Macro to Micro Volatility Trading 1452870624, 9781452870625

Citation preview

MACRO

Macro

to Micro

TO

MICRO

VOLATILITY

TRADING

Strategy Guide for

Intraday & Swing Traders Forex | Stocks | Futures Clarify volatility; see how and why mainstream perceptions are often flawed. Understand how and why volatility and standard deviations are connected...

Aggressive

for

Stocks,

Volatility Forex

and

Trading

Futures

Macro to Micro Volatility Trading By Mark Whistler

MACRO

TO

MICRO

VOLATILITY

TRADING

Specifically, see how to enact Macro to Micro analysis, drilling into short-term time frames to identify high reward, low risk trades.

About Author

Witness how larger market expectations (and uncertainty) can yield big profits for almost any trader.

ee

Mark Whistler is a full-time trader and author. Whistler has appeared on CNBC and is a regular contributor to FXStreet.com, discussing currency trading and global markets. Whistler is also a contributing Senior Market Strategist toTradingMarkets.com. His books include: Macro to Micro Volatility Trading (CreateSpace, 2010) Volatility Illuminated (CreateSpace, 2009) 2034 The Corporation Post 2012 (CreateSpace, 2009)

The Swing Trader's Bible (John Wiley & Sons, Inc.) - co-authored with CNBC/Fox News regular guest Matt McCall Trade With Passion and Purpose (John Wiley & Sons, Inc. 2007)

Trading Pairs (Wiley, 2004) Profit from China (Investment U/Wiley, 2006) Profit from Uranium (Investment U/Wiley, 2006.) Mark Whistler is also the founder of WallStreetRockStar.com, fxVolatility.com, iOnlineForexTrading.com, iForexFeed.com, and iForexMetaTraderRobot.com. Whistler is also a regular columnist for Investopedia.com. In his spare time, Mr. Whistler operates EatsForTheStreets.com, a growing organization - dedicated to helping the homeless across America and the MarkWhistlerGallery.com, an unbiased Internet art gallery open to all artists (globally) seeking to display their works.

Copyright Macro to Micro Trading by Mark Whistler Copyright © 2010 Mark Whistler

All rights reserved. [email protected]

| www.Wallstreetrockstar.com

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopy, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act. In the case of brief quotations, embodies in articles, reviews, scholarly papers, or works of fiction, in compliance with the Copyright Law of the United States of America and Related Laws Contained in Title 17 of the United States Code, reproduction is permitted. Larger reproduction permission will most likely be granted by the Author upon request, as he believes in Capitalism and that free markets provide opportunity. The author requests permission queries be addressed to: [email protected] Limit of Liability/Disclaimer of Warranty: While the author has used his best efforts in preparing this book, he makes no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaims any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The information contained herein may not be suitable to be taken as empirical truth. You should consult with a professional when and where appropriate. Neither the author, the editor, nor the publisher shall be liable for any personal, or commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data: ISBN

1452870624

EAN-13

9781452870625

Printed in the United States of America, New York, New York

First Edition June 2010/First Printing June 2010

Limit of Liability/Disclaimer of Warranty (EXTENDED) PairsTrader.com, Inc. LLC, [WallStreetRockStar.com, FXVolatility.com and Mark Whistler] ("Company") is not an investment advisory service, nor a registered investment advisor or broker-dealer and does not purport to tell or suggest which securities or currencies customers should buy or sell for themselves. The principals, analysts and employees or affiliates of Company may hold positions in the stocks, currencies, and/or industries discussed here. You understand and acknowledge that there is a very high degree of risk involved in trading securities and/or currencies. The Company, the authors, the publisher, and all affiliates of Company assume no responsibility or liability for your trading and investment results. Factual statements on the Company's website, or in its publications, are made as of the date stated and are subject to change without notice. It should not be assumed that the methods, techniques, or indicators presented in these products will be profitable or that they will not result in losses. Past results of any individual trader or trading system published by Company are not indicative of future returns by that trader or system, and are not indicative of future returns which may be realized by you. In addition, the indicators, strategies, columns, articles and all other features of Company's products (collectively, the "Information") are provided for informational and educational purposes only and should not be construed as investment advice. Examples presented on Company's website are for educational purposes only. Such setups are not solicitations of any order to buy or sell. Accordingly, you should not rely solely on the Information in making any investment. Rather, you should use the Information only as a starting point for doing additional independent research in order to allow you to form your own opinion regarding investments. You should always check with your licensed financial advisor and tax advisor to determine the suitability of any investment. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD,

SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING AND MAY NOT BE IMPACTED BY BROKERAGE AND OTHER SLIPPAGE FEES. IN ADDITION, SINCE THE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THE RESULTS MAY HAVE UNDER- OR OVERCOMPENSATED

Table of Contents

FOR THE IMPACT, IF ANY, OF

CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT

About Author

THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN.

Copyright

ADDITIONAL NOTICE TO FOREX/CURRENCY TRADERS

Trading foreign exchange on margin carries a high level of risk and may not be suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to trade foreign exchange, you should carefully consider your investment objectives, level of experience and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with foreign exchange trading and seek advice from an independent financial advisor if you have any doubts. THE INFORMATION AND STRATEGIES

IN THIS BOOK DO NOT MAKE ANY

Limit of Liability/Disclaimer of Warranty (EXTENDED) ADDITIONAL NOTICE TO FOREX/CURRENCY TRADERS.. 4

Chapter 1 | The Nuts and Bolts of Macro to Micro.. 6 What is Macro to Micro?. 7

PROMISE, OR GUARANTEE. MARKET CONDITIONS CONTINUALLY CHANGE AND THUS, INFORMATION PROVIDED IN VOLATILITY UNLIMITED COULD CHANGE AS WELL. YOU SHOULD SEEK PROFESSIONAL ADVICE PROACTIVELY, DURING AND AFTER ATTEMPTING TO IMPLEMENT ANY STRATEGY/INFORMATION NEW TO YOU AND YOUR TRADING KNOWLEDGE,

The mean is the outlier...

8

12

OR STYLE.

NEARLY 95% OF ALL RETAIL TRADERS LOSE. PLEASE DO NOT ATTEMPT TO TRADE FOREX IF YOU FEEL THE AFOREMENTIONED

Chapter 2 | Rethinking the Mean.

EVEN REMOTELY APPROACHES YOUR RISK TOLERANCE.

Chapter 3 | Clarifying Distributions.

15

THE BEST ADVICE TO MOST INDIVIDUAL'S CONSIDERING TRADING

Chapter 4 | Dynamic Volatility via Standard Deviations.

FOREX

17

— IS UNLESS YOU HAVE PROFESSIONAL

HELP — DON'T.

1. Market (Historical) Volatility..

2. Probability Volatility.. 20 3. Mean-period Volatility.. 21 4. Price Volatility.. 21

19

MACRO

Chapter 5 | Reshaping Standard Perceptions

TO

MICRO

_27

Appendix B | The Movement of Subset Distributions. 75 Words of Caution.. 76 Transcending Markets through Volatility and Probability Gaussian Curve Revisited.. 79 The Expansion and Compression of Subset Distributions

_35

Chapter 7 | Macro to Micro.. 38 Chapter 8 | The Reality Adjustment.

TRADING

22

Chapter 6 | Why the Containment Zone is so Important.

The long-term mean is...

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46

Reality Adjustment. 48...

89

Chapter 9 | Short-Term Timing in the Larger Picture.

Monthly Chart - Dow Jones Industrial Average.. 49 Monthly Chart - International Business Machines 52 Weekly Chart - International Business Machines.. 53 Daily Chart | International Business Machines.. 54 4-Hour Chart | International Business Machines.. 56 Hourly Chart | International Business Machines.. 60 30-Minute & 15-Minute Charts| 62 International Business Machines.. 62

Chapter 10 | Pulling it All Together. 63

Appendix A - Commodity Channel Index and the Containment Zone. 66 CCI is calculated as: 67 Standard Stan And The Four Horsemen.. 71 Identifying POTENTIAL Reversals with CCI 73

84

Appendix C | Information and the Volatility Paradigm..

Pre-Loading Major Markets for Volatility.. 90

49.

76

Endnotes. 97

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MICRO

Chapter 1 | The Nuts and Bolts of Macro to Micro Welcome to Macro to Micro, Strategy Guide for Intraday | Swing Traders, one of the most powerful tools in your larger trading repertoire. In principal, the pages you are now reading surfaced only after hundreds of hours of educational Webinars and nearly as many hours answering emails from readers, subscribers, and conversations with real traders, just like you. One of the most common "unclear areas" of trading and markets that seems to plague so many traders is simply a deep understanding of how to determine trend on both short and long-term timeframes.

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TRADING

Likely, yes, it happens to all of us eventually. However, have you ever been in the aforementioned situation, only to click out to a 4-hour, daily, or weekly chart, to suddenly discover, your short-term trade is going against the larger trend? Moreover, have you ever had a solid understanding of the longer-term trend, but just couldn't seem to figure out what the short-term trend was? Even more frustrating, have you ever known what the long-term trend was, and spotted the short-term trend too, but just couldn't see exactly where to time a short-term entry within the chaos? The fundamental question here is: How can one ever see the forest, if they are stuck in the trees? Macro to Micro is not a "fancy pants" indicator, nor is it a red line crosses blue line system... What Macro to Micro is... A real trading methodology, market understanding, strategic positioning, and even better... Actually usable in all markets, in real time, over the long haul. As you're about to see, Macro to Micro is one of the most powerful trading strategies anyone (retail investors and traders...and even the pros!) can utilize to make money with...

What is Macro to Micro? Macro to Micro is a simple, easy to understand "macro down" market analysis tool that allows traders to identify larger points of high probability within the broader market. What's more, Macro to Micro helps traders identify larger trends, while zeroing in on short-term pivots to effectively and accurately time intraday and swingtrading entries, both short and long. Macro to Micro can be used for individual securities, indices, currencies, futures, and commodities. Macro to Micro can be used with broader index and sector movements (for stocks), and even fundamental analysis too.

Have you ever been in a trade, looking at a five, fifteen, or thirty minute chart, when suddenly your position starts moving in the opposite direction?

Macro to Micro is a methodology, where one analyzes larger timeframes first, moving down into intraday charts to unearth possible trading opportunities at hand. The overall methodology and strategy behind Macro to Micro is used by many professional traders, though many may not even know it directly. However, for professional traders to survive, virtually all must have a macro down understanding of markets and the individual components they are trading. Many professional traders just intuitively know Macro to Micro; virtually all know it and use it in some form or

another... In essence, Macro to Micro is a facet of market understanding that no serious trader should be without. Again, Macro to Micro is not complicated, or muddled with countless hours of required research. Though Macro to Micro does take a little effort to adequately master at first, in no time at all, you will find the research takes only minutes each day. Moreover, because of the universally effective principals behind Macro to Micro, the strategy works in virtually any market. We are talking stocks, Forex, commodities, and futures. If there were ever a tool that could seriously help all traders - almost everywhere Macro to Micro is likely the one. Macro to Micro is a fundamental building block of understanding movements within markets, volatility, mass-market sentiment, and even better, high probability pivots, which no trader should be without. Furthermore, Macro to Micro is one of the only methodologies | strategies that every single other technical tool, fundamental analysis, and even news/event triggered trading can be used with. As you're about to see, Macro to Micro is a fundamental pillar of understanding how and why markets move as they do, helping prepare traders before events unfold, not after. To understand Macro to Micro though, we must first touch on two important keys to the larger movements of prices within markets: 1. The concept of mobile distributions and mean. 2. Understanding what a "Containment Zone" is and how and why such is important to the larger movement of prices and sentiment within all markets. 3. Understand that "volatility" is not an all encompassing word that covers all trading action in markets, but rather, is made up of several types of volatility. When we describe automobiles, we use the make and model to identify the type of car we are talking about. However, in markets, traders seem to call all makes and models of volatility - just that: volatility... A fateful error that keeps most from deeply understanding volatility and markets. By the way, I really believe the reason why many traders have so much trouble winning...is because even when they are given the tools to trade, they attempt to make the tools and/or information fit their predetermined opinion of the direction they've

already somehow decided (sometimes subconsciously and for no reason, really) the currency, or stock, or index, should move. Thus, to uncover some sense of truth, we have to examine all options, not just the one, or two that seem to fit the opinion we have somehow arbitrarily already decided fits...

Thus, as we begin digging into Macro to Micro, it is important for readers to keep an open mind, while also understanding that one of the key points behind Macro to Micro is embracing all possibilities within pending movements of markets, steering clear of unjustifiable opinions that as human beings, we all tend to take on from time-to-time, for whatever reason. If we approach our Macro to Micro analysis with rigid opinions of what markets, or an individual component should do, we might as well not even bother with Macro to Micro at all. Fact is, when we're so opinionated that we cannot bring ourselves to attempt to see "the other side of the coin", we likely won't pay attention to any information markets are presenting, except that which fits our already decided outcome of how things must unfold...

I call it the Smart Guy Syndrome. Sadly, Smart Guy Syndrome mostly affects retail traders, especially those who have only been around a short while. Smart Guy Syndrome is where a trader believes he needs to be the smartest person in the room,

and the only way to do so is to be the best stock picker. The Smart Guy believes the best traders are those who can tell you with absolute certainty where a stock, index, currency, or market will be five days, five weeks, or five years from now. He likes to shoot his mouth off about his winners and never talks about his losers. Smart Guy will listen to other's opinions and heaven forbid the person is wrong on his market call. Smart Guy will write him off, or talk him down. The problem with Smart Guy though, is he doesn't understand that everything in markets changes every day. Sort of like weather. One can predict that it will snow next year, but to tell you now, precisely what day it will snow one year from now...is just plain silly. Markets are similar... Everything is constantly changing, including the information that elevates, or lowers our expectations of any particular outcome. Thus, we do not want to be the smartest person in the room, we want to be the profitable trader in the room. To be the most profitable guy in the room, we must understand that markets change, and what was a perceived outcome of an event to happen two weeks ago, may not be at all this morning. Information in the past 12 or 24 hours may have changed the situation, and if we cannot see, understand, or alter our perceptions with the information surfacing, we are stuck in the rigid world of "Smart Guy", who can't change his mind from whatever he was thinking last night, simply because doing so might be too big of a blow to our ego...to admit we were/are wrong. Often though, we were not wrong at all, rather, the information just changed, but Smart Guy can't see that...he just sees right and wrong, which is why he's most always...wrong. Macro to Micro helps us avoid Smart Guy Syndrome, allowing us to "re-think" markets each day. By doing so we begin to clearly see when and where markets are shifting on both a macro and micro basis, while also enabling ourselves to identify high probability short-term entries. In the end, by understanding Macro to Micro, we empower ourselves to dynamically take advantage of the never-ending shifts in market action. With all of the aforementioned in mind, we will now move into Chapter 2, where we will discuss the concept of mobile distributions in markets and revisit a simple, but highly misunderstood concept in trading: the mean. Just as Macro to Micro is a fundamental building block to trading, the next few chapters are precisely the same to the larger concept(s) of Macro to Micro. Please do not "skim over" or pass Chapters 2 thru 6. The chapters are vital to our larger understanding of markets, movements of components within, and of course, macro down analysis, aiding us in identifying high probability entries, from which we

seek to make money.

Chapter 2 | Rethinking the Mean Before we dive too far into the underpinnings of Macro to Micro, I would like to mention that much of what readers are about to take in throughout the following pages is intended to challenge much of what we've traditionally been told about trading and markets... Fact is, the bulk of the information we have come to rely on as factual, empirical and even remotely worthwhile in markets, is sadly, perhaps deeply flawed. Do not listen to a word I say though, just keep reading and simply look at the data for yourself... First though, the question at hand is really, why is it retail investors (and even many professionals) are given daily helpings of not so shiny information, trading tools and even literal "educational" untruths about markets, and never even stop to question why the information may, or may not be relevant? Why is it so that many finance PhD's, economists, six-figure analysts, and fancy-suit pundits are seemingly never able to clearly forecast market conditions accurately? Why exactly do markets dog so many retail investors, media, and even professionals? After a decade and a half in the trenches, one begins to find clues many pundits, analysts, economists, and other financial professionals (not actually putting their own money on the line each and every day) never discover.

Figure 2.1 | Chapter 2: Rethinking the Mean

What is an Outlier?

