Table Of Content

WILEY TRADING ADVANTAGE Trading without Fear / Richard W. Arms, Jr. Neural Network: Time Series Forecasting of Financial Mark& /E. Michael Azoff Option Market Making I Alan I. Baird Money Management Strategies for Futures Traders / Nauzer J. Balsara Genetic Algorithms and Investment Strategies ! Richard Bauer Managed Futures: An Investor’s Guide/Beverly Chandler Beyond Technical Analysis / Tushar Chande The New Technical Trader / Tushar Chande and Stanley S. tioll Trading on the Edge / Guido J. Deboeck Cybernetic Trading Strategies New Market Timing Techniques /Thomas R. DeMark The New Science of Technical Analysis /Thomas R. DeMark Point and Figure Charting/Thomas J. Dorsey Developing a Profitable Trading System with Trading for a Living I Dr. Alexander Elder State-of-the-Art Technologies Study Guide for Trading for a Living ! Dr. Alexander Elder The Day Trader’s Manual I William F. Eng Trading 101 I Sunny Harris Analyzing and Forecasting Futures Prices/Anthony F. Herbst Technical Analysis of the Options Markets I Richard Hexton New Commodity Trading Systems & Methods I Perry Kaufman Murray A. Ruggiero, Jr. Understanding Options/Robert Kolb The Intuitive Trader / Robert Koppel McMillan on Options/Lawrence G. McMillan Trading on Expectations / Brenda” Moynihan Intermarket Technical Analysis /John J. Murphy Forecasting Financial and Economic Cycles I Michael P. Niemira Beyond Candlesticks/Steve Nison Fractal Market Analysis I Edgar E. Peters Forecasting Financial Markets I Tony Plummer inside the Financial Futures Markets, 3rd Edition /Mark 1. Powers and Mark G. Cast&no Neural Networks in the Capital Markets/Paul Refenes Cybernetic Trading Strategies /Murray A. Ruggiero, Jr. Gaming the Market/Ronald B. Shelton Option Strategies, 2nd Edition I Courtney Smith Trader Vie II: Analytic Principles of Professional Speculation I ViCtOr Sperandeo Campaign Trading/John Sweeney Deciphering the Market / Jay Tadion The Trader’s Tax Survival Guide. Revised Edition /Ted Tesser Tiger on Spreads / Phillip E. Tiger The Mathematics of Money Management / Ralph Vine The New Money Management I Ralph Vince Portfolio Management Formulas / Ralph Wince The New Money Management: A Framework for Asset Allocation / Ralph Vince Trading Applications of Japanese Candlestick Charting / Gary Wagner and Brad Matheny Selling Short I Joseph A. Walker JOHN WILEY & SONS, INC. Trading Chaos: Applying Expert Techniques to Maximize Your PrOfitS / Bill Williams New York l Chichester l Weinheim l Brisbane l Singapore l Toronto This text is printed on acid-free paper Universal Seasonal is a trademark of Ruggiero Associates. TradeStation’s EasyLanguage is a trademark of Omega Research. Foreword SuperCharts is a trademark of Omega Research. TradeCycles is a trademark of Ruggiero Associates and Scientific Consultant Services. XpertRule is a trademark of Attar Software. DivergEngine is a trademark of Inside Edge Systems. Copyright 0 1997 by Murray A. Ruggiero, Jr. Published by John Wiley & Sons, Inc. All rights reserved. Published simultaneously in Canada. Reproduction or translation of any part of this work beyond that permitted by Section 107 or 108 of the 1976 United As we approach the end of one millennium and the beginning of another, States Copyright Act without the permission of the copyright computers have changed the way we think and act. In the field of finan- owner is unlawful. Requests for permission or further information should be addressed to the Permissions Department. cial market analysis, the changes have been nothing short of revolution- John Wiley & Sons, Inc. ary. Some of us remember when analysts charted the performance of markets without the aid of computers. Believe me, it was slow and M) fun This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold at all. We spent hours constructing the charts before even getting to the with the understanding fhat the publisher is not engaged in fun part-analyzing them. The idea of experimenting with indicators and rendering legal, accounting. or other professional services. If optimizing them was still decades away. legal advice or other expert assistance is required, the services The computer has removed the drudgery of market analysis. Any in- of a competent professional person should be sought. vestor can buy a computer and some inexpensive software and, in no time Library of