n a high$-$frequency trading $($HFT$)$ system, the main algorithm responsible for making trading decisions must process a vast amount of data in real$-$time to identify profitable trading opportunities. The performance of this algorithm is critical, as even milliseconds of delay can result in significant financial loss. The development team is tasked with optimizing this algorithm to ensure maximum efficiency and speed.Which of the following approaches best exemplifies the application of advanced computational thinking principles for optimizing the algorithm's performance?Pattern Recognition and Algorithm Design: Analyzing historical performance data to identify patterns in successful trades $($Pattern Recognition$)$ and redesigning the algorithm to prioritize these patterns, thereby reducing the computational complexity and improving execution speed $($Algorithm Design$).$Hardware Upgrade: Investing in faster processors and more RAM for the servers running the HFT system, assuming that increased hardware capacity will linearly improve the algorithm's performance.Parallel Processing: Splitting the data processing task into smaller, independent tasks that can be executed simultaneously on multiple processors, without specifically analyzing the algorithm's decision$-$making process or data patterns.Code Refactoring: Reviewing and rewriting the existing code to improve its readability and structure without changing its external behavior, focusing solely on coding best practices.