{"title":"台湾期货市场修正ORB策略之门槛调整","authors":"Jia-Hao Syu, Mu-En Wu, Shin-Huah Lee, Jan-Ming Ho","doi":"10.1109/CIFEr.2019.8759112","DOIUrl":null,"url":null,"abstract":"Opening Range Breakout (ORB) is a fairly intraday trading strategy. We set the resistance and the support levels by the price in opening interval to follow the trend in the futures market. However, such kind of strategies is not profitable for most commodities in recent years in the changing market. In this paper, we attempt to improve the original ORB strategy by considering the effect of trends continuity on the event. We adjust the predetermined threshold for upper bound and lower bound. This strategy is called Threshold Adjusting ORB or TA_ORB. We implement this modified ORB strategy on the Taiwan Index Futures from 2008 to 2012. Compared with the original ORB strategy, we got 145.98% return in 2008 (bear market), 81.86% return in 2009 (bull market) and 32.25% annual return in 2008–2012 (five-year period) which are 4.0 times, 1.4 times, and 2.6 times more than original ORB, respectively. TA_ORB performs outstanding in large fluctuation, especially in the bear market. Performance can verify that the observations of TA_ORB improve the stability of the breakthrough signal, enhance the return, and reduce strategic risk. Further, we plan to use neural network to make more precise predictions and implement these strategies in different commodities.","PeriodicalId":368382,"journal":{"name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Modified ORB Strategies with Threshold Adjusting on Taiwan Futures Market\",\"authors\":\"Jia-Hao Syu, Mu-En Wu, Shin-Huah Lee, Jan-Ming Ho\",\"doi\":\"10.1109/CIFEr.2019.8759112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Opening Range Breakout (ORB) is a fairly intraday trading strategy. We set the resistance and the support levels by the price in opening interval to follow the trend in the futures market. However, such kind of strategies is not profitable for most commodities in recent years in the changing market. In this paper, we attempt to improve the original ORB strategy by considering the effect of trends continuity on the event. We adjust the predetermined threshold for upper bound and lower bound. This strategy is called Threshold Adjusting ORB or TA_ORB. We implement this modified ORB strategy on the Taiwan Index Futures from 2008 to 2012. Compared with the original ORB strategy, we got 145.98% return in 2008 (bear market), 81.86% return in 2009 (bull market) and 32.25% annual return in 2008–2012 (five-year period) which are 4.0 times, 1.4 times, and 2.6 times more than original ORB, respectively. TA_ORB performs outstanding in large fluctuation, especially in the bear market. Performance can verify that the observations of TA_ORB improve the stability of the breakthrough signal, enhance the return, and reduce strategic risk. Further, we plan to use neural network to make more precise predictions and implement these strategies in different commodities.\",\"PeriodicalId\":368382,\"journal\":{\"name\":\"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIFEr.2019.8759112\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIFEr.2019.8759112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modified ORB Strategies with Threshold Adjusting on Taiwan Futures Market
Opening Range Breakout (ORB) is a fairly intraday trading strategy. We set the resistance and the support levels by the price in opening interval to follow the trend in the futures market. However, such kind of strategies is not profitable for most commodities in recent years in the changing market. In this paper, we attempt to improve the original ORB strategy by considering the effect of trends continuity on the event. We adjust the predetermined threshold for upper bound and lower bound. This strategy is called Threshold Adjusting ORB or TA_ORB. We implement this modified ORB strategy on the Taiwan Index Futures from 2008 to 2012. Compared with the original ORB strategy, we got 145.98% return in 2008 (bear market), 81.86% return in 2009 (bull market) and 32.25% annual return in 2008–2012 (five-year period) which are 4.0 times, 1.4 times, and 2.6 times more than original ORB, respectively. TA_ORB performs outstanding in large fluctuation, especially in the bear market. Performance can verify that the observations of TA_ORB improve the stability of the breakthrough signal, enhance the return, and reduce strategic risk. Further, we plan to use neural network to make more precise predictions and implement these strategies in different commodities.