THE POTENTIAL FOR REAL-TIME TESTING OF HIGH FREQUENCY TRADING STRATEGIES THROUGH A DEVELOPED TOOL DURING VOLATILE MARKET CONDITIONS
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In this study, the authors propose a method for testing high frequency trading (HFT) algorithms on the GPU using kernel parallelization, code vectorization, and multidimensional matrices. The method is applied to various algorithmic trading methods on cryptocurrencies during volatile market conditions, specifically during the COVID-19 pandemic. The results show that the method is effective in evaluating the efficiency and profitability of HFT strategies, as demonstrated Sharp ratio of 2.29 and 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.
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