DSpace Repository

AAGFA: Automated ANFIS and GA-Based Forex Agent

Show simple item record

dc.contributor.advisor Gasmen, Perlita E.
dc.contributor.author Ampol, Ariel Kenneth
dc.date.accessioned 2015-07-24T11:12:09Z
dc.date.available 2015-07-24T11:12:09Z
dc.date.issued 2015-06
dc.identifier.uri http://cas.upm.edu.ph:8080/xmlui/handle/123456789/23
dc.description.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. en_US
dc.language.iso en en_US
dc.subject ForEx en_US
dc.subject foreign exchange en_US
dc.subject exchange rates en_US
dc.subject algorithmic trading en_US
dc.subject ANFIS en_US
dc.subject neurofuzzy en_US
dc.subject genetic algorithm en_US
dc.subject NSGA-II en_US
dc.title AAGFA: Automated ANFIS and GA-Based Forex Agent en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account