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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Grasp and apply the basic principles of technical analysis
Savvy traders know that the best way to maximize return is to interpret real-world market information for themselves rather than relying solely on the predictions of professional analysts. This straightforward guide shows you how to put this into profitable action—from basic principles and useful formulas to current theories on market trends and behavioral economics—to make the most lucrative decisions for your portfolio.
The latest edition of Technical Analysis for Dummies includes a brand-new chapter on making the right decisions in a bull or bear market, an updated look at unique formulas and key indicators, as well as refreshed and practical examples that reflect today today's financial atmosphere.
- Become an expert in spotting market trends and key indicators
- Get the skinny on the latest research on behavioral economics
- Take a deep dive into how to read market sentiment and make it work for you
- Get a look at the first innovation in charting for decades—straight from Japan
With comprehensive coverage from charting basics to the cutting edge, Technical Analysis for Dummies includes everything you need to the make informed independent market decisions that will maximize your profits. Happy trading!
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Veröffentlichungsjahr: 2019
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Technical Analysis For Dummies®, 4th Edition
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Published simultaneously in Canada
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Library of Congress Control Number: 2019946701
ISBN: 978-1-119-59655-4
ISBN 978-1-119-59667-7 (ebk); ISBN 978-1-119-59670-7 (ebk)
Technical Analysis For Dummies®
To view this book's Cheat Sheet, simply go to www.dummies.com and search for “Technical Analysis For Dummies Cheat Sheet” in the Search box.
Table of Contents
Cover
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Where to Go from Here
Part 1: Getting Started with Technical Analysis
Chapter 1: Introducing Technical Analysis
Stepping Up to Science
Unpacking Lingo
Buy-and-Hold Is Bunk
Recognizing Who Uses Technical Analysis
Remembering the Trend Is Your Friend
Viewing the Scope of Technical Analysis
Why Technical Analysis Works and What Can Go Wrong
Why Technical Analysis Gets a Bad Rap
Finding Order
What You Need to Get Started
Chapter 2: Tapping into the Wisdom of the Crowd
Comprehending the Conventional Supply/Demand Model
The eBay Model of Supply and Demand
Identifying Crowd Behavior
Defining Normal
Breaking Normal
Accepting When the Crowd Is Extreme
Chapter 3: Trade What You See: Market Sentiment
Where Market Sentiment Comes from and What It’s Good For
Thinking Outside the Chart: Gauging Sentiment
Getting the Lowdown on Volume
Blindsiding Yourself
Thinking Scientifically
Chapter 4: Gaining Critical Advantage from Indicators
Overcoming Noise
Indicators Give You the Edge
Examining How Indicators Work
Establishing Benchmark Levels
Choosing Indicators
Examining Indicators in Detail
Chapter 5: Managing the Trade
Building Trading Rules
Knowing How Much Is Enough
Controlling Losses
Using the First Line of Defense: Stop-Loss Orders
Adjusting Positions
Managing Your Trades Like a Pro
Part 2: Building Indicators from the Ground Up
Chapter 6: Reading Basic Bars: How to Pounce on Opportunities
Building Basic Bars
Putting It All Together: Using Bars to Identify Trends
Overcoming Murky Bar Waters
Framing Your Bars
Applying Bar Reading in Real Time
Chapter 7: Special Bars — An Early Warning System
Finding Clues to Trader Sentiment
Identifying Common Special Bars
Decoding Spikes
Getting Gaps
Filling the Gap
Using the Trading Range as a Tool
Chapter 8: Redrawing the Price Bar: Japanese Candlesticks
Appreciating the Candlestick Advantage
Dissecting the Anatomy of a Candlestick
Sizing Up Emotions
Identifying Special Emotional Extreme Candlestick Patterns
Combining Candlesticks with Other Indicators
Trading on Candlesticks Alone
Part 3: Finding Patterns
Chapter 9: Seeing Patterns
Introducing Patterns
Cozying Up to Continuation Patterns
Recognizing Classic Reversal Patterns
Evaluating the Measured Move
Chapter 10: Drawing Trendlines
Looking Closely at a Price Chart
Following the Rules with Rule-Based Trendlines
Drawing Internal Trendlines
Chapter 11: Transforming Channels into Forecasts
Diving into Channel-Drawing Basics
Riding the Regression Range
Dealing with Breakouts
Examining Pivot Point Support and Resistance Channel
Part 4: Dynamic Analysis
Chapter 12: Using Dynamic Lines
Introducing the Simple Moving Average
Adjusting the Moving Average
Using Multiple Moving Averages
Delving into Moving Average Convergence and Divergence
Chapter 13: Measuring Momentum
Doing the Math: Calculating Momentum
Pondering the Trickier Aspects of Momentum
Applying Momentum
Determining the Relative Strength Index (RSI)
Using the Rest of the Price Bar: The Stochastic Oscillator
Chapter 14: Estimating Volatility
Catching a Slippery Concept
Measuring Volatility
Applying Volatility Measures: Bollinger Bands
Applying Stops with Average True Range Bands
Chapter 15: Ignoring Time to Create Better Timing
Focusing on Tick Bars: In the Spirit of Ignoring Time
Narrowing the Focus to the Move Itself: The Constant Range Bar
Catching the Big Kahuna: Point-and-Figure Charts
Visualizing What’s Important
Applying Patterns
Projecting Prices after a Breakout
Combining P&F Techniques with Other Indicators
Chapter 16: Combining Techniques
Adding a New Indicator: Introducing Complexity
Sailing into Outer Space
Trading with Limited Expectancy: Semi-System, Setup, and Guerilla Trading
Chapter 17: Judging Cycles and Waves
Defining a Cycle and a Wave
Cycling with Supply and Demand — The Pragmatic Mr. Wyckoff
Finding Universal Harmony — Hurst’s Magic Numbers
Looking to the Moon and the Stars
Following the Earth’s Axis: Seasonality and Calendar Effects
Examining Big-Picture Cycle Theories
Shining a Spotlight on the Magnificent Mr. Gann
Embracing the Most Popular Wave Idea — The Elliott Wave
Chapter 18: The Mind-Blowing Ichimoku
Taking a Closer Look at Ichimoku
Grasping Why Analysts Rely on Ichimoku and Why You Can
Using Ichimoku in Your Analysis
Trading with Ichimoku
Part 5: The Part of Tens
Chapter 19: Ten Secrets of the Top Technical Traders
Appreciate Probability
Backtesting Matters
The Trend Is Your Friend
Entries Count as Much as Exits
Stops Aren’t Optional
Treat Trading as a Business
Eat Your Spinach
Technical Stuff Never Goes out of Date
Diversify
Swallow Hard and Accept Some Math
Chapter 20: Ten Rules for Working with Indicators
Don’t Jump the Gun
Defeat Your Math Gremlins
Embrace Patterns
Use Support and Resistance
Follow the Breakout Principle
Watch for Convergence and Divergence
Backtest or Practice-Trade Honestly
Accept That Your Indicators Will Fail
Get Over the Idea of Secret Indicators
Open Your Mind
Appendix: Additional Resources
The Bare Minimum
Additional Reading
Index
About the Author
Connect with Dummies
End User License Agreement
List of Illustrations
Chapter 1
FIGURE 1-1: Uptrend and downtrend.
