Application of differential evolution algorithm for optimization of strategies based on financial time series
Sibirskij žurnal vyčislitelʹnoj matematiki, Tome 19 (2016) no. 2, pp. 195-205.

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An approach to optimization of trading strategies (algorithms) based on indicators of financial markets and evolutionary computation is described. A new version of the differential evolution algorithm for the search for optimal parameters of trading strategies for the trading profit maximization is used. The experimental results show that this approach can considerably improve the profitability of the trading strategies.
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O. G. Monakhov; E. A. Monakhova; M. Pant. Application of differential evolution algorithm for optimization of strategies based on financial time series. Sibirskij žurnal vyčislitelʹnoj matematiki, Tome 19 (2016) no. 2, pp. 195-205. http://geodesic.mathdoc.fr/item/SJVM_2016_19_2_a5/

[1] Fama E., Blume M., “Filter rules and stock-market trading”, J. of Business, 39:1 (1966), 226–241 | DOI

[2] Brock W., Lakonishok J., LeBaron B., “Simple technical trading rules and the stochastic properties of stock returns”, J. of Finance, 45:5 (1992), 1731–1764 | DOI

[3] Gencay R., “The predictability of security returns with simple technical trading rules”, J. of Empirical Finance, 5:4 (1998), 347–359 | DOI

[4] Kestner L., Quantitative Trading Strategies: Harnessing the Power of Quantitative Techniques to Create a Winning Trading Program, McGraw-Hill Professional, Europe–United States, 2003

[5] Allen F., Karjalainen R., “Using genetic algorithms to find technical trading rules”, J. of Financial Economics, 51:2 (1999), 245–271 | DOI

[6] Monakhov O. G., “Parallel genetic algorithm for optimization of trading strategies”, Numerical Analysis and Applications, 1:4 (2008), 347–354 | DOI

[7] Atsalakis G. S., Valavanis K. P., “Surveying stock market forecasting techniques. Part II: Soft computing method”, J. Expert Systems with Applications, 36:3 (2009), 5932–5941 | DOI

[8] Kwok N. M., Fang G., Ha Q. P., “Moving average-based stock trading rules from Particle Swarm Optimization”, Proc. of Int. Conference on Artificial Intelligence and Computational Intelligence, v. 1, 2009, 149–153

[9] Abolhassani A. T., Yaghoobi M., “Stock price forecasting using PSOSVM”, 3rd Int. Conf. on Advanced Computer Theory and Engineering (ICACTE), v. 3, 2010, 352–356

[10] Niu D., Wei S. W., Sun Y., “RBF and artificial fish swarm algorithm for short-term forecast of stock indices”, Second Int. Conf. on Communication Systems, Networks and Applications (ICCSNA-2010), v. 1, 2010, 139–142

[11] Karazmodeh M., Nasiri S., Hashemi S. M., “Stock price forecasting using support vector machines and improved particle swarm optimization”, J. of Automation and Control Engineering, 1:2 (2013), 173–176 | DOI

[12] Devi M. S., Singh Ksh. R., “Study on mutual funds trading strategy using TPSO and MACD”, Int. J. of Computer Science and Information Technologies, 5:1 (2014), 884–891

[13] Liu W., Chen T., Lee Mike Y. J., “A Method for stock trading strategy combining technical analysis and particle swarm optimization”, J. of Convergence Information Technology (JCIT), 9:5 (2014), 44–56

[14] Achelis S. B., Technical Analysis from A to Z, Probus, Chicago, 1996

[15] Artemev S. S., Yakunin M. A., Matematicheskoe i statisticheskoe modelirovanie na fondovykh rynkakh, Izd-vo IVMiMG SO RAN, Novosibirsk, 2003

[16] LeBeau C., Lucas D. W., Computer Analysis of the Futures Market, IRWIN, New-York, 1992

[17] Weissman R. L., Mechanical Trading Systems, John Wiley and Sons, Inc., Hoboken, New Jersey, 2005

[18] Salov V., Modeling maximum trading profits with C++: new trading and money management concepts, John Wiley and Sons, Inc., Hoboken, New Jersey, 2007

[19] Blau W., Momentum, Direction and Divergence, John Wiley and Sons, Inc., Hoboken, New Jersey, 2001

[20] Storn R., Price K., “Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces”, J. Global Optimization, 11:4 (1997), 341–359 | DOI | MR | Zbl

[21] Zaheer H., Pant M., Kumar S., Monakhov O., Monakhova E., Deep K., “A new guiding force strategy for differential evolution”, Int. J. of System Assurance Engineering and Management, 6:4 (2015), 1–14

[22] Monakhov O. G., “Evolutionary synthesis of algorithms based on templates”, Optoelectronics, Instrumentation and Data Processing (New York), 2006, no. 1, 106–116