论文标题
用于算法交易的数据科学管道：财务和加密经济学应用的比较研究
A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics
论文作者
论文摘要
人工智能（AI）的最新进展使算法交易在金融中起着核心作用。但是，当前的研究和应用是断开的信息岛。我们提出了一条通常适用的管道，用于设计，编程和评估库存和加密资产的算法交易。此外，我们演示了我们的数据科学管道如何相对于四种常规算法：移动平均值交叉，体积加权的平均价格，情感分析和统计套利算法。我们的研究提供了一种系统的方式来编程，评估和比较不同的交易策略。此外，我们通过Python3中的面向对象的编程实施算法，该编程是用于未来学术研究和应用的开源软件。
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.
