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DC Field | Value | Language |
---|---|---|
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 |
Appears in Collections: | Computer Science SP |
Files in This Item:
File | Description | Size | Format | |
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SP - Ampol.pdf | 2.81 MB | Adobe PDF | View/Open |
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