TradeZilla Using Algorithmic Trading

Dheeraj Othalasseril, Sana Shaikh
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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.
TradeZilla使用算法交易
随着未来一切都变得自动化,股票市场作为经济引擎的重要组成部分,保持了全球化的很大一部分,需要自己的自动化/人工智能革命。该领域的权威人士和专家将其比作算法交易,算法交易通过为那些适应新技术的人提供最优解决方案来产生最大的收入,从而大大加快了交易过程,而无需人工干预。许多顶级公司已经在使用算法交易,更多的公司正在与许多顶尖大学一起进一步研究这一技术。一般来说,交易需要考虑很多信息,比如公司的信息,阅读每日新闻,公司的报告,它的表现如何,以及人们对公司的总体看法。如此多的信息减慢了交易者的交易过程,可能让他只能接触到整个市场的一部分。正因为如此,本文将使用LSTM(长短期记忆)一种非常先进的RNN(循环神经网络)来帮助我们解决这个问题并得到预测。本文还将使用情绪分析来了解对市场的情绪。使用这些模型,交易者可以作为一个整体进入整个市场,这也将消除人类困惑和情绪的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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