New Frontiers in Technical Analysis E-Book

Paul Ciana

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Bloomberg Professional
• Sprache: Englisch

Seitenzahl: 406

Veröffentlichungsjahr: 2011

Beschreibung

An essential guide to the most innovative technical trading tools and strategies available 

In today's investment arena, there is a growing demand to diversify investment strategies through numerous styles of contemporary market analysis, as well as a continuous search for increasing alpha. Paul Ciana, Bloomberg L.P.'s top liason to Technical Analysts worldwide, understands these challenges very well and that is why he has created New Frontiers in Technical Analysis.

Paul, along with in-depth contributions from some of the worlds most accomplished market participants developed this reliable guide that contains some of the newest tools and strategies for analyzing today's markets. The methods discussed are based on the existing body of knowledge of technical analysis and have evolved to support, and appeal to technical, fundamental, and quantitative analysts alike.

• It answers the question "What are other people using?" by quantifying the popularity of the universally accepted studies, and then explains how to use them
• Includes thought provoking material on seasonality, sector rotation, and market distributions that can bolster portfolio performance
• Presents ground-breaking tools and data visualizations that paint a vivid picture of the direction of trend by capitalizing on traditional indicators and eliminating many of their faults
• And much more

Engaging and informative, New Frontiers in Technical Analysis contains innovative insights that will sharpen your investments strategies and the way you view today's market.

Leseprobe

Contents

Cover

Series

Title Page

Copyright

Dedication

Preface

Acknowledgments

Chapter 1: Evidence of the Most Popular Technical Indicators

Defining Technical Analysis

Defining Chart Types

Evidence of Chart Type Popularity

Evidence of Technical Indicator Popularity

Applying the Most Popular Technical Indicators

Conclusion

Chapter 2: Everything Is Relative Strength Is Everything

“This Time It's Different”

What Is Comparative Relative Strength?

The JdK RS-Ratio and JdK RS-Momentum

Relative Rotation Graphs

Conclusion

Chapter 3: Applying Seasonality and Erlanger Studies

Testing for a Valid Seasonal Cycle

Applying Cycles as a Strategy

Monitoring Seasonal Data

Erlanger Studies: The Art of the Squeeze Play

Chapter 4: Kase StatWare™ and Studies

Introduction to KaseSwing

Kase DevStops

Kase Momentum Divergence Algorithm

Kase PeakOscillator and KaseCD

Why Use KasePO and KaseCD?

Kase Permission Stochastic and Screen

Entering Trades and the Kase Easy Entry System

About the Kase Easy Entry System

Trading with Kase StatWare

Kase Bar Chart (Equal TrueRange Bar Chart)

Summary

Chapter 5: Rules-Based Trading and Market Analysis Using Simplified Market Profile

Technical Analysis Is Simple in Theory—Difficult in Practice

Rules-Based Trading: Automated Strategy Trading versus Discretionary Trading

Balance versus Imbalance: Distinguishing the Two Phases of Market Activity

There Are Only Three Market Segments: Nontrending, Uptrending, and Downtrending

Four Market Participants—and Then a Fifth . . .

Market Profile

Market Movement: The Four Steps of Market Activity

Market Structure

The Relative Speed of the Market's Building-Block Components

Vertical Nondevelopment (“Minus Development”)

Simplifying Market Profile

TAS PRO VAP Map

Rules-Based Trading and Analysis with TAS PRO Navigator

TAS PRO Indicator Application Examples

Conclusion

Chapter 6: Advanced Trading Methods

From the CBOT to the Charts

Trading by Gut Feeling

Understanding the Background of an Opportunity

They Say Entry Is Easy, but Not in My Book

Trade When the Odds Are in Your Favor

Don't Fight the Trend

Trade Location Is Key to Long-term Success

Adjusting to Volatility

Anticipating What Needs to Happen

Using Time as Part of Your Risk Management

Learning to Control Your Emotions

The Hardest Part of Any Strategy Is the Exit

Putting It All Together: Two Examples

Picking Up the Right Tools

Recommended Reading

About the Authors

Paul Ciana, CMT

Phil Erlanger, CMT

Cynthia A. Kase, CMT, MFTA

Julius de Kempenaer

Andrew Kezeli

Rick Knox

Index

Since 1996, Bloomberg Press has published books for financial professionals on investing, economics, and policy affecting investors. Titles are written by leading practitioners and authorities, and have been translated into more than 20 languages.

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Copyright © 2011 by Paul Ciana. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Ciana, Paul, 1983– New frontiers in technical analysis : effective tools and strategies for trading and investing / Paul Ciana. p. cm. – (Bloomberg financial series) Includes index. ISBN 978-1-57660-376-5 (hardback); ISBN 978-1-118-155-608 (ebk); ISBN 978-0-470-879-085 (ebk); ISBN 978-1-118-155592 (ebk) 1. Investment analysis. 2. Investments. I. Title. HG4529.C53 2011 332.63′2042–dc22 2011015868

This book is dedicated to my family, in particular, to the memory of my Grandmother, Charlotte Cianciulli, and her 92 years of inspiring life, laughter, and love.

