A consumer may be interested in buying a bundle of items, where any one item in the bundle may not be of particular interest. The emergence of online auctions allows such users to obtain bundles by bidding on different simultaneous or sequentially run auctions. Because the number of auctions and the number of combinations to form the bundles may be large, the bundle bidding problem becomes intractable and the user is likely to make sub-optimal decision given time constraints and information overload. We believe that an automated agent that can take user preferences and budgetary constraints and can strategically bid on behalf of a user can significantly enhance user profit and satisfaction. Our first step to build such an agent is to consider bundles containing many units of a single a item and auctions that sell only multiple units of one item type. We assume that users obtain goods over several days. Expectations of auctions and their outcome in the future allow the agent to bid strategically on currently open auctions. The agent decides how many items to bid for in the current auctions, and the maximum price to bid for each item. We evaluate our proposed strategy in different configurations: number of items sold, number of auctions opened, expected closing prices, etc. The agent produces greater returns in situations where future auctions can provide better profit, and where not too many agents use our proposed strategy.