Design and Implementation of Machine Learning Based Multi Factor Quantitative Trading Strategy

DOI

10.2991/978-94-6463-052-7_111How to use a DOI?

Keywords

Quantitative trading; Machine learning; Stock indicators; Strategy optimization

Abstract

Quantitative trading is a trading method that combines finance, mathematics, and computer science to achieve a goal. This method can help investors to filter out negative emotional influences effectively so that it is becoming more and more widely used in the Chinese stock market. Traditional quantitative trading strategies predict the trend of stock prices by analyzing fundamental indicators or technical indicators and building formulas quantitatively. However, this paper will use the emerging machine learning technologies to analyze the influence of multiple factors which impacts the stock price, then predict the return of particular stocks and stress the trading strategy.

This research’s main work consists of obtaining financial data from third-party platforms and defining indicators; using Support Vector Machine, Random Forest, and XGBoost machine learning algorithms to build prediction models to predict which stock can bring excess return; generating the stock holding list; and designing the trading strategy accordingly. The outcomes are a multiple factors quantitative trading strategy based on machine learning which brings a steady excess return ratio while bearing low risk. The research achievement solves some problems in the current quantitative trading strategy: the selection of indicators is biased, a single machine learning model is not effective through a long period of time, the process of strategy research is not convenient enough.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yu Zhang
PY  - 2022
DA  - 2022/12/27
TI  - Design and Implementation of Machine Learning Based Multi Factor Quantitative Trading Strategy
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 977
EP  - 984
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_111
DO  - 10.2991/978-94-6463-052-7_111
ID  - Zhang2022
ER  -