Preface

In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.

—Charles Darwin

This book has been assembled in response to the growing demand to diversify an investment strategy through the numerous styles of contemporary market analysis and the ongoing search for increasing alpha. Although the most frequently used style of analysis is fundamental, the adoption of technical analysis as an adjunct or preferred style of analysis is becoming increasingly sought after and accepted.

This evolution has become visible in many ways. One observation discussed in Chapter 1 is the tracking and measurement of the use and growth of charts and technical indicators in different regions of the world. Another observation is the growth rate of the number of market participants specializing in technical analysis. In 2010, the Market Technician's Association announced there were more than 1,000 active Chartered Market Technicians (CMTs) residing in 76 countries, representing a 100 percent increase in only four years. Yet another measure is the growing interest in and reliance on the development and implementation of innovative technical tools and strategies that capitalize on existing methods, such as those presented by the contributors to this book.

The bridge between fundamental and technical analysis continues to strengthen and the sophistication of each continues to develop. About a century ago, Charles Dow, who was a journalist, entrepreneur, and technician, created some of the world's most popular equity indices, which are relied on today by all market participants. About 30 years ago, the fundamental term relative strength had only one meaning, until the publication of the Relative Strength Index by established market technician J. Welles Wilder. The theories of fundamental analysis and technical analysis are evolving together and affecting each other at rates faster than ever before. Therefore, a goal of this book is to properly document and share the gains of this evolution.

This book comprises contributions from five individuals who have spent most of their careers, if not all, studying the financial markets through a “technical” lens with the goal of identifying, developing, and implementing effective trading and investment strategies. These strategies attempt to capitalize on the experiences in their careers and explain how existing market actions will impact the future. Their methods are based on the existing body of knowledge of Technical Analysis, and have evolved to support and appeal to technical, fundamental, and quantitative analysts alike.

I view the contributors as accomplished market participants who do everything they can to continually adapt to the modern-day securities exchange industry. They are constantly modifying and refining their methodic approaches to the markets in order to achieve success, and I feel privileged to be a part of the sharing of their strategies.

These five individuals bring with them a combined 150 years of market experience. Their methods, at some point in time, were likely somewhat simplistic, such as the application of moving averages, overbought and oversold momentum indicators, trending indicators, volume analysis, and so forth. We could ask them to recall how they would use these studies, as I'm certain they remember from their earlier days, but this has been done many times with experienced market professionals.

Rather, Chapter 1 begins with the release of previously undisclosed evidence about the most preferred chart types and technical studies. It continues into a lucid and simple summary of the essential elements of those chart types and indicators. The following chapters continue with in-depth explanations of the work of Julius de Kempenaer, Phil Erlanger, Cynthia Kase, Andrew Kezeli, and Rick Knox. All of the chapters can be considered work that has mostly never been seen before, and if seen, never in this much detail. Where some parts of their work is considered intellectual property and therefore proprietary, subjective discussions provide readers with challenging theories and ideologies for their own use. Other parts certainly are not, and hopefully some, if not all, of the work contained in this book will be published again and again, in the same way that Gerald Appel's MACD indicator was 40 years ago.

Chapter 2 presents the work by Julius de Kempenaer on formalizing a sector rotation strategy for world markets by tracking relative performance, the momentum of, and implementing leading visualizations to hasten the process of this traditional strategy. Chapter 3 presents the quantitative work by Phil Erlanger on investing with seasonality and his four-step approach to trading using Bias, Setups, Triggers, and Monitoring. Chapter 4 is a quantitative and statistical approach by Cynthia Kase, who evolved from an engineer into a market technician. She explains her trading strategies using a multitude of tools that address challenging subjects such as appropriate stop levels, adjusting for volatility, and the confluence of multiple timeframes. Chapter 5 by Andrew Kezeli discusses how Trade Angle Securities has incorporated the advantages of the unorthodox yet extremely powerful Market Profile into a suite of technical indicators that are applied to the more traditional bar chart. Finally, Chapter 6 takes the work of Rick Knox, formerly a pit trader and chart software developer, and emphasizes the importance of improving the clarity of indicators through the use of color and a variety of types of technical tools such as Elliott Waves, cycles, velocity, and also the agreement of multiple timeframes. Additional information on the background of the contributors is provided at the back of the book.

Most of the book's contributing authors also maintain web sites, which are mentioned throughout the text. If you're interested in exploring these valuable resources, go to any of the following:

www.bloomberg.com/professional/charts_launchpad/

http://tamresearch.com/

www.erlanger.com/

www.kaseco.com/

www.atmstudies.com/

www.tradeangle.com

These and other useful resources are listed in the Recommended Reading section.

