Abstract
Nowadays, the increased level of uncertainty in various sectors has posed great burdens in the decision-making process. In the financial domain, a crucial issue is how to properly allocate the available amount of capital, in a number of provided assets, in order to maximize wealth. Automated trading systems assist the aforementioned process to a great extent. In this paper, a basic type of such a system is presented. The aim of the study focuses on the behavior of this system in changes to its parameter settings. A number of independent simulations have been conducted, for the various parameter settings, and distributions of profits/losses have been acquired, leading to interesting concluding remarks.
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References
Tucnik, P.: Optimization of Automated Trading System’s Interaction with Market Environment. In: Forbrig, P., Günther, H. (eds.) BIR 2010. LNBIP, vol. 64, pp. 55–61. Springer, Heidelberg (2010)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modeling. Natural Computing Series. Springer, Heidelberg (2006)
Carter, J.F.: Mastering the Trade-Proven Techniques from Intraday and Swing Trading Setups. McGraw-Hill, New York (2006)
Kaufman, P.J.: New Trading Systems and Methods, 4th edn. John Wiley & Sons, New Jersey (2005)
Tucnik, P.: Automated Trading System Design. In: Godara, V. (ed.) Pervasive Computing for Business: Trends and Applications. IGI Global, Sydney (2010)
Dempster, M.A.H., Jones, C.M.: A real-time adaptive trading system using genetic programming. Quantitative Finance 1, 397–413 (2001)
Dempster, M.A.H., Leemans, V.: An automated FX trading system using adaptive reinforcement learning. Expert Systems with Applications 30, 543–552 (2006)
Kuo, R.J., Chen, C.H., Hwang, Y.C.: An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems 118, 21–45 (2001)
Ghandar, A., Michalewicz, Z., Schmidt, M., To, T.D., Zurbrugg, R.: Computational Intelligence for Evolving Trading Rules. IEEE Transactions on Evolutionary Computation 13(1), 71–85 (2009)
Briza, A.C., Naval, P.C.: Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing 11, 1191–1201 (2011)
Markowitz, H.: Portfolio Selection. The Journal of Finance 7(1), 77–91 (1952)
Dorigo, M., Stultze, M.: Ant Colony Optimization. MIT Press (2004)
More, J.J.: The Levenberg-Marquardt algorithm: Implementation and Theory. Lecture Notes in Mathematics, vol. 630, pp. 103–116 (1978)
Appel, G.: Technical Analysis Power Tools for Active Investors. Financial Times Prentice Hall (1999)
Kuhn, J.: Optimal risk-return tradeoffs of commercial banks and the suitability of probability measures for loan portfolios. Springer, Heidelberg (2006)
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Vassiliadis, V., Dounias, G. (2014). Nature-Inspired Intelligent Techniques for Automated Trading: A Distributional Analysis. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_21
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DOI: https://doi.org/10.1007/978-3-319-07064-3_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
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