Algorithmic Trading

I n his well-received first book Quantitative Trading, Dr. Ernest Chan addressed the essential techniques an algorithmic trader needs to succeed at this demanding endeavor. While some useful example strategies were presented throughout, they were not the main focus of the book. With this in mind, Dr. Chan has created a practical guide to algorithmic trading strategies that can be readily implemented by both retail and institutional traders alike. More than an academic treatise on financial theory, Algorithmic Trading is an accessible resource that blends some of the most useful financial research done in the last few decades with valuable insights Dr. Chan has gained from actually exploiting some of those theories in live trading. Engaging and informative, Algorithmic Trading skillfully covers a wide array of strategies. Broadly divided into the mean-reverting and momentum camps, it lays out standard techniques for trading each category of strategies and, equally important, the fundamental reasons why a strategy should work. The emphasis throughout is on simple and linear strategies, as an antidote to the over-fitting and data-snooping biases that often plague complex strategies. Along the way, it provides comprehensive coverage of: Choosing the right automated execution platform as well as a backtesting platform that will allow you to reduce or eliminate common pitfalls associated with algorithmic trading strategies Multiple statistical techniques for detecting ''time series'' mean reversion or stationarity, and for detecting cointegration of a portfolio of instruments Simple techniques for trading mean-reverting portfolios¿linear, Bollinger band, and Kalman filter¿and whether using raw prices, log prices, or ratios make the most sense as inputs to these tests and…