Abstract
The construction of automated financial trading systems (FTSs) is a subject of high interest for both the academic environment and the financial one due to the potential promises by self-learning methodologies. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that real-time optimize their behavior in relation to the responses they get from the environment in which they operate, without the need for a supervisor. In particular, first we introduce the essential aspects of RL which are of interest for our purposes, second we present some original automatic FTSs based on differently configured RL-based algorithms, then we apply such FTSs to artificial and real time series of daily stock prices. Finally, we compare our FTSs with a classical one based on Technical Analysis indicators. All the results we achieve are generally quite satisfactory.
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.
Similar content being viewed by others
References
Barto, A.G., Sutton, R.S.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. The MIT Press (1998)
Bekiros, S.D.: Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach. Journal of Economic Dynamics & Control 34, 1153–1170 (2010)
Bertoluzzo, F., Corazza, M.: Making financial trading by recurrent reinforcement learning. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007/WIRN 2007, , Part II. LNCS (LNAI), vol. 4693, pp. 619–626. Springer, Heidelberg (2007)
Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific (1996)
Brent, R.P.: Algorithms for Minimization without Derivatives. Prentice-Hall (1973)
Bosq, D.: Nonparametric Statistics for Stochastic Processes. Estimation and Prediction, vol. 110. Springer (1996)
Cuthbertson, K., Nitzsche, D.: Quantitative Financial Economics. Wiley (2004)
Farmer, D., Lo, A.W.: Market force, ecology and evolution. Industrial and Corporate Change 11, 895–953 (2002)
Gold, C.: FX trading via recurrent Reinforcement Learning. In: Proceedings of the IEEE International Conference on Computational Intelligence in Financial Engineering, pp. 363–370 (2003)
Li, H., Dagli, C.H., Enke, D.: Short-term stock market timing prediction under reinforcement learning schemes. In: Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp. 233–240 (2007)
Lo, A.W., Mamaysky, H., Wang, J.: Foundations of technical analysis: Computational algorithms, statistical inference, and empirical (2000)
Lo, A.W.: The Adaptive Markets Hypothesis. Market efficiency from an evolutionary perspective. The Journal of Portfolio Management 30, 15–29 (2004)
Moody, J., Wu, L., Liao, Y., Saffel, M.: Performance functions and Reinforcement Learning for trading systems and portfolios. Journal of Forecasting 17, 441–470 (1998)
Moody, J., Saffel, M.: Learning to trade via Direct Reinforcement. IEEE Transactions on Neural Network 12, 875–889 (2001)
Murphy, J.J.: Technical Analysis of the Financial Markets. A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance (1999)
Jangmin, O., Lee, J., Lee, J.W., Zhang, B.-T.: Adaptive stock trading with dynamic asset allocation using reinforcemnt learning. Information Sciences 176, 2121–2147 (2006)
Ormonet, D.: Kernel-Based Reinforcement Learning. Machine Learning 49, 161–178 (2002)
Smart, W.D., Kaelbling, L.P.: Practical Reinforcement Learning in continuous spaces. In: Proceedings of the 17th International Conference on Machine Learning, pp. 903–910 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bertoluzzo, F., Corazza, M. (2014). Reinforcement Learning for Automated Financial Trading: Basics and Applications. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-04129-2_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
Online ISBN: 978-3-319-04129-2
eBook Packages: EngineeringEngineering (R0)