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

Author

Listed:
  • Luyao Zhang
  • Tianyu Wu
  • Saad Lahrichi
  • Carlos-Gustavo Salas-Flores
  • Jiayi Li
Abstract
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.

Suggested Citation

  • Luyao Zhang & Tianyu Wu & Saad Lahrichi & Carlos-Gustavo Salas-Flores & Jiayi Li, 2022. "A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics," Papers 2206.14932, arXiv.org.
    • Handle: RePEc:arx:papers:2206.14932
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    File URL: http://arxiv.org/pdf/2206.14932
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    References listed on IDEAS

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    1. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    2. Cesari, Riccardo & Marzo, Massimiliano & Zagaglia, Paolo, 2012. "Effective Trade Execution," MPRA Paper 39619, University Library of Munich, Germany.
    3. Luyao Zhang & Yulin Liu, 2021. "Optimal Algorithmic Monetary Policy," Papers 2104.07888, arXiv.org, revised Oct 2021.
    4. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    5. repec:bla:jfinan:v:43:y:1988:i:1:p:97-112 is not listed on IDEAS
    6. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    7. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    8. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    9. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    10. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
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    Cited by:

    1. Yu, Haoyang & Sun, Yutong & Liu, Yulin & Zhang, Luyao, 2023. "Bitcoin Gold, Litecoin Silver: An Introduction to Cryptocurrency’s Valuation and Trading Strategy," OSF Preprints t2fku, Center for Open Science.
    2. Jiasheng Zhu & Luyao Zhang, 2023. "Educational Game on Cryptocurrency Investment: Using Microeconomic Decision Making to Understand Macroeconomics Principles," Papers 2301.10541, arXiv.org, revised Feb 2023.
    3. Zhang, Luyao & Sun, Yutong & Quan, Yutong & Cao, Jiaxun & Tong, Xin, 2023. "On the Mechanics of NFT Valuation: AI Ethics and Social Media," OSF Preprints qwpdx, Center for Open Science.

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