Chapter 2
FIGURE 2-1: Resistance.
FIGURE 2-2: Support and resistance.
FIGURE 2-3: Trend with four retracements.
Chapter 3
FIGURE 3-1: On-balance volume.
Chapter 4
FIGURE 4-1: Count the trends.
Chapter 5
FIGURE 5-1: Parabolic stop.
Chapter 6
FIGURE 6-1: The standard price bar.
FIGURE 6-2: A series of up-days.
FIGURE 6-3: A series of down-days.
FIGURE 6-4: Nontrending bars.
FIGURE 6-5: Fitschen’s simple bar-scoring.
Chapter 7
FIGURE 7-1: Common special bars.
FIGURE 7-2: Uncommon special bars.
FIGURE 7-3: Price gap.
FIGURE 7-4: A breakaway gap and a runaway gap.
FIGURE 7-5: Island reversal.
FIGURE 7-6: Filling a gap.
FIGURE 7-7: Range expansion and contraction.
FIGURE 7-8: The averaging gaps problem.
FIGURE 7-9: The average true range.
FIGURE 7-10: Change in ATR as a warning indicator.
Chapter 8
FIGURE 8-1: Candlestick bar notation.
FIGURE 8-2: Doji candlestick patterns.
FIGURE 8-3: Missing shadows.
FIGURE 8-4: Very long shadows.
FIGURE 8-5: Bar placement.
FIGURE 8-6: Hammer and hanging man patterns.
FIGURE 8-7: Harami.
FIGURE 8-8: Reversal patterns.
FIGURE 8-9: Continuation patterns.
FIGURE 8-10: Candlesticks as confirmation.
Chapter 9
FIGURE 9-1: Find the pattern.
FIGURE 9-2: Pattern revealed.
FIGURE 9-3: Ascending and descending triangles.
FIGURE 9-4: Dead-cat bounce.
FIGURE 9-5: Double bottom.
FIGURE 9-6: Head-and-shoulders patterns.
FIGURE 9-7: Measured moves.
Chapter 10
FIGURE 10-1: A 5 percent zigzag.
FIGURE 10-2: Drawing a support line.
FIGURE 10-3: Drawing resistance lines.
FIGURE 10-4: Classic break of support.
FIGURE 10-5: Simple linear regression.
FIGURE 10-6: Invalid linear regression.
Chapter 11
FIGURE 11-1: A model channel.
FIGURE 11-2: Two standard error channels.
FIGURE 11-3: False breakout.
FIGURE 11-4: Orderly security versus disorderly security.
FIGURE 11-5: Upside breakout in an uptrend.
FIGURE 11-6: Pivot point support and resistance.
FIGURE 11-7: Pivot point levels overlaid with a standard error channel.
Chapter 12
FIGURE 12-1: Simple moving average.
FIGURE 12-2: Trend tidiness and the moving average.
FIGURE 12-3: Types of moving averages.
FIGURE 12-4: Two moving average crossover model.
FIGURE 12-5: Three moving average model.
FIGURE 12-6: A moving average ribbon.
FIGURE 12-7: Convergence and divergence.
FIGURE 12-8: MACD indicator.
FIGURE 12-9: MACD histogram.
Chapter 13
FIGURE 13-1: Momentum predicts price change.
FIGURE 13-2: Momentum and price divergence.
FIGURE 13-3: Relative strength index (RSI).
FIGURE 13-4: Stochastic oscillator.
FIGURE 13-5: Bullish divergence.
FIGURE 13-6: Stochastic oscillator in error.
Chapter 14
FIGURE 14-1: Degrees of volatility.
FIGURE 14-2: Thirty-day minimum and maximum of a stock.
FIGURE 14-3: Orderly and disorderly price series.
FIGURE 14-4: Average true range indicator (ATR).
FIGURE 14-5: Bollinger Bands.
FIGURE 14-6: Average true range band.
Chapter 15
FIGURE 15-1: P&F chart format.
FIGURE 15-2: Patterns on P&F charts.
FIGURE 15-3: Vertical projection.
FIGURE 15-4: Horizontal projection.
Chapter 16
FIGURE 16-1: Trade what you see.
