Ir al contenido

Documat

Trading a través del indicador RSI con la aplicación de Algoritmos Genéticos para la implementación en el sector empresarial de las inversiones

  • Agudelo Aguirre, Alberto Antonio [1] ; Rojas Medina , Ricardo Alfredo [1] ; Duque Méndez, Néstor Darío [1]
    1. [1] Universidad Nacional de Colombia

      Universidad Nacional de Colombia

      Colombia

  • Localización: Desarrollo Gerencial, ISSN-e 2145-5147, Vol. 14, Nº. 2, 2022 (Ejemplar dedicado a: July-December), 24 págs.
  • Idioma: español
  • DOI: 10.17081/dege.14.2.5755
  • Títulos paralelos:
    • Trading Through the RSI Indicator Applying Genetic Algorithms for Implementation in the Business Investment Sector
  • Enlaces
  • Resumen
    • español

      Objetivo: Este artículo propone para la literatura sobre inversión de activos de renta variable una metodología aplicada a través de Algoritmos Genéticos (AG) y su implementación en el sector empresarial de inversión. Método: El estudio se desarrolló mediante simulación de las estrategias de inversión B&H, AT y AG sobre el índice accionario. El proceso de maximización del rendimiento de inversión para los activos financieros se realizó mediante Algoritmos Genéticos, los cuales se basaron en ecuaciones definidas en la tipificación cromosómica con operaciones inmersas en los genes. Se inició con una población aleatoria de individuos con cromosomas representando una solución para lograr el mayor rendimiento posible. Resultados: La aplicación de algoritmos con el RSI generó rendimientos superiores al 9 y 16% respecto a B&H y el análisis técnico, lo que quiere decir, mayor riesgo de inversión para B&H con volatilidad 17,6% pero comparable a las exhibidas por algoritmos genéticos y análisis técnico. Conclusiones: Mejor relación rendimiento-riesgo y eficiencia en los parámetros fundamentales de la Teoría de Portafolio es posible a través de estrategias de inversión basadas en algoritmos genéticos incluyendo el oscilador RSI. Este estudio sugiere que un mejoramiento del rendimiento de inversión puede ser anticipado mediante los parámetros stop loss y take profit y un rango de movimiento del precio del activo previo la toma de posición.

    • English

      Objective: This article proposes a methodology for the literature on equity asset investment applied through Genetic Algorithms and their implementation in the business investment sector. Method: The study was carried out through a simulation of B&H, AT, and AG investment strategies on the equity index. The investment return maximization process for financial assets was developed by means of genetic algorithms, which were based on equations defined in chromosome classification with gene-immersive operations. It began with a random population of individuals with chromosomes representing a solution to achieve the greatest return possible. Results: The application of algorithms with the RSI generated returns that were 9% and 16% higher than with B&H and technical analysis means a greater investment risk for B&H with a volatility of 17.6%, but comparable to those shown by genetic algorithms and technical analysis. Conclusions: An improved risk-return ratio and efficiency in the key parameters of the Portfolio Theory is possible through investment strategies based on genetic algorithms including the RSI oscillator. This study suggests that an improvement of the return on investment may be expected through the stop loss and take profit parameters and a range of motion of the asset price prior to taking position.

