MACD-RSI trading system using neuro-fuzzy system

• Main Author: Lee, Chun Kiat
• Other Authors: Quek Hiok Chai
• Format: Final Year Project
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/75561
• Institution: Nanyang Technological University

Neuro-Fuzzy Systems are made up of Artificial Neural Network (ANN) and Fuzzy Logic which is used in many applications such as predicting trends in stock markets, artificial ventilation modeling, noisy speech recognition and traffic flow conditions. In the financial market world, many traders are buying or selling stock without profiting due to lack of information or making a wrong trading decision. This brings us to use AI in predicting the stock market. However, the trading decision makes by the user itself using the predicted stock market data. This paper proposes a MACD-RSI trading system using the neuro-fuzzy system as a predictor. This system can assist the trader in making a better trading decision by using the MACD and RSI indicators. The proposed method has two phases which the first phase is to find the best prediction model for the historical stock data. For phase 2, we intend to use that model we find in phase 1 and other models we test in phase 1 to predict the future stock market data and use it for RSI and MACD computation which then uses it to compute the overall profit/loss for one trading year for each neuro-fuzzy model. The results obtained from each neuro-fuzzy model will be compared to the ideal result generated from the actual data.