  A universalization of a parameterized investment strategy is an online
algorithm whose average daily performance approaches that of the strategy
operating with the optimal parameters determined offline in hindsight. We
present a general framework for universalizing investment strategies and
discuss conditions under which investment strategies are universalizable. We
present examples of common investment strategies that fit into our framework.
The examples include both trading strategies that decide positions in
individual stocks, and portfolio strategies that allocate wealth among multiple
stocks. This work extends Cover's universal portfolio work. We also discuss the
runtime efficiency of universalization algorithms. While a straightforward
implementation of our algorithms runs in time exponential in the number of
parameters, we show that the efficient universal portfolio computation
technique of Kalai and Vempala involving the sampling of log-concave functions
can be generalized to other classes of investment strategies.