Congress Cataloging-in-P~bficatian Data: at all, have as much data at his or her fingertips as most professional money managers. Any and all markets can be charted, manipulated, over- Ruggiero, Murray A., 1963- Cybernetic trading strategies : developing a profitable trading laid on one another, measured against one another, and so on. In other sysfem with state-of-the-art technologies/by Murray A. Ruggiero, words, we can do pretty much anything we want to with a few keystrokes. Jr. The popularity of computers has also fostered a growing interest in tech- P. cm. -(Wiley trading advantage) nical market analysis. This visual form of analysis lends itself beauti- Includes index. fully to the computer revolution, which thrives on graphics. ISBN O-471-14920-9 (cloth : alk. paper) Up to now, however, the computer has been used primarily as a data- 1. Investment analysis. 2. Electronic trading of securities. I. Title. II. Series. gathering and charting machine. It enables us to collect large amounts of HG4529.RS4 1997 332.6’0285-dc2l 96.53326 Mr. Murphy is CNBC’s technical analyst, and author of Technical Analysis of the Futures Printed in the United States of America. Markets and Inremarker Technical Analysis. His latest book, The Visual Investor (Wiley, 1996). a pplies charting techniquest o sector analysis and mutual fund investing. 1 0 9 8 7 6 5 4 3 2 vi Foreword Foreword vii market information for display in easily understood chart pictures. The by validating what many of us have known for a long time-technical fact is, however, most of us have only been scratching the surface where market analysis does work. But it can also be made better. the computer is concerned. We’ve been using it primarily as a visual tool. There’s much more to this book, having to do with state-of-the-art Enter Murray A. Ruggiero, Jr., and Cybernetic Trading Straregies. thinking-for starters, chaos theory, fuzzy logic, and artificial intelli- I first became aware of Murray’s work when he published an article gence-which leads us to some new concepts regarding the computer it- titled “Using Neural Nets for Intermarket Analysis,” in Futures Maga- self. The computer can do more than show us pretty pictures. It can zine. I subsequently did a series of interviews with him on CNBC in optimize, backtest, prove or disprove old theories, eliminate the bad which he developed his ideas even further, for a larger audience. I’ve fol- methods and make the good ones better. In a way, the computer almost lowed his work ever since, with growing interest and admiration (and oc- begins to think for us. And perhaps that’s the greatest benefit of Cyber- casionally offered a little encouragement). That’s why I’m delighted to netic Trading Strategies. It explores new ways to use the computer and help introduce his first book. I do so for some selfish reasons: Murray’s finds ways to make a valuable machine even more valuable. research validates much of the work I helped develop, especially in the Technical analysis started being used in the United States around the field of intermarket analysis. Murray’s extensive research in that area beginning of the 20th century. Over the past 100 years, it has grown in not only validates my earlier writings in that field but, I believe, raises in- both value and popularity. Like any field of study, however, technical termarket analysis to a higher and more practical level. analysis continues to evolve. Intermarket Technical Analysis, which I Not only does he provide statistical evidence that intermarket linkages wrote in 1991, was one step along that evolutionary path. Cybernetic exist, but he shows numerous examples of how to develop trading systems Trading Strategies is another. It seems only fitting that this type of book utilizing intermarket filters. Most traders accept that a positive correla- should appear as technical analysis begins a new century. tion exists between bonds and stocks. How about utilizing a moving- average filter on the bond market to tell us whether to be in the stock JOHN J. MURPHY market or in T-Bills? One such example shows how an investor could have outperformed the S&P500 while being in the market only 59 percent of the time. Or how about utilizing correlation analysis to determine when intermarket linkages are strong and when they are weak? That insight al- lows a trader to use market linkages