Introduction to Deep Learning Trading in Hedge Funds

Over the past few years, deep neural networks have become extremely popular. This emerging field of computer science was created around the concept of biological neural networks, and deep learning has become something of a buzzword today.

Deep learning scientists and engineers try to mathematically describe various patterns from biological nervous systems. Deep learning systems have been applied to various problems: computer vision, speech recognition, natural language processing, machine translation, and more. It is interesting and exciting that in some tasks, deep learning has outperformed human experts. Today, we will be taking a look at deep learning in the financial sector.

One of the more attractive applications of deep learning is in hedge funds. Hedge funds are investment funds, financial organizations that raise funds from investors and manage them. They usually work with time series data and try to make some predictions. There is a special type of deep learning architecture that is suitable for time series analysis: recurrent neural networks (RNNs), or even more specifically, a special type of recurrent neural network: long short-term memory (LSTM) networks.

LSTMs are capable of capturing the most important features from time series data and modeling its dependencies. A stock price prediction model is presented as an illustrative case study on how hedge funds can use such systems. PyTorch framework, written in Python, is used to train the model, design experiments, and draw the results.

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