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Title: Trading with self-adaptive fuzzy inference system
Authors: Chew, Yao Kang
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2018
Abstract: The 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and traders are required to develop new trading models for the market as their current trading strategies will become outdated quickly.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.
Schools: School of Computer Science and Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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