MetaPS is a simulation-guided framework that enables market agents to adaptively select among programmatic strategies based on market states. It uses simulated markets to generate supervised training data, then selects strategies during inference to produce executable actions. Experiments show MetaPS outperforms fixed strategies and LLM-based agents, with compact models exceeding stronger API models in performance.