in trading decisions only when they are most likely to work. I was amazed at how useful (and logical) these techniques really were. But this book is more than a study of intermarket analysis. On a much broader scale, traditional technical analysts should applaud the type of work done by Murray and young writers like him. They are not satisfied with relying on subjective interpretations of a “head and shoulders pattern” or reading Elliott Waves and candlestick patterns. They apply a statistical approach in order to make these subjective methods more mechanical. Two things are achieved by this more rigorous scientific methodology. First, old techniques are validated by historical backtesting. In other words, Ruggiero shows that they do work. Second, he shows us how to use a more mechanical approach to Elliott Waves and candlesticks, to make them even~more useful; Murray does us all a favor Preface Advanced technologies are methods used by engineers, scientists, and physicists to solve real-world problems that affect our lives in many un- seen ways. Advanced technologies are not just rocket science methods; they include applying statistical analysis to prove or disprove a given hypothesis. For example, statistical methods are used to evaluate the effectiveness of a drug for treating a given illness. Genetic algorithms have been used by engineers for many different applications: the development of the layout of micro processors circuits, for example, or the optimization of landing strut weights in aircraft. In general, com- plex problems that require testing millions or even billions of combi- nations to find the optimal answer can be solved using genetic algorithms. Another method, maximum entropy spectral analysis or the maximum entropy method (MEM), has been used in the search for new oil reserves and was adapted by John Ehlers for use in developing trading strategies. Chaos, a mathematical concept, has been used by scientists to understand how to improve weather forecasts. Artificial intelligence was once used only in laboratories to try to learn how to capture human expertise. Now, this technology is used in everything from cars to toasters. These technologies-really just different ways of looking at the world-have found their way to Wall Street and are now used by some of the most powerful institutions in the world. John ix x Preface Preface xi Deere Inc. manages 20 percent of its pension fund money using neural HOW TO GET THE MOST OUT OF THIS BOOK networks, and Brad Lewis, while at Fidelity Investments, used neural networks to select stocks. This book will introduce you to many different state-of-the-art methods You do not need to be a biophysicist or statistician to understand these for analyzing the market(s) as well as developing and testing trading sys- technologies and incorporate them into your technical trading system. tems. In each chapter, I will show you how to use a given method or tech- Cybernetic Trading Strategies will explain how some of these advanced nology to build, improve, or test a given trading strategy. technologies can give your trading system an edge. I will show you The first of the book’s five parts covers classical technical analysis which technologies have the most market applicability, explain how they methodologies, including intermarket analysis, seasonality, and commit- work, and then help you design a technical trading system using these ment of traders (COT) data. The chapters in Part One will show you how technologies. Lastly, but perhaps most importantly, we will test these to use and test classical methods, using more rigorous analysis. systems. Part Two covers many statistical, engineering, and artificial intelli- Although the markets have no single panacea, incorporating elements gence methodologies that can be used to develop state-of-the-art trading of statistical analysis, spectra analysis, neural networks, genetic algo- systems. One topic I will cover is system feedback, a concept from sys- rithms, fuzzy logic, and other high-tech concepts into a traditional tech- tem control theory. This technology uses past results to improve future nical trading system can greatly improve the performance of standard forecasts. The method can be applied to the equity curve of a trading sys- trading systems. For example, I will show you how spectra analysis can tem to try to