Better Trade Exits for Foreign Exchange Currency Trading using FXGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Loginov:2015:CEC,
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author = "Alexander Loginov and Garnett Wilson and
Malcolm Heywood",
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title = "Better Trade Exits for Foreign Exchange Currency
Trading using {FXGP}",
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booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary
Computation (CEC 2015)",
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year = "2015",
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editor = "Yadahiko Murata",
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pages = "2510--2517",
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address = "Sendai, Japan",
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month = "25-28 " # may,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, Sociology,
Statistics, Resistance, Training, Immune system, Market
research, Decision trees",
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isbn13 = "978-1-4799-7491-7",
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URL = "https://web.cs.dal.ca/~mheywood/OpenAccess/open-loginov15.pdf",
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DOI = "doi:10.1109/CEC.2015.7257197",
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size = "8 pages",
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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.",
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notes = "1010 hrs 15174 CEC2015",
- }
Genetic Programming entries for
Alexander Loginov
Garnett Carl Wilson
Malcolm Heywood
Citations