Whether you're a novice or a seasoned veteran in the subject of technical indicators, there is much to be gained by reading this book. An associate on a trading desk or a beginner in the subject of technical analysis has the opportunity to learn about the universally accepted studies, how to use them, and how the evolution of technical analysis has improved them. An analyst or portfolio manager has the opportunity to discover tools that can bolster his performance by studying the thought-provoking material on seasonality, sector rotation, and market distributions. Technical analysts/strategists will learn about groundbreaking tools and data visualizations to add to and possibly replace some of their preferred indicators. Creative minds will be challenged to brainstorm on which calculations, visual cues, and risk/reward ratios will work the best for them when trading, investing, and creating their own indicators.

On behalf of all of those involved with the writing and editing of this book, thank you for considering this work. We feel confident you will not be disappointed and trust that this book will sharpen your investment strategies and enhance the way you view the market.

Acknowledgments

I would like to express my appreciation for all who were involved in the construction of this book and for their influence on my career.

This includes, but is not limited to, many of my colleagues at Bloomberg LP in the Application Specialist, Sales, Product, Analytics, R&D, News, and Markets groups. In addition, I thank the members and employees of the Market Technicians Association, those who encouraged and supported me in the quest to achieve the Chartered Market Technician (CMT) designation, many of the clients of Bloomberg LP, and, of course, each of the contributors to this book: Julius de Kempenaer, Phil Erlanger, Cynthia Kase, Andrew Kezeli, and Rick Knox.

More specifically, I would like to thank Eugene Sorenson, Karsten Gaebele, and David Keller. You have been great mentors, colleagues, and friends during this project and throughout my career. I look forward to our future endeavors.

Chapter 1

Evidence of the Most Popular Technical Indicators

Paul Ciana, CMT

Bloomberg LP

The application of various technical indicators is nothing new to the majority of financial market participants. The opportunity to trade a moving average cross or an overbought market is a frequent observation during normal market hours worldwide. The challenge that many ponder is which technical indicators to use. In an effort to resolve that challenge, market participants wonder what others are using. If this information can be identified and verified, market participants will likely monitor those indicators to understand what others are thinking and seeing. Therefore, it might be possible to develop a trading strategy based on the most popular technical indicators.

Although I cannot prove the latter as statistically true, this chapter reveals a hierarchy of the most popular technical indicators on the Bloomberg Professional Service. Then it presents the indicators' commonly accepted signals. But first, it attempts to define what technical analysis represents; it would be ill advised to discuss only indicators when technical analysis is much more than that.

Defining Technical Analysis

Sometimes it seems that the majority of market participants may be misled about the broad scope of theories used in the application of technical analysis when trying to understand and forecast the financial markets. My gut feeling is that if we were to sample a random group of market participants to define technical analysis, they would present terms such as price, moving averages, charts, and oscillators. A simple Internet search confirmed my suspicions about what words we would hear. Some of the definitions that can be easily found do a good job of describing parts of the theory, while others should not be read by a technician who lacks a sense of humor.

Three of the better definitions are:

1. Analysis of past price changes in the hope of forecasting future price changes.

2. Analysis based on market action through chart study, moving averages, volume, open interest, formations, and other technical indicators.

3. An approach to forecasting commodity prices that examines the patterns of price change, rates of change, and changes in volume of trading and open interest, without regard to underlying fundamental market factors.1

Technical analysis offers much more than these definitions suggest. The first is so generic it could be used to describe many fields of analysis. It suggests market participants study prices and fails to elaborate on the variety of data types that can be analyzed. The second mentions market action, a common term used in describing technical analysis, but then repeats itself by listing the data sets that represent market action. It assumes that most of the methods of a technical analyst are focused on technical indicators and therefore it does not elaborate on the variety and depth of the theories in this field of study. The third suggests that technical analysis is used in the commodity markets, which is true, but the application of technical analysis is not restricted to only the commodity markets. Technical analysis can be applied to nearly all types of financial markets.

The methods of a technician span a wide array of theories and use countless different tools to strategize, quantify, and discuss the financial markets in ways that other types of analyses don't or can’t. One of my goals in writing this chapter is to create a one-sentence definition that broadens the scope of the known definitions. It has proved to be very challenging to come up with one sentence that defines technical analysis in its entirety. I believe this is a debate for the entire industry to continuously weigh in on, especially as technical analysis evolves; furthermore, I do not mean to suggest that any one definition would ever be universally acceptable. At present, and with the input of a few friends, I lean toward the following definition:

Technical analysis is the extraction of information from market data into objective visualizations through the use of mathematics with an emphasis on investor behavior and supply and demand to explain the current and anticipate the future path of the financial markets.