predict the results of future trades. Another topic is cycle- be used to detect, earlier than shown by classical indicators such as based trading using maximum entropy spectra analysis, which is used in ADX-the average direction movement indicator that measures the oil exploration and in many other engineering applications. I apply this strength of a trend-when a market is trending. I will also show you how method to analyzing price data for various commodities and then use this to evaluate the predictive value of a given classical method, by using the analysis to develop mechanical trading strategies. same type of statistical analysis used to evaluate the effectiveness of Part Three shows how to mechanize subjective methods such as Elliott drugs on a given illness. Wave and candlestick charts. Part Four discusses development, imple- I have degrees in both physics and computer science and have been re- mentation, and testing of trading systems. Here, I explain how to build searching neural networks for over eight years. I invented a method for and test trading systems to maximize reliability and profitability based embedding neural networks into a spreadsheet. It seemed a natural ex- on particular risk/reward criteria. tension to then try and apply what I have learned to predicting the mar- Finally, in Part Five, I show how to use many different methods from kets. However, my early models were not very successful. After many the field of artificial intelligence to develop actual state-of-the-art trad- failed attempts, I realized that regardless of how well I knew the ad- ing systems. These methods will include neural networks, genetic algo- vanced technologies, if I didn’t have a clear understanding of the mar- rithms, and machine induction. kets I was attempting to trade, the applications would prove fruitless. I I would like to point out that many of the systems, examples, and charts then spent the greater part of three years studying specific markets and have different ending dates, even in the same chapter. This occurs be- talking to successful traders. Ultimately, I realized that my systems cause the research for this book is based on over one year of work, and needed a sound premise as their foundation. M)t all of the systems and examples in each chapter were compiled at the My goals are: to provide you with the basics that will lead to greater same time. market expertise (and thus a reasonable premise on which to base your As you read the book, don’t become discouraged if you don’t under- trades) and to show you how to develop reliable trading models using so- stand a particular concept. Keep reading to get a general sense of the sub- called advanced technologies. ject. Some of the terminology may be foreign and may take some getting xii Preface used to. I’ve tried to put the concepts in laypersons’ terminology, but the fact remains that jargon (just like market terminology) abounds. After you get a general feel for the material, reread the text and work through the examples and models. Most of the examples are based on real systems being used by both experienced and novice traders. It has been my goal to present real-world, applicable systems and examples. You won’t Acknowledgments find pie-in-the-sky theories here. MURRAY A. RUGG~ERO, JR. East Haven, Connecticut Mav 1997 Whatever my accomplishments, they have resulted from the people who have touched my life. I would like to thank all of them. First, my loving wife Diana, who has stood beside me my whole career. While I was building my business, she worked full-time and also helped me on nights and weekends. Early in 1996, she left her job at Yale University so we could work together. We make a great team, and I thank God for such a wife, friend, and partner. I also thank my son, Murray III, for or- derstanding why his daddy needs to work and for giving me focus. I know that I must succeed, so that he can have a better life. Next, I thank my parents, who raised me to work hard and reach for my dreams. I am also indebted to Ilias Papazachariou for spending several weekends helping me with researching, organizing, collecting, and editing the material in this book. Several of my professors and colleagues have helped me become who I am. Dr. Charlotte LeMay believed in me more than I believed in myself. It has been 12 years since I graduated from Western