FIGURE 16-2: Confirming indicators.
FIGURE 16-3: Conflicting signals.
FIGURE 16-4: Informal wave with RSI.
Chapter 17
FIGURE 17-1: A sine wave.
FIGURE 17-2: The Wyckoff Model.
FIGURE 17-3: Gann 50 percent retracement rule.
FIGURE 17-4: Wavelike appearance of a trend.
Chapter 18
FIGURE 18-1: The tankan and kijun.
FIGURE 18-2: Parts A and B of senkou-span form the kumo.
FIGURE 18-3: Ichimoku series.
FIGURE 18-4: Ichimoku with crossover.
FIGURE 18-5: Ichimoku with crossover on 60-minute chart.
FIGURE 18-6: Ichimoku on 60-minute chart with stochastic oscillator.
FIGURE 18-7: Ichimoku on 60-minute chart with standard error channel.
List of Tables
Chapter 1
TABLE 1-1 Recovering a Loss
Chapter 4
TABLE 4-1 Results of Simple Moving Average Crossover Backtest on XYZ Stock
Chapter 12
TABLE 12-1 Hypothetical Profit from the Simple Moving Average Crossover Rule
TABLE 12-2 Hypothetical Profit from the Moving Average Level Rule
TABLE 12-3 Hypothetical Profit from the Two Moving Average Crossover Rule
Chapter 15
TABLE 15-1 Approximate Guidelines for Box Size
Chapter 16
TABLE 16-1 Indicator Trading Results
Chapter 18
TABLE 18-1 Comparing Ichimoku and Conventional Technical Analysis
Guide
Cover
Table of Contents
Begin Reading
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Introduction
Timing can be everything.
Timing is critical in cooking, romance, music, politics, farming, and a hundred other aspects of life on this planet. Putting money into a securities market — and taking it out with a gain — is no different: You need good timing to get the best results.
Technical traders all over the world, amateur and professional alike, earn a living using technical analysis to time their trades in many different markets. And they’re still standing after a market crash, unlike many so-called value investors. In this book, I try to explain how they do that and how you can do it, too.
About This Book
The technical analysis industry is expanding at an exponential pace. A few years ago, an Internet search for the term “technical analysis” returned 206 million responses. Now it returns 1.36 billion responses. Even after weeding out duplicates and mismatches, it’s still a huge amount of material. Don’t be intimidated by the sheer size of the available material. In this fourth edition of Technical Analysis For Dummies, I cover the core concepts, most of which you could apply today with no further research. If you were to explore the most advanced entry in the 1.36 billion entries, most of it would be familiar to you from reading this book.
Technical ideas range from the super simple to the tremendously complex. I cover the core concepts that are the building blocks of all, or nearly all, of those tremendously complex systems. It’s up to you to choose to stay with one of two simple ideas or forge onward to the complex. There is no single best technical idea or combination of ideas, for reasons I explain.
Technical analysis is not only a set of tools. It’s also a mindset, a way of looking at securities prices and how they wag and what wags them. The first principle of the technical mindset is to throw conventional wisdom out the window and trade what you see on the chart. Technical analysis is an evidence-based method of making trading decisions, which means you won’t be consulting earning per share, cash flow, management quality, or any of the other fundamentals that lead to an assessment of value. Technical analysis isn’t value investing. Value investing would have you continue to hold a high-value security despite a big drop in price. The technical analysis trader will sell it, knowing he can always come back after the price bottoms and starts recovering.
Try to think like a 10-year-old as you read this book. In fact, go find a 10-year-old, if you have one handy, and ask him, “Which is better to hang on to: a thing that has already let you down (losses) or a different thing that’s delivering exactly what you wanted (profits)?” See?
That doesn’t mean you may not prefer to keep only high-value names in your portfolio and winnow the portfolio for changing value. It does mean your focus is not on the intrinsic value of the securities you’re holding, but rather on the gain you expect to make in each security.
Beating the system is fun and rewarding. The market doesn’t know you, your age, gender, ethnicity, good looks or lack of them, singing talent, or anything else about you except whether you’re a successful trader. The market is blind. In fact, the market is indifferent. It’s the one place you can go to be judged solely on your merits. Use this book to help you find your way.
The good news is that For Dummies books are designed so that you can jump in anywhere and get the information you need. Don’t feel that you have to read every chapter — or even the entire chapter. Take advantage of the table of contents and index to find what you’re looking for, and check it out. Here are a few tidbits that may answer some questions before you jump in:
The point of technical analysis is to help you observe prices in a new way and to make trading decisions based on reasonable expectations about where the market is going to take the price.
Before you plunge into risking hard-earned cash on securities trading, you have to realize that it’s not the security that counts; it’s the trade. Each trade has two parts: the price analysis and you. Price analysis tools are called indicators, and you have to select the indicators that match your personality and preference for risk. But most people don’t know their risk preference when they start out in securities trading (which changes over time, anyway), so you have a chicken-and-egg situation. By studying the kinds of profit-and-loss outcomes that each type of indicator delivers, you can figure out your risk preferences.
The price bar and its placement on the chart deliver a ton of information about market sentiment. It doesn’t take much practice to start reading the mind of the market by looking at bars and small patterns. The payoff is cold, hard cash, but you have to be patient, imaginative, and thoughtful.
Indicators are the workhorses of technical analysis. They help you identify whether your price is trending, the strength of the trend, and when the trend is at a reversal point. Applying these indicators carefully and consistently is the key to trading success.
You don’t have to be math-competent to do excellent technical analysis and make lovely profits. Technical analysis is mostly visual — what you see on the chart and how you interpret it.
Technical analysis is a form of quantitative analysis. Behind every fancy, complicated hedge fund system run by
quants
are the very core concepts I present here. It’s how the quants put these factors together into a system, usually with automated buy/sell execution, that gives the hedge funds their edge. But you can get the same edge without building a system.
