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Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data

Author

Listed:
  • Andrea Frattini

    (Finscience, 20121 Milan, Italy)

  • Ilaria Bianchini

    (Finscience, 20121 Milan, Italy)

  • Alessio Garzonio

    (Finscience, 20121 Milan, Italy)

  • Lorenzo Mercuri

    (Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy)

Abstract

The aim of this paper is to introduce a two-step trading algorithm, named TI-SiSS . In the first step, using some technical analysis indicators and the two NLP-based metrics (namely Sentiment and Popularity ) provided by FinScience and based on relevant news spread on social media, we construct a new index, named Trend Indicator . We exploit two well-known supervised machine learning methods for the newly introduced index: Extreme Gradient Boosting and Light Gradient Boosting Machine . The Trend Indicator , computed for each stock in our dataset, is able to distinguish three trend directions (upward/neutral/downward). Combining the Trend Indicator with other technical analysis indexes, we determine automated rules for buy/sell signals. We test our procedure on a dataset composed of 527 stocks belonging to American and European markets adequately discussed in the news.

Suggested Citation

  • Andrea Frattini & Ilaria Bianchini & Alessio Garzonio & Lorenzo Mercuri, 2022. "Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data," Risks, MDPI, vol. 10(12), pages 1-24, November.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:12:p:225-:d:983774
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    References listed on IDEAS

    as
    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
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    Cited by:

    1. Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.

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