Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
Titulo:

Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
.

Sumario:

La implementación de estrategias de trading a través de herramientas computacionales e inteligencia artificial, entre ellas las redes neuronales artificiales (RNA) y los algoritmos genéticos (AG), ha presentado avances importantes en los últimos años. En este trabajo se implementó un AG para optimizar una estrategia de trading basada en dos promedios móviles en el mercado intradiario de futuros de petróleo crudo WTI. La función objetivo es el retorno global de la inversión. En el documento se presenta la metodología y el diseño de esta estrategia de inversión con resultados consistentes incluso fuera de muestra.

Guardado en:

Revista ODEON

1794-1113

2346-2140

Aragón Bohorquez, Arbey

Mejía Vega, Carlos Armando

Zapata Quimbayo, Carlos Andres

UNIVERSIDAD EXTERNADO DE COLOMBIA

10.18601/17941113.n15.05

2019-05-13

139

160

Colombia

algoritmos genéticos;

promedios móviles;

futuros de petróleo

Arbey Aragón Bohorquez, Carlos Armando Mejía Vega, Carlos Andres Zapata Quimbayo - 2019

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http://purl.org/coar/access_right/c_abf2

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spelling Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
Optimization of trading strategies with moving averages for oil futures using genetic algorithms
La implementación de estrategias de trading a través de herramientas computacionales e inteligencia artificial, entre ellas las redes neuronales artificiales (RNA) y los algoritmos genéticos (AG), ha presentado avances importantes en los últimos años. En este trabajo se implementó un AG para optimizar una estrategia de trading basada en dos promedios móviles en el mercado intradiario de futuros de petróleo crudo WTI. La función objetivo es el retorno global de la inversión. En el documento se presenta la metodología y el diseño de esta estrategia de inversión con resultados consistentes incluso fuera de muestra.
The implementation of trading strategies through computational tools and artificial intelligence, such as artificial neural networks (ANN) and genetic algorithms (AG), have presented important advances in the last years, In this paper it is implemented a ag for the optimization of a trading strategy with two moving averages in the inter-day market of crude oil futures. The objective function is the global return of the investment. The document presents the methodology and the design of this investment strategy with consistent results even with out-of-sample data.
Aragón Bohorquez, Arbey
Mejía Vega, Carlos Armando
Zapata Quimbayo, Carlos Andres
genetic algorithms;
moving average;
oil futures
algoritmos genéticos;
promedios móviles;
futuros de petróleo
15
Núm. 15 , Año 2018 : Julio-Diciembre
Artículo de revista
Journal article
2019-05-13T00:00:00Z
2019-05-13T00:00:00Z
2019-05-13
application/pdf
text/html
Universidad Externado de Colombia
ODEON
1794-1113
2346-2140
https://revistas.uexternado.edu.co/index.php/odeon/article/view/5951
10.18601/17941113.n15.05
https://doi.org/10.18601/17941113.n15.05
spa
https://creativecommons.org/licenses/by-nc-sa/4.0/
Arbey Aragón Bohorquez, Carlos Armando Mejía Vega, Carlos Andres Zapata Quimbayo - 2019
139
160
Alexander, S. (1964). Price movements in speculative markets: trends or random walks. En P. Cootner (ed.), The Random Character of Stock Market Prices (vol. 2), Cambridge: MIT Press.
Allen, F. y Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245-271.
Ayadi, O., Williams, J. y Hyman, L. (2009). Fractional dynamic behavior in forcados oil price series: An application of detrended fluctuation analysis. Energy for Sustainable Development, 13(1), 11-17.
Boboc, I. y Dinica, M. (2013). An algorithm for testing the efficient market hypothesis. PLoS ONE, 8(11), e78177.
Brooks, J. (2006). Mastering Technical Analysis. Using the Tools of Technical Analysis for Profitable Trading. New York: McGraw-Hill.
Cheng, C., Chen, T. y Wei, L. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180(9), 1610-1629.
Conrad, J. y Kaul, G. (1998). An anatomy of trading strategies. The Review of Financial Studies, 11(3), 489-519.
Cuaresma, J., Hlouskova, J., Kossmeier, S. y Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106.
Davey, A. (2010). Deregulation of wholesale petrol prices: What happened to capital city petrol prices? Australian Journal of Agricultural and Resource Economics, 54(1), 81-98.
De Bondt, W. y Thaler, R. (1985). Does the stock market overreact? The Journal of Finance, 40(3), 793-805.
