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Recent advances in Artificial Intelligence (AI) have made algorithmic tradingplay a central role in finance. However, current research and applications aredisconnected information islands. We propose a generally applicable pipelinefor designing, programming, and evaluating the algorithmic trading of stock andcrypto assets. Moreover, we demonstrate how our data science pipeline workswith respect to four conventional algorithms: the moving average crossover,volume-weighted average price, sentiment analysis, and statistical arbitragealgorithms. Our study offers a systematic way to program, evaluate, and comparedifferent trading strategies. Furthermore, we implement our algorithms throughobject-oriented programming in Python3, which serves as open-source softwarefor future academic research and applications.

A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics