Abstract Free and competitive energy markets are a recent and increasing phenomenon in several countries. Understanding these new energy markets and estimating their possible evolutions are current challenges of the research community. To avoid real market risks, the research community has developed autonomous traders and tested them in the Power Trading Agent Competition (Power TAC), a sophisticated energy market simulator. In this paper, we present COLDPower’16, a competitive autonomous trader composed of expert agents in specific kinds of markets and customers that combines local strategies into a global strategy to maximize profit. The local strategy of each tariff expert agent uses reinforcement learning algorithms, while the local strategy of the wholesale expert agent estimates future energy prices and the amount of energy that can be negotiated to buy energy when prices are low and sell energy when prices are high. COLDPower’16 was tested in Power TAC 2016. It achieved 2nd place in the final round of this international competition with 7 autonomous agent brokers.