This definition suggests that technical analysis comprises the following five attributes:

1.Market data: Represents a variety of data sets that includes the most frequently used ones such as price, volume, and open interest, but does not exclude data sets such as volatility, ticks, ratios, and dividend yields.

2.Objective visualizations: A preference for analyzing information in a chart, but visualizations could be more than a chart, such as a figure, table, scatter plot, or query of results.

3.Use of mathematics: The application of measurements and calculations to measure the market actions of an individual security or a group of securities.

4.Emphasis on investor behavior and supply and demand: We have a bias for identifying rational and irrational market actions and look for imbalances in the availability or desire for a security.

5.Explain the current and anticipate the future: We are attempting to understand what the market is telling us about itself to estimate where it may go in the future.

To further explain the definition, we will summarize the three premises of technical analysis (see Figure 1.1) and explain some of the most popular tools (certainly not all) used for this method of analyzing the financial markets.

Figure 1.1 Defining Technical Analysis Principles

The first principle states that market actions discount everything. This premise suggests that all publicly available information—such as company-specific news, political changes, weather, and so forth—is already priced into the current value of a security. Therefore we do not necessarily need to know why something is happening; we need only to understand the reaction of investors to what is happening. If the reaction is positive, market participants will push markets higher. If the reaction is negative, market participants will push markets lower. We then employ a host of tools to decipher the impact of that action on the existing trend.

The second principle states that prices move in trends. This relates to Isaac Newton's first law of motion. It suggests that an object in motion remains in motion until acted upon by an equal or stronger force. This force, depending on its strength, can change the direction of motion from its prior path. In technical analysis, this can be thought of as an event or group of events being discounted into the price of a security, causing price to change direction.

The third principle is that history repeats itself—I can still hear my high school history teacher's voice as he quoted, “Those who do not learn history are doomed to repeat it.” This principle suggests that as the dominant generation or the largest group of market participants transitions out of the financial markets, the incoming generation does not learn or receive enough of the previously accumulated information. Therefore we have an inherent bias to repeat many of the same investment and trading decisions, both correct and incorrect, as did previous generations. Some of this tendency to repeat history is represented by price patterns that form on the chart (i.e., a triangle or head and shoulders).

Now that we have a basis for what technical analysis is, we can discuss the tools that a technician uses. Figure 1.2 is a diagram presenting many of the theories and tools that a technician explores to perform an analysis of the financial markets, but it is certainly not inclusive of all the topics. The goal of this figure is to showcase the broad scope of the theories that encompass technical analysis. There are many books that go into detail about these and other topics. Please see the Recommended Reading section at the back of this book for more information.

Figure 1.2 Methods/Theories Used in the Application of Technical Analysis

The remainder of this chapter will address what the most popular chart types and technical studies are on the Bloomberg Professional Service. We will start with a description of the popular chart types and then break down their popularity. Then we discuss the popularity of technical indicators and break down their applications to the financial markets.

Defining Chart Types

Rarely does any market participant make an investment decision without observing the current trend. By simply looking at a line chart, a market participant can see upward, downward, or sideways movements. The work of a technician starts with price, and to look at price we use many different types of charts, such as those listed in Figure 1.2. Although this list is plentiful, it is far from being all-inclusive. Throughout this book, we will familiarize ourselves with the line, bar, candle, log, and intraday charts and identify their ranks in popularity among market participants. Later, we will do the same for the most-preferred technical indicators.

A line chart is a very elegant and simple type of chart to look at. It provides convenience for faster analysis because it shows the overall direction of trend. It is typically used by an economist analyzing economic data sets, a fundamental analyst scanning a list of securities for performance changes and fundamental trends, and overall very long-term analysis. For example, it could be a historical look at an economic release like gross domestic product (GDP), the price/earnings (P/E) ratio of a stock, or the closing price of a security. Figure 1.3 displays these data sets with added line-chart features that help in differentiating data sets from one another. The middle panel has markers on GDP emphasizing where the closing value was and the bottom panel has shading below the line (P/E ratio) to emphasize the slope of the line.

Figure 1.3 Line Chart Showing Stock Price, U.S. GDP, and P/E Ratio

A bar chart is slightly more complex than a line chart in that it offers three more data points per occurrence, when such data exists. It shows the open, high, and low price in addition to the last or closing price.

A candle chart is similar to a bar chart in that it displays the same data—the open, high, low and closing prices—but it does so in a more descriptive and artistic fashion to allow for a quicker analysis and a clearer understanding of price movement. Figure 1.4 displays all three chart types. The candle chart differs the most because of the “body,” or the rectangular shape in the middle, representing the opening and closing price for a period of time. Typically, when this body is hollow, it represents an up period. When it is dark or filled in, it represents a down period.

Figure 1.4 Three Types of Charts: Line, Bar, and Candle

Figure 1.5 is a historical representation comparing all three chart types and shows an example of how the clarity of a candle chart can offer an advantage in identifying more information faster than other chart types. Here we can quickly see that 13 of the 18 trading days in February were up-days (or hollow-bodied candles) and the other six were down-days (or dark-bodied candles).