Rethinking Old Ideas

It is here that we directly begin our conversation of Macro to Micro, examining one of the most commonly misunderstood concepts within markets: The mean. As you're about to see, because so many have never truly stopped to consider the true paradigm under the statistical equivalent of a child's Tonka Truck, the 'high-brow’ analysts, fancy pundits and the hot shot retail techno-traders, have unfortunately missed one of the most important building blocks of markets, price action, psychology and in the end...profitability within markets. Again, what we are talking about is the simple, good old-fashioned mean/moving average, tracking prices within markets... Here's the thing... It may be possible that even your understanding of what "the mean" truly is within markets... may be slightly damaged. For most, the aforementioned is true, though not because of anything that has to do with smarts; rather, simply because the information that we've all been given to date may have been flawed from the start. After all, conventional definitions of the mean completely miss the point of what the mean even means in markets. But then again,

MACRO

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MICRO

the people who are producing content to sell, but do not actually depend on the same information they are producing to make a living trading with, just wouldn't know in the first place. With the previous in mind, here's what we're about to do: 1. Examine the common definition of "the mean" within statistics and markets, just to make sure we're on the same page. 2. Explore the concept of mean within the larger, longer-term scope of markets (did someone say Macro?), checking to see if there may perhaps be a little "skewness" in the public understanding of the statistical average we're talking about. 3. Take another look at one of the darkest words within statistics and markets: outlier. In Chapter 3, we're going to take a look at whether the simple concept of a "distribution" really applies in trading, and whether...if there is such a thing, it might be dynamic, or static, and perhaps how and why Wall Street Brainpans never seem to get the real picture. Then, in Chapter 4, we're going to look at the short-term mean (did someone just say Micro?), examining whether such might actually have totally different principals (and implications) from its longer-term counterpart. All this about a simple old mean...the boring old line on a chart we refer to as a "moving average"? I mean really, what kind of special information could this ridiculous "simpleton" line really hold, that everyone doesn't already know? Perhaps I might just ask one little question... What do you think when you hear the word outlier? Within the context of our discussion of "the mean", is it possible our conditioned notions of "the norm" may have actually been skewed from the start and perhaps we never even knew it? Let me ask my previous question one more time: What do you think when you hear the word outlier? Take a moment to read the following definition of the term: mean. Mean: Simple or arithmetic average of a range of values or quantities, computed by dividing the total of all values by the number of values. For example, the mean of 1, 2, 3, 4, and 5 is (45 + 5) = 3. It is the most common and best general purpose measure of the mid-point (around which all other values cluster) of a set of values, but is prone to distortion by the presence of extreme values and may require use of a measure of distortion (such as

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mean deviation or standard deviation). See also median and mode."4]

For some, the term "outlier" conjures imagery of foggy, dangerous places far from the center of normality, perhaps a 2nd or 3rd standard deviation (or gasp! further) anomaly where corporations go bankrupt, Governments stumble, and investors lose hordes of money, all because disaster has unexpectedly struck somewhere... On the other hand, some may imagine an outlier as a person or event of innovation and/or prosperity, perhaps a come from behind story, maybe an unexpected discovery. What if I were to say, the first of the two - the darker description of outlier - is precisely wrong in terms of statistics, trading, and markets. Could it be possible? Could the budding fruit of a new idea be right here? We'll get to the more optimistic of the two outliers in just a moment; first though, what if the darker outlier - that place where corporations go bankrupt, investors lose trillions, wars erupt, and society temporarily destabilizes...is in fact...not an outlier at all; rather, the mean. Yes, I am saying the mean is the outlier. Bear with me for a moment, and I think you may leave these pages second-guessing a few common perceptions we’ve all been conditioned to "just believe", without ever questioning. In the above mainstream definition of mean, the source states, "It [the mean] is the most common and best general purpose measure of the mid-point (around which all other values cluster) of a set of values, but is prone to

distortion by the presence of extreme values and may require use of a measure of distortion (such as mean deviation or standard deviation). See

also median and mode."[2!

We've been conditioned to believe the mean is the "mid-point" where all other data resides, which is true... However, we are taught from Statistics 101, data away from the mean is not normal. Case in point, the above statement where the mean, "is prone to distortion by the presence of extreme values and may require use of a measure of distortion (such as mean deviation or standard deviation). See also

median and mode." Please note a few terms in relation to data away from the mean that we just read. Specifically, the terms distortion and extreme values. What’s more, common definitions cite mean deviation and standard deviation as tools to measure the distortion. In essence, commonly accepted principals of statistics tell us that the closer to the mean the data rests, the more normal, calm and regular. From my perspective, though...the perspective of a trader...perceptions of data at the mean as "normal" and "distortion" being that of data falling away from the mean at "extreme values" are dangerously naive. Nevertheless, we are conditioned to just accept that movement away from the mean is truly the data to fear and, we never even question why? I query, why is it that for so many in today's supposedly enlightened Internet age, we just take in the accepted standard merely because it's been published in the public domain... While never even considering the true relevance? Is it just easier to live our lives near the middle, in the cluster, where as long as one does not speak too loudly, or attempt to stray from the group, he or she will be considered ‘normal’, meaning not distorted? Market participants are horribly misled in-terms of what we've been taught to "just accept, just because." We are taught to fear data away from the mean; as all those economists, fancy Wall Street analysts, and PhD's couldn't possibly be wrong about data away from the mean... Again, as we've been told again and again, data away from the mean is distorted (AKA outliers) and indicates something out of the ordinary taking place... Trust me though; something "out of the ordinary" is taking place...what we're talking about is a massive, collective brainwashing... Take a moment now, to read the definition of outlier from Encyclopedia Britannica.

data values (in statistics (science): Outliers) [3] "Sometimes data for a variable will include one or more values that appear unusually large or small and out of place when compared with the other data values. These values are known as outliers and often have been erroneously included in the data set. Experienced statisticians take steps to identify outliers and then review each one carefully for accuracy and the appropriateness of its inclusion..." The common perception of an outlier is that of a rarity, or point within a statistical distribution that generally falls away from the center, or mean. Enron and Long Term Capital Management (1990's $4.6 billion hedge fund failure) are often referred to as outliers. What we are talking about are rare events that generally occur away from the mean of the larger dataset... Distortion... Extreme Values... Perhaps people, places, things, and events to fear. In terms of trading, outliers are commonly thought of as price(s) near the second or third (heaven forbid the fourth and beyond) standard deviations. We are talking about large variance of price that have significant impact on market participants, maybe even the entire retail investing public, corporate substructure, and even possibly Government.

What we're discussing here are events that push markets to places they haven't seen in years... Distortion, crashes, depressions, bailouts, recessions, and the mean. Did someone just bump the record player? Yes, the mean. The long-term statistical mean. See, while we are commonly taught to embrace the mean as a warm fuzzy place of central tendency where tranquil, friendly normalcy resides, in truth, the data at hand shows something remarkably different. The mean is truly the enemy (over the long haul) for investors and markets. Sadly, the public seems to have accepted the conditioned state of believing the precise opposite. Here's what readers should know... When major indices are trading at, or near, the long-term mean, some sort of disaster has struck and markets are flailing.... Badly... Again, when the major indices are trading at, or near a long-term mean, something horrible has occurred, with fear, worry, uncertainty, and hardship likely prevalent within the economy and/or society.

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Need some proof? Please take a moment to examine Figure 2.3. What you are looking at is a 30-year chart of the Dow Jones Industrial Average since 1980. I've drawn a 50-period mean, with 1.25 standard deviations, along with a 14-period mean, also with 1.25 standard deviations. (Please disregard the 14-period mean for the moment, as we will revisit the chart again in Chapter 4, The Containment Zone |

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calmness times the domestic known in

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and normalcy, why is it when looking at the DJIA since 1983, the only two index approached the mean, occurred because of a terrorist attack on soil (leading to war), and the greatest global Financial Crisis the world has modern times?

(By the way, I would like to thank Kirill Melentiev, an incredible young Forex trader from Moscow, Russia, for helping put together the data you are seeing.)

Paradigm on the Move.)

For now, I have drawn arrows illuminating the running 50-year mean, as well as the relevant distribution's 1.25 standard deviations, over the thirty-year history. (As another important, brief note, I will explain standard deviations within markets in just a few moments, for now

though, please just note "traditional statistics" tells us 1-standard deviation measures approximately 68% of the distortion from the mean, 2-standard deviations measures roughly 95.5% and 3-standard deviations encapsulates 99.7%. In addition, the reason for using 1.25 standard deviations over a

simple 1-standard deviation measurement is simply that the decimal point accommodates slight data loss when transferring the formula of standard deviation into electronic format./4] Moreover, the decimal point increase also lines the standard deviations up with one of most commonly misunderstood, but powerful indicators in the market: Commodity Channel Index CCI. For more information, please see Appendix A.) Next, please note the years 1980 to 1982 in Figure 2.3, where the Dow Jones Industrial Average was resting right on the mean, just under 1,000. (Hard to believe the major index was ever that low, right?) Anyway, in 1982, the index began to edge higher, rising well above the 1.25 standard deviation in 1983. The Dow generally remained above the 1.25 standard deviation area for the following 19-years, traveling higher and higher, touching 12,000 at the turn of the century... Here's my question to readers... If the mean is such a comfortable place, where the common perception is that of

Fact is, over the long haul, the exterior of the distribution (data traveling outside of the ist and 2nd standard deviations...sometimes even outside the 3rd standard deviation), are/were actually not outliers at all... On a longer-term basis, data traveling near the exterior of the distribution really translates to "expectations aligned" where the masses are more or less moving in larger lockstep with the belief that greater prosperity is to come... Why do you think the Market Volatility Index (VIX), a measurement of fear, declines in times of market prosperity? The VIX falls during bull markets, as expectations align, and thus, volatility fades. On a longer-term basis, it's really only when fear and uncertainty rise that volatility increases. More on the VIX in a few moments.

Again, as the empirical data in Figure 2.3 shows, data traveling outside of the ist standard deviation, often at or beyond the second standard deviation (over the long haul), provided one of the greatest bull markets in the history of the index.

81 bear market (recession) (mind rate hikes, wars) lead the DOW into containment zone

§2 computer populansation (increaced productmtty of labor)

*rate decreaces - drove DOW out of the containment zone!

gazine votes “Computer” Man of the Year!

Dow trades outside 1.25 Standard Deviation (Ascending!) for Nearly 20-Years_.. After 1982. Index does not tag 50-year mean again until 2008 - The Financial Crisis... Ask yourself. is the mean really the outlier of uncertainty?

50-Period (Month) Mean

|

1990

1982 1985 1954 1985 {908 1957 i985 1988 19aD 1991 19u2 1993 tase (ees 1900 eu) 980

19m

i

I

Bush Tax Cuts & Rising Eamings Restore Confidence |

2000

N

|

|

e

TST NSO - 10M OVZENVE)

710

J902 200) 200 200) 200s M4 2005 Ae s0or sme 20s 2010 201 ani2

“

Monthly Chart of Dow Jones Industrial Average 1980 to 2010 “What is the Meaning of ‘Outlier’

1351

Figure 2.3 Dow Jones Industrial Average Since 1983

TRADING VOLATILITY MICRO TO MACRO

Given the data you are now seeing on the Figure 2.3 with your own eyes, please reconsider the commonly accepted definition of mean and outlier, which you read only a few moments ago. I ask...where is the data most distorted? Was the data distorted away from the mean, during the 19-year bull market, or perhaps, over the past seven years, as the Dow has now traded back to the mean twice during (arguably) the greatest recession in America's history, a major terrorist attack on domestic soil, and the technology-based dot.com bomb? Maybe what is really distorted is the common acceptance of defective information as truth, much like the previously seen definitions of mean and outlier. Who are the people that assume and propagate markets as static little synthetic laboratories and/or economist 'models' that should have even remotely the same rules as their statistical experiments, which historically lead investors to believe away from the mean is data distortion? The academic standard of recording and reporting statistical results of the behavior of mice in a bucket has a finite start and stop... But markets never stop, much like time. Are the so called experts perhaps completely overlooking the fact that this is the real world, where a natural state of skewness persists as time unfolds, all at the mercy of the constant ebb and flow of emotions...of real people...with real money? At the end of the day, over the longer-term in markets, the mean is not a place of calmness and normalcy, rather, the mean is the precise pivot point of pure uncertainty, fear, confusion, economic hardship, and likely, where large losses of wealth from retail investors resides. If you're not quite seeing the picture clearly, don't worry, it will become clear shortly...

The mean is the outlier... What I'm really saying here is outlier events, like terrorist attacks and Financial Crisis's, are indeed outliers in their own Black Swan sort of way... Here's the massive 'aha' point though... The outliers (rare events) do not drive prices to the exterior of the long-term distribution; instead, outlier events drive prices back to the mean...making the mean the real outlier. Am I the first guy to come up with the notion of the mean being the true outlier, in

terms of long-term returns within markets? Well, perhaps in regard to the concept of the mean being "the true outlier." However, not so much in the case of understanding that mean reversion is an eventual problematic situation that hits markets at one time, or another, based on expectations of investors shifting. For the average investor though, the information I'm about to show is not readily available. One must generally sift through hundreds of papers, before finding where these concepts might have been deployed to markets in the past. Even now, the only real market participants who have in their possession, understand, and utilize such, are institutional grade analysts, portfolio managers and traders... In 2003, Eric Hillebrand of CeVis (Center for Complex Systems and Visualization, University Breman, Department of Mathematics, Stanford University) published a paper titled A Mean Reversion Theory of Stock-Market Crashes, from which we find considerable information regarding mean reversion as truly a shift in expectations of market participants. Specifically, Hillebrand states in his Abstract: "Errors in the perception of mean-reversion expectations can cause stockmarket crashes. This view was proposed by Fischer Black after the stock market crash of 1987. I discuss this concept and specify a stock-price model with mean-reversion in returns. Using daily data of the Dow Jones Industrial Average and the S&P 500 index I show that mean-reversion in returns is a transient but recurring phenomenon. In the case of the crash of 19871 show that during the period 1982-1986 mean-reversion was higher than during the nine months prior to the crash. This indicates that mean reversion expectations were underestimated in 1987."l5] [6]

Moreover, as the data at hand in Figure 2.3 shows, what I'm saying in regard to the long-term mean being the real outlier...is correct...and yet, the common perception of the term outlier(s), is to look for distorted data at, near, or beyond, the exterior of the longer-term distribution (like the second and third standard deviations; which, FYI, are where retail traders are commonly trained like chimps to believe reversals will likely occur). Really, given the significant distortion in relation to our common sense understanding of inferential statistics, as applied to markets, it's no wonder retail traders are historically the big losers in trading and investing. The common understanding of what to fear, and what to embrace (that has been engrained in the masses' perception of real, justifiable and common sense), is just plain

muddled in and of itself. Here's the kicker, the data is/was not wrong; rather, the investing public's common perception of the data is and was incorrect...and will (unfortunately) likely remain incorrect, because those who are conveying the concepts may not really understand markets... What I mean is this: Even the people who are supposedly presenting expert educational information to the general investing public, may not fully understand the larger, true paradigm of how "common statistics" apply much differently to the ever-moving dynamic distributions that make up markets, over the static historical models studied in controlled, finite, mainstream Academic settings. What serious traders and investors often come to grips with, over the common public, is that with a slight shift in our understanding of flawed mass-accepted common sense perceptions of information (most often spoon-fed daily by media with an agenda, and those who do not really trade for a living), we may suddenly find ourselves empowered to actually see markets in true light... Hillebrand's 2003 paper definitely breaks the mold of traditional Academia focusing on static distributions, over dynamic models that truly make up markets in real life... Upon deep consideration, I believe Hillebrand was able to do so, as his research focused on expectations, which requires thinking forward, over simply studying historical data, which as any good quant knows, generally involves "curve fitted" results that may not hold true in the future. Looking again at Hillebrand's research, in section two, Mean Reversion Expectations, the author states: "T consider the situation where at time (t) a positive change is observed (Fig. 2). An individual investor with conservative expectations might now think that returns will come down fast. If (») is some parameter in the return generating process that controls the reversion speed, her expectations can be represented by, say, the parameter value (») which stands for a fast reversion. As the individual investor is not alone on the market, her expectations are probably dependent on the behavior of other participants as well. Let us assume that between times (t) and (t+h) she

does not act in any way but observes the behavior of the other market participants to come up with an expectation which is some weighted average of her a priori expectation indicated by (») and the observed market behavior. If the market's sales indicate a reversion expectation like the one represented by (»), the investor recognizes that her a priori

expectation was very conservative relative to the market and consequently adjusts it to (»), for instance. With the above in mind, I have one slight discrepancy with the larger concept of investor expectations, which Hillebrand presented. While I do agree that with Hillebrand's overall theory that historically conservative expectations can induce significant selling within markets, when participants finally realize their previous expectations may have been wrong... However, I do not believe most investors ever think about their positions in relation to potential mean reversion risk, relative to their expectations of return. Fact is, most investors don't even know what mean reversion is...much less being able to identify whether their previous expectations might be out of whack versus the collective perception of other participants. In essence, most participants aren't even aware that it might be a good idea to consider broader market sentiment, as an indication of larger risk within their own portfolio and/or markets on the whole. Thus, we're not really talking about mean reversion expectations whatsoever, we're really talking about expectations for future growth (that which prompts one to buy in the first place), or uncertainty (that which creates fear) related selling, likely based on a deterioration of wealth, as markets tumble. In essence, mean reversion (over the longhaul) is created by those who are in the know, discarding enough positions, that those who never really understood the game from the start, follow suit, based on the fact that they are seeing their accounts lose value, and thus react in fear, and fear alone. If those less savvy (who likely never even had a seriously-solid reason to buy in the first place, other than someone else told them it was a good idea), truly had some type of fundamental reason to remain in their positions, they would hold through major instances of long-term mean reversion. However, without any serious understanding of their investments, previous expectations give way to uncertainty, which in turn, morphs into fear and then...panic selling. What I'm saying here is when expectations are aligned, most participants prosper in continued upward, or downward movement outside the 1st standard deviation of longer-term distributions. However, when price breaks back below (or above) the 1st standard deviation, most average investors are NOT thinking, "Geez, I should perhaps change my market bias from expectations of gains to expectations of mean reversion", as most people don't even know what the hell mean reversion is within markets, in the first place. Instead, the aforementioned participants are thinking, "The value of my account is

going down, I don't know what the hell to do right now, but I'm scared and so I'm going to dump it all." Thus, almost unconsciously, those same participants (the retail public) create a mass wave of selling, which comes only after first tier participants (savvy portfolio managers, institutions and traders) have already bailed out. In the end, when expectations turn to uncertainty, perceptive participants have already likely exited, and thus, leave the common retail public holding a bag of nothingness, other than mediafurthered fear-hype and uncertainty. In the end, "expectations" do not drive prices back to the mean; rather, uncertainty is the true catalyst for long-term mean reversion related market activity. Figure 2.4 | Dow Jones Industrial Average 1983 to 2000

Dow Jones Industrial Average (INDU)

Monthly Chart with 50-Period Mean & 1.2 Standard Deviations 1983 to 2000

A

Becuase standard deviations and distributions within markets are dynamic (mobile) when price moves... The mean

sf

and standard deviations move...

