Machine learning, and in particular deep learning, has shown outstanding performance to solving a wide variety of tasks from almost all fields of science. Application areas in quantitative finance include algorithmic trading, risk management, economic impact studies, asset allocation, and more. Deep learning can detect and exploit complex relationships in the data which are neglected by — or are even unknown to — standard quantitative finance methods today.

Xcelerit get clients ready to apply deep learning algorithms to their financial applications through an in-depth training, consisting of a mix of theoretical and hands-on sessions. Our trainers give practical advice on where to start, which algorithms to apply, and how to tune the learning systems. Topics covered include, but are not limited to, deep neural networks (DNNs), recurrent neural networks (RNNs), long short term memory models (LSTMs), and auto-encoders. The hands-on sessions are using tensorflow, but can be adapted to other frameworks such as Torch, Theano, or Caffe on request. The training also covers performance optimisations of training and inference using GPUs and high-performance hardware.

Whitepaper: A Guide to Deep Learning in Finance

In this paper, we explore the application of machine learning to quantitative finance. Typical quant finance applications depend on vast amounts of economic data with complex relationships which are hard to grasp by humans or traditional quantitative finance approaches. Machine learning has tremendous potential here, producing results far superiour to traditional methods. In particular, machine learning can detect and exploit complex relationships in the data which are neglected by – or are even unknown to – standard quantitative finance methods today.

  • Introduction to machine learning
  • Application areas in quant finance
  • Overview of machine learning architectures
  • Superiour performance compared to traditional methods
  • Insights into learner design
  • Regressions, random forests, deep neural nets
  • Recurrent neural networks (RNNs), long short term memory models (LSTMs), deep believe networks (DBNs)

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About Xcelerit

Xcelerit is a leading provider of acceleration solutions for Quantitative Finance, engineering, and research. Our portfolio of solutions addresses a range of acceleration challenges from algorithmic optimisations to software acceleration. Xcelerit is the maker of the award-winning toolkit that allows domain-specialists to unlock the performance of accelerators (GPUs and Xeon Phi), and optimally deploy the advanced features of conventional multi-core processors. All of this is achieved with minor modifications to the existing source code.

Xcelerit extensive experience enables the firm to deliver a full solution from expert consultancy, bespoke development, training, and software acceleration. Our distinct competitive advantage derives from our unique combination of domain specialist knowledge and High Performance Computing expertise. This allows us to forge the most efficient solutions to better address our clients’ computational challenges.

Xcelerit has received recognition as a finalist in the Red Herring Europe Top 100 award, the Red Herring Top 100 Global award, and a two-time winner of HPC Wire’s “Best use of High Performance Computing in Financial Services” award. Our satisfied customers include the leading firms in investment banking, asset management, and insurance.