Figure 1.5 A Historical Comparison of a Line, Bar, and Candle Chart of the S&P 500 Index

Figure 1.6 Trend Line Analysis Showing Arithmetic versus Log Scale Charts

A logarithmic chart is designed to represent the percent change between price increments on the y-axis. As the values on the y-axis get larger, the distance between them will shrink to a distance that is relative to the percentage change. For example, a security that goes from $10 to $20 has experienced a $10 change or an increase of 100 percent. A security that goes from $100 to $110 has also experienced a $10 change but only a 10 percent increase. Therefore the vertical distance on the y-axis should be greater for the 100 percent increase and smaller for the 10 percent increase. A good rule of thumb is to consider a log chart, in addition to an arithmetic chart, when the value has changed about 30 percent or more and always as an alternative for long-term analysis.

Figure 1.6 displays the price of the S&P 500 from the lows of March 2009 to March 2011, when price gained about 100 percent. The top panel is an arithmetic chart, showing equal price increments on the y-axis, and the bottom panel is a log chart, which adjusts the distance between increments on the y-axis to correspond with percentage change. In the top chart, price is about 50 points above the upward-sloping trend line. In the bottom chart, price is already starting to trade below the upward-sloping trend line. This difference in the display of market actions highlights why it is important to consider both chart types.

The last chart type to introduce is the intraday chart. This chart is used primarily by traders who have a short investment horizon or holding period, in order to track the current day or past few days of price movement. It provides a quick glimpse into what is happening right now for the value of a security and is designed to update in real time. An example of a 10-minute bar chart for the past three days is displayed in Figure 1.7. Each bar displays the open, high, low, and close for that 10-minute period of market activity.

Evidence of Chart Type Popularity

Now that we are familiar with the line, bar, candle, log, and intraday charts, we can discuss the preference of these chart types by market participants who analyze the financial markets through interaction with the Bloomberg Professional Service.

The measurable sample size of these regions is approximately 44 percent in the Americas, 38 percent in Europe, 12 percent in Asia, and 2 percent in the Middle East and South Africa (MESA). In other words, of a hypothetical 100 market participants, 44 were in the Americas, 38 in Europe, 12 in Asia, and 2 in MESA.

Figure 1.8 displays the average chart-type preference of market participants from 2005 to 2010. This reveals, on average, that the line chart is preferred about half the time, the bar chart about one quarter of the time, the candle chart about one fifth of the time, and that the log chart is rarely preferred.

Figure 1.9 displays the average preference for historical charts and intraday charts by market participants from 2005 to 2010. This reveals, on average, that the historical chart is chosen more than twice as often as the intraday chart, or about 69 percent of the time, while the intraday chart is preferred about 31 percent of the time.

Table 1.1 reveals the average preference for each year of the statistics shown in Figure 1.8 and 1.9. This data suggests that the preference for line charts is slowly growing, the preference for bar charts is gradually declining, and the preference for candle charts is steady. It also shows that the preference for historical charts is declining and the preference for intraday charts is rising.

Figure 1.7 Three-Day, Ten-Minute Bar Chart

Figure 1.8 Average Chart Type Preference from 2005 to 2010

Figure 1.9 Average Historical and Intraday Chart Type Preferences

Table 1.1 Yearly Averages of Chart Types and Chart Periods

There are three large shifts in the data in this table. The first is in log chart preference from 2008 to 2009. The second is the historical chart preference from 2007 to 2009. The third is the intraday chart from 2007 to 2009. During this two-year period, from high to low, the S&P 500 declined about 56 percent. Therefore the rise in preference for log-scale charts makes sense because the markets experienced a large percentage move. The decline in historical chart preference and the rise in intraday chart preference could represent a few things. It could represent the urgent and repeated desire of market participants to see short-term impacts on the value of their holdings. It could represent investor indecision about what to do with their holdings. Or it could also represent the fear of further losses or hopes of a reversal. Overall it suggests that market participants choose intraday charts more frequently in bear markets than they do in bull markets.

Table 1.2 measures chart type preference of market participants with respect to a region. It answers the question, “What chart type does a region prefer?” Based on the average user preference in 2010, we can conclude:

Table 1.2 Chart Type Preference of Each Region

The Americas, Europe, and MESA prefer a line chart about half the time.After the line chart, the Americas prefer bar charts considerably more than candle charts, while Europe has equal preference for bar and candle charts.Asia is the only region that does not prefer the line chart more than the candle chart. Asia prefers the candle chart the most, and prefers it considerably more than the other regions.MESA, like Europe, prefers first the line chart and then the candle chart.Log chart preference is higher in Europe and the Americas than in Asia and MESA.

