A common goal of a prediction market maker is to upper bound the worst-case loss since it makes sense for a market maker not to lose an arbitrarily large amount of money. Nevertheless, another objective of profit for a market maker has also been considered in some works. None of these previous works on market makers that ensure profit exploits a connection to no-regret online learning. The goal of regret minimization for learners corresponds to the goal of minimizing the worst-case loss for market makers. With the insights from online learning about designing no-regret algorithms under a “friendlier” loss environment (e.g., specifically, low deviation or variation), which corresponds to some “patterns” of trade sequences in market making, we aim to achieve market making that furthermore guarantees profits, i.e., negative regrets, under appropriate patterns of trade sequences, which may require conditions other than those suggested by just low deviation or variation. Besides, we also want to model strategic behavior and thus “truthfulness” in prediction market making.