Automated Feature Discovery with Autoencoders for Market Prediction

In this tutorial, we will explore the concept of automated feature discovery using autoencoders for market prediction. Autoencoders are a type of unsupervised learning algorithm that can be used to identify novel predictors in a dataset. By training an autoencoder on a set of input features, we can learn a compressed representation of the data that captures the most important information. This compressed representation can then be used as input to a predictive model for market prediction.

Photo by Adam Nowakowski on Unsplash

We will start by discussing the theory behind autoencoders and their application in feature discovery. Then, we will dive into the implementation of an autoencoder using Python and the Keras library. We will use real financial data downloaded from Yahoo Finance to train our autoencoder and identify novel predictors for market prediction. Finally, we will build a predictive model using the discovered features and evaluate its performance.

Table of Contents

1. Theory
2. Data Preparation
3. Autoencoder Implementation
4. Feature Discovery
5. Market Prediction
6. Conclusion

Theory