Table 1.3 allows us to understand the figures in Table 1.2 in more detail by comparing chart type preference of a region to chart type preference of the world. In other words, the Americas, or 44 percent of the sample size, prefer the line chart 43 percent of the time, or they about equally prefer the use of the line chart. The conclusions we can draw from this table that weren't clear in Table 1.2 are:

Table 1.3 Comparison of Regional Chart Type Preference to World Preference

Although Asia used line charts the least of all the regions in Table 1.2, its preference for line charts in Table 1.3 is 25 percent greater than its sample size. Asia's preferences for a bar or line chart is about equal.Although MESA preferred the line chart most of all charts in Table 1.2, its candle chart preference in Table 1.3 is greater than its sample size, and the line chart preference is less. Candle chart preference is well represented by MESA.The log chart is greatly preferred in Europe and equally preferred in the Americas, while Asia and MESA do not prefer it.

Evidence of Technical Indicator Popularity

Regardless of the chart type that you prefer, chartists and technicians take price and apply an abundance of calculations to it in order to gain a better understanding of what price or market actions are telling them. A question I frequently hear from those who are starting to use technical analysis is “What indicators (calculations) should I use?” In my opinion, there is no “right” technical indicator. The selection and application of one or a handful of studies is based on a person's investment style, trading strategy, risk tolerance, goals, and available time commitment to learn the ins and outs of those indicators independently and together. We could back-test these indicators and strategies, but perhaps that will be in another book. Overall indicator preference can be defined with the data we discuss in the next few pages.

The first step to learning about them is to read some reliable information that provides an introduction into the many indicators that exist. While reading about them, you could select half a dozen studies and dig deeper into their calculations and tendencies. A strong recommendation would be to choose a set of indicators that have different objectives, such as a smoothing study like MACD, a momentum study like RSI, and a distribution study like Bollinger Bands. The next step would be to start applying them individually to a chart to see how they react to price movements, and finally applying them together.

For reference, the following studies and abbreviations will be used when discussing the indicators. Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands (BOLL), Stochastics (STO), Ichimoku (GOC), Directional Movement Index (DMI), Average Directional Movement (ADX), Volume at Time (VAT).

The graph in Figure 1.10 displays the most-preferred indicators, which are a convenient group of studies to be familiar with. The legend lists them in the order of most to least preferred. Please note that the simple moving average (SMA) is most certainly a highly preferred indicator, but it has been excluded because its application is not only for technical use.

Figure 1.10 Most Preferred Indicators

Table 1.4 compares the preference of an indicator to the total preference of all indicators of that region. The world column presents the same data as in Figure 1.10 and is listed for ease in comparison. This table answers questions such as “In what order does a region prefer these popular indicators?” It shows that the world as a whole prefers RSI the most, or about twice as much as it prefers MACD. The Americas favor volume at time (VAT) over DMI and Ichimoku. It also shows that Asia prefers GOC over DMI and STO.

Table 1.4 Comparing Regional Indicator Preferences to All Indicator Preferences

Table 1.5 displays how much a specific indicator is preferred in a particular region relative to the indicators' total preference worldwide. Although Table 1.4 showed RSI as the most-used indicator, it is less preferred by the 44 percent of the sample size in the Americas, about equally preferred by the 38 percent in Europe, is preferred more by the 12 percent in Asia, and much more by the 2 percent in MESA.

Table 1.5 Regional Indicator Preferences Compared to Total Indicator Preference

Some bigger-picture conclusions we can draw from this table are as follows. First, the preference for almost all technical indicators in MESA is more than double its sample size. This shows a strong overall preference for technical indicators in this region. Asia's preference for technical indicators substantially outperforms its sample size, but not as much as MESA. Europe's preference for the top five technical indicators is slightly more than its sample size. The Americas substantially underperform in all categories except VAT.

Figure 1.11 displays the overall growth in indicator use in 2009 and 2010 and is normalized for changes to sample size. This answers a question such as “What indicators are market participants preferring more often?” The average growth of technical indicators over these two years is quite substantial. Their preference on average grew 23 percent in 2009 and another 10 percent in 2010. Interestingly, the most-preferred study, RSI, had double-digit growth rates for both years. Of all the studies, preference for RSI, VAT, and BOLL grew more than average during both years.

Figure 1.11 Growth Rates of Popular Technical Indicators Adjusted for User Growth

Table 1.6 is displaying the preference of the other six studies in terms of RSI. We already know the order of the most-preferred studies but this table addresses a question like “What indicator does a region prefer in addition to RSI?” For example, Asia prefers MACD 59 percent of the time that RSI is preferred, which didn't stand out nearly as much in the other tables. Europe prefers MACD about half of the time, and the Americas and MESA prefer it about two fifths of the time. MESA's lack of preference in STO is emphasized here. Further confirmation for the Americas’ preference for VAT and Asia's preference for GOC is also provided.

