AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural networks to predict the return of stocks. My goal for the viewer is to understand the core principles that go behind the development of such a multilayer model and the nuances of training the individual components for optimal predictive ability. Once the core principles are understood, the various components of the model can be replaced with the state of the art models available at time of usage. The workflow is similar to the approach in the excellent text Advances in Financial Machine Learning by Marcos Lopez de Prado, which I recommend to anyone who wants to learn about applying machine learning techniques to financial data. The data that was used for this project is not in the repository due to size constraints in GitHub, but the raw data was open sourced from Tick Data LLC, but now I believe is not available. In essense, we will be making bars (tick, volume or dollar) based on the tick data, apply feature engineering, reduce the dim

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