Foolish Assumptions
Every author must make assumptions about her audience, and I make a few assumptions that may apply to you:
You’ve dabbled in securities trading but without much luck. You want to become successful and make some money.
You’re reasonably well versed in the trading game, but you’re looking for new tools to become a more effective trader and improve your profits.
You’re tired of the buy-and-hold approach in which your returns seem unrelated to the supposed quality of the security you bought.
You want to find out how to sell. You know how to buy, but timing your sales ties you up in knots.
You’ve experienced some setbacks in the market, and you need an approach to make that money back.
You want to know whether technical analysis has any basis in reason and logic — or whether all technical analysts are crackpots.
If any of these descriptions fits the bill, then you’ve picked up the right book.
Icons Used in This Book
Icons are small pictures in the margins of this book that flag certain material for you. The following icons highlight information you want to pay special attention to.
When you see this icon, you don’t want to forget the accompanying info — pretty subtle, huh?
This icon clues you in on hands-on time- and hassle-saving advice that you can put into practice. In many cases, this icon tells you directly how to conduct a trade on a technical principle, usually an indicator crossing something, breaking something, or dancing a jig.
Ignore this information at your own financial peril. I use this icon to warn you about mistakes, missteps, and traps that can sink even the best trading professional.
This icon flags places where I get really technical about technical analysis. Although it’s great info, you can skip it and not miss out on the subject at hand.
Where to Go from Here
If you’re new to technical analysis, take a close look at Parts 1 and 2 for the scoop on the field. If you are already a good chart reader, what you probably need is help on managing the trade (Chapter 5). Applying indicators is better than willy-nilly trading decisions, but to get The Traders’ Edge, you also need the discipline of a winner. How do you become a winner? The same way you get to Carnegie Hall — practice, practice, practice, and hanging out with other winners. Figuring out how to trade technically is a journey of self-discovery, corny as that sounds. Luckily, it’s a journey with a lot of fellow travelers to keep you company. I hope you enjoy the road.
Part 1
Getting Started with Technical Analysis
IN THIS PART …
Find out what technical analysis is and isn’t. Technical analysis bypasses fundamentals to make trading decisions on indicators that reveal market sentiment.
Understand supply and demand. Securities trading deals with two forms of supply and demand, the old-fashioned kind and also auction-style. You want to join the trading crowd to take advantage of momentum but very carefully, without going overboard.
Appreciate that indicators work most of the time because of the law of large numbers, but not always, and that’s because the market is made up of humans who behave irrationally sometimes.
Check out sentiment indicators and some useful measurement methods to get an overview of the trading environment, especially volume as an indicator of what the crowd is doing — as contrasted with what they might be saying.
Protect your capital from random moves and from manias and panics by managing the trade from entry to exit, including exits that mean you’ll be taking a loss. All trading involves taking losses and the secret of success lies in controlling them.
Chapter 1
Introducing Technical Analysis
IN THIS CHAPTER
Knowing what certain words mean
Accepting the idea that the trend is your friend
Figuring out what can go wrong
Technical analysis is the study of price behavior in financial markets in order to forecast the next price movement and to trade on that forecast with cold, hard cash. Focusing on price behavior gives you a window into the mind of the market — what the majority of key players are thinking — and helps you make better trading decisions. Technical analysis seeks to identify and measure market sentiment, described as optimistic (bullish), pessimistic (bearish), or uncertain about future prices (sideways range-trading).
To become a technical analyst, you need to figure out how to draw lines on your security price chart and work up the courage to place the buy and sell order with your broker. Each type of line is named an indicator, and I cover every major type of indicator in this book. You need to figure out whether the line/indicator embodies a bullish or bearish outlook (price rising or falling). Many lines/indicators contain a handy built-in buy-and-sell signal, but following those probably won’t match your risk appetite given the amount of capital you have. In practice, you’ll use a computer program to draw the lines (and to do whatever math underlies the lines). You’ll also have many books and websites to guide you in deciding which lines to draw.
Knowing how to draw lines is relatively easy. Placing the trades is hard. This is because your technical-based buy/sell decisions don’t come packaged with how much to trade, how long to hold, how much risk to take, or even how much risk is involved.
To help you start, this chapter provides an overview to this book and what you can expect. Consider it your jumping board into this book and the world of technical analysis.
FINDING THE ORIGINS OF TECHNICAL ANALYSIS
Technical analysis isn’t some newfangled flash in the pan. Observing prices and shutting out other noise has been in development for more than a century. Charles Dow, one of the founders of The Wall Street Journal, observed around the turn of the 20th century that the price of a security neatly cuts through all the clutter of words and is the one piece of hard information you can trust, no matter what the other facts about a security and what people are saying about it.
Here are some basic observations underlying technical analysis that are attributed to Dow himself:
Securities prices move in trends much of the time.Trends can be identified with patterns that you see repeatedly (which I cover in Chapter 9) and with support and resistance trendlines (see Chapter 10).Primary trends (lasting months or years) are punctuated by secondary movements (lasting weeks or months) in the opposite direction of the primary trend. Secondary trends, today called retracements, are the very devil to deal with as a trader. (See Chapter 2 for more on retracements.)Trends remain in place until some major event comes along to stop them.These ideas are part of what is called Dow theory, although Charles Dow himself never called it that and many ideas are called Dow theory that would surprise Dow. An Internet search of the phrase Dow theory yields 23 million hits. A key point is that traders were using technical ideas long before the advent of electronic communication and software programs — technical analysis is hardly a gimmicky fad that will have a short shelf life.
