## AbstractWe present a simple online two-way trading algorithm that exploits ﬂuctuations in the unit price of an asset. Rather than analysing worst-case performance under some assumptions, we prove a novel, un- conditional performance bound that is parameterised either by the actual dynamics of the price of the asset, or by a simplifying model thereof. The algorithm processes T prices in O(T^2) time and O(T) space, but if the employed prior density is exponential, the time requirement reduces to O(T). The result translates to the prediction with expert advice frame- work, and has applications in data compression and hypothesis testing.
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