Connecticut State University and she is still a good friend. She made me believe that if I could dream it, I could do it. Many friends in the futures industry have also helped me along the way. I thank Ginger Szala, for giving me the opportunity to share my research with the world in Futures Magazine, and John Murphy for giving me a chance to meet a larger audience on CNBC, for being a good friend and colleague, and for agreeing to write the Foreword of this book. xiv Acknowledgments Finally, I thank Larry Williams. Larry has been very good to me over the years and has helped me understand what it takes to be successful in this business. Inside Advantage, my newsletter, began based on a sugges- tion from Larry Williams. Larry has been a valued colleague, but, most importantly, he is a friend whom I can always count on. I know that I am forgetting people here; to everyone else who has Contents helped me along the way: Thank You! M.A.R. introduction 1 PART ONE CLASSICAL MARKET PREDICTION 1 Classical Intermarket Analysis as a Predictive Tool 9 What Is Intermarket Analysis? 9 Using Intermarket Analysis to Develop Filters and Systems 27 Using Intermarket Divergence to Trade the S&P500 29 Predicting T-Bonds with Intermarket Divergence 32 Predicting Gold Using Intermarket Analysis 35 Using Intermarket Divergence to Predict Crude 36 Predicting the Yen with T-Bonds 38 Using Intermarket Analysis on Stocks 39 2 Seasonal Trading 42 Types of Fundamental Forces 42 Calculating Seasonal Effects 43 Measuring Seasonal Forces 43 The RuggierolBarna Seasonal Index 45 Static and Dynamic Seasonal Trading 45 Judging the Reliability of a Seasonal Pattern 46 Counterseasonal Trading 47 xvi contents contents xvii Conditional Seasonal Trading 47 Using Cycles to Detect When a Market Is Trending 109 Other Measurements for Seasonality 48 Adaptive Channel Breakout 114 Best Long and Short Days of Week in Month 49 Using Predictions from MEM for Trading 115 Trading Day-of-Month Analysis 51 Day-of-Year Seasonality 52 8 Combining Statistics and Intermarket Analysis 119 Using Seasonality in Mechanical Trading Systems 53 Counterseasonal Trading 55 Using Correlation to Filter Intermarket Patterns 119 Predictive Correlation 123 Long-Term Patterns and Market Timing for Interest Using the CRB and Predictive Correlation to Predict Gold 124 Rates and Stocks 60 Intermarket Analysis and Predicting the Existence of a Trend 126 Inflation and Interest Rates 60 9 Using Statistical Analysis to Develop Intelligent Exits 130 Predicting Interest Rates Using Inflation 62 Fundamental Economic Data for Predicting Interest Rates 63 The Difference between Developing Entries and Exits 130 A Fundamental Stock Market Timing Model 68 Developing Dollar-Based Stops 13 1 Using Scatter Charts of Adverse Movement to Develop Stops 132 Trading Using Technical Analysis 70 Adaptive Stops 137 Why Is Technical Analysis Unjustly Criticized? 70 Profitable Methods Based on Technical Analysis 73 10 Using System Feedback to Improve Trading System Performance 140 The Commitment of Traders Report 86 How Feedback Can Help Mechanical Trading Systems 140 What Is the Commitment of Traders Report? 86 How to Measure System Performance for Use as Feedback 141 How Do Commercial Traders Work? 87 Methods of Viewing Trading Performance for Use as Feedback 141 Using the COT Data to Develop Trading Systems 87 Walk Forward Equity Feedback 142 How to Use Feedback to Develop Adaptive Systems or Switch between Systems 147 PART TWO STATISTICALLY BASED MARKET PREDICTION Why Do These Methods Work? 147 A Trader’s Guide to Statistical Analysis 95 11 An Overview of Advanced Technologies 149 Mean. Median, and Mode 96 The Basics of Neural Networks 149 Types of Distributions and Their Properties 96 Machine Induction Methods 153 The Concept of Variance and Standard Deviation 98 Genetic Algorithms-An Overview 160 How Gaussian Distribution, Mean, and Standard Developing the Chromosomes 161 Deviation Interrelate 98 Evaluating Fitness 162 Statistical Tests’ Value to Trading System Developers 99 Initializing the Population 163 Correlation Analysis 101 The Evolution 163 Updating a Population 168 Cycle-Based Trading 103 Chaos Theory 168 The Nature of Cycles 105 Statistical Pattern Recognition 171 Cycle-Based Trading~in the Real World 108 Fuzzy Logic 172 . . . X”lll contents Contents xix PART THREE MAKING SUBJECTIVE METHODS