Raúl Gómez-Martínez, Carmen Orden-Cruz, maRía lUISa meDRanO-GaRCía
{"title":"在趋势跟踪指标上使用人工智能的量化交易:以2020年为例","authors":"Raúl Gómez-Martínez, Carmen Orden-Cruz, maRía lUISa meDRanO-GaRCía","doi":"10.3905/joi.2022.1.235","DOIUrl":null,"url":null,"abstract":"Currently, algorithmic trading systems are one of the biggest challenges for machine learning (ML) and artificial intelligence (AI). In this article, an AI model is proposed using predictor variables based on trend-following momentum indicators. Using a data sample of highly traded futures contracts and their technical indicators, the results show a predictive capacity greater than 50% of the market trend of the next session. However, ML did not allow a profitable algorithmic trading system during the testing process.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"35 - 49"},"PeriodicalIF":0.6000,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative Trading Using Artificial Intelligence on Trend-Following Indicators: An Example in 2020\",\"authors\":\"Raúl Gómez-Martínez, Carmen Orden-Cruz, maRía lUISa meDRanO-GaRCía\",\"doi\":\"10.3905/joi.2022.1.235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, algorithmic trading systems are one of the biggest challenges for machine learning (ML) and artificial intelligence (AI). In this article, an AI model is proposed using predictor variables based on trend-following momentum indicators. Using a data sample of highly traded futures contracts and their technical indicators, the results show a predictive capacity greater than 50% of the market trend of the next session. However, ML did not allow a profitable algorithmic trading system during the testing process.\",\"PeriodicalId\":45504,\"journal\":{\"name\":\"Journal of Investing\",\"volume\":\"32 1\",\"pages\":\"35 - 49\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Investing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3905/joi.2022.1.235\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Investing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3905/joi.2022.1.235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Quantitative Trading Using Artificial Intelligence on Trend-Following Indicators: An Example in 2020
Currently, algorithmic trading systems are one of the biggest challenges for machine learning (ML) and artificial intelligence (AI). In this article, an AI model is proposed using predictor variables based on trend-following momentum indicators. Using a data sample of highly traded futures contracts and their technical indicators, the results show a predictive capacity greater than 50% of the market trend of the next session. However, ML did not allow a profitable algorithmic trading system during the testing process.