Building on Dow theory were Robert D. Edwards and John Magee, whose Technical Analysis of Stock Trends (St. Lucie Press) was the first major book to use the term “technical analysis” in the title. It was published in 1948 and has been in print ever since. Edwards and Magee expanded on Dow’s observations, covering many of the core concepts of technical analysis such as support and resistance, breakouts, retracements, many patterns, and more. Edwards and Magee noted the universal pattern of a primary upmove followed by a shallower secondary pullback and then another upmove. You’ll see this configuration repeatedly as you explore technical analysis. Edwards and Magee were the first to introduce the tools still in use today to evaluate the pattern and to trade it profitably.
What’s different from Dow’s day and Edwards and Magee’s day is the advent of computers that take drawing lines and calculating indicators away from paper and colored pencils to a screen and cursors. Something else that differs from Dow’s day is the continuous incursion of the scientific method into everyday life. At the turn of the last century, the scientific method was confined to scientists. Regular people would take a homemade folk remedy for a malady because their grandmother swore by it. Today the average person wants to know if that remedy was scientifically tested in double-blind tests and the results peer-reviewed. The comparable development in technical analysis is to take an observation about market prices and volume and to test what percentage of the time the observation results in a correct deduction.
Stepping Up to Science
It may be hard to consider drawing lines on a chart as “scientific” in any sense of the word, but it’s through the scientific method that scientists can forecast outcomes in the physical world and through the scientific method that you can forecast price outcomes in financial markets. Technical analysis of securities prices follows the scientific method in that it entails systematic observation of the subject with standard measurement methods to form a hypothesis and then testing the hypothesis many, many times to validate the theory. But there’s a problem. In a hard science like fluid dynamics, the thing being observed and measured is an object — in this case, water. In technical analysis, the thing being measured isn’t hard — it’s market sentiment generated by human beings, who have far more variability than physical objects. Even given human variability, market sentiment tends to move in repetitive and predictable ways. Technical analysis gives you the tools to identify which sentiment the market has on display at any one time.
Today’s technical analysis has a wider understanding and appreciation of statistics and probability, and thus the value and pitfalls of forecasting. The theory of probability originated in the 16th and 17th centuries, but dealt mostly with the outcome of games and thus the best way to bet on games. Not until the 1920s and 1930s did statistics and probability enter the general public mainstream. Today even ordinary people routinely ask health questions of their doctors in probability terms, such as what percentage of small children without the measles vaccination does it take for the rest of the school class to risk a measles outbreak?
Throughout this book I use words like “highly likely” and “forecast.” I say, for example, technical analysts use lines and indicators to identify price moves that provide a fairly reliable forecast of upcoming future price moves. The word “forecast” makes everybody squeamish because everyone knows stories of catastrophically bad ones. History is full of them, like a top economist saying in 1929 that the stock market was in fine fettle — just before the crash.
But don’t be misled. Although the word is riddled with negative implications, everyone makes forecasts all the time. They just don’t think of them as forecasts. In fact, you make forecasts many times every day. You take this travel route over some other route because you forecast it will save time or aggravation. On a larger scale, when you move to a new city, take a new job, get married, have children, or buy a house, you’re making a forecast about the outcome. Every life decision you make is a forecast — a bet — almost always made on incomplete or hidden information. Technical analysis entails forecasting, but don’t let that scare you. You’ll have plenty of data in dozens of formats to help you, and I describe nearly all of them in this book.
Unpacking Lingo
To get you started, most of the vocabulary associated with technical analysis that you need to know you learned in grade school. Here is a list of some of the important lingo to know:
Chart:
The workspace of technical analysis is a
chart
. Much of the time the chart will show time along the horizontal axis and price along the vertical axis, but not always. (Some charts are formed differently, as I discuss in
Chapter 15
).
Bar:
The price information on the chart is presented in several different formats, but usually you’ll see each period’s price as a standard
bar
showing the price open, high, low, and close. (Refer to
Chapter 6
for more about bars.) Bars, or a series of bars, can be used alone to detect patterns that reveal how participants in that market feel about the security and therefore what might happen next to the price. (
Chapters 7
,
8
, and
9
discuss how technical analysts use bars to forecast prices.)
Candlesticks:
Another method of showing the same information that the bar does is the
candlestick bar
(see
Chapter 8
).
Lines:
Drawing
lines
on the chart helps forecast future prices. For example, you may draw a line connecting a series of price lows and name it “support,” meaning you expect the traders in this security will see the next low as a buying opportunity, raising the price again. (
Chapters 10
and
11
provide more detail.)
Indicators:
You want to enhance the information in the price data by arithmetic manipulation, creating
indicators.
Indicators comes in all shapes and sizes. For example, you see a price chart where the price jumps all over the place. You have no idea whether to buy it or at what price. Now take an average of the closing price over the past 20 days to smooth out the price jumps. Does that line of averages point up or down? Aha! You may have identified a tradeable trend. I describe a wide range of indicators in
Chapters 12
,
13
, and
14
.
Buy-and-Hold Is Bunk
You may think that active trading is too much work and too uncertain to spend the time on. Why not just buy-and-hold? Buy-and-hold is a philosophy that says most equities are best left unattended for long periods of time in your portfolio. They’ll rise more or less in sync with the overall economy so that avoiding turnover saves you transactions costs and taxes. Besides, who are you to suggest a security is over or undervalued?
One reason to distrust buy-and-hold is that over really long periods, returns aren’t very good at all. Stocks from 1950 to 2018 returned 11.1 percent annually. Bonds returned 5.8 percent. If you had a 50/50 stock and bond portfolio, you averaged 8.8 percent. The average return on the S&P 500 over the past 30 years is only about 8 percent. In order to get higher returns, you had to pick one of the periods when market was in a bull market phase. In the United States, from 1927 to 2018, the Standard & Poor’s equity index has been in 25 bull market phases, meaning it rose more than 20 percent. Each one averaged about three years, and the average return of each of the bull markets was 127.36 percent. But the S&P also fell 20 percent or more, defined as a bear market, 25 times over that same period. Timing counts.
