Improving High-Frequency Trading – Traders Magazine
Improving high-frequency trade: Using artificial intelligence, machine learning and software-defined radios to break down technological barriers
By Tamara Moskalyuk, Marketing Director, Per Vices
High frequency trading (HFT) has become a race in the armament of data collection and execution the fastest. With the multimillion-dollar benefits of nanosecond differences, commercial companies benefit from latency optimizations in link latency and application / algorithm processing latency.
By subtracting the emotion driven by human emotions from the equation using algorithmic trading, firms can profit by exploiting market conditions that are indistinguishable to humans and for a very short period of time that would otherwise be physically impossible to perform. As the deals are happening at an incredible rate, proponents say HFT creates high liquidity and facilitates market fragmentation. Overcoming competition through this “superhuman” advantage, opponents say there is a high risk of exploitation and fraudulent activities such as fraud and high entry costs due to algorithmic development, hardware / network infrastructure costs and subscription fees for data emissions. Even through controversy, high-frequency trading remains here and continues to push the boundaries of the technologically possible through the integration of new systems, programs, hardware and software.
With so many low latency addicts, one of the latest technologies used to improve HFT is software-defined radio; an advanced, flexible, multi-channel transmission and reception solution that can be easily integrated into existing systems and is able to process data with the speeds and accuracy required for high-frequency trading. SDR consists of a radio interface and a digital backup with FPGA (field programmable gate array), which is a reprogrammable chip that allows processing of ultra low latency of complex algorithms. The parallel hardware architecture of the FPGA makes it a solution to reduce return delays in receiving data for exchange and execution of trade orders. The flexible and low group delay architecture allows SDRs to offer the lowest latency using HF, while combining point-to-point radio resources with FPGA on-board processing capabilities. Figure 1 shows the structure of the SDR. The best SDRs offer up to 16 channels and include radio circuits with very low latency and can be customized to compromise between latency and reliability according to user requirements.
[Figure 1: The composition of an SDR allows for low latency.]
To complement the processing power of SDR, machine learning (ML) and artificial intelligence (AI) are integrated into HFT algorithms and programs. By using support vector machines (SVMs) that analyze data for regression analysis and classification, you can have statistical data matching, linear programming, optimization, and decision theory. Algorithms can be retrained (without ML supervision) to learn and notice new patterns and trends and to automatically apply the best trading strategies. You can use it to predict the mood of people using natural language processing by reviewing it in market publications, financial magazines, opinions and reports. It is a powerful tool for capturing subtle trends and nuances through a sea of information and for fast and efficient analysis and impact on data.
However, there are trade-offs between hardware-managed and software designs. In order to obtain HFT market data in the first place, very efficient network hardware is needed. The high-frequency network architecture involves the use of ultra-fast network communications, high-performance switches and routers, specialized servers, and operating system optimization, such as kernel bypass. There may be significant costs associated with the supply, maintenance and updating of hardware, which can create barriers to entry for companies wishing to enter the market. In terms of software, FPGAs are flexible and can be reprogrammed to do things like backtest-testing new algorithms for trading historical market data to determine if the algorithm would make the company more profitable and change the trade-offs between latency and reliability for transactions. They do not have a fixed architecture that relies on operating system overheads, interfaces, interrupts, and generally the consistency found in typical processors. In terms of software, the Big O notation is used to sort algorithms, understand their performance, and choose the right algorithm for their situation based on datasets and hardware – or just how a particular algorithm will perform if the input size it grows to infinity. This is not enough here, as each operation / function in the algorithm is added to create greater computational complexity, so even if the algorithm is more predictable using several market data analysis strategies, it will perform one with fewer analysis strategies due to execution time speed. The predictability of opportunities means nothing if you can’t make a quick deal.
Optimizing non-limiting software and hardware is a way to overcome inefficiencies and blocks that would hinder the successful execution of a trade and mitigate errors. A combination of software-defined radios, machine learning and artificial intelligence are the way to gain an advantage in trade and further drive technology and financial markets into the future.
Tamara Moskalyuk holds dual degrees in finance and economics from McMaster University and worked in banking and venture capital before migrating her career to technology startups. She is currently Marketing Director at Per Vices, a leading manufacturer of high-performance radios and low-latency software.