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Forerunne Fault Tolerant Intelligent Indicator Based Forex Forecasting System

Sarala Sewwandi Kumarage

Knowledge of the current situation of the currency market in terms of the relative strength of the buyers and sellers is essential to make a good trading decision. It is advisable to buy the currency pairs if the buyers are high in the market. If the sellers dominate the market it is recommended to sell the currency pairs. There is already existing indicator known as Relative Strength Indicator which was developed by J. Welles Wilder. The mentioned indicator shows a figure called relative strength index calculated using the average gain and average loss during a given time period relative to a particular timeframe. Existing indicators calculate the RSI value with some time lag. Purpose of this study is to come up with a forecasting system to support manual currency trading with RSI indicator with a solution to the time lag of the existing indicator. RSI value also gives an idea about the current trend. If its value passes the halfway in a scale of 0 to 100 it confirms a forming trend in either direction. Neural networks have been identified as an emerging technique to predict the price actions of the currency markets. Advantage of using the neural networks is its ability to handle the nonlinearity of the price actions in a highly dynamic environment. This paper proposes a fault tolerant neural network model to predict the future price actions, minimize the time lag of the existing indicator along with the type of order, current trend, and take profit level and the stop loss levels for the next trade.