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

Andrea Frattini, Ilaria Bianchini, Alessio Garzonio and Lorenzo Mercuri ()
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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

Risks, 2022, vol. 10, issue 12, 1-24

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.

Keywords: trading strategy; XGBoost; LightGBM (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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