  • Referencias bibliográficas
    • Abrams, J. R., Celaya-Alcalá, J., Ryan Gonda, D. B., & Chen, Z. (2017). Analysis of equity markets: a graph theory approach. https://evoq-eval.siam.org/Portals/0/Publications/SIURO/Volume%2010/Analysis_Equity_Markets_A_Graph_Theory_Approach.pdf?ver=2018-02-28-145946-083
    • AFI Escuela de Finanzas España. (2017). AFI guías. Análisis técnico-17. https://docero.es/doc/508v50
    • Agudelo Aguirre, A. A., Rojas Medina, R. A., & Duque Méndez, N. D. (2020). Machine learning applied in the stock market through the Moving...
    • Allen, F., Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economic, 51(2), 245-271....
    • Boboc, I-A., & Dinică, M-C. (2013). An algorithm for testing the efficient market hypothesis. PLoS ONE, 8(10), e78177. https://doi.org/10.1371/journal.pone.0078177
    • Burduk, A., Musiał, K., Kochańska, J., & Łapczyńska, D. (2019). Tabu search and genetic algorithm for production process scheduling problem....
    • Chen, Y., Mabu, S., & Hirasawa, K. (2011). Genetic relation algorithm with guided mutation for the large-scale portfolio optimization....
    • Cheng, C-H., Chen, T-L., & Wei, L-Y. (2010). A Hybrid model based on rough sets theory and genetic algorithms for stock price forecasting....
    • Chou, C.-C., & Lin, K.-S. (2019). A fuzzy neural network combined with technical indicators and its application to Baltic dry index forecasting....
    • Cohen, G., & Cabiri, E. (2015). ¿Can technical oscillators outperform the buy and hold strategy? Applied Economics, 47(30), 3189-3197....
    • Cortés-Villafradez, R. A., y Hernández-Luna, Y. (2014). Impacto del desarrollo del sistema financiero en el crecimiento económico en países...
    • Ding, S., Cui, T., Xiong, X., & Bai, R. (2020). Forecasting stock market return with nonlinearity: a genetic programming approach. J Ambient...
    • Drake, A.E., & Marks, R.E. (2002). Genetic algorithms in economics and finance: forecasting stock market prices and foreign exchange—a...
    • Evans, C., Pappas, K., & Xhafa, F. (2013). Utilizing artificial neural networks and genetic algorithms to build an algo-trading model...
    • Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
    • Farias-Nazário, R. T., Lima e Silva, J., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets....
    • Fernández-Rodríguez, F., González-Martel, C., Sosvilla-Rivero, S. (2001). Optimization of technical rules by genetic algorithms: evidence...
    • Figueroa-Pelayo, A. P., y Gualdron-López, A. E. (2014). Importancia de los índices bursátiles en el mercado de Colombia. Innovando en la U,...
    • Forrest, S. (1993). Genetic algorithms: Principles of adaptation applied to computation. Science 261 (August 13), 872- 878. https://doi.org/10.1126/science.8346439
    • Forrest, S. (1996). Genetic Algorithms. ACM Computing Surveys, 28(1), 77-80. https://dl.acm.org/doi/10.1145/234313.234350
    • García, M. C., Jalal, A. M., Garzón, L. A., & López, J. M. (2013). Methods for predicting stock indexes. Ecos de Economía, 17(37), 51-82....
    • Gold, S. (2015). The Viability of six popular technical analysis trading rules in determining effective buy and sell signals: MACD, AROON,...
    • Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: machine learning techniques applied to financial market prediction....
    • Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.
    • Holland, J. H. (1982). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial...
    • Isidore, R., Christie, P. (2018). Fundamental analysis versus technical analysis-A comparative review. Int J Recent Sci Res, 9(1), 23009-23013....
    • Khan, M. A., Aman, Q., & Khan, N. (2016) Technical analysis: concept or reality?. Pakistan Business Review: 732-751.
    • Levine, R. (1997). Financial development and economic growth: views and agenda. Journal of Economic Literature, 35(2), 688-726. https://www.jstor.org/stable/2729790
    • Llorente-López, M. A. (2012). Programación Genética en Mercados Financieros - Construcción automática de reglas de inversión utilizando programación...
    • Luukka, P., Pätäri, E., John, E., & Garanina, T. (2016). Performance of moving average trading rules in a volatile stock market: The Russian...
    • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 1952, 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
    • Metghalchi, M., Chen, C., & Hayes, L. (2015). History of share prices and market efficiency of the Madrid general stock index. International...
    • Mohd-Nor, S., & Wickremasinghe, G. (2014). The profitability of MACD and RSI trading rules in the Australian stock market. Investment...
    • Moosa, I., & Li, L. (2011). Technical and fundamental trading in the Chinese stock market: Evidence based on time-series and panel data....
    • Murphy, J. (1998). Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Pearson Professional...
    • Murphy, J. (2016). Análisis Técnico de los Mercados Financieros. Editorial Paidós.
    • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions....
    • Papailias, F., & Thomakos, D. D. (2015). An improved moving average technical trading rule. Physica A: Statistical Mechanics and its Applications,...
    • Petrusheva, N., & Jordanoski, I. (2016). Comparative analysis between fundamental and technical analysis of stocks. Journal of Process...
    • Radeerom, M. (2014). Automatic trading system based on genetic algorithm and technical analysis for stock index. International Journal of...
    • Restrepo-Gaviria, A. M. (2015). El sistema financiero y su importancia en el crecimiento económicos. [Tesis de pregrado, Universidad Pontificia...
    • Rodrigues-Leles, M. C., Amaral-Mozelli, L., & Nogueira-Guimarães, H. (2017). A new trend-following indicator: Using SSA to design trading...
    • Rosillo, R., de la Fuente, D., & Burgos, J. A. L. (2013). Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum...
    • Rosillo, R., Giner, J., & de la Fuente, D. (2014). Stock market simulation using support vector machines. Journal of Forecasting, 33 (6),...
    • Sevilla-Arias, A. (2012). Índice bursátil. https://bit.ly/3u5KvZi
    • Shangkun, D., Yizhou, S., & Akito, S. (2012). Robustness test of genetic algorithm on generating rules for currency trading, Procedia...
    • Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442....
    • Straßburg, J., Gonzàlez-Martel, C., & Alexandrov, V. (2012). Parallel genetic algorithms for stock market trading rules. Procedia Computer...
    • Suresh, A. S. (2013) A study of fundamental and technical analysis. International Journal of Marketing, Financial Services & Management...
    • Swain, K.R. (2012). Book Review: Technical Analysis Ideal for Beginners. Asia-Pacific Journal of Management Research and Innovation, 8(4),...
    • Taylor, N. (2014). The rise and fall of technical trading rule success. Journal of Banking & Finance, 40, 286-302. https://doi.org/10.1016/j.jbankfin.2013.12.004
    • Tharavanij, P., Siraprapasiri, V., & Rajchamaha, K. (2015). Performance of technical trading rules. SpringerPlus, 4(1). 1-40. https://doi.org/10.1186/s40064-015-1334-7
    • Vora, M. N. (2011). Genetic algorithm for trading signal generation. Solution to trader’s dilemma: Is it right time to trade?. International...
    • Wang, C.-F., Liu, K., Shen, P.-P. (2020) A novel genetic algorithm for global optimization. Acta Mathematicae Applicatae Sinica, English Series,...
    • Wang, L., An, H., Liu, X. & Huang, X. (2016). Selecting dynamic moving average trading rules in the crude oil futures market using a genetic...
    • Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
    • Zapranis, A., & Tsinaslanidis, P. E. (2012). A novel, rule-based technical pattern identification mechanism: Identifying and evaluating...
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno