{"title":"商品期货价格预测与交易策略——一种信号噪声差分方法","authors":"Jinhao Zheng, Shoukang Peng","doi":"10.1109/ISCC-C.2013.60","DOIUrl":null,"url":null,"abstract":"This paper introduces the signal noise difference method and applies this method into the commodity futures price prediction. Based on the prediction rules mined from the data of 25 potential prediction indicators of SHFE CU, a corresponding transaction strategy is established. And we use the market data from 2009 to 2013 to test our transaction strategy, which obtains a result of 147.85% annual yield. In addition, several improvements are discussed to optimize this model.","PeriodicalId":313511,"journal":{"name":"2013 International Conference on Information Science and Cloud Computing Companion","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Commodity Futures Price Prediction and Trading Strategies -- A Signal Noise Difference Approach\",\"authors\":\"Jinhao Zheng, Shoukang Peng\",\"doi\":\"10.1109/ISCC-C.2013.60\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the signal noise difference method and applies this method into the commodity futures price prediction. Based on the prediction rules mined from the data of 25 potential prediction indicators of SHFE CU, a corresponding transaction strategy is established. And we use the market data from 2009 to 2013 to test our transaction strategy, which obtains a result of 147.85% annual yield. In addition, several improvements are discussed to optimize this model.\",\"PeriodicalId\":313511,\"journal\":{\"name\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC-C.2013.60\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Information Science and Cloud Computing Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC-C.2013.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Commodity Futures Price Prediction and Trading Strategies -- A Signal Noise Difference Approach
This paper introduces the signal noise difference method and applies this method into the commodity futures price prediction. Based on the prediction rules mined from the data of 25 potential prediction indicators of SHFE CU, a corresponding transaction strategy is established. And we use the market data from 2009 to 2013 to test our transaction strategy, which obtains a result of 147.85% annual yield. In addition, several improvements are discussed to optimize this model.