Abstract
We consider an online adaptive forecasting algorithm for time series elements. Based on this algorithm, we define a universal strategy for the financial market: such a strategy ensures asymptotically maximal profit compared to any trading strategy where decisions are made based on rules that depend continuously on the input information. To reduce risk, in simultaneous trading of several financial instruments we perform adaptive redistribution of the current capital among them according to the AdaHedge algorithm. We propose variations of a combined game with various algorithmic trading strategies. We give results of numerical experiments based on historical data of the MICEX and BATS (US) trading platforms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Dawid, A.P., Calibration-Based Empirical Probability [with Discussion], Ann. Statist., 1985, vol. 13, pp. 1251–1285.
Foster, D.P. and Vohra, R., Asymptotic Calibration, Biometrika, 1998, vol. 85, pp. 379–390.
Cover, T., Universal Portfolios, Math. Finance, 1991, vol. 1, pp. 1–29.
Cover, T. and Ordentlich, E., Universal Portfolio with Side Information, IEEE Trans. Inform. Theory, 1996, vol. 42, pp. 348–363.
Kakade, S.M. and Foster, D.P., Deterministic Calibration and Nash Equilibrium, in Lecture Notes Comput. Sci., Berlin: Springer, 2004, vol. 3120, pp. 33–48.
Vovk, V., On-line Regression Competitive with Reproducing Kernel Hilbert Spaces (extended abstract), in Lecture Notes Comput. Sci., Berlin: Springer, 2006, vol. 3959, pp. 452–463.
V’yugin, V.V. and Trunov, V.G., Adaptive Forecasting and Its Application for Technical Analysis of Financial Instruments, Inform. Protsessy, 2011, vol. 11, no. 1, pp. 46–75.
V’yugin, V.V. and Trunov, V.G., An Adaptive Universal Trading Strategy, Inform. Protsessy, 2013, vol. 13, no. 4, pp. 237–264.
V’yugin, V.V., Universal Algorithm for Trading in Stock Market Based on the Method of Calibration, in Lecture Notes Artific. Intelligence (LNAI), vol. 8139, pp. 53–67.
Scholkopf, B. and Smola, A., Learning with Kernels, Cambridge, MA: MIT Press, 2002.
de Rooij, S., van Erven, T., Grunwald, P.D., and Koolen, W.M., Follow the Leader If You Can, Hedge If You Must, J. Machine Learning Res., 2014, vol. 15, pp. 1281–1316.
V’yugin, V.V., The Following the Perturbed Leader Algorithm and Its Application for Constructing Game Strategies, J. Commun. Technol. Electron., 2015, vol. 60(6), pp. 647–657.
V’yugin, V. and Trunov, V., Universal Algorithmic Trading, J. Investment Strateg., 2012/13, vol. 2(1), pp. 63–88.
Vovk, V., Takemura, A., and Shafer, G., Defensive Forecasting, Proc. 10 Int. Workshop Artific. Intelligence Statist., Cowell, R.G. and Ghahramani, Z., Eds., Cambridge: Society for Artificial Intelligence and Statistics, 2005, pp. 365–372.
Steinwart, I., On the Influence of the Kernel on the Consistency of Support Vector Machines, J. Machine Learning Res., 2001, vol. 2, pp. 67–93.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © V.V. V’yugin, V.G. Trunov, 2016, published in Avtomatika i Telemekhanika, 2016, No. 8, pp. 136–158.
Rights and permissions
About this article
Cite this article
V’yugin, V.V., Trunov, V.G. Applications of combined financial strategies based on universal adaptive forecasting. Autom Remote Control 77, 1428–1446 (2016). https://doi.org/10.1134/S0005117916080099
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0005117916080099