A Great Introduction to Fully Automated Algorithmic TradingI was drawn to Dr. E.P. Chan's Quantitative Trading (2009) by a process of elimination. After losing half of my buy-and-hold retirement portfolio in the 2007-2009 bear market, I tried and rejected a variety of both fundamental and technical trading strategies. Fear and greed invariably blocked my path to success in any of these trading endeavors. Then I discovered momentum trading strategies that could be automated. Trend following strategies, such as, those proposed by Tom Lydon in The ETF Trend Following Playbook: Profiting from Trends in Bull or Bear Markets with Exchange Traded Funds (2009), were especially appealing to me. For example, buy when an equity's price moves above its 200 day moving average. Sell when the price falls below its 200 day moving average. What could be simpler than that? Even more appealing, however, were the relative strength, risk adjusted trading strategies that I discovered at the ETF Replay website. There I found a momentum based, quantitative, statistical model that was mechanical in operation and that separated my trading activities from my emotions, keeping fear and greed in check.Was Chan's book written for traders like me? Not exactly. The author had in mind a reader who wants to know (1) how to start a quantitative trading business or (2) how to work as a quantitative trader at a major institution. I, on the other hand, was simply looking for ways to enhance my skills as an independent trader for managing my personal accounts. In reading this book, I felt like a minor league player asking for help from a major league coach. Chan is a true quant with both institutional and independent trading experience. Chan offers way more expertise than I can use. For instance, Chan's book applies to trading that can be characterized as algorithmic, mean-reverting, fully automated, intraday, high frequency, leveraged, risk adjusted, and benchmarked with high Sharpe ratios and low drawdowns. My trading, on the other hand, is mechanical, not algorithmic; momentum based, not mean-reverting; semi-automated, not fully automated, monthly traded, not intraday; low frequency, not high frequency; unleveraged, not leveraged; risk adjusted and benchmarked with high Sharpe ratios and low drawdowns -- these last items being significant points of agreement with Chan. Chan moves his readers step-by-step from determining their aptitude for quantitative trading in Chapter 1 to growing a quantitative trading business in Chapter 8. Along the way, Chan tells his readers how to select a trading strategy, how to backtest their strategy using MATLAB (or Excel), how to build an automated trading system, how to manage their money and risks, and how to refine and improve their trading strategies.Quantitative trading is known by several other names: "algorithmic trading," "automated trading," "computer trading," and Chan's favorite, "statistical arbitrage trading." Incidentally, for statistical arbitrage trading to work, both Random Walking and the Efficient Market Hypothesis must fail. Are there prerequisites? To benefit from Chan's book, the reader needs to have at least a first year college proficiency in statistics, algebra, and computer programming. Given this minimal background, the reader can then proceed to become an independent trader who will be able to outperform institutional money managers at their own game, namely, statistical arbitrage trading.By the time you finish Chan's book, your statistical arbitrage trading kit will include such tools as geometric mean, moving average, standard deviation, linear regression, Gaussian distribution, mean-reverting time series, half-life time series, principal components analysis, Kelly Formula, and Sharpe ratio -- and the means to achieve a consistent monthly stream of revenue.