Making Smarter, Faster Trades with AI and HPC

Financial firms draw on the power of artificial intelligence and high-performance computing to make smarter, faster trades.

In the new digital economy, data and the IT solutions used to harness it are often a financial services company’s primary source of competitive advantage. This is especially true for algorithmic trading, a highly automated investment process where humans train powerful software applications to select investments and implement trades automatically.

The ultimate evolution of algorithmic trading is high‑frequency trading, where the algorithms make split‑second trading decisions designed to maximize financial returns. Automating trading and removing humans from the hands-on process has several advantages, including reduced costs, greater speed and improved accuracy.

High‑frequency trading platforms deliver competitive advantage through their ability to place thousands of trades before the market can react. Given this new reality for the industry, high‑frequency trading has led to competition in computational speed, automated decision making, and even connectivity to the execution venue to shave off microseconds and beat other traders to opportunities.

In light of these compelling business benefits, it’s no surprise that algorithmic trading is becoming more the norm than the exception for financial trading firms.

Developing the algorithms

To develop trading algorithms, financial firms typically leverage a proprietary mix of data science, statistics, risk analysis capabilities and DevOps processes. Then the algorithm is tested against historical data and refined until it produces the desired profits. The algorithm is then put into production, making trades in real time on behalf of the firm. The real‑world yields produced by the algorithm generate even more data, which is used to continually train the algorithm and improve its performance.

This training feedback loop is a data‑intensive process that often includes machine learning, a subset of artificial intelligence. Developers leverage machine learning techniques to improve predictive capabilities, using deep neural networks to find trends that trigger buy or sell decisions.

This is a never-ending cyclical process. Financial trading firms are continually developing, implementing and perfecting algorithmic trading strategies to stay a step ahead of the competition. This puts significant stress on infrastructure because the algorithm must continuously adapt to new input to remain relevant. As such, the back‑end infrastructure must make accommodations for live‑data feeds and the quick processing of large amounts of data. Databases, in turn, must be able to feed the compute engine in real or near‑real time to update the algorithm.

The data‑intensive training requirements and the need for high speed and low latency mean that these sophisticated algorithms are typically trained and run on high performance computing systems to provide the speed and accuracy required to dominate the market. An HPC system that supports algorithmic trading should be able to accommodate current workloads seamlessly and provide the flexibility, performance and scaling needed to continually train and update algorithms to help firms stay ahead of the market.

The power of partnerships

The realities of today’s algorithmic trading processes, including model development, dictate that financial firms form close partnerships with technology providers who have the breadth of products and the technical expertise to build systems that span from the edge to the cloud to the core data center.

Working together, Dell Technologies and NVIDIA provide integrated and successful builds for GPU‑enabled solutions for the financial services industry. These jointly engineered solutions leverage NVIDIA GPUs, which are the accelerator of choice for algorithmic trading since they have obvious logic for parallelizing computing streams with straightforward code development and mature numerical libraries.

For a quick route forward, NVIDIA GPUs are available in solutions based on the Dell EMC HPC Ready Solution for AI and Data Analytics. This system provides high-level guidance for building a converged architecture that allows organizations to run HPC, AI, and data analytics workloads on a single infrastructure.

And for firms that are looking to gain experience with new AI and HPC solutions, Dell Technologies offers the resources of its HPC & AI Innovation Lab in Austin, Texas. This 13,000 square foot data center houses thousands of servers, a TOP500 cluster, and a wide range of storage and network systems. The lab offers products from NVIDIA, Intel, AMD and other technology leaders to allow IT teams to test applications and gain hands-on experience with the latest and greatest technologies.

To learn more

For a deeper dive into this topic, see the Dell Technologies Algorithmic Trading HPC & AI Reference Guide. This guide provides an in‑depth technical analysis of a range of solutions for financial trading firms, including a deep dive into the options available for specific use cases, workloads and emerging trends. It also includes considerations for software, services and infrastructure design with complete architectural design examples.

Gabriel Pirastru

About the Author: Gabriel Pirastru

Gabriel is passionate about Cloud, AI and HPC. His interests focus on technology’s ability to solve real-life problems for society and businesses alike. Gabriel serves on the data-centric workloads team and acts as PreSales Engineer for the EMEA region and as the expert for the Financial Services Industry.