Back
Quside Blog
Quside RPU for High Frequency Trading in the Cloud

HFT strategies have evolved considerably over the last two decades. Companies are continually looking to improve their high-performance trading capabilities in search for improved capital gains and better risk management. Emerging technologies are clearly top of mind for financial institutions employing HFT techniques, and in 2024, developments in random number generation and the speed thereof has emerged as a central theme to better account for market randomness in their trading operations and portfolio optimisation.

18 de marzo de 2024
5 min read

High-frequency trading is a subset of algorithmic trading that focuses on executing trades at incredibly high speeds, and has become a topic of great interest in the financial industry. This type of trading involves the use of powerful computers and algorithms and is a major focus of research and development in the financial industry. Here, Quside RPUs emerge as a pivotal tool, notably enhancing simulation speeds and algorithm efficiency through its stochastic optimization solutions.

Evolution and Current Trends in HFT

Although the practice traces its roots back to the 1990s when exchanges developed electronic trading platforms, high-frequency trading really came into prominence in the 2000s and 2010s, as firms and traders began using computer algorithms for high-speed, high-volume trades over increasingly shorter periods. 

The switch to decimal pricing in 2001 helped the HFT trend, while various regulatory changes also assisted its utilisation, such as the SEC’s Regulation National Market System (NMS) in 2005. But overall, advances in computing technology have been primary drivers of HFT, with machine learning, AI and quantum-optimised random number generation pushing further development in the industry.

Technologies Used in High-Frequency Trading

  • High-speed data feeds: Financial institutions employ speedy, real-time data streaming to provide up-to-the-millisecond market information.
  • Trading algorithms: Complex algorithms which analyse market data to identify trading opportunities and smarter order routing.
  • Hardware acceleration: Financial institutions use field-programmable gate arrays (FPGAs) and GPUs to speed up processing and decision-making.
  • Risk management systems: Due to the speed and volume of trading, financial institutions critically rely on robust, real-time monitoring to control exposure to market risks fueled by random number generators (RNGs).
  • Machine learning and AI: Complex pattern recognition, predictive analytics and Natural Language Processing are explored to measure trends and market sentiment and to optimise trading strategies in general.

Core Principles of High-Frequency Trading

High-frequency trading’s competitive advantage stems from several approaches to exploiting market conditions. HFT sifts through vast amounts of data such as order book dynamics, price movements and liquidity patterns, and employing fast algorithmic trading based on that data synthesis means potential greater capital gains. In this way, HFT algorithms search for statistical patterns and signals in market data such as short-term trends and correlations that may be predictive of upcoming price movements.

Thus, micro- and even nanoseconds are of the essence where financial institutions conduct high-volume, algorithmic trading. High-frequency algorithm trading crucially depends on being most up-to-date on new technologies and computational models, from the simple, such as moving averages, to the most complex ones, such as Markov Chain Monte Carlo methods.

The Role of Technology in HFT

The need for speed means that financial firms see latency as a paramount concern. Delays in receiving market data are one form of latency, as are execution latency, which accounts for the time between the decision to make a trade and the actual execution. Finally, network latency deals with the lag time in delivering information from a firm’s servers to those of the exchange. 

Using high-speed connectivity, fast data feeds and high-performance servers helps with latency, through co-location. In other words, situating one’s operations physically close to an exchange server  decreases latency and trading speed from this proximity. Firms also use computers with parallel processing and specialised hardware and software to ramp up computational power and decision-making time.

Today’s HFT technology is on algorithmic optimisation to improve code and streamline the decision-making process for micro- and nano-second gains, used for gauging potential loss in value and managing risk exposure. Financial firms use both deterministic and stochastic processes, such as moving averages and Monte Carlo simulations respectively, in an attempt to better emulate market dynamics and identify trading opportunities.

Stochastic processes are mathematical models that describe the evolution of a system over time in a random or probabilistic manner. These processes incorporate uncertainty, representing how variables change over time due to random factors. In high-frequency trading, stochastic algorithms simulate price movements, assess market volatility, and generate realistic scenarios for testing trading strategies. However, stochastic algorithms aren’t limited to price-related workloads; they can also assume the heavy lifting of the portfolio optimization workloads that underlie the decision-making process of even the best-performant trading strategies. 

Advanced Strategies in High-Frequency Trading

Latency Reduction Techniques

Speed is definitely of the essence in high-frequency trading, and thus, financial institutions employ a variety of techniques to deal with latency. Co-location is pivotal in cutting down transmission time and overall latency, as is the use of high-speed, dedicated network connections.

In terms of processing, algorithms break down complex computation into smaller, independent tasks that can be executed in parallel, while on the hardware side, companies use customised, programmable field-programmable gate arrays (FPGAs) rather than off-the-shelf processors to speed up computation and optimise their trading strategies.

Find out more about stochastic processes in portfolio optimisation and the pros and cons of PRNGs compared to QRNGs in our free report, “Ensuring Speedy Accuracy in Financial Risk Assessment”.

Risk Management in High-Speed Trading Environments

Quantifying potential losses is key to risk management, and for HFT operations this process entails a variety of analytical techniques particular to the high-speed, high-volume trading environment.

The result is an approximation of randomness but one which has its drawbacks in the world of trading and finance. Namely, PRNGs can be taxing in their use of computational power and they can potentially introduce biases into HFT systems. Neither are their outputs truly random, as they result from algorithmic-deterministic processes. Thus, PRNGs can present challenges for firms looking to strengthen the risk management practices.