Deng, S., Yoshiyama, K., Mitsubuchi, T. y Sakurai, A. (2015). Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Computational Economics, 45(1), 49-89.
Dong, H. y Huang, W. (2014). Prediction of geothermal resources by means of wavelet neural network optimized by genetic algorithm. Res. Ind, 16(3), 101-106.
Esfahanipour, A. y Mousavi, S. (2011). A genetic programming model to generate riskadjusted technical trading rules in stock markets. Expert Systems with Applications, 38(1), 8438-8445.
Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.
Fama, E. y Blume, M. (1966). Filter rules and stock market trading. Security prices: A supplement. Journal of Business, 39(1), 226-241.
Fama, E. y French, K. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465.
Glabadanidis, P. (2015). Market Timing and Moving Averages: An Empirical Analysis of Performance in Asset Allocation. New York: Palgrave MacMillan.
Holland, J. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology. Ann Arbor: University of Michigan Press.
Kabundi, A. y Mwamba, J. (2012). Appying a genetic algorithm to international diversification of equity portfolios: A South African investor perspective. South African Journal of Economics, 80(1), 91-105.
Li, J. y Tsang, E. (1999). Improving Technical Analysis Predictions: An Application of Genetic Programming. En flairs Conference, 108-112.
Lin, L., Cao, L., Wang, J. y Zhang, C. (2004). The applications of genetic algorithms in stock market data mining optimisations. En Conference on Data Mining, Text Mining and Their Business Application. Wessex Institute of Technology Press, 273-280.
Liu, X., An, H. y Wang, L. (2015). Performance of generated moving average strategies in natural gas futures prices at different time scales. Portfolios: A South African investor perspective. Journal of Natural Gas Science and Engineering, 24(1), 337-345.
Lo, A. y MacKinlay, A. (1990). An econometric analysis of nonsynchronous trading. Journal of Econometrics, 45(1), 181-211.
Matilla, M. y Argello, C. (2005). A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the spanish stock market. Applied Economics Letters, 12(5) 303-308.
Metghalchi, M., Marcucci, J. y Chang, Y. (2012). Are moving average trading rules profitable? Evidence from the European stock markets. Applied Economics, 162(15), 1539-1559.
Muñoz, M., Corchero, C. y Heredia, F. (2013). Improving electricity market price forecasting with factor models for the optimal generation bid. International Statistical Review, 81(2), 289-306.
Murphy, J. (1999). Technical analysis of the financial markets. New York: Penguin Group.
Potvin, J., Sorianoa, P. y Valléeb, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 44(12), 1033-1047.
Qu, H., y Li, X. (2014). Building technical trading system with genetic programming: A new method to test the efficiency of Chinese stock markets. Computational Economics, 43(3), 301-311.
Roberts, M. (2005). Technical analysis and genetic programming: Constructing and testing a commodity portfolio. The Journal of Futures Markets, 25(7), 643-660.
Routledge, B. (2001). Genetic algorithm learning to choose and use information. Macroeconomic dynamics, 5(2), 303-325.
Straßburg, J., González, C. y Alexandrov, V. (2012). Parallel genetic algorithms for stock market trading rules. Procedia Computer Science, 9(1), 1306-1313.
Trippi, R. y Turban, E. (1992). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. New York: McGrawHill.
Tsang, R. y Lajbcygier, P. (2002). Optimizing technical trading strategies with split search genetic algorithms. En Chen (ed.), Evolutionary Computation in Economics and Finance (pp. 333-358), Berlin: Springer-Verlag.
Wang, C., Wu, C. y Tzang, S. (2012). Implementing option pricing models when asset returns follow an autoregressive moving average process. International Review of Economics & Finance, 24, 8-25.
Wang, L., An, H., Xia, X., Liu, X., Sun, X., Huang, X. (2014). Generating moving average trading rules on the oil futures market with genetic algorithms. Mathematical Problems in Engineering, 1, 1-10.
Wang, L., An, H., Xia, X., Liu, X., Sun, X. y Huang, X. (2016). Selecting dyna- mic moving average trading rules in the crude oil futures market using a genetic approach. Applied Energy, 162(15), 1608-1618.
Wiesinger, J., Sornette, D. y Satinover, J. (2013). Reverse engineering financial markets with majority and minority games using genetic algorithms. Computational Economics, 41(4), 475-492.
https://revistas.uexternado.edu.co/index.php/odeon/article/download/5951/7676
