The Development of Prediction Indicators on Currency Market Using Neuro-Fuzzy Method

Lie, Ronald Limowa and Santoso, Murtiyanto and Pasila, Felix and Sutjiadi, Raymond and Lim, Resmana (2020) The Development of Prediction Indicators on Currency Market Using Neuro-Fuzzy Method. In: E3S Web of Conferences. The 4th International Conference on Electrical Systems, Technology and Information (ICESTI 2019), 188 . EDP Sciences.

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Abstract

A technical indicator is an analysis instruments to help traders analyzing forex price movements through charts. Prediction indicators are artificial technical indicators that can help traders to analyse forex price movements in the future. This prediction information becomes one of the bases in making trading decisions. This project aims to develop prediction indicators on MetaTrader that can provide information on forex price predictions using Neuro-Fuzzy method. The Neuro-Fuzzy System requires input parameters in the system prediction process obtained from the system training process. These parameters can be through or without optimization process. The prediction indicator will also make trading decisions based on prediction indicators analysis, RSI, and Stochastic. Finally, the information on trading decisions will be displayed on Facebook pages. The prediction indicator testing run well on a trading system. Prediction indicators with parameters before optimization were well used in the H4 EURUSD pair (data 2012) with a predicted profit of USD 16 499. While the prediction indicators with parameters after optimization were well used in the H1 EURUSD pair with a predicted profit of USD 21 945. The information on trading decisions were also successfully displayed on Facebook pages.

Item Type: Book Section
Uncontrolled Keywords: Artificial technical indicator, facebook, forex, forex price analysis, metatrader
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Information Technology
Faculty of Information Technology > Informatics Department
Depositing User: P3M IKADO
Date Deposited: 09 Sep 2020 04:01
Last Modified: 09 Sep 2020 04:13
URI: http://repository.ikado.ac.id/id/eprint/129

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