Abstract
We present a universal method for algorithmic trading in Stock Market which performs asymptotically at least as well as any stationary trading strategy that computes the investment at each step using a continuous function of the side information. In the process of the game, a trader makes decisions using predictions computed by a randomized well-calibrated algorithm. We use Dawid’s notion of calibration with more general checking rules and some modification of Kakade and Foster’s randomized rounding algorithm for computing the well-calibrated forecasts. The method of randomized calibration is combined with Vovk’s method of defensive forecasting in RKHS. Unlike in statistical theory, no stochastic assumptions are made about the stock prices.
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V’yugin, V. (2013). Universal Algorithm for Trading in Stock Market Based on the Method of Calibration. In: Jain, S., Munos, R., Stephan, F., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2013. Lecture Notes in Computer Science(), vol 8139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40935-6_5
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DOI: https://doi.org/10.1007/978-3-642-40935-6_5
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