Co-evolving online high-frequency trading strategies using grammatical evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/cifer/GabrielssonJK14,
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author = "Patrick Gabrielsson and Ulf Johansson and
Rikard Konig",
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title = "Co-evolving online high-frequency trading strategies
using grammatical evolution",
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booktitle = "IEEE Conference on Computational Intelligence for
Financial Engineering Economics (CIFEr 2104)",
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year = "2014",
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pages = "473--480",
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address = "London",
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month = "27-28 " # mar,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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bibdate = "2014-11-06",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cifer/cifer2014.html#GabrielssonJK14",
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URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6901616",
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DOI = "doi:10.1109/CIFEr.2014.6924111",
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abstract = "Numerous sophisticated algorithms exist for
discovering reoccurring patterns in financial time
series. However, the most accurate techniques available
produce opaque models, from which it is impossible to
discern the rationale behind trading decisions. It is
therefore desirable to sacrifice some degree of
accuracy for transparency. One fairly recent
evolutionary computational technology that creates
transparent models, using a user-specified grammar, is
grammatical evolution (GE). In this paper, we explore
the possibility of evolving transparent entry- and exit
trading strategies for the E-mini S&P 500 index futures
market in a high-frequency trading environment using
grammatical evolution. We compare the performance of
models incorporating risk into their calculations with
models that do not. Our empirical results suggest that
profitable, risk-averse, transparent trading strategies
for the E-mini S&P 500 can be obtained using
grammatical evolution together with technical
indicators.",
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notes = "Also known as \cite{6924111}",
- }
Genetic Programming entries for
Patrick Gabrielsson
Ulf Johansson
Rikard Konig
Citations