Table 1.6 Indicator Use in Terms of RSI

In conclusion, of these findings, the following are some of the general preferences of the market participants utilizing the Bloomberg Professional Service.

Asia strongly prefers candle charts, then line charts.MESA has a relatively strong preference for candle charts, although not as strong as Asia’s, and then it prefers line charts.The Americas prefer line and bar charts, but overall have less preference for other charts and indicators.Asia prefers GOC and tends to complement it with MACD.Europe uses all chart types and indicators and has the largest representation of chart preference. It also has the most preference for log scale charts.The Americas prefer VAT.MESA's preference for indicators is very high when compared to its sample size.

Growth in technical indicator preference is strong and in fact was one of the driving factors in producing this book to discuss newer and more advanced technical indicators.

The rest of this chapter reviews the generally accepted methods for using these technical indicators.

Applying the Most Popular Technical Indicators

I have used the technical indicators discussed here for many years and through study and experience I feel I have come to understand their movements very well. This is something I highly recommend to all readers, as it will increase your confidence in using them as part of an investment decision-making process. Having spent a significant amount of time discussing indicators with market participants, I've come up with succinct yet informative descriptions to explain their workings. Once you've read about the indicators, refer to the box presented further on in the chapter, titled “Generally Accepted Rules for Popular Indicators.”

Relative Strength Index

The relative strength index is usually referred to as a momentum indicator or oscillator. It is called “relative” because the calculation compares the average size of the up-days to the average size of the down-days over a specified timeframe. For example, if we analyze the price change of a security for each of the past 14 days and notice that price went up $1.00 ten times and down $0.25 four times, we can quickly and easily say that price went up $10 and down $1, or that on average it went up more than down. In reality, we cannot quickly and easily see this on a chart, nor can we compare it historically. This is the relationship that the RSI is extracting from market actions on a rolling basis, but is not the exact calculation.

The indicator is scaled onto an axis that has a low of 0 and a high of 100. Usually by default, horizontal lines are drawn at 70 and 30 to signify momentum in the upward and downward direction, or what is commonly referred to as overbought and oversold respectively. It is also important to point out that an RSI level of 50 signifies equal performance of up-periods versus down-periods. The most common look-back period for RSI is 14. Some prefer 9 or 21, and I've seen some go as low as 3 and 5.

RSI is traditionally interpreted as “Sell when overbought and buy when oversold.” This interpretation can be meaningful primarily in range-bound markets with areas of predefined support and resistance. It is important for RSI to confirm the direction of price. If price and RSI fail to confirm each other near support or resistance or during breaks of these levels, a change in trend may be near. We can define confirmation as new highs or new lows in both instruments at approximately the same time.

If RSI travels above 70 while price fails to break resistance, you may choose to be bearish in anticipation of a pullback because high levels of momentum did not lead to price breaking resistance. Alternatively, if price pierces resistance but RSI does not reach overbought, momentum is not behind the new highs so you may choose to be bearish.

The opposite would be true for a bullish view. When RSI travels below 30 and price is holding above support, you might choose to be bullish because large downside momentum did not force a break of support. Alternatively, when RSI is above 30 and price pierces support you may choose to be bullish because momentum to the downside is not strong and the break of support may only be temporary.

Volume analysis is very complementary to these methods of using RSI. If volume is light near resistance or support, it suggests market participants have finished pushing price in that direction. There will be more discussion of volume later.

RSI in a trending market is viewed differently. When a market is trending, all we want to know is if it's going to continue or reverse. If the overall trend is down and RSI reaches an overbought reading, the trend may be changing to an upward direction. The start of a trend change usually appears like sideways movement. Therefore the rally that occurred in the downtrend to cause the overbought reading is likely to at least partially correct itself because the market isn't fully confident in a change in trend yet. A trend change can be confirmed if RSI stays above oversold in the correction and when price starts to break resistance or set higher highs.

RSI analysis can be more complex than what was just discussed. Another way to interpret RSI is to identify periods of divergence. Divergence acts as a warning sign that the trend may be changing. Bearish divergence (an opportunity to sell) occurs when price is making higher highs and RSI is making lower highs. Bullish divergence (an opportunity to buy) is the opposite, when price is making lower lows and RSI is making higher lows. The reason bearish divergence is a warning sign of a change in trend is because price is getting more expensive, but it is doing so at a slower rate. Market participants are still pushing price higher but not as fast as they were when price was cheaper.

Finally, it is fairly common for RSI to be biased to the larger trend for that security and the overall market. During uptrends, the RSI level tends to become more overbought and less oversold. During downtrends, the RSI line tends to become more oversold and less overbought. Therefore, in uptrends you could anticipate the overbought level to be more like 75–80 and oversold levels to be 35–40. In downtrends you could anticipate overbought levels of 55–60 and oversold levels of 20–25. These levels are a rule of thumb. What is important is that when you use RSI, you start seeing the transition of RSI levels in a downtrend to RSI levels in a range to RSI levels in an uptrend. The violation of these levels is alerting and confirming to a change in the behavior of trend. Let's take a look at an example of both divergence and overbought/oversold bias.