Moreover, from 1983 to 2000

[

MASSIVE bull market, the data

trades AWAY from the mean the ENTIRE time...

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Price trading AWAY from long-term mean over almost 20-years - is not DISTORTED, rather, shows “expectations were aligned” for greater prosperity within

markets...

f

Dow back under 1.2

i

standard deviation. But it does temporarily push Dow:

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Even crash of 1987 does not FULLY bring

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Piz

Upper 50 Month 1.2

Standard

Deviation...

Lower 50- a 1.2 Standard Deviation 1997 1998 1999 201

Let's take a closer look at what I'm talking about... The above chart zooms in on the Dow Jones Industrial Average from 1983 to 2000. Immediately, traders should recognize that the Dow never even approached the 50-period monthly mean during the entire upward during the 1980's and 1990's! Really, the massive bull rally from 1983 to 2000 was because "expectations were aligned" for growth, prosperity and if nothing else, opportunity for the aforementioned. Moreover, even the crash of 1987 did not bring the Dow fully back to the mean; however, it did bring the Dow towards the mean. Do you see what I'm saying? If we are normally taught the mean is where normalcy resides, then why is it catastrophic events bring prices back towards their long-term mean - not away? Clearly, calmness and normalcy in markets resides when data is falling away from the long-term mean, not towards. Moreover, calmness can even fall outside of the mean on the downside, as at least even then expectations are aligned for further valuation deterioration. (Only when prices come back to the mean - again, on a longer-term basis - does uncertainty rip through markets, while volatility crushes average investors trying to just make a few extra bucks on their hard earned money.) Why hasn't this information come forward to the investing public yet? Not only is the accepted standard perhaps inaccurate, but is continually being dished up to common investors DAILY - today - right now - here in 2010. Why? It really upsets me, and makes me very sad about how badly common investors are almost ‘duped' within markets daily. In the end, we're perhaps merely talking about a breakdown in the semantics, which many statisticians and economists may already be aware of... However, as we already noted in the common definitions of Mean and Outlier (as presented to the general public) the information is NOT clearly or effectively disseminated or relayed to the average Joe - at least NOT in terms of markets anyway. Thus, if the common understanding of outlier is data away from the mean - not a horrible event that drives prices back towards mean - no wonder investors never really understand how to apply "common sense" to markets and trading...and always seem to be the last guys holding the bag of nothingness, while Wall Street insiders walk away with hordes of cash. So how can investors and traders put the odds back in their favor again? First, we must understand that long-term data approaching the mean

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indicates "expectations" are NOT aligned, whereby fear and uncertainty are prevalent in markets. Second, we must understand that data falling away from the long-term mean, outside the 1.2 standard deviation, indicates expectations are likely aligned for greater value, earnings, or wealth (hence, a bull market), or deterioration of value, earnings or wealth (accordingly, a bear market.) Third, we must understand that the mean is the outlier, not the second or third standard deviations (and even sometimes beyond), which are really indications of subset distributions moving because they are dynamic, not static, like many economists' ivy-tower - removed from reality - models. Fourth, we must understand that the information is not complicated, nor does it require a deep understanding of complicated math, we must just understand that THEORY behind what is happening.

Fifth, we must understand what causes expectations to align, while also being able to spot when a possible real outlier is present in markets; thus, potentially cluing us into when true uncertainty is about to push prices back to the mean. Sixth, we must be willing to challenge the status quo of flawed information and/or deployment of such by mainstream media... The bottom line is up that up to this point in history, the information (or perhaps relay of) presented to regular investors has proven to be clearly broken for average Joe's who continually get burned anytime markets take a massive nosedive, seemingly out of the blue. But the nosedives aren't always out of the blue (at least not always, though occasionally markets catch EVERYONE by surprise), rather the drops are often foreseeable by you, me and anyone else who is willing just take a little time to understand what's really happening with the mean, volatility, mass expectations, and how even sometimes, commonly accepted information is flawed within markets. With all of the aforementioned in mind, throughout the following chapters I will do my best to not only provide readers with even more information on how the mean is

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the true outlier, but will also endeavor to clarify how we can truly and accurately attempt to perceive when expectations are aligning for a bull or bear trend... At the same time, I seek to also provide real, quantifiable, empirical probability and volatility theory to assist all traders, in all markets. Finally, as we work through Macro to Micro, I will attempt to show traders how they can utilize the Macro to Micro strategy to drill into short-term periods, making the required Reality Adjustment to time trades on an intraday and swing trading basis. In the end, I hope to provide traders with NEW and FRESH information about trading that isn't just another REHASH of the same old junk that never really worked in the first place... I do currently use - and plan to keep using - what you are reading here... However, because what you are about to read is NOT a red line crosses blue line "don't have to do no research" get rich quick black-box super signal system, I know many in markets will never even bother to take the time to understand what I'm presenting... Regardless, I am introducing the information because there is hordes of money to be made in markets for everyone, and even if I tell you what I know, I don't believe it would diminish my ability to hopefully see ALL of my own personal dreams come true trading... And if I do something dumb in the future and blow up my account, hopefully the revenue from this book will have padded my ticket into the retirement home where with any luck they might serve chocolate ice cream during bingo hour. In the end, it appears to me there is plenty of money for everyone on Wall Street, though up 'till now, it seems the only guys really pulling it out (from what I can tell), are institutions and Wall Street insiders - and a few very savvy investors. I believe in sharing information and trying to help other people, not just trying to help myself, like so many investment banks, brokers, funds and other institutional Wall Street suits. The little guy has been burned for way too long... I'm an independent trader, not a suit with a fat firm gorging clients on commissions, while taking positions against the same instruments they're recommending. Seriously, it's time for small investors and independent traders to get their chance too. All I ask of you is this: If you do find the information here helpful - HELP SOMEONE ELSE. Help break the cycles of greed, ego, soullessness, and selfishness on Wall Street.

Chapter 3 | Clarifying Distributions Understanding why widespread -flawed- perceptions of statistics and markets continues day-by-day, even though the data tells us something different, requires a brief explanation of why the common perception of the mean and the term outlier are flawed conceptually and theoretically. Don't worry though, the explanation (from the perspective of statistics) is not complicated, math-riddled, or time consuming. We simply need to take a moment to digest the theoretical underpinning of why and how incorrect perceptions of the mean and the term outlier have indeed infected markets. To begin, what do you think of when you hear the term distribution? Do you think of a Bell Curve measuring a bunch of boring data? It might be slightly odd if you did not (unless you are a statistician, quant, or data junky), as the common explanation of a distribution within markets is an empirical attempt to measure probability of data within a given timeframe... In addition, most often we associate the term distribution with Gaussian, or Bell Curve. However, the shape of the curve doesn't even matter, to tell you the truth. The shape of the curve is a common argument that keeps most from ever seeing the true paradigm under the surface. The point here is that much like time, markets continually move forward with each day that surfaces... In effect, time is skewed, meaning we will never return to any moment on the linear axis, as in, this precise moment will never return again, at least not unless you happen to have time machine conveniently parked in your garage. What I am saying is the larger set of market-data is skewed, because time is skewed. Thus, the argument of the "shape of the distribution" is moot. All that matters are the underlying principals of probability theory remain intact, and whether or not, such is/are empirically justifiable. What I'm saying is I don't care whether the "shape" of the distribution is a round, square diamond monkey poissossian green triangle (whatever), so long as the results hold constant the probability of 99.7% of all the data residing within three standard deviations of the mean. (FYI, in Appendix B, readers will be able to verify for themselves that nearly 99.7% of the data does indeed rest within three standard deviations of the mean - on all time frames.)

Furthermore, we must understand distributions in markets are not static; in other words, our mean is mobile and dynamic, in that, prices are constantly moving, and thus, the mean and distributions are itinerant as well. What's more, prices will never remain static, as fear, greed, and expectations of market participants will always prompt continual buying and selling. In other words, the up and down movement we see day in and day out will never cease, much like time, unless time stops, prices go to zero, or people stop hoping for and working towards greater prosperity and wealth. Over the long haul, we can assume most people seek higher prices, as hope, hard work, and the desire for greater prosperity naturally prompt buying, greater earnings (both personal and corporate), increased wealth, and an overall higher standard of living. Thus, as society continues to push forward towards innovation, a better standard of living, while continually overcoming adversity, naturally, the means of the major indices will continue to rise over the long haul as well, even though temporary setbacks will occur. Case in point, the Dow's rally from 1,000 in the early 1980's to the present value near 10,000, at the time of this writing.

The bottom line is: For prices to ascend further, they must trade away from the mean, above the first standard deviation, pulling the mean higher, for the larger distribution to ascend as well. Thus, we can assume price action near the edge of a distribution (as in at, or near the second or third standard deviations), is not an outlier occurrence like most believe... Rather, prices at or near the previously perceived areas of "outlier" often directly reveal aligned expectations of prosperity by market participants, prompting the larger distribution to ascend as well. In reality, only when uncertainty and disaster strike, will prices move back towards the mean. Consequently, the mean is the true outlier, which directly contradicts conventional thought. Again, we're talking about the big picture here, not intraday prices, as the intraday mean is vastly different. Prices at the mean on an intraday basis indicate a risk adverse "reload" buy or sell point (likely aligned with expectations of institutional participants), or a pivot point of a possible Reality Adjustment pending. Just FYI, We will cover intraday distributions and mean-related trading in Chapter 9 | Trade Timing and Chapter 10 | Pulling the Pieces Together, as intraday trading requires a conscious Reality Adjustment within the larger macro picture. Again, we will cover the

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Reality Adjustment (which is both a paradigm shift and trading tool) in Chapters 9 and 10. For now though, traders must simply understand the theoretical paradigm beneath the larger macro movements of dynamic distributions in markets... By doing so, we will possibly begin finding brilliant clarity in perceiving market movements, while also distinguishing sentiment of the larger market, and thus, uncovering an uncanny awareness of how and where to time trades (both long and short), throughout the larger cyclical swings within markets. At this point, we should be clear on a few things:

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Peeking at Figure 3.2, readers will notice a new set of volatility bands (AKA 2nd standard deviations) drawn above and below the index.

Figure 3.2 | Dow Jones Industrial Average Showing 2nd Standard Deviations me ncu

News

Stories

BBands (51, 2%

mm SMA (0)

Dow Jones Industrial Average (INDU) Trading pot com Outside of 2nd Standard Deviation... ome | (Normally thought of as pushing the bounds of

“Outlier’)

1. Over the long haul, the mean is the real outlier where something has gone horribly wrong within markets and participants are confused, worried, and frustrated. 2. Trading outside the 1st standard deviation, even at the 2nd and 3rd standard deviations, are not outlier events at all, rather, indicate market perceptions are aligned and the larger distribution is on the move up, or down. 3. Because the mean of the distribution is mobile, the distribution is not static; rather, the distribution is dynamic, much like time. 4. The shape of the distribution does not matter, what matters is that we understand we simply need a point to gauge if and where the larger distribution will begin to ascend, or descend, which for our purposes here are the 1st and 2nd standard deviations.

Of course, we also need to be able to spot exuberance and/or blind devotion, where the investing public has fallen off its rocker, and could be setting up for a massive unforeseen move by most... Through the concepts presented in Macro to Micro, readers should have no trouble spotting such in the future. Before we dig too deep, let us take one more moment to look at another chart of the Dow Jones Industrial Average from 1980 to 2010, hammering home why and how movement away from the mean is truly about expectations aligned, and not distorted data, as mainstream explanations of mean and outlier would have us believe.

9°"

Financial Crisis

Public Expectations of Prosperity Aligned... Source: Dow Jones & Company

The Real Outlier... The Mean, Where Uncertainty and Confusion Resides...

Figure 3.2 shows another shocker, which readers will hopefully immediately recognize. See, because mainstream explanations of moving averages, market-related statistics, and standard deviations almost never incorporate the concepts of dynamic moving distributions, and a mobile mean into their elucidation, we are commonly taught that a tag of the second standard deviation indicates a potential reversal. However, as you will note, price not only traded at, but above (during the steepest portion of the rally) the 2nd standard deviation for a significant duration of the 19-year bull run, as noted on the monthly chart above. Fact is, all of the traders and investors who took contrarian (short) positions based on price touching the second standard deviation of the 50-month distribution probably: 1. Took HUGE losses.

2. Walked away saying, "Those dang-ity-dang Bollinger Bands don't work." Right... Tires don't work well in the back seat either. Dig? You can't plop a tire in the backseat of a car and expect to get somewhere, simply based on the fact that just ‘cause the tires ez on ta car sumwherre, the car should roll. See, prices would likely

ONLY reverse at the second, or third standard deviations on a constant basis, if the mean were bolted to one price and one price only. Like, the mean was IMMOBILE in the MIDDLE OF A CLUSTER OF DATA.

Sound familiar? If the mean was anchored at $50 no matter what, and you'd measured 100 years of data with outliers coming in near $90 and $10, than sure, it would seem reasonable a tag of the second or third standard deviation near $10 or $90 would indicate prices would come back toward the mean. When prices breached $10 and $90, we would in fact see DISTORTED DATA and OUTLIERS. Moreover, when real outliers surfaced, like the seventh or tenth standard deviations, because the distribution was static from the get go, the outliers would hurt like heck... See how traditional explanations of the MEAN and OUTLIER are totally screwed up?

Traditional expectations, as commonly relayed within markets, do not assume a mobile mean, or dynamic standard deviations, or never ending data rolling in, like the little thing called time that (for some weird) reason won't seem to go away or stop. Time has stopped though, if you are studying HISTORICAL data in markets, which is why so many super-black-box hype-trading systems fail in real time... They were/are curve fitted to historical (static) data, much like a form fitted plaster cast to a statue of

a lion. Try to slap some plaster onto a live lion though, and you're likely going to get a different result. Real traders just can't live in a vacuum like Academia-trained-econo-pros, media, or those who pump out information just to sell it, but have never really used it themselves in real time, in with their own cash. If they had, they'd likely have changed their cruddy explanations real quick. Fact is, markets are NOT static - they are constantly moving, AKA dynamic - and guess what, there is no ceiling, and there is no floor, except what the market decides, not stationary-historical-I-live-in-a-bubble-data. So all those traders (I've met plenty over the years, just FYI) who assume prices that hit a second standard deviation must reverse, never even understood what was

happening in the first place. They got a flat, took off the bum tire, pulled the spare out of the trunk - tossed both tires in the back seat - and then just sat there and pouted when the car wouldn't move. Why do so many retail traders and investors believe the 2nd-standard deviation is a surefire reversal point then? Here's why... Because the people who have engrained this belief in masses and markets are not

real traders slugging it out on the front lines every day. The statisticians and analysts who have not actually been in the trenches and take some licks with their own money, likely missed the key point that markets are dynamic, not static theoretical - historical studies - like the ones they've curve fitted their schemes too. They just don't get that as time will always be skewed in motion, markets and distributions will be too. Another common misconception is that of whether the Central Limit Theorem holds true that a small subset of data plucked from the larger dataset, will resemble that of a normal distribution. Here's the thing though... The shape of the distribution really doesn't matter...what matters is that we merely have some way to measure whether expectations are aligned, or abound with confusion and uncertainty, packaged with verifiable probability theory, to help us from making dumb mistakes like chasing data that is ripe for a quick-pop short-term shakeout pullback, before heading in the former direction. Alright, enough about the mainstream flawed explanations of the mean (without clarification of the dynamic attributes of markets) really hurting traders and investors. Let us move on to another subject... Maybe how the concept of standard deviations as non-linear volatility completely missed making it to investors too? I'm not going to rant, but the basic concept is this: Measuring the linear movement of the larger distribution (meaning predicting whether markets will move up, or down) requires us to also understand the non-linear movement of volatility, as measured through dynamic standard deviations. That was a mouthful, but bear with me for a moment and I will break it down to another tire in the back seat type of example in just a few moments...

