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Trading With The Odds: Using the Power of Statistics to Profit in the futures Market PDF

162 Pages·2004·1.52 MB·English
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CYNTHIA A. KASE Boston, Massachusetts Burr Ridge, Illinois Dubuque, Iowa Madison, Wisconsin New York, New York San Francisco, California St. Louis, Missouri McGraw-Hill All names of indicators are Copyright 0 by Ruse, Inc. All charts created using Tradestation” byOmega Research, Inc. ORICHARD IX IRWIN, A Times Mirror Higher Education Group cumpm,y, IW6 Ail rights resewed. No part of this publication may be reproduced, stored in a ret,rieval system, or transnrilted, in any form or hg any means, elrctmnic, mechanical. photocopying, rccurding, or otherwise, wit,hout the prior writt,en permission of the publisher. This publication is designed to provide accurate and authoritative informution in regard to the subject matter covered. It is sold with tho understanding that neither the author or the publisher is engaged in rendering legal, accnunting, or other professional service. If logal advice UT other expert assistance is required, the services of R ccmpctent professional persrm shrmld he sought. Hypothrt,ical or simulated performance results have certain inherent limitations. Unlike an actual pcrfornmm rewrd, simulated results do not represent actual trading. Also, since the trades have nut actually hem executed, the results may have under or ovcrcompensaled Ibr the impact, if any, of certain market factors, such as lack of liquidity Simulsled trading programs in general are also subject to the fact that they are desi&med wit.h the benefit of hindsight. No representation is being made that any accourd will or is likely to achieve profits or losses similar to those shown. The risk of loss in trading commodities can be substantial. You should thcrcforc carefully consider whether such trading is suitahlr for you-in light of your financial condition Inlimnation contained in this report is not to bc considered as an offer to sell or a solicitutim to buy commodities, nor do WC make any guuranters. &se will not he respomihir for any typographical crmrs. Expressions of opinion are subject to change without notice. Printed in the United States of America 4 5 6 7 8 9 0 BKMBKM 909 F O R E W O R D Several years ago I had the pleasure of taking Cynthia Kase on a speaking (teaching) tour to Italy and throughout many mid-east- ern countries, I could easily discern that her mind was always at work. She would not take the traditional, commonly used technical analysis studies for granted, but would investigate carefully where others had blazed a trail, using their observations as a jumping off place from which to begin truly unique research. An outside observer could see at the time that she had already mined the rough gems. I can tell it took work and dedication to pol- ish these ideas into the methodology described in this book. The book is filled with unique observations. They are best summed up by Cynthia’s own comments on the present “state of the art” of the common routines published and used by technicians today She feels that today, even with the availability of powerful computers, we are still living too close to the past where most technical analysis was done by hand, or, at best, using spreadsheets on fairly crude com- puters. Cynthia believes that we must make today’s powerful com- puters WORK and work hard. With the increase in versatility of today’s PCs, they are now capable of NEWER types of analysis if we tell them where to look. I could easily cite many new ideas illustrated in this book, but I will choose just one and, for brevity, I will greatly simplify the con- cept. A trader who trades in two time frames traditionally uses the longer (weekly) chart and its signals to confirm the shorter (daily) chart. The trader’s recurring dilemma is that he or she must wait for Friday’s close to get the weekly confirmation. The trader would like to get his/her signals earlier, but the system specified requires a weekly confirmation. Cynthia asks why a week must end on a spe- cific day By using a “rolling” week for the last five trading days and their cumulative signal as the confirmation in building the system, both the daily chart and the weekly rolling chart can be evaluated EACH day This example demonstrates Cynthia’s dimensional expan- sion of a particular technique-breaking the traditional mold and looking for the trading edge. To sum up, at this time I feel Cynthia’s present work and the research evidenced in this book represents a new view of techniques. ” vi TRADING WITH THE ODDS If computer users or experienced technicians are looking for a trad- ing edge, then