2022/06/29 by Luyao Zhang, Tianyu Wu, Zhang, Luyao +7
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #General Economics (econ.GN) #Human-Computer Interaction (cs.HC) #Stock Market Forecasting Methods
paper · doi:10.48550/arxiv.2206.14932
openalex publication_date 2022/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.