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Title: AAGFA: Automated ANFIS and GA-Based Forex Agent
Authors: Gasmen, Perlita E.
Ampol, Ariel Kenneth
Keywords: ForEx
foreign exchange
exchange rates
algorithmic trading
genetic algorithm
Issue Date: Jun-2015
Abstract: With Forex as the largest and most liquid financial market, the practice of algo- rithmic trading has become of interest in the market, as well as in research. This study explores the use of the Adaptive Neuro-Fuzzy Inference System as a pre- dictor, combined with the Non-Dominated Sorting Genetic Algorithm II for trade timing to create a smart autonomous Forex trading agent that produces sizable profits. Upon performing a backtest, the agent was shown to be able to garner approximately $80 in profit in a span of two months and nearly $500 in profit for a one-year period. Empirical evidence is also provided that the trading agent running live is able to open trades which profitably closed.
Appears in Collections:Computer Science SP

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