https://revistas.uexternado.edu.co/index.php/odeon/article/download/5951/7888
info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
http://purl.org/redcol/resource_type/ARTREF
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Text
Publication
institution UNIVERSIDAD EXTERNADO DE COLOMBIA
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country_str Colombia
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title Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
spellingShingle Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
Aragón Bohorquez, Arbey
Mejía Vega, Carlos Armando
Zapata Quimbayo, Carlos Andres
genetic algorithms;
moving average;
oil futures
algoritmos genéticos;
promedios móviles;
futuros de petróleo
title_short Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
title_full Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
title_fullStr Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
title_full_unstemmed Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
title_sort optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos
title_eng Optimization of trading strategies with moving averages for oil futures using genetic algorithms
description La implementación de estrategias de trading a través de herramientas computacionales e inteligencia artificial, entre ellas las redes neuronales artificiales (RNA) y los algoritmos genéticos (AG), ha presentado avances importantes en los últimos años. En este trabajo se implementó un AG para optimizar una estrategia de trading basada en dos promedios móviles en el mercado intradiario de futuros de petróleo crudo WTI. La función objetivo es el retorno global de la inversión. En el documento se presenta la metodología y el diseño de esta estrategia de inversión con resultados consistentes incluso fuera de muestra.
description_eng The implementation of trading strategies through computational tools and artificial intelligence, such as artificial neural networks (ANN) and genetic algorithms (AG), have presented important advances in the last years, In this paper it is implemented a ag for the optimization of a trading strategy with two moving averages in the inter-day market of crude oil futures. The objective function is the global return of the investment. The document presents the methodology and the design of this investment strategy with consistent results even with out-of-sample data.
author Aragón Bohorquez, Arbey
Mejía Vega, Carlos Armando
Zapata Quimbayo, Carlos Andres
author_facet Aragón Bohorquez, Arbey
Mejía Vega, Carlos Armando
Zapata Quimbayo, Carlos Andres
topic genetic algorithms;
moving average;
oil futures
algoritmos genéticos;
promedios móviles;
futuros de petróleo
topic_facet genetic algorithms;
moving average;
oil futures
algoritmos genéticos;
promedios móviles;
futuros de petróleo
topicspa_str_mv algoritmos genéticos;
promedios móviles;
futuros de petróleo
citationissue 15
citationedition Núm. 15 , Año 2018 : Julio-Diciembre
publisher Universidad Externado de Colombia
ispartofjournal ODEON
source https://revistas.uexternado.edu.co/index.php/odeon/article/view/5951
language spa
format Article
rights https://creativecommons.org/licenses/by-nc-sa/4.0/
Arbey Aragón Bohorquez, Carlos Armando Mejía Vega, Carlos Andres Zapata Quimbayo - 2019
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
references Alexander, S. (1964). Price movements in speculative markets: trends or random walks. En P. Cootner (ed.), The Random Character of Stock Market Prices (vol. 2), Cambridge: MIT Press.
Allen, F. y Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245-271.
Ayadi, O., Williams, J. y Hyman, L. (2009). Fractional dynamic behavior in forcados oil price series: An application of detrended fluctuation analysis. Energy for Sustainable Development, 13(1), 11-17.
Boboc, I. y Dinica, M. (2013). An algorithm for testing the efficient market hypothesis. PLoS ONE, 8(11), e78177.
Brooks, J. (2006). Mastering Technical Analysis. Using the Tools of Technical Analysis for Profitable Trading. New York: McGraw-Hill.
Cheng, C., Chen, T. y Wei, L. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180(9), 1610-1629.
Conrad, J. y Kaul, G. (1998). An anatomy of trading strategies. The Review of Financial Studies, 11(3), 489-519.
Cuaresma, J., Hlouskova, J., Kossmeier, S. y Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106.
Davey, A. (2010). Deregulation of wholesale petrol prices: What happened to capital city petrol prices? Australian Journal of Agricultural and Resource Economics, 54(1), 81-98.
