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Quantitative Trading Strategies
This book presents an in-depth look at today’s top technical trading strategies – and how you can incorporate them into your personal trading program. By combining historical market performance with modern-day technology, technical traders often exhibit uncanny, seemingly intuitive abilities to control money-draining losses while letting profits run.”Quantitative Trading Strategies” reviews today’s most popular and effective methods, and explains how to incorporate their quantitative strengths into your own trading system to dramatically improve both your entry and exit timing and risk management. Exploring a wide range of systematic trading techniques and strategies for risk and money management, “Quantitative Trading Strategies” examines every vital aspect of today’s technical trading arena to provide you with: performance summaries of specific trading strategies; all-new money management approaches based on optimal leverage; and step-by-step directions for creating a system built around our own trading style.For decades, millions of successful traders have relied on technical analysis to not only improve the timing of their entries and exits but also to see and avoid dangerous trades and situations. Let “Quantitative Trading Strategies” introduce you to the best-of-the-best, and provide you with the knowledge and tools you need to create and implement a trading methodology designed to fit your trading strengths – and improve your performance in virtually any market environment. ‘First and foremost, this book explores the ability of quantitative trading strategies to time the markets. My goal in writing it is to set the record straight with time tested statistics – not untested theories and market lore passed down through the ages’ – From the Prologue.Technical traders study – and build their trading programs around – aspects of market and investor behavior that lead to regularly occurring patterns in stock prices. These patterns can help traders dramatically improve the timing of when, and when not to, place buys and sells. And while there is never a guarantee whether a given trade will generate a profit or a loss, quantitative tools can show technicians how to identify, measure, and act upon opportunities for both reward and risk. “Quantitative Trading Strategies” examines today’s most popular and proven technical trading strategies, explaining their pluses and minuses while providing the necessary data and research findings for determining which will work best for you.Drawing on current market research as well as strategies that are both statistically sound and rigorously backtested to determine their accuracy and effectiveness, this results-focused book features: 11 new techniques for trading stocks, futures, and the newly popular relative value markets; money management guidelines that can mean the difference between prospering – and going broke; methods for creating and implementing your own technical trading strategies; technical traders know that what has occurred before is destined to occur again, and they use this knowledge to enhance their trading performance across the board.”Quantitative Trading Strategies” takes you through the development and evaluation stages of today’s most popular technical trading techniques and – requiring nothing more than average market knowledge and math background – shows you how to accurately detect and exploit profitable patterns. From deciding which markets to trade to developing personalized trading strategies and money management plans, “Quantitative Trading Strategies” will give you the quantitative foundation you need to accurately buy and sell financial assets while controlling the risk associated with those assets. Along the way, it debunks numerous myths and misconceptions, and provides a clear understanding of the many profitable benefits quantitative analysis can provide traders and investors in today’s technically driven marketplace.
I’m an active quant trader. I bought this book because I was impressed by the K-Ratio, described as a reward-to-risk ratio to complement the Sharpe Ratio (See Kestner’s “(Re)Introducing the K-Ratio”” available for download on SSRN). Briefly, the K-Ratio is obtained by taking the slope of cumulative returns over time, from linear regression, divided by the sum of residual errors between the fitted line and the actual data, and adjusted for the number and frequency of observations. I’ve found it to be useful in comparing the relative linearity between securities.
So I got this book to see what other ideas he had. The answer is, unfortunately, worse than none. The book is a rehash of decades-old systems based on familiar oscillators (MACD, %R, etc.), which have pretty much proved useless when backtested. The book itself provides many examples of such failures. More disturbing though is that much of what he says is just plain wrong.
For example, he states that market returns fit normal or gaussian distributions, when in fact distributions of returns are skewed (e.g., the mean, median, and mode are not aligned) and exhibit kurtosis or fat tails. The latter in particular is hazardous to your portfolio because fat tails in the negative part of the distribution indicate higher levels of risk than indicated by the narrower normal distributions. Unless your trading system takes this into account, it’s likely to be pretty much a loser. By way of demonstrating the concept of normal distributions, he presents an Excel spreadsheet obtained by sampling a normal distribution and then generates a plot that demonstrates this sample follows a normal distribution. To see what is really going on, take a large random sample of actual log-returns for the S&P and construct a Q-Q Plot comparing the empirical distribution versus a normal distribution – there are many examples online. Notice that instead of being a 1:1 correspondence, the tails of the actual returns significantly deviate from linearity.
To get a better understanding of representative distributions for stock returns, see Portfolio optimization for Student t and skewed t returns by Hu and Kercheval. Fair warning – it took me about 6 months of constant study to understand the math involved, much less how to apply it. I’m good at math and better at coding. Don’t be daunted though. After publishing his paper on special relativity, Einstein spent several years learning new math before he could explain his concept of general relativity. There’s a reason that big firms hire PhD physicists and mathematicians to figure this stuff out.Amazon Reader
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