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.

Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data / A. Frattini, I. Bianchini, A. Garzonio, L. Mercuri. - In: RISKS. - ISSN 2227-9091. - 10:12(2022), pp. 225.1-225.24. [10.3390/risks10120225]

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

L. Mercuri
Ultimo
2022

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.
trading strategy; XGBoost; LightGBM
Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie
2022
https://www.mdpi.com/2227-9091/10/12/225
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/955580
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