A Fuzzy Logic-Based Stock Market Trading Algorithm Using Bollinger Bands

Stock market price forecasting with the use of Technical Analysis is not precise Mathematics. Mostly, prediction is only based on probabilities supported by historical data and patterns. With these, several technical strategies were made by traders to produce signals on trading execution. This study proposes an algorithm that undergoes a certain trading strategy using three fuzzy logic controllers. Technical indicators such as candlestick parameters and Bollinger Bands (BB) were used for triggering the strength of buy, hold, and sell signals. Stock price data were gathered from a certain stock company. These data contain the opening and closing prices that are utilized for computing the BB. The raw and the computed values are the crisp input parameters for the Fuzzy Inference System (FIS). The membership functions were classified to very low, low, high, and very high levels depending on the input default parameters used by traders. Sets of rules were created fuzzy logically to produce signals indicating the strength of an execution recommendation. The system is implemented using NI LabVIEW and MATLAB, proving that the tests are yielding acceptable result of about 94.44%.