Sparse Coding-Inspired Optimal Trading System for HFT Industry PROJECT TITLE :Sparse Coding-Inspired Optimal Trading System for HFT IndustryABSTRACT:The monetary trade has witnessed an exceptionally fast progress of incorporating info processing techniques in designing knowledge-primarily based automated systems for high-frequency trading (HFT). This paper proposes a sparse coding-impressed optimal trading (SCOT) system for real-time high-frequency monetary signal representation and trading. Mathematically, SCOT simultaneously learns the dictionary, sparse features, and the trading strategy in a very joint optimization, yielding optimal feature representations for the specific trading objective. The learning method is modeled as a bilevel optimization and solved by the online gradient descend methodology with quick convergence. During this dynamic context, the system is tested on the $64000 money market to trade the index futures in the Shanghai exchange center. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Ivy Bridge Server: A Converged Design Robust Histogram Shape-Based Method for Image Watermarking