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Abstract
Given the increase in the popularity of algorithmic trading resulting from an increase in market participants, more considerations are now required to prototype a profitable trading strategy. Trading strategies, which require optimization of parameters based on linear or nonlinear relationships, cause an increase in complexity, which in turn increases computational run time. We find that C provides the best performance for prototyping quantitative trading strategies; however, it is the most time-consuming to implement. Among the languages that allow for faster development times, the difference between Cython and Julia is relatively small, so choice between them comes down to user preference and other factors. We find Julia to be the standout programming language due to its simplicity and high performance.