this book, with its new look at technical analysis, is one they will want to study and execute or make part of their trading plan(s). Ms. Kase has found the gems and polished them, and leaves the reader to put them in their setting. Timothy C. Slater Managing Director T&rate Seminars C O N T E N T S FOREWORD V INTRODUCTION XI Chapter 1 Increasing the Probability of Success with Science and Statistics 1 Replace Empirical Methods with Mathematically Derived Models 1 Manipulate Data to Improve Performance 1 Condense Information 1 Automatically Adaptive Indicators 2 Science, Not Magic 3 New Ideas Challenge Old Beliefs 3 Corporate Trading Must Be Accurate 4 WbileNcver Easy, Trading Can at Least be Simple 6 Chapter 2 The True Nature of the Market 7 What is Important to Understand about the Market? 7 The Market Is Symmetrical Across Tine Frames 7 Elliott’s Wave Theory Is Essentially Correct 8 Forecasting uersus Trading 8 The Market Is Mostly Predictable 9 Market Extremes Are Unstable and Unpredictable 10 The Logarithmic Spiral Describes Market Behavior 10 There Is No Magic Formula or Easy Answer I1 Cl~pter2Append~:StatisticsOverview 1 2 vii “Ill TRADING WITH THE ODDS Chapter 3 Developing a Strategy with Accurate Forecasting 20 Can People Really Forecast the Market Accurately’! 20 The SixKaseBehavioralLawsofFnrecasting 21 Market Geometry 25 Forecasting Methods 26 Pattrrnw and Ru.les 27 The Math 29 Corrccliue Mow Relr-accmmls 30 Th,e Rule of Three 31 Applying th,e Rules 31 Shorter Than Rule 31 Equa.l To Rule 33 Longer Than Rule 34 I?: IIf and IX Rules 35 The Rule of Three 3 5 Retracements 35 The Forecasting Grid 38 Forecasting Grid 38 Forecasting GridLegerld 3 8 Chapter 3Ap~endix:UsingChart Formations In Forecasting 40 Chapter 4 Improving the Probability of Success with Time Diversification 48 Screening Trades 49 5’creenin.g lising Trending Filters 50 Screening Using Momentum Filtela 53 Bar Nmberirzg Protocol 54 The Kase Permission Stochastic: Redefining Time 55 The Kase Permission Stochastic: A Better Screen 57 Kase Permission Stochastic Filters 58 Condensing the Information 59 KaseWarning Signs 62 Scaling In Trades 63 Setting Up Charts 64 Scaling Up in Time Examples 65 Trade One Example: Loss Minimized by Scaling Tech.niques 67 Trade Two Example: GainMnximizrrlbySc~lin.gTechn.ique 6 7 Determining True Range 68 Empirical Evidence that Price and Volume am Fine-Tuning Entries 69 Price andVolumeProportiona1 to the Square Root of Time 70 Chapter4Appendir:The Traditional Stochastic Indicator 72 Chapter 5. Increasing the Probability of Catching Market Turns 73 Why Traditional Momentum Indicators Cannot Be Evaluated Statistically 74 what IfWe Could Define Overbought and Oversold? 75 The Solution: The Statistically Based Kase peakOscilla@r 77 PeakOscillator Works while Other Indicators Do Not 78 Improving Divergence Signals with the KascCD (KCD) 83 Using the PeakOscillator in Trading 83 Stochastic Processes, Monte Carlo Simulations, andRandom Walk Mathematics 87 Stochastic Processes 87 Monte Carlo Simulations 88 The Kase Twist on the RW I 8 9 Chaoter 6 Using Statistics to find Optimal Stop: Kase’s Adaptive Dev-Stop 91 The Old Mousetrap: Stops Based on Fe a r 9 2 What Risk Does the Market Impose? 92 Stops Must Relate to the Market’s Threshold of Uncertainty 93 The Wilder and Bookstaber Volatility Method 94 VarianceofVolatility 9 5 TheSkewofVolatility 9 6 Engineering a Better Stop: the Kase Dev-Stops 96 The Dev-Stop is as Close as Possible to the Best Balance 97 Charting the Dev-Stop 97 Using CandlestickPatterns to Accelerate Exits 97 Five Important Candlestick Patterns for Finessing Exits 98 AcceleratingExits Using CandlestickPatterns 101 An Example ofAccelerated Exits Using Candlestick Patterns 102 Using the Dew-Stop in Trading 103 Chapter Six Appendti: Gaps 10 6 X TRADING WITH THE ODDS Chapter 7 Walking Through Trades 111 Trade Plan for Example Trades 111 Timing Signals 112 Monitor/Timing Chart,Exit Rules and Stops 113 Daily Chart, Exit Rules and Stops 113 Forecasting Rules 113 Walking through a Trade Using The Kase Rules and Indicators 114 Example One: August 1995Natural Gas 114 Example Two: July 1995 126 Chapter 8 Freedom from Time and Space with Universal Bars 139 Rules for Formatting EqualRange Bars 140 References 145 Index 147 OrderingInformation 151 I N T R O D U C T I O N “I can’t believe that God plays dice with the universe.” Albert Einstein My educational background was in engineering, while my trading background was as a corporate trader with a large oil company and then with a money center bank. Both these experiences have had a major impact on how I view the markets and how I trade. Accord- ingly, this book is about understanding the market from both an engineer’s and a trader’s