Robust technical trading strategies using GP for algorithmic portfolio selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Berutich:2016:ESA,
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author = "Jose Manuel Berutich and Francisco Lopez and
Francisco Luna and David Quintana",
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title = "Robust technical trading strategies using {GP} for
algorithmic portfolio selection",
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journal = "Expert Systems with Applications",
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year = "2016",
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volume = "46",
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pages = "307--315",
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keywords = "genetic algorithms, genetic programming, Algorithmic
trading, Portfolio management, Trading rule, Finance,
buy and hold",
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ISSN = "0957-4174",
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URL = "https://e-archivo.uc3m.es/rest/api/core/bitstreams/169bcfb0-d6cc-4f0a-870b-dd9915924000/content",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417415007447",
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DOI = "doi:10.1016/j.eswa.2015.10.040",
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size = "12 pages",
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abstract = "This paper presents a Robust Genetic Programming
approach for discovering profitable trading rules which
are used to manage a portfolio of stocks from the
Spanish market. The investigated method is used to
determine potential buy and sell conditions for stocks,
aiming to yield robust solutions able to withstand
extreme market conditions, while producing high returns
at a minimal risk. One of the biggest challenges GP
evolved solutions face is over-fitting. GP trading
rules need to have similar performance when tested with
new data in order to be deployed in a real situation.
We explore a random sampling method (RSFGP) which
instead of calculating the fitness over the whole
dataset, calculates it on randomly selected segments.
This method shows improved robustness and out-of-sample
results compared to standard genetic programming (SGP)
and a volatility adjusted fitness (VAFGP). Trading
strategies (TS) are evolved using financial metrics
like the volatility, CAPM alpha and beta, and the
Sharpe ratio alongside other Technical Indicators (TI)
to find the best investment strategy. These strategies
are evaluated using 21 of the most liquid stocks of the
Spanish market. The achieved results clearly outperform
Buy and Hold, SGP and VAFGP. Additionally, the
solutions obtained with the training data during the
experiments clearly show during testing robustness to
step market declines as seen during the European
sovereign debt crisis experienced recently in Spain. In
this paper the solutions learned were able to operate
for prolonged periods, which demonstrated the validity
and robustness of the rules learned, which are able to
operate continuously and with minimal human
intervention. To sum up, the developed method is able
to evolve TSs suitable for all market conditions with
promising results, which suggests great potential in
the method generalization capabilities. The use of
financial metrics alongside popular TI enables the
system to increase the stock return while proving
resilient through time. The RSFGP system is able to
cope with different types of markets achieving a
portfolio return of 31.81percent for the testing period
2009-2013 in the Spanish market, having the IBEX35
index returned 2.67percent.",
- }
Genetic Programming entries for
Jose Manuel Berutich
Francisco Lopez Valverde
Francisco Luna Valero
David Quintana Montero
Citations