Universal Algorithm for Online Trading Based on the Method of Calibration
This work addresses the problem of technical trading in stock markets for traders seeking non-stochastic strategies, though it is incremental as it builds on existing calibration and forecasting methods.
The authors tackled the problem of online trading without stochastic assumptions by developing a universal algorithm that asymptotically matches or exceeds any stationary trading strategy in a given RKHS, and empirical results on historical markets showed it can 'beat the market' when transaction costs are ignored.
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universal kernel, we extend this result for any continuous stationary strategy. In this learning process, a trader rationally chooses his gambles using predictions made by a randomized well-calibrated algorithm. Our strategy is based on Dawid's notion of calibration with more general checking rules and on some modification of Kakade and Foster's randomized rounding algorithm for computing the well-calibrated forecasts. We combine the method of randomized calibration with Vovk's method of defensive forecasting in RKHS. Unlike the statistical theory, no stochastic assumptions are made about the stock prices. Our empirical results on historical markets provide strong evidence that this type of technical trading can "beat the market" if transaction costs are ignored.