In other words, to buy and hold securities for a long period of time is a well-documented path to accumulating capital, but only if you got in at the best time. Otherwise, buy-and-hold is a path to the poorhouse. Consider the following:
If you had bought U.S. stocks at the price peak just ahead of the 1929 crash, it would’ve taken you more than 20 years to recover your initial capital.
Since the end of World War II, the Dow Jones Industrial Average has fallen by more than 20 percent on 14 occasions.
From January 2000 to October 2002, the S&P 500 fell by 50 percent. If you owned all the stocks in the S&P 500 and held them throughout the entire period, you lost 50 percent of your stake, which means you’d now need to make a gain equivalent to 100 percent of your remaining capital to get your money back. Ask yourself how often anyone makes a 100 percent return on investment.
During the Crash of 2018, the S&P fell 6.2 percent and the Dow 5.6 percent, the worst performance in a decade — during a year that saw the highest economic growth in a decade.
That covers the factual aspect of buy-and-hold — you need to get lucky in your entry. Now consider the underlying assumption that all information is already incorporated into the price, the so-called efficient markets hypothesis. Even in the “weak” form of the argument, the assumption is patently untrue.
For one thing, if markets were actually efficient, you shouldn’t get bubbles and crashes, and yet undeniably they happen. Behavioral economists have found that prices are influenced by all kinds of bias, including overconfidence, wishful thinking, and the whole panoply of possible errors in both reasoning and in evaluating information that’s not always unambiguous.
Can you beat the market using timing over buy-and-hold? Yes. Timer Digest has tracked dozens of timers in gold, bonds, and equities who publish newsletters over the past 35 years (www.timerdigest.com). In 2018 alone, the S&P ex-dividends fell 6.24 percent. The top ten timers had gains ranging from 12.86 percent to 40.32 percent. Over the past ten years, the S&P rose a cumulative 177 percent. The top timer had a return of 249 percent. The timers aren’t getting one-time lucky. You see the same names over and over again.
The emphasis in technical analysis is to make profits from trading, not to consider owning a security as some kind of savings vehicle. In buy-and-hold investing, you hardly ever sell, sometimes waiting until you have a catastrophic loss. In technical trading, when you sell is just as important as when you buy.
Before diving into technical analysis, first you have to appreciate that it’s the chart that determines the trading decision, not the underlying fundamentals of the security. You don’t have to follow earnings, management style, new inventions and designs, or any other qualitative aspect of an equity security. In commodities, that might be the weather in Brazil or Chinese demand for rare metals. In foreign exchange, you can ignore inflation, GDP, and central bank forward guidance.
You can still use fundamentals if you want to. Although technical analysis is the central factor in the trading decision, it doesn’t have to be the only factor. Many technical analysts use programs to winnow out the best candidates in a list of securities based on fundamentals like earnings, dominance in its sector, sales forecasts, dividends, and so on, and then apply technical analysis to the select few that remain.
Fundamental analysis and technical analysis aren’t enemies. They can be combined to complement one another.
Recognizing Who Uses Technical Analysis
Both traders and investors use technical analysis. So what’s the difference between a trader and an investor? Most people consider that a trader is someone who holds securities for only a short period of time, anywhere from a minute to a year. An investor is someone who holds securities from many months to forever. You may also think of an investor as someone who seeks income from dividends or bond coupon payments.
Actually, the dividing line between trader and investor isn’t fixed except for purposes of taxation. Be careful not to fall into the semantic trap of thinking that a trader is a wild-eyed speculator while an investor is a respectable guy in a pinstriped suit. I use the word trader in this book, but don’t let it distract you. People who consider themselves investors use technical methods, too.
You can use technical methods over any investment horizon, including the long term. If you’re an expert in Blue Widget stock, for example, you can use technical analysis to add to your holdings when the price is relatively low, take some partial profit when the price is relatively high, and dump it all when it falls more than you can stomach, only to buy it back when it bottoms. Technical analysis has tools for identifying each of these situations. You can also use technical tools to rotate your capital among several securities, allocating more capital to the ones delivering the highest gains or the lowest risk. At the other end of the holding period spectrum, you can use technical analysis to spot a high-probability trade and execute the purchase and sale in one hour.
Remembering the Trend Is Your Friend
You can look at most charts and see that securities prices tend to move in trends, and trends often persist for long periods of time. Opinion varies as to how long any specific security remains in trending mode. It may be 20 percent or it may be 80 percent. A trend is a discernible directional bias in the price — upwards, downwards, or sideways. Many people don’t consider sideways a trend in its own right, but rather a departure from an upward or downward direction.
And yet it can be useful to consider sideways a trend because when you widen the time frame to include more time, you often see that a sideways move is a transition phase from one direction to the other, often on a sudden breakout. You gain an edge when you can forecast a change in direction, even if you don’t know yet which way. The secret to successful trading is to buy at a low price and sell at a higher price. The chart displays lows and highs, and your charting work should indicate where you can next buy low and sell high.
In these sections, I show a model for identifying trendedness and how technical analysts use the model to make money.
Charting your path
The price chart is the primary workspace of technical analysis. Many technical analysts work only with mathematical manipulation of prices in order to devise probabilistically optimum trades, but the chart is the starting point for nearly everyone and remains the main workspace for the majority. Figure 1-1 shows a classic uptrend following a downtrend.
© John Wiley & Sons, Inc.
FIGURE 1-1: Uptrend and downtrend.
