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

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Autores

Arbey Aragón Bohorquez
Carlos Armando Mejía Vega
Carlos Andres Zapata Quimbayo

Resumen

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.

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Referencias

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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