{"title":"TradeZilla Using Algorithmic Trading","authors":"Dheeraj Othalasseril, Sana Shaikh","doi":"10.1109/INDISCON53343.2021.9582206","DOIUrl":null,"url":null,"abstract":"As everything in the future is getting automated and the stock market which is a very important part of the economic engine, which keeps a big part of globalization moving, needs its own revolution in automation/AI. Pundits and experts alike of this field have likened it to algorithmic trading which is considerably speeding up the trading process by generating maximum revenue with the most optimum solution for those who are adapting to the new technology, without the need for human intervention. Already many top companies are using algorithmic trading and many more are further researching this along with many top universities. For trading in general a lot of information needs to be taken into consideration like information about the company, reading of daily news, reports of the company, how it is performing and all and how the general outlook of people towards the company is. So much information slows down the trading process for the trader and maybe gives him access to only part of the whole market. Because of all this, the paper will be using LSTM(long short term memory) a very advanced RNN(recurrent neural network) to help us solve this problem and get predictions. The paper will also be using sentiment analysis to get an idea about the sentiment towards the market. Using these models the trader gets access to the whole market as a whole and this will also eliminate the problem of human confusion and emotion.","PeriodicalId":167849,"journal":{"name":"2021 IEEE India Council International Subsections Conference (INDISCON)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE India Council International Subsections Conference (INDISCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDISCON53343.2021.9582206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
As everything in the future is getting automated and the stock market which is a very important part of the economic engine, which keeps a big part of globalization moving, needs its own revolution in automation/AI. Pundits and experts alike of this field have likened it to algorithmic trading which is considerably speeding up the trading process by generating maximum revenue with the most optimum solution for those who are adapting to the new technology, without the need for human intervention. Already many top companies are using algorithmic trading and many more are further researching this along with many top universities. For trading in general a lot of information needs to be taken into consideration like information about the company, reading of daily news, reports of the company, how it is performing and all and how the general outlook of people towards the company is. So much information slows down the trading process for the trader and maybe gives him access to only part of the whole market. Because of all this, the paper will be using LSTM(long short term memory) a very advanced RNN(recurrent neural network) to help us solve this problem and get predictions. The paper will also be using sentiment analysis to get an idea about the sentiment towards the market. Using these models the trader gets access to the whole market as a whole and this will also eliminate the problem of human confusion and emotion.