Examinando por Autor "Soltero, Francisco José"

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A parallel evolutionary algorithm for technical market indicators optimization
(Natural Computing, 2012-09-28) bodas, Diego; Fernández, Pablo; Hidalgo, José Ignacio; Soltero, Francisco José
This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.
A technique for the optimization of the parameters of technical indicators with multi-objective evolutionary algorithms
(IEEE, 2012-06-10) Bodas, Diego; Soltero, Francisco José; Fernández, Pablo; Hidalgo, José Ignacio
Technical indicators (TIs) are used to interpret stock market and to predict market trends. The main difficulty in the use of TIs lies in deciding which their optimal parameter values are in each moment, since constant optimal values do not seem to exist. In this work, the use of Multi-Objective Evolutionary Algorithms (MOEAs) is proposed to obtain the best values of the parameters in order to help to buy and sell shares. Those parameters are applied in real time and belong to a collection of indicators. Unlike other previous approaches, the necessity of repeating the parameter optimization process each time a new data enters the system is justified, searching for the best adjustment of the parameters (and hence the TIs) in every moment. The Moving Averages Convergence-Divergence (MACD) indicator and the Relative Strength Index (RSI) oscillator have been chosen as TIs, so the MOEAs will provide the best parameters to use them on investment decisions. Experiments compare up to nine different configurations with the Buy & Hold strategy (B & H). The obtained results show that the Multi-Objective technique proposed here can greatly improve the results of the B & H strategy even operating daily. This statement is also demonstrated by comparing the results to those previously presented in the literature.
Clasificadores inductivos para el posicionamiento web
(EPI SCP, Barcelona, Spain, 2012-06-28) Soltero, Francisco José; Bodas, Diego
En este trabajo se muestra cómo el estudio individual de los distintos atributos básicos de un recurso web no es suficiente para inferir las distintas estrategias de posicionamiento de un motor de búsqueda. El problema fundamental que se plantea es cuál es la relación entre los distintos elementos que componen la página y el peso que cada uno de ellos aporta al posicionamiento final. Como alternativa a este problema se propone la utilización de técnicas de aprendizaje inductivo, más concretamente, clasificadores arbóreos. Los resultados se ven reflejados en dos experimentos, fruto de la aplicación de dos algoritmos de aprendizaje distintos. Como resultado final se observa que la aplicación de esta técnica puede ser un punto de partida muy interesante para la optimización del posicionamiento web.
Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
(Applied Sciences, 2023-11-19) Soltero, Francisco José; Fernández, Pablo; Hidalgo, José Ignacio
Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations.
Optimization of technical indicators in real time with multiobjective evolutionary algorithms
(Association for Computing Machinery New York, NY, United States., 2012-07-07) Soltero, Francisco José; Bodas, Diego; Fernández, Pablo; Hidalgo, José Ignacio
Technical analysis uses technical indicators to identify changes in market trend. These are composed by a set of parameters and rules, whose values try to determine the future movements of the assets. This paper addresses the optimization of these values depending on the current market, allowing better returns with less risk. The use of Multi-objective Evolutionary Algorithms (MOEAs) is proposed in this work to obtain the best parameter values in real time belonging to a collection of indicators that will help in the buying and selling of shares. Unlike other previous approaches, the necessity of repeating the parameters optimization process each time a new data enters the system is justified, searching for the best adjustment in every moment. This technique can greatly improve the results of Buy & Hold (B & H) strategy even operating daily. This statement will be demonstrated by comparing the results to those presented in the literature.
Technical market indicators optimization using evolutionary algorithms
(Association for Computing Machinery New York, NY, United States, 2008-07-12) Fernández, Pablo; Soltero, Francisco José; Hidalgo, José Ignacio
Real world stock markets predictions such as stock prices, unpredictability, and stock selection for portfolios, are challenging problems. Technical indicators are applied to interpret stock market trending and investing decision. The main difficulty of an indicator usage is deciding its appropriate parameter values, as number of days of the periods or quantity and kind of indicators. Each stock index, price or volatility series is different among the rest. In this work, Evolutionary Algorithms are proposed to discover correct indicator parameters in trading. In order to check this proposal the Moving Average Convergence-Divergence (MACD) technical indicator has been selected. Preliminary results show that this technique could work well on stock index trending. Indexes are smoother and easier to predict than stock prices. Required future works should include several indicators and additional parameters.