De Bondt, W. y Thaler, R. (1985). Does the stock market overreact? The Journal of Finance, 40(3), 793-805.
Deng, S., Yoshiyama, K., Mitsubuchi, T. y Sakurai, A. (2015). Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Computational Economics, 45(1), 49-89.
Dong, H. y Huang, W. (2014). Prediction of geothermal resources by means of wavelet neural network optimized by genetic algorithm. Res. Ind, 16(3), 101-106.
Esfahanipour, A. y Mousavi, S. (2011). A genetic programming model to generate riskadjusted technical trading rules in stock markets. Expert Systems with Applications, 38(1), 8438-8445.
Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.
Fama, E. y Blume, M. (1966). Filter rules and stock market trading. Security prices: A supplement. Journal of Business, 39(1), 226-241.
Fama, E. y French, K. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465.
Glabadanidis, P. (2015). Market Timing and Moving Averages: An Empirical Analysis of Performance in Asset Allocation. New York: Palgrave MacMillan.
Holland, J. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology. Ann Arbor: University of Michigan Press.
Kabundi, A. y Mwamba, J. (2012). Appying a genetic algorithm to international diversification of equity portfolios: A South African investor perspective. South African Journal of Economics, 80(1), 91-105.
Li, J. y Tsang, E. (1999). Improving Technical Analysis Predictions: An Application of Genetic Programming. En flairs Conference, 108-112.
Lin, L., Cao, L., Wang, J. y Zhang, C. (2004). The applications of genetic algorithms in stock market data mining optimisations. En Conference on Data Mining, Text Mining and Their Business Application. Wessex Institute of Technology Press, 273-280.
Liu, X., An, H. y Wang, L. (2015). Performance of generated moving average strategies in natural gas futures prices at different time scales. Portfolios: A South African investor perspective. Journal of Natural Gas Science and Engineering, 24(1), 337-345.
Lo, A. y MacKinlay, A. (1990). An econometric analysis of nonsynchronous trading. Journal of Econometrics, 45(1), 181-211.
Matilla, M. y Argello, C. (2005). A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the spanish stock market. Applied Economics Letters, 12(5) 303-308.
Metghalchi, M., Marcucci, J. y Chang, Y. (2012). Are moving average trading rules profitable? Evidence from the European stock markets. Applied Economics, 162(15), 1539-1559.
Muñoz, M., Corchero, C. y Heredia, F. (2013). Improving electricity market price forecasting with factor models for the optimal generation bid. International Statistical Review, 81(2), 289-306.
Murphy, J. (1999). Technical analysis of the financial markets. New York: Penguin Group.
Potvin, J., Sorianoa, P. y Valléeb, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 44(12), 1033-1047.
Qu, H., y Li, X. (2014). Building technical trading system with genetic programming: A new method to test the efficiency of Chinese stock markets. Computational Economics, 43(3), 301-311.
Roberts, M. (2005). Technical analysis and genetic programming: Constructing and testing a commodity portfolio. The Journal of Futures Markets, 25(7), 643-660.
Routledge, B. (2001). Genetic algorithm learning to choose and use information. Macroeconomic dynamics, 5(2), 303-325.
Straßburg, J., González, C. y Alexandrov, V. (2012). Parallel genetic algorithms for stock market trading rules. Procedia Computer Science, 9(1), 1306-1313.
Trippi, R. y Turban, E. (1992). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. New York: McGrawHill.
Tsang, R. y Lajbcygier, P. (2002). Optimizing technical trading strategies with split search genetic algorithms. En Chen (ed.), Evolutionary Computation in Economics and Finance (pp. 333-358), Berlin: Springer-Verlag.
Wang, C., Wu, C. y Tzang, S. (2012). Implementing option pricing models when asset returns follow an autoregressive moving average process. International Review of Economics & Finance, 24, 8-25.
Wang, L., An, H., Xia, X., Liu, X., Sun, X., Huang, X. (2014). Generating moving average trading rules on the oil futures market with genetic algorithms. Mathematical Problems in Engineering, 1, 1-10.
Wang, L., An, H., Xia, X., Liu, X., Sun, X. y Huang, X. (2016). Selecting dyna- mic moving average trading rules in the crude oil futures market using a genetic approach. Applied Energy, 162(15), 1608-1618.
Wiesinger, J., Sornette, D. y Satinover, J. (2013). Reverse engineering financial markets with majority and minority games using genetic algorithms. Computational Economics, 41(4), 475-492.
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