MECHANICAL PART FIVE USING ADVANCED TECHNOLOGIES TO DEVEIO_P-. TRADING STRATEGIES 12 How to Make Subjective Methods Mechanical 179 Totally Visual Patterns Recognition 180 17 Data Preprocessing and Postprocessing 241 Subjective Methods Definition Using Fuzzy Logic 180 Developing Good Preprocessing-An Overview 241 Human-Aided Semimechanical Methods 180 Selecting a Modeling Method 243 Mechanically Definable Methods 183 The Life Span of a Model 243 Mechanizing Subjective Methods 183 Developing Target Output(s) for a Neural Network 244 Selecting Raw Inputs 248 13 Building the Wave 184 Developing Data Transforms 249 An Overview of Elliott Wave Analysis 184 Evaluating Data Transforms 254 Types of Five-Wave Patterns 186 Data Sampling 257 Using the Elliott Wave Oscillator to Identify the Wave Count 187 Developing Development, Testing, and Out-of-Sample Sets 257 TradeStation Tools for Counting Elliott Waves 188 Data Postprocessing 258 Examples of Elliott Wave Sequences Using Advanced GET 194 18 Developing a Neural Network Based on Standard 14 Mechanically Identifying and Testing Candlestick Patterns 197 Rule-Based Systems 259 How Fuzzy Logic Jumps Over the Candlestick 197 A Neural Network Based on an Existing Trading System 259 Fuzzy Primitives for Candlesticks 199 Developing a Working Example Step-by-Step 264 Developing a Candlestick Recognition Utility Step-by-Step 200 19 Machine Learning Methods for Developing PART FOUR TRADING SYSTEM DEVELOPMENT AND TESTING Trading Strategies 280 Using Machine Induction for Developing Trading Rules 281 15 Developing a Trading System 209 Extracting Rules from a Neural Network 283 Steps for Developing a Trading System 209 Combining Trading Strategies 284 Selecting a Market for Trading 209 Postprocessing a Neural Network 285 Developing a Premise 211 Variable Elimination Using Machine Induction 286 Developing Data Sets 211 Evaluating the Reliability of Machine-Generated Rules 287 Selecting Methods for Developing a Trading System 212 Designing Entries 214 20 Using Genetic Algorithms for Trading Applications 290 Developing Filters for Entry Rules 215 Uses of Genetic Algorithms in Trading 290 Designing Exits 216 Developing Trading Rules Using a Genetic Algorithm- Parameter Selection and~optimization 217 An Example 293 Understanding the System Testing and Development Cycle 217 Designing an Actual System 218 References and Readings 307 16 Testing, Evaluating, and Trading a Mechanical Index 310 Trading System 225 The Steps for Testing and Ev&ating a Trading System 226 Testing a Real Trading System 231 Introduction During the past several years, I have been on a quest to understand how the markets actually work. This quest has led me to researching almost every type of analysis. My investigation covered both subjective and ob- jective forms of technical analysis-for example, intermarket analysis, Elliott Wave, cycle analysis, and the more exotic methods, such as neural networks and fuzzy logic. This book contains the results of my research. My goal was to discover mechanical methods that could perform as well as the top traders in the world. For example, there are technologies for trading using trend following, which significantly outperform the leg- endary Turtle system. This book will show you dozens of trading systems and filters that can increase your trading returns by 200 to 300 percent. I have collected into this volume the best technologies that I have discov- ered. This overview of the book’s contents will give you the flavor of what you will be learning. Chapter 1 shows how to use intermarket analysis as a predictive tool. The chapter first reviews the basics of intermarket analysis and then, using a chartist’s approach, explains the many different intermarket re- lationships that are predictive of stocks, bonds, and commodities. That background is used to develop fully mechanical systems for a variety of markets, to show the predictive power of intermarket analysis. These markets include the S&P500, T-Bonds, crude oil, gold, currencies, and more. Most of these systems are as profitable as some commercial systems cost- ing thousands of dollars. For example, several T-Bond trading systems have averaged over $10,000 a year during the analysis period. Chapter 2 discusses seasonal trading, including day-of-the-week, monthly, and annual effects. You will learn how to judge the reliability 1