  Stock Market can be easily seen as one of the most attractive places for
investors, but it is also very complex in terms of making trading decisions.
Predicting the market is a risky venture because of the uncertainties and
nonlinear nature of the market. Deciding on the right time to trade is key to
every successful trader as it can lead to either a huge gain of money or
totally a loss in investment that will be recorded as a careless trade. The aim
of this research is to develop a prediction system for stock market using Fuzzy
Logic Type2 which will handle these uncertainties and complexities of human
behaviour in general when it comes to buy, hold or sell decision making in
stock trading. The proposed system was developed using VB.NET programming
language as frontend and Microsoft SQL Server as backend. A total of four
different technical indicators were selected for this research. The selected
indicators are the Relative Strength Index, William Average, Moving Average
Convergence and Divergence, and Stochastic Oscillator. These indicators serve
as input variable to the Fuzzy System. The MACD and SO are deployed as primary
indicators, while the RSI and WA are used as secondary indicators. Fibonacci
retracement ratio was adopted for the secondary indicators to determine their
support and resistance level in terms of making trading decisions. The input
variables to the Fuzzy System is fuzzified to Low, Medium, and High using the
Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference
rules were used for combining the trading rules for each input variable to the
fuzzy system. The developed system was tested using sample data collected from
ten different companies listed on the Nigerian Stock Exchange for a total of
fifty two periods. The dataset collected are Opening, High, Low, and Closing
prices of each security.

Artificial Intelligence;Machine Learning;Logic in Computer Science