Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating the algorithmic trading of stock and crypto assets. Moreover, we demonstrate how our data science pipeline works with respect to four conventional algorithms: the moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage algorithms. Our study offers a systematic way to program, evaluate, and compare different trading strategies. Furthermore, we implement our algorithms through object-oriented programming in Python3, which serves as open-source software for future academic research and applications.
翻译：人造情报(AI)的最近进展使算法交易在金融中发挥了中心作用。然而,目前的研究和应用是互不相连的信息岛屿。我们建议为设计、编程和评估股票和加密资产的算法交易提供一个普遍适用的管道。此外,我们展示了我们的数据科学管道如何在四种常规算法方面发挥作用:移动式平均交叉、量加权平均价格、情绪分析和统计套利算法。我们的研究为规划、评估和比较不同的贸易战略提供了系统的方法。此外,我们通过在Python3中以对象为导向的程序制定我们的算法,作为未来学术研究和应用的开源软件。
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