In Figure 1.12 there are seven zones to discuss where price and RSI movements depict trend direction. In zone 1, price is in a downtrend and is being coffered lower by a downward-sloping resistance line. Price set four lower lows while RSI made three higher lows and did not get oversold on the fourth low (and was only .37 below the third low). This tells us price is reaching the lowest levels in a long time but at a slower and slower rate. Bullish divergence had presented itself and warned of a potential bottom.

Figure 1.12 Alcoa, Inc., with Examples of Bullish and Bearish RSI Divergence and Multiple Overbought and Oversold Levels

In zone 2, price breaks above the downward sloping resistance line but RSI fails at 60, the overbought level for a downtrend. In zone 3, price stays above the lows of zone 1 and RSI reaches 34, which is closer to oversold in an uptrend than a downtrend. June, July, and August is starting to look more and more like a range-bound market, or a double bottom, than a downtrend, as RSI stays above 30 and below 70.

In zone 4, price breaks the range-bound highs and RSI breaks above 60 and then through 70, confirming the uptrend. All RSI lows between zone 4, 5, and 6 are above the oversold level of 40; in fact they are at least 45, or showing very bullish momentum. In zone 6, price has a huge thrust to the upside with RSI exceeding 80. Price continued to higher highs after that, but RSI did not reach overbought, showing lack of momentum into higher prices. Price and momentum were diverging, bearishly, warning that price may be forming a top.

In zone 7, RSI crossed below 45 for the first time in a long time while price broke down through multiple support levels. We can look for one of two situations to occur that will specify a change in trend from up to sideways. The first is if price continues to decline and RSI reaches 30–35. The second would be if price moves higher and fails to exceed the prior highs of 17–17.50, all while RSI does not exceed 60 (the overbought level for a downtrend).

Moving Average Convergence/Divergence

Also known as MACD, moving average convergence/divergence, this indicator falls in the trending category of studies mostly because it is based on moving averages. By design, trending studies will experience some lag in their signals, so they are best when used to confirm signals from other indicators. Something that is noteworthy about MACD and perhaps contributes to its popularity is that it weights the most recent data points more, or exponentially calculates to reduce its lag.

The default settings for this study across all systems are largely the same. The MACD1 line is the spread between the 12- and 26-period exponential moving average. The signal line is the 9-period exponential moving average of the MACD1 line. I find that very few people actually change these periods. Hopefully this explanation will encourage you to experiment. In Chapter 2, by Julius de Kempenaer, you'll see that he prefers the spread between the 10- and 30-week moving averages. Interestingly enough, almost everyone seems to keep the signal at a period of 9.

This produces an indicator that will oscillate between a positive and negative value. A rule of thumb for many indicators is that when the value of a line in an indicator turns positive, it is bullish, and when it turns negative, it is bearish. A second rule of thumb to consider is that when a faster-moving line (in this case the MACD1 line) crosses above a slower-moving line (signal), a buy signal has occurred, and when a faster-moving line crosses below a slower-moving line, a sell signal has occurred. Remember, these are rules of thumb, not guarantees.

In Figure 1.13 there are two exponential moving averages on the price chart. The dashed line is a 12-day average and the solid line is a 26-day average. Below that is the MACD indicator where the MACD1 line is dashed and the signal line is solid. According to the legends, the EMAVG (12) is 70.315 and the EMAVG (26) is 68.4248. The 12-day average minus the 26-day average equals 1.8902, which is equal to the MACD1 line in the bottom panel. If the shorter-term average is less than the longer-term average, the MACD1 line value will be negative. If the shorter-term average is greater than the longer-term average, the MACD1 line value will be positive. Therefore, the MACD1 line is visualizing the crossing of the exponential moving averages on the price chart.

Figure 1.13 Moving Average Convergence/Divergence Example: Boeing Company

The other component of the MACD indicator is the signal line. This line is plotted to trail or smooth the MACD1 line for two reasons. First, it allows the indicator to generate earlier signals of a potential change in trend. It provides earlier sell signals when the MACD1 line crosses below the signal line and earlier buy signals when the MACD1 line crosses above the signal line. Considering where these crosses occur is important. The MACD1 line crossing below the signal line while positive is an early sell signal. If the MACD1 line crosses above the signal line while positive and far from the zero line, a buy signal has not occurred because the trend is already very bullish. Second, the signal line confirms a trend change when it turns into a positive or negative value after the MACD1 line turns.

Last, the slope of the MACD1 and signal line—positive, negative, or transitioning—can have a bearing on the overall direction of trend. In situations shown in zone 2 of Figure 1.13, you'll see how this can become important.

In Figure 1.13