{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:07:31Z","timestamp":1777705651061,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:p>Trend following strategies have a wide-ranging role in quantitative trading fields, which can capture important unilateral market trends for large gains, while this is vulnerable to losses in the period of consolidation. In this paper, we explored the trend trading system in the Chinese futures market based on machine learning techniques and statistical methods. This research utilized the Long-Short-Term Memory network to extract features of time series then predicted the price movements by Machine Learning classifiers. Moreover, based on rebar futures data, the results reveal that the annualized return improved from 6.39% to 15.68% after the trading signals generated in the trading strategy were filtered using the XGBoost model. Also, futures on gold and soybean were used to further test the integrated strategy and the results of the experiment show the effectiveness of the model in filtering false trading signals.<\/jats:p>","DOI":"10.3233\/jifs-223873","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T12:22:24Z","timestamp":1673612544000},"page":"6131-6149","source":"Crossref","is-referenced-by-count":1,"title":["Trade filtering method for trend following strategy based on LSTM-extracted feature and machine learning"],"prefix":"10.1177","volume":"44","author":[{"given":"Jun","family":"Liang","sequence":"first","affiliation":[{"name":"School of Mathematics and Informatics, South China Agricultural Univeristy, Guangzhou, Guangdong, People\u2019s Republic of China"},{"name":"Xinyan IT Co., Ltd., Guangzhou, Guangdong, People\u2019s Republic of China"}]},{"given":"Keyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Extra-IT Co., Ltd., Guangzhou, Guangdong, People\u2019s Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4138-2514","authenticated-orcid":false,"given":"Shaojian","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Informatics, South China Agricultural Univeristy, Guangzhou, Guangdong, People\u2019s Republic of China"}]},{"given":"Hai","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Mathematics and Informatics, South China Agricultural Univeristy, Guangzhou, Guangdong, People\u2019s Republic of China"}]},{"given":"Keng","family":"Lian","sequence":"additional","affiliation":[{"name":"Extra-IT Co., Ltd., Guangzhou, Guangdong, People\u2019s Republic of China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-223873_ref1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.eswa.2016.01.018","article-title":"Evaluating machine learning classification for financial trading: An empirical approach","volume":"54","author":"Gerlein","year":"2016","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.3233\/JIFS-223873_ref2","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s11277-017-5224-x","article-title":"Development and application of artificial neural network","volume":"102","author":"Wu","year":"2018","journal-title":"Wireless Personal Communications"},{"issue":"0","key":"10.3233\/JIFS-223873_ref3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1002\/9781119509875.ch5","article-title":"Augmenting Dental Care: A Current Perspective","volume":"4","author":"Nayyar","year":"2018","journal-title":"Emerging Technologies for Health and Medicine: Virtual Reality, Augmented Reality, Artificial Intelligence, Internet of Things, Robotics, Industry"},{"issue":"2","key":"10.3233\/JIFS-223873_ref6","first-page":"112","article-title":"Quantitative Trading Strategies of Shanghai and Shenzhen 300 Index Furtures Based on SVM","volume":"37","author":"Zhang","year":"2017","journal-title":"Mathematical Theory and Applications"},{"issue":"2","key":"10.3233\/JIFS-223873_ref8","first-page":"125","article-title":"An arbitrage strategy model for ferrous metal futures based on LSTM neural network","volume":"48","author":"Long","year":"2018","journal-title":"Journal of University of Science and Technology of China"},{"issue":"2","key":"10.3233\/JIFS-223873_ref9","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.3233\/JIFS-179505","article-title":"Quantitative trading system based on machine learning in Chinese financial market","volume":"38","author":"Zheng","year":"2020","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-223873_ref10","doi-asserted-by":"crossref","first-page":"153083","DOI":"10.1109\/ACCESS.2021.3127570","article-title":"Can Deep Learning Improve Technical Analysis of Forex Data to Predict Future Price Movements","volume":"9","author":"Fisichella","year":"2021","journal-title":"IEEE Access"},{"issue":"04","key":"10.3233\/JIFS-223873_ref17","doi-asserted-by":"crossref","first-page":"1850037","DOI":"10.1142\/S2424786318500378","article-title":"Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals","volume":"5","author":"Giribone","year":"2018","journal-title":"International Journal of Financial Engineering"},{"issue":"1","key":"10.3233\/JIFS-223873_ref18","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.1080\/1331677X.2021.1974921","article-title":"Are technical indicators helpful to investors in china\u2019s stock market? 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