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Chapter 4 |

Dynamic Volatility via Standard Deviations

When you think of standard deviations in a distribution, what do you think of? For most, we think of static points away from the mean. In other words, we think of standard deviations as the "wingspan" of the distribution. Here's the problem, we are conventionally taught these points are "static" as well, meaning that they do not compress and expand, as prices dynamically move up and down. Figure 4.1 | Plot of Normal Distribution!7] +

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Standard deviations measure the

Second and

“wingspan” of

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width, or

deviations

%o | distribution.

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Wikipedia states: "In probability theory and statistics, the standard deviation of a statistical population, a data set, or a probability distribution is the square root of its variance. Standard deviation is a widely used measure of the variability or dispersion, being algebraically more tractable though practically less robust than the expected deviation or average absolute deviation. It shows how much variation there is from the 'average’ (mean). A low

standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values. For example, the average height for adult men in the United States is about 70 inches (178 cm), with a standard deviation of around 3 in (8 cm). This means that most men (about 68 percent, assuming a normal distribution) have a height within 3 in (8 cm) of the mean (67—73 in (170-185 cm)) — one standard deviation, whereas almost all men (about 95%) have a height within 6 in (15 cm) of the mean (64-76 in (163-193 cm)) — 2 standard deviations. If the standard deviation were zero, then all men would be exactly 70 in (178 cm) high. If the standard deviation were 20 in (51 cm), then men would have much more variable heights, with a typical range of about 50 to 90 in (127 to 229 cm). Three standard deviations account for 99.7% of the sample population being studied, assuming the distribution is

0.0

normal (bell-shaped)."[8] -30

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lo

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& plot of a normal distribution (or bell curve). Each colored

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band has a width of one standard deviation.

Figure 4.1 shows the 2nd and 3rd standard deviations as the width, or wingspan of a distribution. Please read the following very closely... To show just how horribly broken even statistical information is presented to markets; take a moment to read the following explanation of standard deviation from the mass-accepted open source Website Wikipedia.com. (By the way, I'm not faulting Wikipedia, as it is a great source for general information... However, for those seeking information about markets though, common resources can sometimes do more damage than good.)

In the mainstream description of Standard deviation above, readers will hopefully notice a few points that will raise a few more questions about much of the information we receive within the public domain. Foremost, the explanation states, "Standard deviation is a widely used measure of the variability or dispersion, being algebraically more tractable though practically less robust than the expected deviation or average absolute deviation. It shows how much variation there is from the "average" (mean)." What is critical to understand here is the above explanation tells us we are measuring "average deviation” from the mean... However, as we have already seen on our 30-year chart of the DJIA, movement away from the mean (to the upside for 19 years from the early 1980's to 2000 in our example) is actually indicative of

expectations aligned and a possible bull market in place. Thus, we really want to see variation from the mean, as the long-term average or mean, is really the outlier. Moreover, nowhere in the above statement is there any mention of a dynamic mean or the possibility that distributions might just be dynamic in and of themselves. Clearly, the common discussion of standard deviations assumes a static distribution and an immobile mean, which is not true in markets. Though I have yet to explain what's really happening within volatility and standard deviations, what readers can note for now is... In the common explanation of standard deviations, there appears to be no mention of the possibility of DYNAMIC standard deviations - at least not upon first glance. More on this in a moment... Please continue reading the explanation of standard deviation from Wikipedia.com. "In addition to expressing the variability of a population, standard deviation is commonly used to measure confidence in statistical conclusions. For example, the margin of error in polling data is determined by calculating the expected standard deviation in the results if the same poll were to be conducted multiple times. The reported margin of error is typically about twice the standard deviation — the radius of a 95% confidence interval. In science, researchers commonly report the standard deviation of experimental data, and only effects that fall far outside the range of standard deviation are considered statistically significant —normal random error or variation in the measurements is in this way distinguished from causal variation. Standard deviation is also important in finance, where the standard deviation on the rate of return on an investment is a measure of the volatility of the investment." Taking a deeper look at the above passage, we are primarily focused on the later part, which (again) reads, "In science, researchers commonly report the standard deviation of experimental data, and only effects that fall far outside the range of standard deviation are considered statistically significant—normal random error or variation in the measurements is in this way distinguished from causal variation. Standard deviation is also important in finance, where the standard deviation on the rate of return on an investment is a measure of the volatility of the investment." Again, hopefully readers have noticed the common perception of "outlier" (the unpredictable, devastating price movements within, or outside of a distribution) as,

"effects that fall far outside the range of standard deviation." However, as the data we've already seen in the DJIA presents... The return to the mean after 19-years of ascending price action, was and will continue to be based on catastrophic events, like 9/11 and the Financial Crisis. When 9/11 hit and the Financial Crisis ensued, as we've already seen in the long-term charts of the Dow, the data did NOT fall outside of the range of standard deviations...

In fact, the 50-month mean is dead center inside the range of standard deviations, which is where the major indices headed directly towards when the Crash of 1987 took place, as the dot.com bomb unfolded, when 9/11 occurred and as the Financial Crisis ensued. Again, the outlier is the mean. We are conditioned to think of "statistically significant" as data outside of the first, second and third standard deviations, not a return to the mean, which in the case of markets over the past 30 years, couldn't be any more wrong, or further from the truth. What all of the above tells us is the common information (even within statistics) presented to the common public often falls way short of explaining, or even touching on in many cases, how distributions and standard deviations apply to real - dynamic markets and trading. Furthermore, as I'm about to show, the final statement of the above explanation of standard deviation, "the standard deviation on the rate of return", completely misses the mark too...in-terms of real-time application to markets, from the perspective of active traders and investors. Looking at Figure 4.1 again, standard deviations measure the "wingspan" of a distribution, which are thought of (by most people) as static, much like the larger concept of mean and distribution as immobile, having infiltrated markets as well. However, neither the means or distributions within markets are static; rather, both are dynamic. What's really important to dig into though...is understanding the concept of standard deviations as dynamic, organic, and mobile. When applied to markets, the standard deviations of a distribution not only move up and down with the distribution, but also expand and compress, seemingly independently. What we're talking about is a "double dynamic" distribution that not only ascends and declines, but also expands and compresses, all at the same time. We are talking jelly fishy almost. Is the expansion and compression of the standard deviations significant? You know it. We will cover volatility and the expansion and compression of standard deviations (as applied to trading) in detail throughout the following pages... For now though let us just understand some vital theoretical concepts.

In terms of markets, when the formula for standard deviation is applied to prices, the actual standard deviations expand and contract... In other words, at moments, the standard deviations will be trading far from the mean, while at others; the statistical benchmarks will mysteriously pull in close towards the mean. Why does such occur and what is the significance? The standard deviations expand and compress, not because of bullish or bearish sentiment, but because of the real time, relevant volatility displayed in prices. (In times of low volatility, the standard deviations will compress, however, during times of high volatility, standard deviations will expand.) Under the surface though, what's really happening is nothing more than a natural luxury of the formula for standard deviations providing markets with a "little something extra." As John Bollinger stated in Chapter 6 (endnote #6) in his breakthrough book Bollinger on Bollinger Bands, "It is this squaring of the deviations from the average that makes Bollinger Bands so adaptive, especially to sudden changes in the price structure. "[9]

It is here, thatI would like to start clarifying the larger concept of volatility for readers. Foremost, please understand that "volatility" is a broad general word used hurtfully within markets, by those who do not really understand the game in the first place... Here's whatI mean... have you ever sat there watching one of the mainstream financial media stations, when suddenly one of the anchors, or pundits states, "Volatility is high with markets right now." Well what does that mean? Seriously. Stating, "Volatility is high" only does one thing and one thing only for common investors: invoke fear. Furthermore, when I hear a pundit state, "Volatility is high," the first thing I think is, "that guy doesn't know jack, and probably doesn't trade with real money." Here's why... Stating, volatility is high, is about the same as saying, "cars perform well in the snow." Let me clarify... "Four-wheel drive SUV's perform well in the snow" would be a much more accurate statement, because we are describing what type of car generally has better traction in the winter months, over the entire category of automobiles. Thus, we must be able to identify what type of volatility is, or is not, high at particular moments within trading, otherwise we are just lumping a blanket statement of nothingness over markets, thus showing the world we do not know what the hell we're talking about in the first place.

Sadly, the only real people within markets who understand volatility are options floor-traders, as they take on significant time-related volatility risk when writing options. However, even in the world of options, the common definitions of volatility are relatively worthless to average investors. Case in point, in the world of options we have implied volatility (how much the market expects price to move) and statistical volatility, better known as the historical measurement of price volatility, over a given period of time. Options traders also know about the "Greeks" (Delta, Gamma, Lambda, Rho, Theta and Vega), which are...well...Greek to the average Joe. Furthermore, in the world of equities, most lump volatility into "historical volatility, implied volatility, the Volatility Index, and intraday volatility", showing again, even the expert news sources, pundits and educational writers don't know jack... At least, that they don't trade with real money... To finally clarify volatility for regular investors and traders, we really have four types of volatility that affect all markets daily. (By the way, I would like to mention "the Greeks" - in options trading - are by far the most effective understanding of volatility, though because this work is not meant to focus on options pricing and valuation, we will not cover the aforementioned here. I recommend, Option Volatility & Pricing by Sheldon Natenberg, which is the floor-trader's bible, for those who would like to know more.)

The four types of volatility that affect common trading and markets are: 1. Market Volatility 2. Probability Volatility 3. Mean-period Volatility 4. Price Volatility For a quick explanation of each type of volatility, we will first start with Market Volatility, which is part in parcel, what we must be on the lookout for, in relation to longer-term mean reversion trading, where expectations for higher, or lower ground are no longer aligned... When long-term mean reversion ensues, Market Volatility is likely high.

1. Market (Historical) Volatility Foremost, many often separate Historical Volatility from Market Volatility, as

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measured by the Market Volatility Index (VIX). However, I would like to argue that for the purposes here, historical volatility and market volatility are the same. It may be said that historical volatility in energy companies is less than the historical volatility in technology companies, if we were to split hairs. However, I believe most investors have this much common sense and can distinguish the two. By merging historical volatility with market volatility here, I am saying: Historically, when larger market volatility kicks up, so does that of the broader market, which I believe readers can validate on their with two minutes of free time and access to historical chart on the Internet. In addition, I am merging the two terms here, as really, they are the same. If one wanted to know the historical volatility on their particular stock, they would be seeking out "stock specific volatility", which in my eyes, should not be encompassed in the broader category of "historical volatility". See what I'm saying? If you want specifics, than be specific, please do not lump a broad word like "historical" into what I believe most would refer to as a much more explicit category like stock, option, commodity, or currency-specific volatility. With the previous in mind, by definition, the CBOE defines the Market Volatility Index (VIX) as: "The CBOE Volatility Index® (VIX®) is a key measure of market

expectations of near-term volatility conveyed by S&P 500 stock index option prices. Since its introduction in 1993, VIX has been considered by many to be the world's premier barometer of investor sentiment and market volatility." Let me break it down: When the writers of options (AKA sellers, or those who are taking on the risk to give others the right to purchase a put or call at a later date) believe volatility is about to see (or is in the process of seeing) a reasonable uptick, will increase premiums to be compensated for the additional time-related risk taken on. Thus, when the VIX rises, higher fear levels are beginning to surface within markets, and thus, expectations are no longer aligned. Figure 4.2 | Market Volatility Index with S&P 500

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If you remember our previous discussion of the mean as the true outlier, when uncertainty shows up in markets, the first place prices head to is: the long-term mean. Perfectly in-line with what I am proposing here, we see that in 2008/2009, and in early June of 2010, when the VIX rallied, the S&P 500 declined. Thus, we must understand that Market Volatility is a measurement of larger market fear (uncertainty prompting mean reversion trading - on a LONGER TERM basis), based on option premiums.

2. Probability Volatility As we already know, when measuring probability of a distribution, approximately 68% of the data should rest within one standard deviation of the mean, 95% within two standard deviations and nearly 99% within three standard deviations. However, while the above probabilities are thought of as reliant only with a normal (bell shaped) distribution, we know as time is skewed, so are distributions within markets. The common argument or whether distributions are, or are not "bell shaped" in markets is moot though, as regardless of the shape, because of the squaring of the deviations in the formula for standard deviation, the probability holds, which is all we care about. As Figure 4.3 shows, regardless of trading action, because of the expanding and

compressing nature of the standard deviations, as measured in our image here, the theory that three standard deviations should contain 99.7% of the data, holds true. I want to mention that the reason why I truly love the larger volatility/probability price mass paradigm is because not only do we have empirical reasoning to explain why what is happening...is happening, but we can also empirically validate the theory over and over - with our own eyes - on the charts we trade from. No other indicator within markets provides this type of validation. Figure 4.3 | Probability Volatility Lote #IBM,H1

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probability volatility (the standard deviations) compresses, the likelihood of lateral consolidation increases. I would like to mention that we must use common sense when understanding probability volatility in markets... When prices are trending over a long period of time, while probability volatility would have spiked when the trend first began, towards the end of the trend, probability volatility will almost always compress, even though prices are still moving upward, or downward... Why? Because on a common sense basis, we're talking about TWO SIDES of a distribution that cannot move away from one another forever. When prices begin trending, probability volatility will spike in both directions, however, as price continues trending, eventually, the probability volatility band opposite the trend, will roll over toward the direction of the trend... Imagine you are standing at the foot of a mountain, and suddenly you decide to begin running upa trail... Initially you would fail your arms outward, as you start in motion; however, as you start running, you would pull your arms and legs inward to maintain a more stable core, as you continued onward. If you were to stop abruptly, your arms would likely flail outward again, to stabilize your body. Such is the same with a distribution ceasing a trend as well. When prices stop moving in one direction, the immediate halt of the trend (seen through prices reversing through the mean of the shorter-term distributions) causes probability volatility to spike again, and then begin compressing, as prices move laterally and consolidate. We're talking about understanding the COMMON SENSE of the cycles of probability volatility, as related to distributions within markets. Probability Volatility can both be independent and a consequence of Market Volatility, as probability volatility is stock, currency, commodity (whatever) specific, while also (at times) influenced by larger market action, should all of the market begin rolling upward, or downward, based on larger fears (uncertainty), or moments of calm trending (expectations aligned.) Common sense is required.

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3. Mean-period Volatility Mean period volatility is simply the paradigm where shorter-term distributions will likely show greater volatility than that of their longer-term counterparts. In addition, the shorter the period measured, the greater the volatility of the same mean measured. By this, I'm saying a 50-period mean on 15-minute chart will show greater volatility than a 50-period mean on a 4-hour chart. Figure 4.4 | Mean-Period Volatility

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What we know of mean-period volatility is a 14-period distribution will show quicker, sharper volatility than a 50-period, based on the fact that it simply takes less data to move the shorter-term distribution, over that of the longer-term counterpart. However, because the longer-term distribution does indeed take more data to move, the longer-term probability volatility will show a greater range, than that of the shorter-term distribution. In the end, when shorter-term probability volatility is outside of longer-term probability volatility, prices are likely trending. Conversely, when shorter-term probability volatility is compressing underneath longer-term probability volatility (unless price is towards the end of a sustained trend, or has just ceased trending), prices will begin consolidating (moving laterally), with shorter-term mean-period volatility trading above and below longer-term mean-period volatility.

