This is the world’s first attempt to verify the effectiveness of quasi-quantum computers when they are applied to the HFT strategies in actual financial trading systems.

Realising financial markets in which all investors can trade at fair values requires improvements in market efficiency and liquidity. High frequency traders, who continue to become more important as trading entities in financial markets, are characterised by repeating buying and selling of financial products at high-speed and high-frequency using trading algorithms that enable them to make high-speed decisions. It is believed that such HFT activities contribute to improving market efficiency and liquidity. 

For example, in the stock market, such factors as excessive responses to the news by the media can cause prices of individual stocks to deviate significantly from their fair values (“mispricing”), which may cause investors to trade at unfavorable prices. High frequency traders have the ability to quickly eliminate these mispricings and improve market efficiency and liquidity by executing transactions that match overvalued/undervalued financial products even when markets are in such volatile situations.

Conventional HFTs tend to focus on self-evident arbitrage opportunities where high speed is the main source of competitiveness. An integration with mathematical models that require high-precision wide-area search using sophisticated evaluation functions to detect mispricings has not necessarily progressed over the years. On the other hand, in recent years, there has been rapid progress in the development of quasi-quantum computers that can solve large-scale combinatorial optimisation problems at high speeds and low latency, which was previously difficult.

By combining this quasi-quantum technology with conventional HFT technology, it is now possible to search a wider area, which includes statistical arbitration opportunities that have not been targeted before, with a sufficient level of low latency against market price fluctuations. Establishing trading systems that can quickly detect ever-untargeted mispricings and eliminate them is expected to further improve market efficiency and liquidity. The arrival of quasi-quantum technology envisions the new concept of algorithmic trading, but due to the nature of finance and especially of HFT, it needs to be validated in the actual market first. (Fonte:

Grand View Research

The global high-frequency trading server market size is expected to reach USD 501.0 million by 2028, registering a CAGR of 3.5% from 2020 to 2028, according to a study conducted by Grand View Research, Inc. In the trading industry, servers play a pivotal role in reducing tick-to-trade delays; this is driving the product demand. Furthermore, with improvements in server technology over the years, high-frequency trading (HFT) servers, in particular, have witnessed several advancements in terms of processor technology, which is creating opportunities for industry growth. These advancements are fueled by the need to track stock markets where every nanosecond counts and are expected to become an indispensable element of the finance sector over the coming years.

Increased adoption of algorithmic trading in global financial markets has encouraged companies in the financial sector to opt for high-speed transactions. Technological advancements, such as integrating AI and social media feeds with electronic trading, are expected to drive the demand for high-speed trading transactions. Thus, the demand for low-latency trading servers has increased tremendously among the derivatives, quantitative, and proprietary trading firms. Asia Pacific has become one of the new revenue pockets for market growth.

Favorable government regulations for the implementation of automated trading and new investment law in China have emerged as potential revenue streams for the vendors. Furthermore, the surge in adoption of Artificial Intelligence (AI) and machine learning technology by small-sized hedge fund firms, is anticipated to drive the overall product demand over the forecast period. A competitive edge is now determined by nanoseconds and microseconds. Speed is important to market participants, such as large investment banks, hedge funds, and other financial companies, because it impacts profitability, and hence the deployment of HFT servers is of paramount importance.

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