The design of trading algorithms requires specialized mathematical models that rely on reliable data. In this manual, the authors develop models for algorithmic trading in environments such as executing large orders, market making, targeting VWAP and other programs, trading pairs or collections of assets, and executing in dark pools. These models are grounded in how markets operate, whether the algorithm is trading against better-informed traders (adverse selection scenario), and the type of information available to market participants at both extremely high and low frequencies. Algorithmic and High-Frequency Trading is the first book to combine complex mathematical models, empirical data, and financial economics, guiding the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, which information provides an edge in trading, and how other market participants can affect the profitability of algorithms, then this is the book for you.

Pages: 356