At the most basic level, your goal as a technical trader is to sit on your hands while the security is falling and wait to identify the reversal point — the best place to buy (shown in the circle) — as early as possible. Figure 1-1 is a good example of the kind of chart with which you’ll spend most of your time. Unfortunately, most charts aren’t as clear-cut as to the correct trading decision as this one.
Trendiness versus trendedness
To say that something is on a trend is to say that it’s moving in a specific direction and exhibits evidence of a tendency to continue in that same direction. The use of social media like Twitter and Facebook is a trend. What’s the difference between trendiness and trendedness?
Trendiness
implies a fashion or fad that may wither and blow away, like skinny jeans.
Trendedness
refers to a measurable directional bias. It’s a more serious word reflecting a more serious and enduring phenomenon.
Skinny jeans may be out of fashion 30 years from now, but social media will probably still be around and securities charts will still be exhibiting price trendedness.
Picking a time frame
You don’t have to select a time frame right away. In fact, don’t rush. You may fancy yourself a conservative person who would never want to join the ranks of those flibberty-gibbet day traders, but the fact remains that day trading is a deeply risk-averse form of trading when properly executed. And your position on life’s timescale can be important, too. You can’t day trade when you have a day job, but you can when you’ve retired or are temporarily out of work. I know of one auto company president (yes, president) who got through a rough patch (meaning unemployed) by day trading.
Complicating your decision about what time frame to trade is the weird and wonderful aspect of market prices named their fractal property. Fractal refers to the odd fact that a price chart of a security on a one-hour time frame basis can’t be told apart from the price chart on a four-hour time frame or daily time frame or even weekly. If the chart isn’t labeled along the bottom horizontal x-axis to disclose its period, you can analyze it using any indicator and get the same outcome as you would get on any other time frame.
BECOMING A QUANT
You don’t need to know any math at all in order to use technical analysis. If the word “algorithm” makes you feel faint, fear not. It’s enough to know the difference between big and small, up and down, black and white.
Math is a shorthand method of expressing what nonmath types (me included) put into words. For example, I may say, “The price is moving upward at a faster pace than before.” The math person measures the exact extent of the upmove in arithmetic terms such as momentum. He takes today’s price and divides it by the price times number of days ago. He does this for five days in a row and gets a momentum indicator that is a higher number every day. Just because the math person has a number doesn’t make his trading decision any better than yours without the number — the observation of the price event is equally valid however you express it.
You can use indicators that you put on the chart yourself from your charting program, broker platform, or website that lets you fool around with their indicators — without knowing or caring about the math behind the indicator. Your job as a student of technical analysis is not to know exactly how an indicator is calculated arithmetically. It’s to know what the indicator is indicating and what decision the indicator is suggesting to you.
Don’t worry about finding a user-friendly program to do the math work for you. Twenty years ago, both the data and the software were very expensive — now they’re a given. Do a web search of “free charting software” and you get more than four million hits, including reviews of the programs.
Viewing the Scope of Technical Analysis
Technical analysis focuses on prices and often on the accompanying volume. Analyzing prices can take many different forms — from drawing lines on a chart by hand to using high-powered computer software to calculate the most likely path of a price out of all possible paths. Technical analysis is sometimes called by other names, such as charting, market timing, and trend-following. The press, the public, and even technical-analysis authors all use these terms interchangeably. All technical analysis methods fall under the broad term quantitative analysis to set it apart from fundamental analysis.
When you see these terms in this book and elsewhere, don’t fret over a strict interpretation — and don’t accept or reject a technical idea because it has a particular label. You can put ten technical traders in a room and get ten definitions of each term. The following sections are my interpretation of these terms and their nuances.
Charting
Charting is probably the oldest generic term used for technical analysis. I cover charting techniques in Parts 3 and 4. Charting refers to reading supply and demand into bars and patterns. Some technical analysts reject the term charting because it harkens back to the days of colored pencils and rulers. They see charting as subjective, whereas statistics-based indicators (which I cover in Part 5) are objective. But many traders use charting conventions developed over decades because they work.
Market timing
Market timing is another term used in place of technical analysis. All technical trading involves timing, but this term refers to statistical analysis that goes beyond a single chart. It encompasses many techniques, such as sentiment indicators and calendar effects, that many self-described chartists say aren’t charting, and at least some technical analysts say aren’t technical analysis. I cover these and other tools in Chapter 3.
Trend-following
The very first question to ask when you look at any chart is, “Is the price trending?” Because so much emphasis is put on the presence or absence of a trend, technical analysis is sometimes named trend-following.Parts 4 and 5 contain techniques that are trend-following. Some analysts object to the term because you aren’t always following, but often anticipating, a trend such as when you use momentum indicators (see Chapter 13).
Technical analysis
Technical analysis is the broadest of the terms. It’s a term encompassing all techniques, but at heart technical analysis seeks to measure and quantify market sentiment.
Technical analysis isn’t confined to just math-based techniques, as some folks may think. Using math is a breakthrough and a curse. Math may outperform human judgment and the human eye, as many an optical illusion has proved, but it’s not true that numbers never lie. Numbers lie all the time in price analysis. You can have a textbook-perfect trend with ten confirming indicators, and it can still run into a brick wall — really bad news that trashes the price of the security overnight. Math can never overcome the inconvenient fact that a Shock, which no one can predict, may overwhelm any price trend. Shocks in capital letters are events like 9/11.
In your quest to define trendedness and formulate trading rules to maximize profits and reduce risk, don’t run the risk of turning into an obsessed, nerdy number cruncher. Don’t forget that behind the numbers are other human beings who often behave in irrational ways. Technical analysis (so far) remains an art, not a science, even when it uses scientific methods.