4. Price Volatility Price volatility is a both a cause of, and derivative of market volatility, probability volatility and mean-period volatility. While price volatility is really nothing more than an extra description of the total low-to-high range of prices in any given period measured, the label is required to separate "price action" from the other three volatility descriptions... If price volatility is high, prices are likely moving from the bottom to the top of shorter-term 3.2 standard deviation probability volatility, even though the total range can be either lateral, or trending. In the end, when probability volatility is collapsing, the less likely price is to put in new highs and lows, and thus, will stall when hitting the third standard deviation. Should price strike a third standard deviation and then commence trending in the direction of the third standard deviation tagged, we should also see shorter-term mean-period volatility start showing slope, while at the same time, short-term third standard deviation probability volatility would spike outside of longer-term third standard deviation probability, confirming that the smaller subset distribution was on the move. It is important to note that elevated price, probability and mean-period volatility can occur in the absence of market volatility, however, are also likely a consequence of market volatility, when fear levels rise. In essence, price, probability and mean-period volatility are both independent and a consequence of market volatility. At the same time, market volatility is really independent of price, probability and mean-period volatility of individual components of markets. Only when many components show similar extensively elevated price, probability, and mean-period volatility, do they influence market volatility. What the above tells us, is when an instrument begins to display price action that breaks the relevant short, or long-term trend (up, down, or sideways) probability volatility initially expands, only to later compress as participants ease into expectations aligned. Furthermore, while a larger surprise move within markets may initially spike probability volatility outward, as prices ‘consolidate’ afterward, probability volatility may initially spike as the sustained trend suddenly stops, however, probability volatility eventually compresses towards the mean, as mean-period volatility begins to flatten out. Here's what's pretty amazing though... While price volatility (price action) taking out highs or lows, initially triggers probability volatility to spike, at times, probability volatility itself, can be a massive leading indicator alerting us to the fact that prices are about to spike... Sounds a little weird right? The aforementioned would happen if the

stock, currency, commodity, or whatever we're watching was consolidating for a longperiod of time and short-term probability volatility had returned to its natural state, collapsing underneath long-term probability volatility, with both coming in very close to their means. In this example, the total distributions (measured through probability volatility) would have shed significant mass, and thus a small movement in price, would cause short-term probability volatility to spike outside of long-term probability volatility, alerting us ahead of time, that prices were likely about to move. Please just note, when probability volatility increases, the standard deviations move away from the mean, and when probability volatility decreases, the standard deviations move toward the mean, all because of the natural luxury of the squaring of the deviations in the formula. To explain why the expansion and compression of standard deviations is so incredibly significant, we must not only dump what we've traditionally come to understand as statistically significant outliers of a distribution, but also about how distributions and statistics apply to trading and markets on the whole. Again, what we're really getting at here is a larger, commonly misunderstood perception of what standard deviations are, how they interact with distributions in markets, what the data is truly presenting, and even, how, why and what amazing data standard deviations hold in terms of leading information, which traders can use in real markets, in real time. By the way, I know that for many, the mere mention of statistics and standard deviations can instantly cause some to nod off into slumber-land; however, don't worty, we're not going to cover a bunch of boring math here. We are going to cover the theory behind why what is happening is indeed happening and then directly apply the concepts to markets. There are tons of resources available to double check the formula for standard deviation and the like, so I won't waste your time here... Readers will find a plethora of educational resources all over the Internet to learn how to calculate standard deviation, etc... If the information is coming from a mainstream portal though, I might recommend checking two sources, just to be on the safe side. J Anyway, we already have tools within markets to do the bulk of the work for us, one of which you already know: Bollinger Bands. I do have to mention that while John Bollinger is one of my personal idols in markets, I really wish he had not named the application of standard deviations to price data (electronically) within markets 'Bollinger Bands.' It is my understanding that the naming of his incredible breakthrough was an accidental occurrence on CNBC, but the fact remains, by taking one thing, and then naming it something completely unrelated,

we just make markets more difficult for average investors to comprehend. Anyway, it is what it is...at the end of the day, I'm incredibly grateful for Bollinger's genius in bringing forward the "bands" (standard deviations, or probability volatility) to markets. We've begun to touch on why and how some information within markets may not be making it the investing public correctly (like the long-term mean as the real outlier), while also scraping the surface on how and why distributions and their means are dynamic in markets, not static. Moreover, we've also just slightly introduced the concept of standard deviations as doubly dynamic, organic and nearly jelly fishy... I'm guessing the latter probably still needs a little clarification, which we will now make sure to cover in Chapter 5. In addition, we're also clarified volatility within markets, understanding that there are really four different types which affect traders on a regular basis: Market Volatility, Probability Volatility, Mean-period Volatility and Price Volatility. Now, in Chapter 5, we will clarify Probability Volatility and dynamic distributions within trading - even more...

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Digging into the details of how our perceptions of statistics, distributions, and standard deviations may have been slightly led astray by mainstream sources over the years, I sincerely hope the following pages open a few eyes within markets... At the end of the day, we cannot just accept information as truth, simply because it is in the public domain, without ever questioning its validity. Again, all too often, many mainstream outlets providing market education and information, are put forth by people who do not trade with real money, in the trenches every day, and thus, can be quite mistaken about what they learned on paper somewhere, and about how markets and real trading work... By the way, I hold myself out to the same standard. If you find an error in these pages, let me know. Anyway, please ask yourself, would you ever take flying lessons from an instructor who didn't even fly? Please take a minute to observe Figure 5.1 below, showing a chart of the Dow Jones Industrial Average with a 50-period mean, 1st, 2nd, and 3rd standard deviations from August of 2009 to April of 2010. The sole reason for drawing the standard deviations (probability volatility) is simply that I would like readers to actually see the full distribution and standard deviations, on a chart... We immediately observe that during the ascending move from August to November of 2009, the index bounced from the mean to the 2nd standard deviation, most often trading above the 1st standard deviation in the period shown.

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Can you see the same distribution as we witnessed in Figure 3.3? Just in case, I have re-posted the image to the right, showing the mean and standard deviations of a normal distribution. While I'm fairly certain readers likely see the distributions on the charts, I would like to take a moment to alter the common display of the data (the actual price chart) which we normally see. In doing so, I hope to also prompt readers to question whether the way information and data is presented in markets could be better received if it were to be reshaped slightly.

By simply repositioning the way we view the data daily, we may suddenly find a few new perceptions of the movement of subset distributions within markets. Figure 5.3 below is the exact same chart you just saw a moment ago, however, I have taken the image and simply rotated it 90 degrees to the left, while laying the image out, almost like a map resting flat on a table. At this point, there should be no mistaking the distribution at hand...

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move forward, both rising and falling... If we make the fatal mistake of assuming the above distribution is static, while also not fully perceiving and understanding the expansion and compression nature of standard deviations, what would our conclusions be when prices started to move up, or down? Here's what would happen... When prices moved up or down with time - and they always will - we would suddenly be shocked and upended when the third standard deviation is breached - and it will be - it's just a matter of time. Historically, this is where investors, media and pros stand there scratching their heads, often coming to the only conclusion that it's impossible to measure standard deviations, volatility and/or probability within markets. However, the fact remains, they never understood the data to begin with and thus, the 5th, 6th, 7th (and beyond!) outliers that rocked their portfolios had nothing to do with extreme events in markets; rather, their own misunderstanding that distributions actually move with prices in markets... Figure 5.4 | Fatal Flaw of Assuming Static Distributions in Markets

Here's the problem though... Because we are conventionally conditioned to believe that distributions are static, or rather, to put it another way, simply not made aware that distributions in markets are dynamic, when we hear "outlier" we often think of a movement beyond the 2nd and 3rd standard deviations. We've also been trained to think touching the 2nd or 3rd standard deviations indicates a reversal pending. Add in the misunderstanding of distribution probability theory to boot and most traders are doomed from the start. Think about it for a moment, if we're told there is a 99.7% probability all of the data will reside within three standard deviations of the mean, why wouldn't we think 'reversal' when price tags a third standard deviation? (By the way, the probability is not wrong, as you're about to see, rather, the mainstream understanding and application of standard deviations and probability within markets is where the flaw has occurred.) As Figure 5.4 (below) presents, much like time never stops, prices will continue to

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deviations as outliers and begin to see that price must lean on one side of the distribution (at, or near the second and/or third standard deviations) in order for the

distribution to move! The second and third standard deviations are not distorted data at all, but rather are almost a sort of "steering wheel" of the distribution. Moreover, the standard deviations (probability volatility) ALSO expand and contract because of the squaring of the deviations in the formula for standard deviation, thus rapidly moving outward when volatility spikes...and thus, uphold a 99.7% probability that all the data will rest within three standard deviations of the mean! Figure 5.5 | Dynamic Movement of Distribution and Standard Deviations $INDU (Dow Jones Industrial Average) INDX

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There's one more crucial eye opener to take in during our present conversation... We just covered how assuming a static distribution is a fatal flaw that will bankrupt investors, funds, and even governments who assume such, while also briefly touching on the concept of organically expanding and contracting standard deviations (probability volatility) too... Just to make sure we're clear though, let's talk a little more about the ‘jelly fishy' organic action of the standard deviations I've just mentioned. Again, by squaring the deviations in the formula for standard deviation, we suddenly see a miraculous expansion and compression effect within the 'wingspans' of the distributions we're measuring probability through. While I have not stated the following yet, what much of the above translates to is

this: The common perception of a standard deviation in markets is a bit off the mark... Standard deviations are not points that measure potential outliers within markets, but rather, are direct, empirical probability volatility indicators helping us to precisely gauge when trending is about to begin and when trending is about to stop. In essence, standard deviations are a leading indicator of volatility and thus, price movement within markets. When prices lean towards the left-most standard deviations, the distribution will naturally begin moving upward (bull market). When prices start leaning to the rightmost standard deviations, the distribution will naturally begin moving downward (bear market).

Furthermore, because the standard deviations (probability volatility) naturally expand and compress because of the squaring of the deviations, there's even more to the story as you're about to see... Nearly beating a dead horse, standard deviations (probability volatility), as we are conventionally taught through many public channels, are measurements of data where, "effects that fall far outside the range of standard deviation are considered statistically significant—normal random error or variation in the measurements is in this way distinguished from causal variation." As we already witnessed through 30-years of DJIA movement in Chapter 1, when prices are trading outside the 1st standard deviation, and are at or even beyond the 2nd standard deviation (probability volatility), what we’re not talking about is a definite reversal signal or outlier; rather, we are witnessing expectations aligned, attempting to fiercely drive prices up or down. Moreover, because the distribution is mobile (dynamic) in and of itself, when prices are moving fiercely up or down, the distribution is also moving. In effect, price is like the horse pulling a cart... Inevitably, where the horse goes, the cart goes, though at times the horse may have more trouble pulling the cart than others, based on the size of the cart, the weight of the load, condition of the wheels and axles, and of course grade of the terrain. Thus, price moving "outside the range of standard deviation" are not "statistically significant," but actually required, much like time will never stop, prices will never remain stagnate at one level forever. Even so, the common supposition of a static distribution also generally assumes the standard deviations do not move dynamically either. As we've seen though, the standard deviations (probability volatility) do move expanding and contracting, creating a whole other dynamic effect within markets... In addition, because the standard deviations do indeed move, the occurrence verifies the

MACRO

TO

MICRO

VOLATILITY

probability theory that over 99% of the data should reside within three standard deviations (probability volatility) of the mean. The real kicker here is this: If we have a clearly verifiable gauge (based in rock solid probability) of the larger wingspan market data will fall within - in real time... Wouldn't what we're talking about truly be a probability-based, real time, dynamic and organic indicator of volatility? You bettcha. As Figure 5.6 below shows, when the DJIA was moving abruptly downward in the thick of the Financial Crisis, the 1st, 2nd and 3rd standard deviations (probability volatility) were expanding and compressing... Again, if we were to possibly miss the point that standard deviations do indeed expand and compress as applied to price action, we might be looking for a breach of the outer standard deviations (probability volatility) when prices are moving up, or down. However, prices hardly ever breach the third standard deviation of the distribution, because as prices are rapidly moving either up, or down at, or near the second, or third standard deviations, away from the mean, the standard deviations are expanding and when prices are moving rapidly downward, the standard deviations are also rapidly expanding. Again, this little mobility effect in the width or wingspan of the distribution is a luxury of the squaring of the deviations.

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What this also means is that it is very, very difficult for prices to stay outside of the third standard deviation for very long, because not only is the distribution moving toward price, but the standard deviation is moving away from price during periods of rapidly ascending, or descending prices, as well. (By the way, I expect that what I've just mentioned might be a little confusing at first, though please bear with me for just a moment longer.) Insightful readers may have already stumbled onto a few incredible questions that must be asked with presentation of the concept that standard deviations expanding directly indicate prices are in the midst of a large move, or are about to move... Such queries might be... 1. Can you prove volatility (standard deviations) spiking outward indicate a burst in prices? 2. What happens when standard deviations are contracting and why are they even contracting at all? 3. What is the practical use of this information in trading? Firing right into question one, please take a moment to observe Figure 5.7 below...

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Amazingly, we clearly see in the entire move downward from about 13,000 in May of 2008 to roughly 8,000 in February of 2009, the standard deviations were expanding and contracting the whole way down... In effect, it's almost as if the standard deviations (probability volatility) of the 50-period distribution witnessed here have some sort of organic tie to the "energy" of the smaller bursts of descending price action in the larger move... In reality, there's no "almost as if" about it, the fact of the matter is standard deviations are actually gauges of price action volatility, directly letting us know when to expect a directional burst and even telling us right when and where the move has ceased and lateral trading will begin... See, when prices consolidate, the squaring of the deviations naturally vacuums the standard deviations in towards the mean. In other words, if and when price is trading outside of the ist standard deviation (probability volatility), and then falls back under the first standard deviation towards the mean, not only is trending likely either pausing

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What takes place is this: because the standard deviations (probability volatility) have shed weight and come in closet to the mean - and because we know there is a

the distribution to edge higher at the same time. When prices begin to move downward, they naturally slide underneath the lower 1st standard deviation (probability volatility), naturally prompting the distribution to edge lower at the same

99.7% probability that prices will remain within 3-standard deviations of the mean, price must either back off the standard deviations (probability volatility), or the standard deviations must quickly (almost exponentially) spike outward to

ume. .. .. What happens when the standard deviations (probability volatility) have compressed (literally meaning moved in towards the mean), after a period of lateral

accommodate for the movement of price... Adding a little more information to the same chart as above, Figure 5.8 below shows that every single time in the period measured, probability volatility (the standard

trading though:

deviations) compressed prices moved exactly sideways, while in every instance that probability volatility (the standard deviations) spiked outward, a burst of downward price action surfaced...

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The aforementioned is direct proof that volatility expanding (as seen through movement away from the mean by the standard deviations) is a leading indicator of price action about to bolt upward, or downward... In addition, standard deviations moving inward toward the mean allude to "mean reversion" in price action as well, while also directly telling us that significant trending is not possible now. Can you think of a single other indicator in the entire markets that directly tells you, "please take notice, trending has ceased for now...prices are headed back to the mean"? What I've just mentioned is indeed practical information for both short and longterm traders, but there's even more amazing trading potential in store... We're just getting started; we haven't even begun to touch on the truly incredible trading information unveiled by understanding the proper paradigm of volatility and the movement of distributions, which investors, media and so many financial professionals have pretty much completely missed - up until now. To quickly recap what we’ve covered in Chapter 5 before taking on too much too soon, over the past pages, we've uncovered a definite need for traders and investors to

re-evaluate the common syndication and acceptance of concepts like outlier, distributions, and standard deviations (probability volatility). Moreover, when examining longer-term timeframes, we must recognize the real outlier of uncertainty, where confusion, market downturns and disruption occur are not at or outside the standard deviations, but really, is the mean itself. On the contrary, when prices are traveling outside the first standard deviation (probability volatility), at, or even beyond the 2nd and 3rd standard deviations, the occurrence is not an "outlier" but rather the empirical occurrence of expectations aligned, also known as a trend. Throughout the past pages, we have also directly seen how and why assuming a static distribution and standard deviations (probability volatility) is a surefire way to lose, based on the fact that much like time will never stop...prices will never remain "locked" within the confines of the standard deviations (probability volatility) of an immobile distribution. Finally, we also now know precisely why standard deviations are more than just measurement of total distribution width, and are in fact, gauges of volatility indicating when prices are about to, or are in the process of bursting upward, or downward, while also providing significant indication of lateral trading to come. With all of the aforementioned in mind, we will now move on to Chapter 6, coving the subject of "Containment Zone" within markets, while also uncovering how a deep understanding of prices in relation to standard deviations (probability volatility) can help us identify when markets are trending because expectations are aligned, irrational exuberance has set in, or uncertainty has begun to surface. Moreover, we will also identify why and how we can directly identify situations where the larger investing public has become too greedy, or fearful (opening significant opportunity for adept volatility-aware traders), but also when it may be a good idea to just sit out of the markets altogether.

