Better Trade Exits for Foreign Exchange Currency Trading using FXGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Loginov:2015:CEC,

• author = "Alexander Loginov and Garnett Wilson and Malcolm Heywood",
• title = "Better Trade Exits for Foreign Exchange Currency Trading using {FXGP}",
• booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
• year = "2015",
• editor = "Yadahiko Murata",
• pages = "2510--2517",
• address = "Sendai, Japan",
• month = "25-28 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, Sociology, Statistics, Resistance, Training, Immune system, Market research, Decision trees",
• isbn13 = "978-1-4799-7491-7",
• URL = "https://web.cs.dal.ca/~mheywood/OpenAccess/open-loginov15.pdf",
• DOI = "doi:10.1109/CEC.2015.7257197",
• size = "8 pages",
• abstract = "Retracement is the tendency of markets to move between upper resistance and lower support price levels. Human traders frequently make use of visual tools to help identify these resistance and support levels so that they can by used in their trading decisions. These decision can be put into trading strategies composed of rules designed to mitigate losses after a trade is started, often called stop loss orders, or to take profit at a near optimal time, often called take profit orders. However, identifying such resistance and support levels is notoriously difficult given market volatility. Indeed, the levels need recalculating on a continuous basis, and only hold to an approximate degree. In this work we describe an approach for evolving buy-stay-sell currency trading rules using genetic programming. These rules are explicitly linked to technical indicators that incorporate features characterizing retracement. Benchmarking is then performed using the most recent three years of data from the EURUSD foreign exchange market with three different methods of identifying retracement based on moving average, pivot points and Fibonacci ratios. Investment strategies employing Fibonacci ratios and found to provide superior performance among the strategies examined.",
• notes = "1010 hrs 15174 CEC2015",

}

Genetic Programming entries for Alexander Loginov Garnett Carl Wilson Malcolm Heywood

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