Algorithmic trading
Algorithmic trading, also referred to as algo trading, uses a specific set of conditions, including technical analysis indicators, to trigger a trade. After the computer identifies that all conditions are met, it automatically sends the trading order to the broker. Computer programs are faster than human brains or fingers, so ordering a trade as quickly as possible is one of the benefits of algo trading. Some algo trading companies pay for data collection sites a few seconds closer to the physical location of exchange data. Conditions can include time of day, volume, and other parameters as well as nuts-and-bolts technical analysis indicators. Algo trading, like technical analysis, removes emotion from the trading decision.
If you’re a beginner in technical analysis, you won’t much care about algo trading, but you should have at least an outline of what it is in order to know the lingo and be able to judge commentary and promotions.
Understanding how algo trading works
Here is algo trading in a nutshell: A modeler designs a set of indicators, all expressed mathematically, and devises a buy/sell program to execute the trades automatically with no human participation after the original design. As in all technical analysis, the design relies upon present price behavior repeating past price behavior. The modeler is using the very same indicators you’ll be using after you get started in technical analysis.
Algo trading gone berserk has been blamed for a rise in volatility, especially in the U.S. stock markets, and for at least two flash crashes (a flash crash is an abrupt, unexpected, extreme drop in prices in minutes):
In 2010, the Dow fell 998.5 points in 36 minutes before recovering.
In June 2016, algo trading was blamed for the collapse of sterling after the Brexit vote. Sterling has yet to recover.
In 2018, the Dow fell 800 points in ten minutes before recovering.
Algo models often use a strategy of systematically placing slices of the total order at specific time intervals to conceal the trader’s position from the rest of the market, such as 20 percent of the capital stake at ten-minute intervals as long as the price is moving favorably. When the model identifies the end of the price move, the algo system will dump all the slices, all at once.
Noting the benefits of algo trading
The primary benefits of algo trading are as follows:
It takes human decision-making out of the immediate trading decision.
It’s much faster. The computer can do in nanoseconds what takes the normal person at least a minute or two.
Much algo trading is high frequency trading (HFT), meaning in and out again in seconds. This is “trading without emotion” in spades!
The predecessor of algo trading was HFT, which is now a feature of algo trading, too. HFT is based on the high speed of computerized trading. The value comes from being able to get in and out of a trade very, very fast, and generally with a large number of trades and small gains or losses per trade. You wouldn’t buy and sell a stock for a one penny profit, but if you’re doing it with $1 million and doing it dozens of times per day, if you get it right, you’ll make hundreds of thousands.
Rocketing to the moon
What is the difference between algo trading and a robot? None. A robot performs the same functions — to find a defined set of conditions expected to deliver a gain and to place the orders with the broker without human action. Many brokers offer the capability of designing your own algo system, although transaction fees can be high. What they offer is special algo-building software and a trading platform that allows automated trades. Automation is the key to the speed, but you can also find programs that require manual approval before the trade is placed. You don’t have to be a programmer to engage in algo trading; you can buy algo formulas online.
Or you can buy a black box algo trading systems from many different vendors, including brokers. They’re named black box because the indicators dictating the trades are, usually, a secret. The trading rule for an entry may be something as simple as “buy if the open is higher than the close the day before.” Some are undoubtedly far more complicated.
ALGO TRADING BECOMES AI
When an algo trading system gets the ability to think, otherwise known as machine learning, it becomes AI or artificial intelligences. The essence lies in the computer program being able to modify some aspects of its performance using feedback. Say you have a system that always buys a security when its price has fallen by x percent, based on the observation that an x percent drop is the most probable norm for a bottom over many instances in the price history. But now market volatility is higher — prices change direction faster and after lesser moves. The program records these lesser moves and, without human judgment, alters the buy rule to (say) 15 percent. At the minimum, AI makes a technical trading system more adaptive and does it far more quickly than a human can do.
The fancier AI story would be when the machine can observe dozens of nonprice factors, such as the overall index to which the security belongs, economic data, or news events like central bank policy changes, and can simulate the price change in this one security in a flurry of mock trials — in nanoseconds. It calculates the probability of each of those events and all combination of those events as a price influencer. As the actual information becomes available, like an economic data release, the program starts all over again with the new information and its now-known effect. This effect becomes an input in the next round of mock trials. And as I keep saying, the rules of probability hold that the more trials you can run, the more reliable your forecast.
This form of AI is growing by leaps and bounds in medical diagnosis and treatment as well as other fields, including the rapidly improving hurricane forecasting. There is now facial recognition, self-driving cars, Apple’s Siri and Google’s Alexa, and thermostats that track your habits to adjust the temperature in your house.
This form of AI will someday become the true pinnacle of technical analysis. It’s possible that some fund managers are already there, or close to it. More than one big-shot fund manager has designed algo systems to mirror his own thinking in a way that is mathematically expressible and thus programmable, but going beyond technical analysis. In the U.S. equity market, JP Morgan estimated in early 2019 that various quant strategies manage at least $1.5 trillion. Another scary idea from JP Morgan is that traditional nonquant investors account for only about 10 percent of U.S. equity trading.
But note that somebody — a human — has to design the feedback protocol in the first place. A researcher can discover that a central bank rate decision affects a stock, bond, or currency price on x percent of the occasions and generates a y percent move. But if you get an y percent price change in the absence of the trigger event, the computer program is at a loss as to where to look for the cause in order to include it in the next iteration. The program can always go hunting for some data event that seems correlated and possibly causative, but the program won’t know whether the data event makes sense. You could end up with the price of oranges in Marrakech as a determining factor in a buy signal for IBM. A computer program can beat a human at chess, but it can’t invent chess in the first place. The point is that while employing technical analysis qualifies you as a quant, not all quants are technical analysts.
