A constant rebalanced portfolio is an investment strategy which keeps the same distribution of wealth among a set of stocks from day to day. There has been much work on Cover's Universal algorithm, which is competitive with the best constant rebalanced portfolio determined in hindsight. While this algorithm has good performance guarantees, all known implementations are exponential in the number of stocks, restricting the number of stocks used in experiments. We present an efficient (polynomial-time) implementation of the Universal algorithm that is based on non-uniform random walks that are rapidly mixing. The same implementation also works for non-financial applications of the Universal algorithm, such as data compression and language modeling.