designing an automatic system for converting news into stock trading strategy
Wagh Sayali,More Nikhil,Nirmal Akshay,Yele Jyoti
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 22 March,2015 Pages:384-391
Abstract
The most important resource for companies to raise their money is the stock marketing. A majority of traders who invest in the stock market, regardless of what they feel , uses their own stock trading strategies, help them decide what stocks would be best to buy and when. In this we present a framework for comparing the approaches which exploit the news into stock trading strategies. This framework associates the information related to news releases and technical indicators to the daily stock price tendency. Dataset are being used to store the information related news. Even accomplished Genetic algorithms are there to find optimal trading strategies but there is no collateral to find the optimized solution. Here we are presenting the Association rule mining approach which is pre-owned to measure the effect of real time news from different techniques to predict ups and downs. In Our proposed system we are introducing the criteria which provide the optimized solution over Genetic algorithm and gives output of technical trading strategy whether to hold/buy/sell the shares related to particular company which improves the predictive capacity.
Kewords
Computer application, evolutionary computing and Association Rule Mining, learning, natural language processing, web text analysis.
Reference
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