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State-of-the-art spectrum auctions are designed under a primary market paradigm to conduct spectrum trading between legacy owners and large cognitive service providers. In our previous work, we established a spectrum secondary market based on double auctions, and showed that it significantly improves spectrum utilization and user performance by allowing secondary users to dynamically trade among themselves their channel holdings obtained in the primary market. In this paper, we devise a channel portfolio optimization framework in order for users to make intelligent trading decisions without burdensome overhead. By viewing each channel in the secondary market as a stock, users assess its characteristics, and derive which channels to buy or sell at what price and quantity as a portfolio optimization problem to maximize the expected utility. Coupled with the robust secondary market design, the channel portfolio optimization framework offers salient performance with low complexity as corroborated in our simulations.