Abstract
In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algorithm. In general terms, this kind of approach consists in specifying a trading policy based on some predetermined investor’s measure of profitability, and in setting the financial trading system while using it. In particular, with respect to the prominent literature, in this contribution: first, we take into account as measure of profitability the reciprocal of the returns weighted direction symmetry index instead of the wide-spread Sharpe ratio; second, we obtain the differential version of this measure of profitability and obtain all the related learning relationships; third, we propose a procedure for the management of drawdown-like phenomena; finally, we apply our financial trading approach to some of the major world financial market indices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abecasis, S.M., Lapenta, E.S., Pedreira, C.E.: Performance metrics for financial time series forecasting. Journal of Computational Intelligence in Finance, 5–23 (July/August 1999)
Bishop, C.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Corazza, M., Vanni, P., Loschi, U.: Hybrid automatic trading system: technical analysis & group method of data handling. In: Marinaro, M., Tagliaferro, R. (eds.) Neural nets. 13th Italian workshop on neural nets, WIRN Vietri 2002, Vietri sul Mare, Italy, May/June 2002, pp. 47–55. Springer, Berlin (2002) (revised paper)
Gold, C.: FX trading via recurrent reinforcement learning. In: Proceedings of IEEE international conference on computational intelligence in financial engineering, pp. 363–370. IEEE Computer Society Press, Los Alamitos (2003)
Lo, W.A., Mamaysky, H., Wang, J.: Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. Journal of Finance LV, 1705–1769 (2000)
Moody, J., Saffell, M.: Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks 12, 875–889 (2001)
Moody, J., Wu, L., Liao, Y., Saffell, M.: Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting 17, 441–470 (1998)
Murphy, J.J.: Study guide to technical analysis of the financial markets. Prentice Hall Press, New York (1999)
Sutton, R.S., Barto, A.G.: An introduction to reinforcement learning. MIT Press, Cambridge (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bertoluzzo, F., Corazza, M. (2007). Making Financial Trading by Recurrent Reinforcement Learning. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-74827-4_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74826-7
Online ISBN: 978-3-540-74827-4
eBook Packages: Computer ScienceComputer Science (R0)