High-frequency trading (HFT) is a subset of algorithmic trading that focuses on executing trades at incredibly high speeds, that 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.
Algorithmic trading has revolutionized 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 computerized 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 minimize risk and maximize 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.
We have studied the sequential application of the Correlation Diversified Passive Portfolio (CDPP) strategy  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).
1.- 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 summarizes the averaged percentual change of risk and returns due to the application of the CDPP strategy to multiple initial investments, back tested in S&P500 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.
2.- 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.
3.- 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 specialized 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 .
 Yutaka Sakurai, Yusuke Yuki, Ryota Katsuki, Takashi Yazane, and Fumio Ishizaki. Correlation diversified passive portfolio strategy based on permutation of assets. SSRN Electronic Journal, November 2019
 K Hukushima, Y Iba, and Y Iba. Population annealing and its application to a spin glass. AIP Conference Proceedings, 690:200–206, 11 2003
 Francesco Cesarone, Andrea Scozzari, and Fabio Tardella. An optimization–diversification approach to portfolio selection. Journal of Global Optimization, 76:245–265, 2 2020.