TensorTrade provides composable components for environments, action schemes, reward functions, and data feeds that can be combined to create custom trading systems. It leverages Ray and RLlib for distributed training and evaluation of reinforcement learning trading agents. The framework is designed to benchmark whether RL-based trading agents can outperform traditional strategies like buy-and-hold.