Chapter 6 | Why the Containment Zone is so Important Why 1.25

Throughout Chapter 6, we will begin to dig into one of the fundamental mainstays of Macro to Micro analysis, the Containment Zone, seeing precisely why and how such is so important to understanding the movement of prices, subset distributions and of course, allowing us to recognize why and how volatility remains persistent within markets. The trader who has an adept understanding of Containment Zone analysis stands head and shoulders above his colleagues, who at most, are generally left guessing why and how markets are unfolding as they are, at any given time. Moreover, the trader who understands Containment Zone analysis, finds himself amazingly empowered to know when to "trade with the trend", while also having clear insight into when trending has ceased and lateral trading action has begun. Thus, throughout Chapter 6, we will specifically cover several areas... Foremost, what the Containment Zone (1.25 standard deviations, or 1.25 probability volatility) is, and why the levels are so important to all traders, regardless of the time frames navigated. Secondly, we will see precisely how and why watching the Containment Zone allows us to spot subset distributions on the move, while also noting when trending has potentially ceased and lateral "chop" action has begun. Third, we will cover why and how the Containment Zone has a direct relationship to the mean and the Commodity Channel Index (CCD), a tool that may help traders infer trending, and potential cyclical reversals, when all else is unclear. Finally, we will briefly discuss why the Containment Zone is so important in the Macro to Micro methodology, not getting too far ahead of ourselves before we really delve into the larger strategy at hand. Figure 6.1

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The Containment Zone in Trading

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Diving in, the main question at hand is: What is the Containment Zone? The Containment Zone is basically the "belly" of whatever distribution we are measuring... In essence, we are talking about 1.25 standard deviations from the mean. Normally, we measure 1-standard deviation (probability volatility) from the mean, where we know there is a 68% probability of all of the data residing. However, by notching up the standard deviation slightly at 1.25-standard deviations (probability volatility), we are really measuring roughly 75% of the data. We're upping the standard deviation "belly" for two reasons... First, because as you are about to see in a few moments, by doing so, we actually line the indicator up with the Commodity Channel Index (CCI) indicator in terms of cyclical swings and indication of trending breakouts, or not. Second, because CCI is calculated using Mean Absolute Deviation instead of normal standard deviations, while utilizing something called Chebyshev's Theorem, we are

able to slightly increase the "belly" area of the distribution, and thus, are able to obtain better guidance into the larger movement of the distribution, and hence, are able to more clearly spot trending, or not. By the way, if any of this sounds confusing, please don't worry too much, just keep reading and everything will become clear. Readers need to be less concerned with the math under what is happening here, versus the theory. However, for those who really like to know the math, we will make sure to cover all our bases as well. With regard to Chebyshev's Theorem, the conjecture basically says, "The proportion of any set of data lying within K standard deviation of the mean is 1. For K=2 At least mean." At least

always at least 1-1/K?, where K is any positive number greater than and K=3, we get the following results: 3/4 (or 75%) of all values lie within 2 standard deviations of the 8/9 (or 89%) of all values lie within 3 standard deviations of the

mean."L11]

Okay, so remember our 30-year chart of the Dow Jones Industrial Average, where we witnessed precisely how (and why) data traveling away from the mean (like beyond the first standard deviation, at, or beyond the second and third standard deviations) provoked a 19-year bull rally? Well, the concept of the Containment Zone is precisely the justification of why and how data at, or near the mean indicates market participants are uncertain about the larger outcome of prices. Data near the mean indicates uncertainty, while data consistently away from the mean (above, or below, but on the same side for a prolonged period) precisely alludes to expectations aligned. Moreover, when expectations are aligned, meaning the larger public and institutions have the same expectations for a particular outcome, prices will trend either up, or down. What you've just read is possibly some of the most important material in this book... Just to make sure we are clear; please take second note of the concepts at hand.

[IMPORTANT] Over the long haul: 1. Data at, or near the mean, indicates participants are uncertain about current events, unsure about the future, or unclear about what could

unfold tomorrow. What we are talking about is a lateral market, likely consolidating, or experiencing very choppy trading. (When data is at, or near the mean, uncertainty and fear are likely the highest, and thus, market, price and probability volatility will all be at the greatest.) 2. Data consistently away from the mean (above, or below, but on the same side for a prolonged period of time) precisely alludes to expectations aligned. 3. Data falling outside of the 1st-standard deviation (the Containment Zone) indicates expectations are aligned and prices are likely in the process of, or about to begin trending. 4. With data consistently away from the mean on a longer term basis, outside of the 1.25 standard deviation (on a longer-term basis), expectations are aligned and trending is in effect. What's more, with expectations aligned, fear and uncertainty have likely significantly decreased and thus, probability volatility decreases. Again, to reiterate, we are able to track whether expectations are aligned, or not, by measuring data outside of the 1.25 standard deviation area, which is known as the Containment Zone.

With some basics in place, we will now look at a few charts showing the 1.25 standard deviation Containment Zone. What I would like readers to do at this point is simply observe the data... Please do not attempt to jump to any conclusions just yet... Again, simply observe the data. I say this because it is in our nature as traders and people to always attempt to find the "gems" of knowledge, while not actually taking time to just take in what's towards us. However, it is by simply spending a little time to really just look at - take in - the data that we begin to really see the larger picture at hand. This is part of the Macro to Micro process... Actually taking time to become cognizant of the larger picture at hand, which ultimately will become the "gem" helping us to find where and how to place our trades. However, if we constantly rush through the greater process, in search of "trigger" type information to act on, we unfortunately miss the real "meat" or wealth the data is presenting. From an Eastern perspective, this is known as Mindfulness in Buddhism, which translates to the articulate process of

staying centered in the moment. To remain mindful of the moment, if we quietly say "out" when we exhale, we are fully cognizant of the outward breath we are in the midst of...perfectly centered in the moment of exhaling, at the moment of exhaling. Then, when inhaling, if we quietly say in" during the breath filling our lungs, we are letting go of the constant clutter of thought running through our head, thus focusing on one thing only, the breath we are taking... In essence, by centering ourselves in the moment, we are pulling ourselves back into right now, which is something that most of us are completely unaware of each day, in the never-ending light speed bombardment of information. Thus, please take a moment to simply look over the following charts at hand, not trying to jump to wildly brilliant assumptions, or unearth a possible signal to jump into

GBPUSD,H4

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200-Period 1.25 Standard Deviations, otherwise known as

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Now, please note Figure 6.3 below, which shows the same time period for the GBP|USD, however, we are now looking at a daily chart, instead of a 4-hour period. Notice, how in the 4-hour chart, the GBP|USD was in a torrid uptrend, however, ona daily chart basis, the GBP|USD is actually trading laterally. Please also note we are viewing the 200-period mean and 1.25 standard deviation Containment Zone (probability volatility) on the daily chart, just like the 4-hour chart. Again, without jumping to any conclusions yet, please just take in the data at hand... Figure 6.3 | Daily GBP|USD 200-Period Containment Zone

MACRO

TO

MICRO

VOLATILITY

TRADING

GBPUSD,Weekly GRPUSO Daly

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Now, when taking a peek at the weekly chart, we suddenly see that the rally on the

4-hour chart (in May of 2009) was actually part of a "bounce" within a larger one and a half year bear decline for the GBP|USD. Notice how looking at the larger picture gives us a dramatically different view of the trading action, versus just staying focused on the 4-hour chart?

Figure 6.4 | Weekly GBP|USD 200-Period Containment Zone

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Next, if we zoom all the way out to the monthly chart, we see the previously

mentioned one and a half year decline in the GBP|USD with even more clarity, while also seeing that the decline was preceded by a bull rally into highs, which stalled in the fall of 2007. Figure 6.5 |

Monthly GBPUSD 200-Period Containment Zone

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We have yet to see how the 4-hour chart, daily, weekly and monthly charts all come into play with the Containment Zone (probability volatility), but readers hopefully are already starting to form a few ideas... I know I mentioned not to jump to any conclusions; however, if you're reading this book in the first place, you're likely a bright star already, and would have trouble not already connecting a few dots on your own. Regardless, please try to not make too many huge assumptions just yet, allowing me adequate time to explain what is really happening here... In Chapter 7 we will go through the GBP|USD charts one more time, really pulling out rock solid trading information, but first, we need to cover a few more basics... Our main goal here (without getting too far ahead of ourselves) is to truly attempt to understand what the Containment Zone is and why it is so important. We will get into the important details of how to use the information at hand - to actually trade with - throughout the following chapters; however, without a deep understanding of what's happening, and why it's happening first, we will lack the fundamental underpinning required to truly use the Macro to Micro strategy to make money with on a consistent basis.

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Working from left to right, we see that when prices first burst above the upper Containment Zone in late 2005, early 2006, the first candle to attack the Containment Zone (the fourth candle from the left side of the chart) stalled almost precisely at the 1.25 benchmark. The following month (the fifth candle from the left side of the chart)

attempted to boldly move above the Containment Zone. However, in the following two months, bulls and bears slugged it out in the GBP|USD (the sixth and seventh candles from the left), creating wicks underneath the bodies of both candles. Of importance, notice how the wicks stalled almost exactly at the 1.25 standard deviation Containment Zone (probability volatility). Coincidence? Heck no... You are going to see this type of behavior over and over - in all markets - as the Containment Zone is more than just a fancy term. What we're talking about is a

statistically relevant point, which is quantifiable both in terms of risk management and probability of price movement, both of which, if you were a billion dollar fund, you would likely want to know about... Do you see it? Because we're talking about a relevant point of statistical probability | distribution analysis, we're talking about taking note of points where big money institutions (and their valuation algos) are watching as well. The empirical evidence right before your eyes (meaning the data stalling directly at the 1.25 Containment Zone) should be proof of the pudding that something is up... Fact is, given nearly $3.5 trillion is traded in currency markets daily, with about $2.5 trillion of such directly in spot markets, I can assure you, it isn't a few retail traders causing price to stall at points of high probability, like the mean and 1.25 standard deviation Containment Zone. We are talking about big money... Institutional money... Moreover, given that this information is really NOT available to most at-home traders in the current market, I can also assure you, the coincidences you have just seen (and will continue to see directly see) are not savvy at-home traders, because: 1. This information is not really available to retail traders, in this format, in the current market...at all, at least to my knowledge.

2. Even if a thousand retail traders did have the same information, they would not be able to "stall" a currency at any level, if only a few institutions had a different opinion. Next in Figure 6.6, the GBP|USD continued upward, seemingly climbing into the open air whiteness of the chart.* As the rally faded, price then fell directly back into the upper monthly Containment Zone, plummeting directly through... Notice, as price was ascending upward atop the Containment Zone, the monthly bars (e.g. monthly price swings) are actually slighter than when price falls through the upper Containment Zone, through the mean, all the way down to the lower Containment Zone. Again, when prices (for stocks, indices, currencies, or whatever...) are solidly above, or below the upper, or lower Containment Zone, expectations are aligned and thus, probability volatility decreases. In the case of the GBP|USD, the closer prices get to the mean, the larger the range of highs and lows in the candles. To reiterate, as prices near

the mean, expectations are not aligned, uncertainty increases, while at the same time, probability volatility increases.

Hammering a dead horse, prices away from the mean on a longer-term basis allude to expectations aligned, and thus, lower probability volatility. As prices approach the mean, uncertainty increases, and thus, probability volatility increases as well, as traders, investors and institutional players become more nervous and less likely to hold positions through short-term price volatility. The bottom line here is, when uncertainty surfaces, players have less certainty about the true long-term direction of whatever the financial instrument, economy, or market, and thus, are less resilient to shorter-term swings, quickly discarding positions, should reversal risk increase, even remotely. Thus, because all players are less certain when prices are near the long-term mean, price, market, and (initially) probability volatility increases, which again, is better known as expectations not aligned. Please also note that while the GBP|USD falls straight through the mean in 2008, the currency pair stalls right on the lower Containment Zone in the final month of 2008 and the first three months of 2009. The aforementioned should be another revelation just how pertinent the 1.25 standard deviation Containment Zones are, especially on a longer-term basis. (In just a moment, we will also see how the Containment Zone also comes into play on shorter-term timeframes, for now though, please simply take in the data at hand.) Figure 6.7 | Monthly GBPUSD Figure 6.6 Shown Again

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1Aug2008

EXACTLY at

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TRADING

to 1 in equities.) In essence, traders had a high probability entry point, risking $250 to make $3,790 in one month. Opportunities like the aforementioned in the GBP|USD do not come around too often; however, by watching the macro picture within stocks, indices, currencies and futures, traders often find opportunity reasonably abundant. FOREX PROFIT CALCULATOR Primary Account Currency | USD [x]

tue

Currency Pair | GBP/USD

1.7470

Opening Rate

|1.4475

1.6790

Closing Rate

|1.4854

1,6090

USD

Rate Action | Buy [>|

Number Of Units

1Dec 2009

Above, I have re-posted Figure 6.6, just so you don't have to flip back so many pages to reference the image. Please note how after price stalls at the lower Containment Zone in early 2009, the GBP|USD bounces right up into...you guessed it, the mean. After trading around the mean for seven months from June to December of 2009, as the United Kingdom's financial woes increased in 2010, the pair took another tumble, with prices once again attacking the lower Containment Zone. What's more, we have once again witnessed price stalling directly at the lower Containment Zone, as the GBP|USD did in May of 2010. During the month of May, the GBP|USD hit a low or 1.4475, with the lower Containment Zone resting at 1.4491. We are talking about a level, which stalled long-term monthly prices within 16-pips of the low. For those unfamiliar with Forex (currency) trading, taking a position with 20 to 25 pips risk is not a bad proposition at all. In fact, from the low print at 1.4475, the GBP|USD had bounced 379-pips into mid-May 2010, translating to a 1 to 15.2 risk to reward ratio (assuming a 25-pip stop loss on the 1.25 Containment Zone), yielding $3,790.00 for every $10,000 invested at 100 to 1 leverage. (Currencies are vastly different from stocks in that retail traders can leverage up to 200 to 1, unlike a mere 2

100000 Calculate

1.4030.

Profit ( |\USD

'owet 1.25 C.Z.| ....5

1Apr2009

/\USD

) 3790.00

fxuniversal.com

Of important note, when the GBP|USD tagged the lower Containment Zone in May of 2009, savvy traders would have likely been asking, "How do I know the pair will bounce right here?" First, no one knows anything in markets for certain, and anyone who says they do, is likely partying with Santa and the Easter Bunny at night, if you know what I mean. However, what we're talking about is attempting to identify high-probability entry points, where should price volatility persist, or the high probability entry not hold, we are able to quickly cut our losses, thus taking on as little risk as possible. Moreover, right now, we're talking about the monthly (macro) Containment Zone, without having taken any other data, time frame, news, or fundamentals into account. It is VITALLY important that readers not assume that when prices near the mean, or either upper or lower Containment Zone on a monthly chart, the trade is a "sure thing." The whole point of the Macro to Micro strategy is taking a slight bit extra time to be more cognizant of the whole picture, over what most retail traders usually look at (and/or consider adequate research), which is a 15-minute chart, coupled with five minutes of watching CNBC. I hate to sound like a jerk, but it's true. Retail traders are

the laziest lot of the entire trading community. I cannot tell you how many times I've given a Webinar and the second I say "standard deviation" or "Bollinger Band", someone blurts, "that doesn't work" without even hearing the information at hand. The reply in my head is, "You're right pal, if you attempt to use standard deviations (probability volatility) in the traditional format, they don't work, but if you'd clap your trap for a moment and open your ears, you might learn something new and helpful." The aforementioned is precisely the same trader who does absolutely no fundamental research at all, gets his opinions from mainstream media, and likely still uses broken indicators like Stochastics for signals. If 95% of all retail traders lose, it's probably a good idea to look beyond the conventional, accepted standard, for something that truly bucks the larger trend at hand, which is the very real paradigm of retail traders losing, almost all the time. One last point here, and then I will get off my soapbox... What we do not do, is look at the monthly chart only, thinking that taking a position on a major probability point, without any more macro-down research, fundamental due-diligence, or notice of the news at hand, will be a sure thing. What we are doing, is attempting to identify high probability entry points and then drilling down into smaller time frames (with fundamental and news-event research) to confirm, or deny a possible opportunity at hand. If we believe we can simply make big bets when price tags a Containment Zone, without more research and analysis, we might as well just go to Vegas and put it all on 32 red, because really, we're doing nothing more than gambling. With all of the aforementioned in mind, please now peek at Figure 6.9, which shows the same monthly chart of the GBP|USD. However, in Figure 6.9, readers will hopefully notice that I have drawn dotted lines on either side of the upper and lower Containment Zones, while also doing the same for the mean. On a common sense basis, when we simply take a look at the Containment Zones

and mean, while also remembering that the Containment Zones and mean are really visual representations of the larger movement(s) of the subset distribution(s) - and are

dynamic and price reactive - if we do not see significant slope in the mean, or Containment Zone, we might want to consider the possibility of whether expectations are not aligned... When expectations are not aligned, prices trade near the long-term mean, as players remain uncertain about current and future circumstances... Figure 6.9 | Monthly GBPUSD Lateral Containment Zone Volatility

PACT

GBPUSD,Monthly 1.5314 1.5314 1.4475 1.4854

it

i" i

I,

Common sense tells us, when the “belly” of the data (1.25 STD } 2.0910 DEV), the Containment Zone is moving laterally (not showing } 2.0230 tt emeeh slope) prices will likely rT display lateral CHQP ..., {high volatility) } 1.8850 } 1.8170 } 1.7470 } 1.6790 } 1.6090

Lateral mean confirms volatility conditions of uncertainty...

|,

Lateral Contaiment Zone indicates volatility ahead... Without “slope” expectatiqns 1Dec 2005

T

T

= 1Aug 2006 = 1 Apr 2007

T

1Dec 2007

T

are NOT aligned. T

1Aug 2008 = 1 Apr 2009

T

1 Dec 2009

} 1.5410

} 1.4030

+ 1.3350

Case in point, into early summer of 2010, with the Financial Crisis still at hand (globally), national debt nearing record levels for many nations, sovereign debt continually problematic (think Greece), unemployment still near highs, unusual current events stirring headlines (think terrorist attacks, earthquakes, volcanic eruptions halting air travel, and other unforeseen whoppers - still uncertain in total outcome (like the oil leak in the Gulf of Mexico), who the hell really knows what will

happen in the next six months? Given the massive amount of unusual circumstances hitting markets, no one really knows for sure whether global recovery is eminent in the short-run, or if larger bombshells still reside under the surface. What I am talking about is... Expectations' are not aligned, and thus, it makes perfect sense that the mean and Containment Zone (1.25 probability volatility) of the GBP|USD are traveling laterally, at the same time as prices trades near the monthly mean... Again, contrary to what we are traditionally taught, the mean is the outlier, not when prices are traveling consistently above, or below the 1.25 standard deviations (Containment Zone), with/where expectations are aligned.

