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dc.contributor.authorChew, Yao Kang
dc.description.abstractThe need to constantly predict stocks and making accurate decisions based on past experience of the ever-changing market has never been an easy task even for experienced traders. It is very time consuming to study historical data of the market to predict trends, not to forget about unpredictable events that can easily trigger the market and cause a huge change. Nonetheless, the use of trading applications to help assist traders to better predict stock prices prove to be possible using historical data and knowledge of experts. On top of that, Artificial intelligence is also used to forecast the price fluctuations of the markets. Trading applications are widely used today to automate trading. Compared to a human trader, the advantage is that applications do not use emotions to trade. These applications are implemented using thoughts of experienced traders, historical data of trades and trading models. Based on the rules set by traders, the algorithm will capture patterns of stock movements according and start trading and react to the trader’s rules. However, such algorithm is not permanent and trading models do not last forever, market changes quickly and traders are required to develop new trading models for the market as their current trading strategies will become outdated quickly. The objective of this project is to help traders and investors to inquire a smart tool that is able to provide an accurate prediction of market movement.en_US
dc.format.extent69 p.en_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleTrading with self-adaptive fuzzy inference systemen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorQuek Hiok Chaien_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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