The paper aims to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize the short-term profit. It proposes a short-term planning framework to forecast the load under dynamic tariffs and construct biding curves. Stochastic programming is applied to manage the uncertainties of spot price, regulating price, consumption behaviors, and responsiveness to dynamic tariffs. A case study based on data from Sweden is carried out. It demonstrates that a real-time selling price can affect the aggregate load of a residential consumer group and lead to load shift toward low-price periods. The optimal bidding curves for specific trading periods are illustrated. Through comparing the bidding strategies under different risk factors, the case study shows that a risk-averse retailer tends to adopt the strategies with larger imbalances. The benefit lies in the reduction of low-profit risk. However, the aversion to risk can only be kept in a certain level. A larger imbalance may lead to a quick reduction of profit in all scenarios.