The financial industry has witnessed an exceptionally fast progress of incorporating information processing techniques in designing knowledge-based automated systems for high-frequency trading (HFT). This paper proposes a sparse coding-inspired optimal trading (SCOT) system for real-time high-frequency financial signal representation and trading. Mathematically, SCOT simultaneously learns the dictionary, sparse features, and the trading strategy in a joint optimization, yielding optimal feature representations for the specific trading objective. The learning process is modeled as a bilevel optimization and solved by the online gradient descend method with fast convergence. In this dynamic context, the system is tested on the real financial market to trade the index futures in the Shanghai exchange center.