This paper concerns online portfolio selection problem. In this problem, no statistical assumptions are made about the future asset prices. Although existing universal portfolio strategies have been shown to achieve good performance, it is not easy, almost impossible, to determine upfront which strategy will achieve the maximum final cumulative wealth for online portfolio selection tasks. This paper proposes a novel online portfolio strategy by aggregating expert advice using the weak aggregating algorithm. We consider a pool of universal portfolio strategies as experts, and compute the portfolio by aggregating the portfolios suggested by these expert strategies according to their previous performance. Through our analysis, we establish theoretical results and illustrate empirical performance. We theoretically prove that our strategy is universal, i.e., it asymptotically performs almost as well as the best constant rebalanced portfolio determined in hindsight. We also conduct extensive experiments to illustrate the effectiveness of the proposed strategy by using daily stock data collected from the American and Chinese stock markets. Numerical results show that the proposed strategy outperforms all expert strategies in the pool besides best expert strategy and performs almost as well as best expert strategy.