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The potential for real-time testing of high-frequency trading strategies through a developed tool during volatile market conditions

Vaitonis Mantas, Korovkinas Konstantinas

Applied Computer Science

2023

Vol. 19, no 2

63--81

This study presents a method for testing high-frequency trading (HFT) for algorithms on GPUs using kernel parallelization, code vectorization, and multidimensional matrices. The research evaluates HFT strategies within algorithmic cryptocurrency trading in volatile market conditions, particularly during the COVID-19 pandemic. The study's objective is to provide an efficient and comprehensive approach to assessing the efficiency and profitability of HFT strategies. The results show that the method effectively evaluates the efficiency and profitability of HFT strategies, as demonstrated by the Sharp ratio of 2.29 and the Sortino ratio of 2.88. The authors suggest that further study on HFT testing methods could be conducted using a tool that directly connects to electronic marketplaces, enabling real-time receipt of high-frequency trading data and simulation of trade decisions. Finally, the study introduces a novel method for testing HFT algorithms on GPUs, offering promising results in assessing the efficiency and profitability of HFT strategies during volatile market conditions.

Bayesian multidimensional-matrix polynomial empirical regression

Mukha Vladimir S.

Control and Cybernetics

2020

Vol. 49, No. 3

291--314

The problem of parameter estimation for the polynomial in the input variables regression function is formulated and solved. The input and output variables of the regression function are multidimensional matrices. The parameters of the regression function are assumed to be random independent multidimensional matrices with Gaussian distribution and known mean value and variance matrices. The solution to this problem is a multidimensional-matrix system of the linear algebraic equations in multidimensional-matrix unknown regression function parameters. We consider the particular cases of constant, affine and quadratic regression function, for which we have obtained formulas for parameter calculation. Computer simulation of the quadratic regression function is performed for the two-dimensional matrix input and output variables.