Multi-objective Optimization of Technical Stock Market Indicators using GAs

financial researches showed that technical indicators are useful tools for stock prediction. Technical indicators are used to generate trading signals (buy/sell) signals. The main problem of an indicator usage is to determine its appropriate parameters. In this paper a new GA based technique for optimizing the parameters of a collection of technical indicators over two objective functions Sharpe ratio and annual profit is proposed. The technique handles four indicators DEMAC (Double Exponential Moving Average Crossovers), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and MARSI (Moving Average RSI) indicators. The technique was tested on 30 years of historical data of DJIA (Dow Jones Industrial Average) stock index. Results showed that the optimized parameters obtained by the proposed technique improved the profits obtained by the indicators with their typical parameters, the Buy and Hold strategy and the random strategy.

[1]  Nuno Horta,et al.  An Innovative GA Optimized Investment Strategy based on a New Technical Indicator using Multiple MAS , 2010, IJCCI.

[2]  Vladimir Filimonov,et al.  On the Modeling of Financial Time Series , 2015 .

[3]  J. Murphy Technical Analysis of the Financial Markets , 1999 .

[4]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[5]  V Kapoor,et al.  Genetic Algorithm: An Application to Technical Trading System Design , 2011 .

[6]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[7]  José Ignacio Hidalgo,et al.  Multiobjective optimization of technical market indicators , 2009, GECCO '09.

[8]  Hitoshi Iba,et al.  Optimization of the trading rule in foreign exchange using genetic algorithm , 2009, GECCO.

[9]  David W. Corne,et al.  Multiobjective algorithms for financial trading: Multiobjective out-trades single-objective , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[10]  Jan Korbel,et al.  Modeling Financial Time Series , 2013 .

[11]  N. P. Landsman,et al.  A random walk down Wall Street , 2008 .

[12]  S. N. Sivanandam,et al.  Introduction to genetic algorithms , 2007 .

[13]  Y. Ong,et al.  An empirical study of Genetic Programming generated trading rules in computerized stock trading service system , 2008, 2008 International Conference on Service Systems and Service Management.

[14]  Byung Ro Moon,et al.  Finding attractive rules in stock markets using a modular genetic programming , 2009, GECCO '09.

[15]  José Ignacio Hidalgo,et al.  Technical market indicators optimization using evolutionary algorithms , 2008, GECCO '08.

[16]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[17]  Andreas S. Weigend,et al.  Nonlinear Trading Models Through Sharpe Ratio Maximization , 1997, Int. J. Neural Syst..