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Foreign exchange market is based on the movement or fluctuation between one country currency to another, because both of currency are connected one to another. There are a lot of analyze methods for use to forecasting foreign exchange, some of the methods are : Stochastic Oscilator, Relative Strengt...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/16379 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Foreign exchange market is based on the movement or fluctuation between one country currency to another, because both of currency are connected one to another. There are a lot of analyze methods for use to forecasting foreign exchange, some of the methods are : Stochastic Oscilator, Relative Strength Index (RSI), SMA, MACD, etc. For input parameters are using a few type of price, like : open, high, low, and close price. Besides that, the writer also use the stochastic forecasting result, RSI forecasting result and the actually result in reality.<p>Artificial neural network have been implemented in a various application, especially for forecasting, recognition, marketing, health, investment, etc. This final project will discuss about clever system to back up the decision in foreign exchange forecasting using the neural network backpropagation which is used to optimized neural network architecture. The data that writer use in this project is exchange rate for GBPUSD with four hours time frame.<p>Neural network structure in this forecasting needs could be made with 6-n-1 architecture which is 6 neuron for input layer, n is for hidden layer and 1 for output layer. This final project could be use for advice to foreign exchange trader in making a decision. <br />
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