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

被引：4

作者：

Frattini, Andrea ^{[1
]}

Bianchini, Ilaria ^{[1
]}

Garzonio, Alessio ^{[1
]}

Mercuri, Lorenzo ^{[2
]}

机构：

[1] Finscience, I-20121 Milan, Italy

[2] Univ Milan, Dept Econ Management & Quantitat Methods, I-20122 Milan, Italy

来源：

RISKS | 2022年 / 10卷 / 12期

关键词：

trading strategy; XGBoost; LightGBM;

D O I：

10.3390/risks10120225

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页数：24

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