A Study on the Combination Strategy of Quantitative Investment Trend Tracking EMA Triple Averages

DOI

10.2991/978-94-6463-010-7_86How to use a DOI?

Keywords

Formatting; Style; Styling; Insert (Key Words) Python; Moving Average; EMA Triple Moving Average

Abstract

In this paper, we use python to code implementation of the classic moving average double mean model in quantitative investment strategies and improve the original model. The improved algorithm generates trading signals on specific breakouts of the EMA triple averages for automatic trading and sets floating stop-loss and take-profit points. The experimental results show that taking CSI 300 as an example, the moving average model can achieve 150% excess return in the decade from 2010 to 2020, and the improved triple-mean model can achieve 555% excess return, which has great investment potential in the experimental sense.

Copyright

© 2023 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  - Shiyi Jiang
AU  - Xianxin Liao
AU  - Yunshuai Tai
PY  - 2022
DA  - 2022/12/02
TI  - A Study on the Combination Strategy of Quantitative Investment Trend Tracking EMA Triple Averages
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 860
EP  - 868
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_86
DO  - 10.2991/978-94-6463-010-7_86
ID  - Jiang2022
ER  -