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To optimize performance in algorithmic trading, it is important to choose appropriate data ___ and algorithms. This ensures efficient execution and improves the overall speed of the trading strategies. By using efficient data structures and optimizing algorithms, you can reduce time complexity and eliminate unnecessary computations. Additionally, parallelization techniques such as multiprocessing or multithreading can be utilized to perform computations in parallel and take advantage of multi-core processors. Regularly profiling and benchmarking the code helps identify performance bottlenecks and areas for improvement. Proper memory management techniques, such as smart pointers, minimize memory leaks and improve memory utilization. By optimizing code for readability, simplicity, and efficiency, you further enhance the performance of trading strategies. Overall, optimization and performance improvements involve choosing efficient data structures and algorithms, utilizing parallelization techniques, profiling and benchmarking, and optimizing code for optimal execution speed.