Fact is... These are uncertain times... Sure, maybe nothing will ever be certain, but historically things have been calmer (and likely will be again in the future), though right now, we must simply embrace the fact that markets will likely remain volatile as no one really knows which countries will recover the quickest, what financial and political fallout still resides ahead, or how other factors like the massive oil spill in the Gulf could impact the environment and/or, GDP growth of affected nations. It doesn't take a rocket scientist, or a Nobel Prize winner, to see the direct correlation between a lateral long term mean and Containment Zone (probability volatility) and elevated uncertainty and market volatility...but it does take common sense and the openness to take a step back and really re-examine what we've traditionally been conditioned to perceive as the accepted standard, while also taking note of the larger picture at hand.

At the end of the day, when the longer-term mean and Containment Zone are traveling laterally, expect price volatility. So why do prices often stall at the mean and Containment Zone points? Concerning the mean, the concept is simple... As Figure 6.8 shows, we are viewing the mean (simple moving average) of prices for the past 200-months... Let me put it another way; we are seeing the visual representation of the average price over the past 16.7 years, even though we are only viewing 5 years on the chart. When prices near the major long-term benchmark (the mean) of nearly two decades worth of data, and uncertainty persists, funds, traders, and investors are likely to pause aggressive buying, or selling campaigns while seeking additional information and/or guidance regarding the situation at hand. Moreover, there are likely some huge players out there saying, "T have the opportunity to buy/sell at a two and half decade average price, perhaps I should begin to think about taking action." What we're talking about is a combination of uncertainty creating price, market and probability volatility, while also triggering thoughts of opportunity at the same time. Good old fear and greed. In addition, we're also talking about a point of possible fair historical valuation, where expectations are already, or in the process of being priced in. Think about it for a moment, if you own a share of stock at $1 and are expecting the price of that stock to be worth $1 a year from now, how much are you likely to pay for another share? One buck... Why would you pay more? However, if someone dropped the asking price for their share to 80-cents, a savvy trader would likely jump in to snap up the discounted share, taking advantage of the 20-cent profit on the table, despite the lack of trend.

Again, we are talking about trading around the mean - or average price - and why and how prices seem to "consolidate" when trading at, or near the mean. Fact is when there are no ultra-clear reasons for expectations to be aligned (one direction or another), those who understand what the instrument is worth (fundamentally), step in to take advantage of the short-term fluctuations, when dips or pops around the mean (and/or within the larger Containment Zone) surface. It's not until the larger savvy investing public really begins to perceive future growth (or deterioration) in the value of the instrument, that prices seriously rise, or fall, making new highs, or lows. Thus, the longer-term mean is where current (or with lack of better information, historical) fair valuation presumptions reside, given uncertain market conditions. In a long-term ascending market, as the mean rises (as noted in the Dow chart in Chapter 2), the overall value of whatever instrument we're tracking, should (in theory) be increasing at the same time. If the mean were rising over the long haul, while value was decreasing, most certainly, exuberance (blind devotion) is/was in effect, and a major price correction would ensue when players sober up. Sometimes this is known as the Greater Fool Theory, where if you buy into a massive rally, late in the game, without a rock solid fundamental reason to be buying in the first place, there must be a greater fool out there who is willing to buy higher, for you to get out profitably. We're talking about a fine line between valid, solid, reasoning for expectations of greater future value to increase, versus simply buying in because the opportunity looks hype... Whoops, I meant hot, did I say hype? There's more to the story though... Say for instance longer-term prices were headed upward, with the mean and Containment Zone (probability volatility) all showing ascending momentum (slope) as well... Then, perhaps a short-term catalyst triggers a decline into the mean, though overall, longer-term expectations remained both valid and aligned for the future value to increase... Would there be opportunity at hand? You bet. What we're talking about is a pullback, and is where short-term valuations/expectations have exceeded long-term valuations/expectations...to a point where rational minds can no longer justify paying a premium... Thus, while expectations remain aligned for a future value increase, while those who really understand the situation might like to buy more, they comprehend that the short-run risk of taking on additional positions is just too high. We're talking about risk-management, where savvy players know when short-term exuberance runs amuck, a temporary re-shuffling must take place, allowing real valuation to catch up with expectations. During the pullback, some take profit, some buy more into the dip, and some - who didn't really even know what the hell was really going on in the first place -

MACRO

TO

MICRO

are likely shaken out, or in many circumstances, taken out by fear, margin calls, and/or improperly placed stops. (We will cover technical pullbacks over fundamental shakeups (larger expectation(s) vs. real valuation(s) earthquakes) in greater detail later in

VOLATILITY

TRADING

deviation.

Second, the Containment Zone is where we know probabilistically, about 75% of the data should reside.

the book, but for the time being, please simply note there is a difference.) Overall, in terms of the long-term mean(s), readers should be aware:

The long-term mean is: 1. Where uncertainty about the future resides in the absence of a mere "technical" pullback. 2. Very likely at, or near where current and/or historical "perceived" fair value could be reasonably obtained. 3. Where price, market and probability volatility will boldly persist, when future value is significantly uncertain. 4. The direct middle of the Containment Zone (where nearly 75% of the data should - theoretically - reside. 5. A possible risk-averse entry point for those who know (with reasonable certainty) whether the future value will exceed (or fall below) current values.

6. In the absence of clarity and/or guidance (for the present and/or future), or in the midst of unforeseen information unfolding (i.e. a situation that catches the bulk of participants off guard), the most likely place where prices will initially revert to, before taking on a longer-term bias. With all that we have covered on the monthly chart of the GBP|USD, readers may also now be asking, "What about the Containment Zone?" Attempting to keep a long-story short, let us cover some quick basics. Foremost, the Containment Zone is all of the area on our chart from the mean to the upper 1.25 standard deviation to the lower 1.25 standard

Third, when data falls into the Containment Zone, on a longer-term basis after a sustained rally, expectations are likely not aligned, thus alluding to mass uncertainty and consequently, then verified by elevated price, market and probability volatility as prices approach the mean.

Fourth, without some type of clear reason for the masses to come together with reasonably clear understanding (expectations) of greater (or lesser, in some cases) future value ahead, price, market, and probability volatility will likely remain elevated while participants hash it out in the absence of some type of binding information to calm fears. Funny thing, when we look at trading action (post New Millennium) in the Dow chart from Chapter 2, part of the Dow beginning the larger 19-year rally that commenced in 1982 - 1983 can be attributed to the invention of the computer and other technological innovations; however, "In 1981, Congress enacted the largest tax cut in U.S. history, approximately $750 billion over six years. The tax reduction, however, was partially offset by two tax acts, in 1982 and 1984, that attempted to raise approximately $265 billion."!2] For some the argument of whether the 1981 tax cuts were good or bad politically will come up... However, politics are not my concern here whatsoever... What is my concern is simply observing the data, which in the case of markets commencing a huge bull rally in the early 1980's was coincidentally timed just after the "largest tax cut in U.S. history." My point is tax cuts are something that everyone understands, and can align expectations with. Regardless of whether the tax cuts were the right decision for the early 1980's national debt issues is moot; the bottom line is that it's pretty difficult to look at the massive tax cuts in the early 1980's, and not see how such helped kick off a massive 19-year rally. Certainly, other factors came into play, including the invention of the computer (indicating greater productivity and wealth for the American public); however, at the end of the day, larger market rallies are generally fueled by what all the people can understand... And nearly everybody understands less taxes. What we're talking about is:

MACRO

TO

MICRO

expectations and hope of greater future wealth and prosperity aligned on a mass scale, henceforth driving prices above the 50-month Containment Zone, away from the mean, upwards and onwards. Reiterating my entire argument that the long-term mean is the outlier (while also reaffirming the concept that expectations must be aligned because of something understandable, transpiring on a mass-scale), The Jones and Growth Tax Relief Act of 2003 finally stalled the major indices downward spiral leftover from the Dot.com bomb and 9/11, lifting the DJIA upward from the mean... Again, no matter how you really slice it, big tax cuts really saved the day. Two more tax bills in 2005 and 2006 (referencing the 2003 favorable rates on capital gains), with concurrent low interest rates finally nudged the Dow Jones Industrial Average above the upper Containment Zone in 2006.

VOLATILITY

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-

LATA SOO - ION 12012

Two tax bills signed in 2005 and 2006

POT ROUESELLM | extended through 2010 the favorable SARECUCRICUME | cotos on capital gains an¢ dividends * Bele that had been enacted in 2003, raised

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13,500

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Figure 6.10 | DJIA - A New Millennium of Uncertainty

Dow

reverses

from

the mean

and heads back towards the upper Containment Zone...

The Jobs and Growth Tax Relief and

Reconciliation Act of 2003 accelerated Ne tax rate cuts that had been enacted in 2001, and temporarily reduced the tax rate

In the current financial crisis.

Toughly one-third of the $800-billion

two-year US stimulus package

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I really don't care what political stance readers take on all this, because at the heart of the issue, it's not really a political thing... It's a data thing. The data shows us - as unbiased traders - when uncertainty persists, the mean will come into play, and when expectations once again become aligned (most often in a format the masses can easily understand) prices move upward. By the way, all of the above tax cuts directly influenced income taxes of individuals... Less taxes, more cash to burn, which everyone just gets, I believe. Again, the point isn't whether the tax cuts were the correct political, or longer-term economic move, the point is when prices are trading at, or near the mean (with fear and uncertainty prevalent in markets), it generally takes something all the people can comprehend and embrace, to save the day. And tax cuts are the historical rabbit in the hat to drive prices away from the real outlier of

distortion, the long-term mean, back outside the upper Containment Zone, where prosperity theoretically ensues, as prices once again begin gaining ground. We all know there were other factors influencing higher ground in all of the above points within the Dow's history, but the bottom line as a real world trader is: An ounce of common sense is worth a pound of economists. Common sense says, when an event occurs (like tax cuts) where all the people can infer the possibility of more cash in their pocket, it's probably a good idea to look for higher ground in the major indices, if even only for a short-while. Into May of 2010, the Dow Jones Industrial Average is trading right back at the mean, where uncertainty persists... I have some theories about what is about to unfold, though I will save them for the end of the book... The larger point here though, is when prices are trading within the longer-term Containment Zone, at or near the mean, price volatility, and uncertainty is prevalent within markets... There should be no doubt about it at this point... Remember, we've always been taught an "outlier" is distorted data at the exterior, or outside of a distribution... However, the statisticians probably never really traded with their own money, in real life, which is why common sense is telling us something remarkably different, which you've now seen with your own eyes.

standard deviation and then revert to the mean (allowing the dynamic probability volatility band, otherwise known as the third standard deviation) time to ascend higher, or lower, so price can soon after follow. Only when we assume a static distribution, do we fall victim of the fatal flaw of haughty thinking that prices will see an all out reversal when touching a second, or third standard deviation, when in fact, the situation could be really stating, "Get ready, a trend is in effect." In the case of the GBP|USD in Figure 6.11 (below), notice how price directly tagged the lower Containment Zone in May of 2010, before bouncing... Why did price stall at precisely this point? Again, prices (as seen through monthly closes significantly beyond) above or below the Containment Zone, allude to expectations aligned. Into May of 2010, the GBP|USD had lost nearly 30% in value since the high of 2.1161 in November of 2008. On a monthly basis, a drop below the lower Containment Zone at 1.4491 would be clearly indicating, expectations are aligned for the pound to lose further value.

So why do prices so often stall right at, or near the upper and lower 1.25 standard deviations, AKA upper and lower Containment Zones? Prices above, or below the Containment Zone indicate future expectations are aligned either positively, or negatively, and thus are leading the movement of the mean and distribution. Shorter-term distributions will have more erratic upward and downward movement, versus the longer-term distribution, as obviously, near-term shifts in expectations cause fluctuations in price. Prices trading at, or near the second and/or third standard deviations do NOT always mean "reversal" as we have been taught to believe. While we do know there is a 95% probability all the data will rest within 2-standard deviations of the mean, and a nearly 99.7% probability prices will reside within 3-standard deviations of the mean, price pushing on the second, or third standard deviation could be indicating prices (AKA subset distribution) on the move directionally. While price may tag a third

ut

it

i"" a

Upper 200-Month } 2.0910 1.25 Standard Deviation Contaiment Zone } 2.0230 } 1.9530 } 1.8850

200-Month Mean

} 1.8170 } 1.7470 } 1.6790

Lower 200-Month 1.25 Standard Deviation Containment Zone

} 1.6090 } 1.5410

} 1.4030

1Dec 2005

T 14ug 2006

T 1Apr2007

T 1Dec2007

T 1Aug2008

T 1Apr2009

T

1Dec 2009

+ 1.3350

However, given the U.S. holds significant national debt, unemployment remains high, and GDP growth has yet to fully show sustained recovery, why would the greenback prompt (with certainty) further buying in May of 2010. It's not to say that such won't occur in the future, but if such did, we would have a pretty good idea larger players have concluded the greenback is a better bet for the future, over the pound. As of May, 2010 though, despite the negative news coming out of Europe, the U.S. wasn't exactly in the "free and clear" itself, and thus, expectations for a further rally - with the information at hand, in the moment - in the greenback are/were not aligned. Again, a move below the 1.25 standard deviation Containment Zone would indicate expectations are aligned for lower ground in the pound, and inversely, higher ground in the U.S. Dollar; however, as the GBP|USD bounced precisely at the lower Containment Zone, the statistical "belly" of the distribution is basically calling the market's bluff, at least in the short-term. Prices below the lower Containment Zone could quickly kick off a landslide trend south for the GBP|USD, which clearly, participants were ready to embrace into May of 2010. What we're really saying here is the people with the money, resources and motivation to drive prices in a larger direction (up, or down trend) aren't putting their full resources to work, because though they likely do have the resources to drive prices, they do not have the motivation... Their expectations are/were not aligned for a longerterm trend downward right now, and hence, uncertainty and price volatility persist within the Containment Zone. Thus, the Containment Zone is right where prices should stall, implying larger players and Governments are equally uncertain about the future. Why? Again, if the GBP|USD were to start to see closes boldly below the lower monthly Containment Zone, the instance would be begin to prompt a larger subset distribution move lower, while also indicating expectations aligned for lower ground in the pound. One more time, while prices remain inside the Containment Zone, with the mean and upper | lower Containment Zone 1.25 standard deviations traveling laterally, prices will likely run amuck around the mean, while institutional players attempt to decipher greater guidance into future valuation. The bottom line here is, when prices remain inside the Containment Zone, savvy retail traders can anticipate extra price volatility, perceiving that even the big guys aren't fully sure what the outcome of the situation that initially drove price to the mean in the first place...will be. What's more, as prices near high-probability points where a larger trend could begin, we can surmise those with deep pockets aren't likely to endlessly bluff, attempting to drive prices artificially above or below the Containment Zone, because if and when the bluff is up, without

longer-term expectations aligned, prices could just a quickly drop back towards the mean, which is the natural resting place of uncertainty. Thus, as prices near the Containment Zone, without reasonable certainty of greater (or lesser) future valuation, participants are likely to cease, or pause mean divergence buying or selling, with risk increasing the more price moves away from the mean, without expectations aligned. Moreover, should expectations not be aligned, savvy traders will begin taking 'mean reversion’ positions, knowing continued uncertainty will likely quickly punch prices back to...the mean. As you're about to see, there's actually more information here, but to find it, we likely must look into shorter-term timeframes, AKA Macro to Micro.

®% GBPUSD. Monthly GBPUSD,Monthly

a

10) x|

1.5314 1.5314 1.4475 1.4854

May 2009 rally adds back a whopping

Chapter 7 | GBPUSD

Macro to Micro

} 1.8850

int

Revisiting our previous monthly, weekly, daily and 4-hour GBP|USD charts, let's take a quick look at what unfolded in recent history... Figure 7.1 shows the same monthly chart of the GBP|USD we've been using throughout Chapter 6. However, I have made specific note of the months March through June of 2009. Readers will kindly notice the spring months of 2009, specifically observing the largest ascending candle in the series, May of 2009. Moreover, please also take note of the bear candle six months previous to May 2009 (November of 2008), where "Cable" (nickname for the GBP|USD) tagged the lower Containment Zone and then bounced directly from, closing the month 852 pips off the low. (By the way, the lower Containment Zone for November rests at 1.4510, just 43pips below the monthly low. Pretty amazing right?) The U.S. Dollar picked up steam into late 2008 and early 2009, as UK credit conditions worsened (while U.S. stock markets trudged towards fresh lows), with the

greenback's saving grace being the perceived notion of safety and liquidity. However, after the U.S. dollar put in relative highs in January and March (the latter being the same month U.S. indices put in lows), Cable proceeded to add back a massive 1,374-pips during a torrid rally in May of 2009. Figure 7.1 |GBPUSD 2009 Monthly Chart

} 2.0230

cloforsCaebl)e. _| {335%

it

|

(i

} 2.0910

} 1.8170 } 1.7470 } 1.6790 } 1.6090 November, 2008

} 1.5410

Monthly close, nearly 850 pips off of low at 1.4553

1 Dec 2005

T

November monthly lower Containment Zone @ 1.4510.

