends.
Nowadays, machine learning and neural networks are perfect tools for pattern recognition.
Coming to the question.
Assuming one wants to trade price patters and is knowledgeable of machine learning and other statistical concepts.
How should the dependent variable of a classification algorithm be defined? Is it better to use a simple 0/1 (down/up price) or to use the traditional patterns as the dependent variables?
Alternatively, are the price patterns to be used as independent variables? Or are the machine learning and neural network models able to recognize patterns without levering on the empirically available pattern rules such as flags, pennants, etc., rather by using other predictors?
Would be really nice to hear someone's opinion.