Bodas, DiegoFernández, PabloHidalgo, José IgnacioSoltero, FranciscoRisco, José Luis2024-01-312024-01-312009-07-12@inproceedings{10.1145/1570256.1570266, author = {Bodas-Sagi, Diego J. and Fern\'{a}ndez, Pablo and Hidalgo, J. Ignacio and Soltero, Francisco J. and Risco-Mart\'{\i}n, Jos\'{e} L.}, title = {Multiobjective optimization of technical market indicators}, year = {2009}, isbn = {9781605585055}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/1570256.1570266}, doi = {10.1145/1570256.1570266}, abstract = {This paper deals with the optimization of technical indicators for stock market investment. Price prediction is a problem of great complexity and usually some technical indicators are used to predict the markets trends. The main difficulty in the use of technical indicators lies in deciding the parameters values. We proposed the use of Evolutionary Algorithms (EAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. This paper extends the work presented on previous works by including additional indicators and applying them to more complex problems. In this way the Moving Averages Convergence-Divergence (MACD) indicator and the Relative Strength Index (RSI) oscillator have been selected to obtain the buying/selling signals. The experimental results indicate that our EAs offer a solution to the problem obtaining results that improve those obtained through technical indicators with their standard parameters.}, booktitle = {Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers}, pages = {1999–2004}, numpages = {6}, keywords = {technical trading rules, stock market data mining, optimization, finance, evolutionary algorithms, decision making}, location = {Montreal, Qu\'{e}bec, Canada}, series = {GECCO '09} }9781605585055https://hdl.handle.net/10115/29326This paper deals with the optimization of technical indicators for stock market investment. Price prediction is a problem of great complexity and usually some technical indicators are used to predict the markets trends. The main difficulty in the use of technical indicators lies in deciding the parameters values. We proposed the use of Evolutionary Algorithms (EAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. This paper extends the work presented on previous works by including additional indicators and applying them to more complex problems. In this way the Moving Averages Convergence-Divergence (MACD) indicator and the Relative Strength Index (RSI) oscillator have been selected to obtain the buying/selling signals. The experimental results indicate that our EAs offer a solution to the problem obtaining results that improve those obtained through technical indicators with their standard parameters.engAlgoritmos evolutivos, indicadores bursátiles, optimizacióninfo:eu-repo/semantics/article10.1145/1570256.1570266info:eu-repo/semantics/embargoedAccess