Enhancing High-Frequency Trading Strategies with Edge Computing and Deep Learning

As financial markets continue to evolve, high-frequency trading (HFT) has become an important force in the market, which involves the execution of large numbers of orders at extremely fast speeds. These strategies rely on the ability to process large amounts of real-time data in order to make split-second decisions using small price differences between various trading venues. However, the efficiency and effectiveness of HFT strategies are largely affected by delays in data processing and order execution. In order to overcome data processing and execution delays, edge computing technology has gradually emerged. Edge computing allows real-time data processing closer to the data source, reducing data transfer times and improving response speed. In high-frequency trading, this means faster decision making and order execution, allowing traders to better take advantage of price movements in the market. This paper introduces data privacy and security through edge computing, as data does not have to travel long distances over the network, but can be processed locally. This is particularly important for high-frequency trading, where leakage or tampering of trading data can lead to significant risks and losses. In conclusion, the application of edge computing technology in high-frequency trading is expected to improve the efficiency and reliability of strategies and enhance traders' competitiveness in a rapidly changing market environment. This development reflects the ongoing exploration of emerging technologies in the financial sector to improve the competitive advantage of trading strategies and highlights the potential applications of edge computing in financial markets.