Abstract
In this treatise, we describe some of the basic and common deep learning architectures amenable to objectives in finance and give heuristic arguments as to which of those seems preferable for applications in financial trading. A specific deep architecture, allowing information cascades and different feature weights for different market environments, is described. Applicable objectives and input feature engineering for this architecture are discussed.
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