text: abstract
  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.