Successes and Failures in HFT

HFTs now play a significant role in financial markets, acting as ‘market makers’ by providing liquidity, but also, through their pursuit of speed and efficiency, these financial institutions have been central to the evolution of trading technology through their market-making strategies and HFT trading software. 

The ups and downs of HFTs over the years have been well documented, with the “Flash Crash” of May 2010 and its associations with HFTs often being noted.

In that event, the Dow Jones Industrial Average dropped almost 1,000 points in a matter of minutes, only to recover most of those losses by the end of the trading day. Regulators later identified automated HFT strategies as contributing factors to the crash. They said HFT algorithms responded to an initial massive sell order in the E-Mini S&P 500 futures market by triggering a high volume of sell orders across various markets.

The 2010 Flash Crash prompted significant changes, including in the practice of HFT. Companies sought to bolster their risk controls and circuit breakers to prevent extreme market disruptions by temporarily halting trading in the event of rapid and severe price movements. Firms have also focused more stringently on how their algorithms respond to unexpected market events and put more emphasis on stress testing to reveal performance under adverse conditions. 

Quside’s dedicated and physically deployable random number generator, the RPU One produces 10x speed in high-frequency trading (HFT) compared to classical methods potentially increasing trading gains.

The Future of High-Frequency Trading

Algorithmic trading has revolutionised the financial industry in recent years. This type of trading involves the use of complex computer programs to analyse market data and execute trades with minimal human intervention. High-frequency trading (HFT) is a subset of algorithmic trading that focuses on executing trades at incredibly high speeds, sometimes measured in milliseconds, and has become a topic of great interest in the financial industry in recent years. This type of trading involves the use of powerful computers and algorithms and is a major focus of research and development in the financial industry, with many firms investing heavily in the technology and expertise needed to stay ahead of the competition.

High-speed trading strategies use computerised quantitative models that identify which type of financial instrument to buy or sell and the trades’ quantity, price, timing, and location. This can result in significant profits and improved investment performance for those who use HFT effectively. By staying ahead of the curve with the latest HFT technology and expertise, financial institutions and investors can position themselves for success in today’s dynamic financial landscape.

Dynamic portfolio hedging is a powerful and effective strategy for high-frequency trading that can extract profits from all the high-frequency data generated by the markets. Dynamic hedging can minimise risk and maximise returns in even the most fast-paced and volatile markets by constantly adjusting portfolio allocations based on real-time market data. With the ability to quickly adapt to changing market conditions and identify new opportunities for profit, dynamic portfolio hedging is an essential tool for any high-frequency trader.

CDPP strategy for HFT with Quside’s RPU

We have studied the sequential application of the Correlation Diversified Passive Portfolio (CDPP) strategy [1] as a fast and efficient method to improve a given investment following market evolution. This strategy diversifies the total investment by grouping the stocks that correlate in returns by an optimization process, to then leverage the investment decreasing the total risk. When the leverage is done at high speeds, low latency optimization allows HFT traders to correct the investment’s weights at small times to continuously be in an advantageous position. To overcome the optimization challenge we use a Stochastic Optimization approach in tandem with our Quside’s Randomness Processing Unit (RPU).

CDPP strategy improves initial investment

CDPP strategy makes the portfolio’s total risk lower and, in general, increases the expected returns. This behaviour makes the new investment more profitable, demonstrating the strategy is worthy of being applied. The table below summarises the averaged percentual change of risk and returns due to the application of the CDPP strategy to multiple initial investments, back tested in S&P 500 stocks with historic data since 2013, and the percentual change in the merit figure Sharpe Ratio, proving that CDPP strategy is useful in today’s market.

Why Stochastic Optimization?

By searching for the best possible combination within a large, complex solution space, stochastic optimization algorithms can approach the best solution at lightning-fast speeds, overcoming deterministic algorithms and avoiding non-optimal solutions that usually appear due to local minima in the evaluation of functions.

Why Quside’s RPU?

Quside’s RPU is a perfect tool for stochastic optimization problems where a high rate of random numbers is needed. Randomness generation is a bottleneck when the optimization is based on random numbers, then the use of a purpose-specific device allows to overcome this pain point, improving simulation speeds of the optimization to increase the algorithm’s efficiency.

To implement the CDPP strategy, the complete program includes three main steps: processing market data, finding the best permutation using a Population Annealing permutation specialised algorithm, and outputting the corrected investment weights.

With the implementation of CDPP strategy using the RPU Cloud available in AWS marketplace we obtain outstanding execution times, taking less than a second for problems up to 100 assets. These execution times are up to X20 smaller than previously reported times in other works using the same strategy or an alternative diversification scheme.

Conclusion

The exploration of cutting-edge High-Frequency Trading (HFT) strategies is the quest for optimal speed in financial trading. Through algorithmic precision and the integration of advanced technologies like Quside RPUs, the financial industry is adapting to manage market volatility more adeptly. Stochastic optimisation, as a beacon of innovation, empowers firms by enhancing the speed and accuracy of simulations, pivotal for risk management and portfolio optimisation. Firms leveraging these advancements in 2024 are strategically positioned to navigate the complexities of the financial markets, marking a significant evolution in high-frequency trading efficiency and speed.