points of view. It is about looking at the markets scientifically and accurately, without making the procedure for doing so too complex. The book also offers views of the market from new perspectives. The reader will learn that simultaneously viewing the markets from multiple vantage points can provide profitable insights; that definitions and relationships based upon tradition are not neces- sarily the most accurate (15th-century mapmakers, for example, defined the world as flat); that an examination of statistically de- pendent and independent relationships can provide universal views of the market that are not impeded by differing units of measure in time or volume; and that, by combining statistics with common sense, aggressive stops can be placed with confidence and without fears of missed opportunities. Where many older indicators are based strictly on empirical ob- servations, we now have the tools to derive indicators from the natu- ral structure of the market itself. Patterns that were difficult to observe with primitive tools now emerge for examination, and the reader is thereby led through complete and detailed step-by-step trades, utilizing his intellectual capacity and application of new tools to better understand the market. Because I spent 10 years as a design and construction engineer and Naval Reserve engineering duty officer before I became a trader, I view the markets with an engineer’s eye. Like pure research sci- entists, engineers think about the world in abstract mathemati- cal terms. Unlike them, however, engineers are paid to convert their abstract mathematical understanding into practical appli- cations. This book adopts the engineer’s understanding of the mar- ket and applies practical and real-world terms, thus improving trading strategies and generating superior trading results. xi xii TRADING WITH THE ODDS Admittedly, this approach requires crunching lots of numbers quickly and accurately, an overwhelming obstacle in the past be- cause the tools required for these calculations were extremely in- timidating. The computational power of early computers was recognized, but getting at that power was tedious; computers were neither user-friendly nor affordable. Today, however, computer- phobia is rapidly vanishing, and many people in the vast major- ity of developed nations are as familiar with their computers as they are with their microwave ovens and telephone answering machines. We have powerful, affordable, and user-friendly com- puters. I say, let’s use them and make them work hard for us. Once the reluctance to use new tools is overcome, all kinds of possibilities unfold. Markets can be explored in entirely new ways that can broaden our understanding by astronomical proportions. Those early mapmakers, for example, were exceedingly accurate in the things they could measure, but their perspective was limited to the use of the tools of their day. Consider the differences in their calculations and resultant maps if satellite imagery had been avail- able to them. One early technical indicator, developed in the late 1950s and early 1960s by Investment Educators, Inc., was the Stochastic, the most sophisticated tool extant. Though the Stochastic utilizes fairly rudimentary mathematical principles, calculating it by hand was still a tedious endeavor. During the ensuing 20 years, the pro- grammable calculator, reverse polish notation (RPN) programming language, and the first affordable personal computer (PC) were developed. As these tools became available, traders took advan- tage of this increase in available computational speed, using it to perform many tasks. In the late 196Os, Richard Donchian used the new calculators to test moving average systems (see Sidebar, “Moving Averages”) and, in the early 197Os, published the results. In 1978, shortly after Hewlett Packard introduced RPN, Wells Wilder published a book called New Concepts in lkchnical Dading, which contained the directional movement indicator (DMI), parabolic indicator, relative strength index (RSI), and other indicators still popular today. (This book included steps for programming a calculator in RPN, making, for the first time, such sophistication available to the average trader.) In the late 197Os, Gerald Appel introduced the moving average convergence divergence indicator (MACD), which is derived from exponential moving averages, again add- ing a layer of mathematical complexity to calculations that would have been too time consuming to perform by hand. These indicators became popular among technicians-and re- main perennial favorites today-yet they viewed the market in terms of rudimentary, programmable calculators. No matter how in-

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