1 Aug 2006

T

= 1 Apr 2007

T

1 Dec 2007

4030 T

1 Aug 2008 =

T

1 Apr 2009

J

1 Dec 2009

>

On the forefront of our minds should be the initial test of the lower Containment Zone (probability volatility) in November 2008, which stalled almost precisely at the 1.25 standard deviation, or lower Containment Zone. In the five months following the initial test of the lower Containment Zone, Cable bounced around the 1.25 standard deviation, but failed to put in significant closes below the benchmark. Why? Into early 2009, the U.S. was facing its own set of credit related issues, massive national debt uptick through stimulus, equity markets approaching new lows, while at the same time; unemployment was edging into the upper 9 percent area. Though the U.S. Dollar is perceived as a risk-averse safe haven (backed by the U.S. Government), there's a point when even the good word of the U.S. Government (and U.S. Treasury) just aren't enough considering the larger spending habits at hand. Bottom line, given the United States own problems, expectations just weren't clearly aligned for an all-out greenback breakout higher... Thus, Cable teetered around the 1.25 lower Containment Zone until April of 2009, when the UK currency began to show signs of recovery. Again, there simply was not enough reason for global expectations to align for a move higher. At the same time, money began moving out of the U.S. dollar, into more risky

MACRO

TO

MICRO

global assets, as super-savvy investors perceived the worst might be over... (Just FYI, interest rates also came into play, with the Bank of England yielding 3.5% on the pound, while the Fed Funds Rate sat at an effective rate of 0.12%.)

VOLATILITY

TRADING

"% GBPUSD Weekly GBPUSD,Weekly

1.4879 1,5053 1.4495 1.4535

} 2.0800 } 2.0260

In summary:

|

Weekly 200-period Mean

y Bete? } 1.9180

1. The monthly chart showed us the GBP|USD declined directly into the 1.25 standard deviation lower Containment Zone, but was unable to close significantly below, because expectations for an even stronger dollar (enough to trigger a subset distribution on the move, AKA prices outside the Containment Zone) just weren't aligned. 2. The GBP|USD then bounced around the lower Containment Zone until April of 2009, when the pair began to slowly lift off the benchmark standard deviation. Next, in Figure 7.2 (below) traders will notice price held the lower monthly lower Containment Zone (drawn as a black horizontal line) in April of 2009, making a fresh 17-week high in the first week of May 2009. Moreover, the weekly chart shows us that in the fall of 2008, the GBP|USD breached the lower 1.25 standard deviation Containment Zone with successive closes below the benchmark standard deviation. °

Figure 7.2 | GBPUSD 2009 Weekly Chart

} 1.8640 } 1.8100

} 1.7560 May of 2009 Solid (with weekly closes) breach of the lower 1.25 standard deviation Containment Zone.

Ws

} 1.7020

aes

al lower Containment Zone

1.5940

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} 1.3780 } 1.3240

13Jul 2008

=

7 Sep

=

2008

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=

28Dec

=

2008

22 Feb 20

:

2003

9 Aug

=

2003

+ 1.2700

4 Oct 2009

:

The burst below the fall 2008 lower Containment Zone (on a weekly basis)

indicated expectations aligned for a probable move lower, likely into...perhaps the monthly lower Containment Zone? If things seem unclear right now, bear with me for a moment and the picture should come lucid... The monthly chart showed the GBP|USD bouncing around the lower Containment Zone from November of 2008 to the start of April 2009, which we see more clearly, with the monthly lower Containment Zone drawn on the weekly chart, again, as the lowermost horizontal black line. Amazingly in April of 2009, when price tested the monthly Containment Zone, a wick did not appear under the 1.25 standard deviation, meaning even on a market and price volatility basis, bearish momentum was beginning to fade... Then, as the

GBP|USD began to make 17-week and then 19-week highs, bulls stepped fiercely back into the pound driving the currency back upward towards the weekly lower Containment Zone.

In summary: 1. In the fall of 2008, savvy traders watching the weekly chart of the GBP|USD with the 200-week mean and Containment Zones visible, would have been tipped off to a pending move lower, when price showed successive closes below the lower Containment Zone. 2. Aligned expectations (on a weekly basis) for a lower GBP|USD began to dwindle into December 2008, as noted in price stalling almost exactly at the lower Containment Zone, just days before the New Year. Then, in midJanuary, 2009, the GBP|USD breached the monthly Containment Zone, but recovered the lost ground the very next week. The move below and then, rebound back into the monthly Containment Zone set the tone for uncertainty at the key statistical benchmark for the next nine weeks, until price began to really show some resilience above the standard deviation. 3. While the GBP|USD began making 17-week and then 19-week highs, given the pair was still below the lower 1.25-standard deviation 200-week Containment Zone, traders may have been a little unsure about whether the bounce was "for real." 4. Once the GBP|USD solidly popped above the 19-week high though, the race back to the lower weekly Containment Zone was on... Here's where the information starts to get really good... Though the monthly chart gave us some clue as to the larger trend at hand, while also indicating perhaps expectations were no longer aligned for a stronger U.S. dollar in May of 2009 (as seen through price stalling at the lower Containment Zone), the info really hasn't been extremely precise (think tradable) to this point. To rephrase, while we have seen that initial tests of a Containment Zone (on a monthly basis) can often bounce without clear

guidance to align expectations, we certainly aren't going to base our entire trading

strategy on a few points within the monthly charts... Furthermore, though we took notice of expectations aligned when the pound fell out of bed in early fall of 2008, the little data we observed, was certainly not enough to actually take a position with. What's more, while the weekly chart also displayed prices headed back upward into the lower Containment Zone (probability volatility) in a fast May 2009 dead cat bounce, we still don't really have enough information to piece together a risk adverse trading strategy, that clearly shows us what and why the Containment Zone really matters. But we will... To get there though, we will have to drill down into the daily chart, and perhaps even the 4-hour chart for greater guidance. By the way, have you noticed anything going on here? Just to make sure we're on the same page, let me divulge a couple things happening... Foremost, once in a rare while, a precise point of low risk, high probability trade entry will surface on the monthly chart... But it's rare. Thus, what we're not doing is trying to use the monthly chart as our specific trigger for entry and/or exit. Rather, we are using the macro charts to identify larger instances of expectations aligned, or not, thus helping us to also be on the lookout for possible subset distributions on the move in shorter-term time frames. Please also note that nowhere in the above am I using any garbage language like "triple top monkey break out", "afternoon star humming bird reversal pattern", "stochastics in the overbought area" or any of that other junk that (to quote the movie Wall Street), is nothing more than a pig with some lipstick slapped on it. Scrape off the paint and you still have a pig, know what I mean? Again, we are determining larger market sentiment on macro timeframes, based on where price is trading in relation to the long-term mean, and then drilling down into the shorter-term charts, using applicable inferential statistics to help find clear - low risk - trading opportunities based on the effective movement of subset distributions... Not hopeful, wishful, red-line-crosses-blue-line-no-brainer-tin-can indicators that likely also jingle on a Christmas tree. Moreover, because we understand why and how the longer-term mean is truly the outlier, we are also able to identify whether expectations are aligned, or whether uncertainty is prevalent... What's more, we also know whether to consider if perhaps other market participants are focused on simply capturing profit when discrepancies occur within the current or historical fair value, or whether the larger focus has shifted to expected, greater growth (or deterioration) in future values, earnings, or wealth. Now, taking a look at figure 7.3 below...

We will look at two daily charts of the GBP|USD, first looking at the picture from a distance, all the way back to August of 2008. After Figure 7.3, we will peek at the scenario slightly closer up, taking special notice of the May 2009 rally, which we've been discussing throughout Chapter 7. Figure 7.3 shows the GBP|USD 1.25 standard deviations (Containment Zone) with a roughly 900-pip spread (upper band minus lower band) in August of 2008. In the same month, the GBP|USD breached the lower Containment Zone and began a torrid move south, losing nearly 5,400-pips by March of 2009. We want to take special notice of the Containment Zone spread in August 2008, versus March 2009. What readers will notice is while the Containment Zone was showing a spread of nearly 900-pips in August 2008, by March of 2009, the two Containment Zone standard deviations were trading over 5,400-pips apart. Why? If you remember in Chapter 5 | Reshaping Standard Perceptions, we learned that not only are the larger distributions dynamic; but because of the squaring of the deviations when calculating standard deviations, applying the formula to continually moving data within markets, the standard deviations of the distribution also expand and compress dynamically, as well. Again, as we covered in Chapter 5, distributions are doubly dynamic, in that the mean (and larger distribution) are mobile, while the standard deviations (probability volatility) expand and contract too, as market, price, and mean-period volatility increases and decreases with each day's trading action. Moreover, when the data (on a longer-term basis) is trading near the mean, we also know some sort of uncertainty is present within markets. Figure 7.3 | GBPUSD

2009 Daily Chart, From a Distance

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The longer uncertainty persists, the greater the likelihood of prices (highs to lows) generally moving closer to the mean, as participants become less and less willing to take on risk, without any fundamental or news related events providing reason for expectations to align. What I've just mentioned is often much of the reason prices "consolidate" when traveling laterally. Moreover, as you will see in Chapter 9, even uncertainty eventually gives way to decreased price and probability volatility, thus releasing mass from the distribution, making it easier for price to begin trending again in the future, once expectations again align. In the absence of fundamental news prompting significantly higher highs or lower lows outside of the lateral range, probability volatility (standard deviations) eventually collapse into the mean. What we're talking about is a situation where even when prices travel in a lateral (sometimes choppy) range...eventually even

probability volatility compresses towards the mean. In the absence of clear information indicating aligned expectations for greater future value (like earnings growth) or deterioration, participants eventually struggle less and less to push prices outside of the lateral range... After all, in the absence of news or a fundamental catalyst to move price higher or lower, if price were to approach a range it had failed at the previous two times, only a fool would buy (or sell), at the exterior of the lateral distribution, in anticipation of a move beyond the relative range. Unless the trader had some sort of true reason to take a mean aversion position in a lateral range, doing so would really be nothing more than hope and hope alone. However, prices can't stay lateral forever, given that people aren't going to stop working, innovating, developing, and attempting to grow their own personal wealth. In fact, the only time price will travel laterally for eons is when there is some sort of artificial hedging taking place, pinning the instrument to a specific value, or when the instrument is trading at zero, like, the company went bankrupt. As Corporations press forward, Wall Street's future earnings expectations increase (unless something is not working right), thus prompting participants to push prices upward, paying a premium (multiple to future earnings) to own the stock now, in anticipation of greater value tomorrow. For currencies, when a country appears to be

well positioned for strong GDP growth and higher interest rates (contrary to some common thought, the latter is actually a healthy occurrence in a low debt, solid employment situation), participants will likely buy the currency with ‘expectations aligned' for future prosperity of the underlying country and henceforth, currency. Moreover, when one country is paying a higher interest rate over another country, institutions and retail traders holding the currency with the higher of the two interest rates are actually paid the interest rate (divided by 365) nightly into their accounts... Known as a roll or carry trade. A carry trade is where one borrows from low interest rate lender and then parks the cash somewhere else so they can receive a higher interest or return. Anyway, what I'm saying is this: When trending ends, market and probability volatility will strike up as prices revert to the mean; however, the longer prices trade in a lateral range, the greater the likelihood probability volatility will eventually collapse onto the mean (as the distribution sheds mass), thus setting price to trend again when expectations align once more. In addition, as prices breakout of the lateral range, probability volatility will again spike, as price moves towards the exterior of the distribution, thus taking higher/lower ground, while prompting the distribution to begin moving again as well.

Two key points to remember are: 1. The longer the lateral range persists, the higher the likelihood probability volatility will collapse. 2. Prices will not trade laterally forever, and thus, probability volatility cannot collapse into the mean endlessly... When expectations for greater (or lesser in the case of the GBP|USD in Figure 7.3) future value, income, and/or wealth again align after a sustained period of lateral trading, prices will first move outside of the 1.25 standard deviation Containment Zone...thus telling participants the uncertainty is over, and expectations are once again aligned for higher or lower ground. After a prolonged period of lateral trading (eventually causing probability volatility to decrease), when prices move above, or below the Containment Zone again, the standard deviations will begin to expand (assuming adequate compression was allowed during the lateral trend) indicating the distribution is likely about to start moving again. In essence, trending cannot take place in a collapsing probability volatility situation, unless we are in the final stages of a longer-term trend. The bottom line in all of the above is: In the case of the GBP|USD in August of 2008, the price breach of the lower Containment Zone alluded to a possible larger move at hand...which was confirmed via expanding probability volatility, coming out of the pre-August lateral range. In the Macro to Micro strategy, we're not only attempting to identify high probability entry points, but are also tracking the macro down movements of subset distributions, in order to identify longer-term trends (up, down, or sideways) and then isolate high-reward, low risk short-term entries. What's NEW though, as savvy readers likely already inferred, we are also using the expansion and contraction of standard deviations (probability volatility) for guidance into the possibility of a larger move upward, or downward in prices. What I'm saying is this: When prices move outside a Containment Zone, the expansion of probability volatility actually provides a leading indicator, telling us whether prices might truly begin trending... (More on the expansion and compression of probability volatility in Chapter 9)

MACRO

TO

MICRO

Again, traders will want to take specific notice of the fact that the entire time the GBP|USD (Figure 7.3) was dumping from August 2008 to March 2009, the pair traded underneath the 200-period 1.25 standard deviation Containment Zone. Clearly, expectations were aligned for a lower pound, and common sense Containment Zone traders who were short, could have simply waited until price reattacked the Containment Zone, before closing positions. (By the way, trading is not as easy as I've just made it sound...so please do not assume we can always just take a position outside of the Containment Zone and then go play golf... Occasionally super clear, situations like the GBP|USD August 2008 decline do occur, but not always, and thus, we have to know Macro to Micro in greater detail to avoid head fakes... FYI, head fakes do happen...in all markets. Okay, so now that we've covered a little ground on price trading outside the longterm Containment Zone indicating expectations aligned, while also having touched on why and how probability volatility eventually compresses in a sustained lateral range, let's get back to our analysis of the May 2009 GBP|USD rally. To do so, we will use the daily chart; however, we will zoom into the data, grabbing a clearer snapshot of the period in question. Take quick look at Figure 7.3 again, and then take a moment to examine Figure 7.4 below. In Figure 7.3 the GBP|USD traded under the lower Containment Zone from August 2008 all the way until March of 2009. However, in October of 2008, though the downtrend still seemed intact, Cable actually began trading laterally, before putting in a new brief low in January 2009. Figure 7.4 | GBPUSD

2009 Daily Chart; A Closer Look

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What we should notice here is the larger fierce move downward began to taper in October, though the actual trend wasn't really over until March... I want to mention that in the aforementioned, I'm talking about the visual tapering of the landslide selloff seen in the early fall of 2008, however, while the lateral trading (as seen on the daily chart) appears relatively harmless, the January lows and bounce back from made up almost 1,000-pips, a massive move by most standards..,just a point to note, merely for the sake of noting. January lows and retracement back to the Containment Zone aside, when we look at Figure 7.4, we see that after the big 1,000-pip pop downward and back up (capitulation almost), the GBP|USD tagged the lower Containment Zone on February 6, 2009 for the first time since August of 2008. The following Monday, Cable tried to push over the

Containment Zone, closing on atop the benchmark standard deviation at 1.4894. Then, on Tuesday, February 10, the GBP|USD again failed the Containment Zone, dropping nearly 500-pips in the following six sessions. On February 23rd, 24th and the 25th, the pair again attempted to trade above the Containment Zone, but failed once more seeing another quick (nearly) 400-pip range downward and back up over the next four sessions. On March 6, the GBP|USD tried to climb over the Containment Zone a third time, but failed to hold ground above the lower 1.25 standard deviation in the same the session. The following Monday, Cable dropped a massive 350-pips... Throughout all of the above, readers may have noticed a common theme: in a pronounced trend where prices are trading outside the long-term (daily 200-period in

this example) Containment Zone, when prices attempt to re-attack the 1.25 standard deviation, but fail to hold ground INSIDE the Containment Zone, such often provides a

great short-term "with the trend" entry. We will dig deeper into the details of timing the entries in a few minutes, but please just notice the scenario for now. Still observing Figure 7.4, readers will notice the GBP|USD gave the lower Containment Zone a fourth test March 16, closing just barely inside the benchmark. The following day, though Cable closed red for the session, the pair held ground inside the 1.25 standard deviation. Then, on March 18, Cable did something it had not done since August of 2008, the pair put in a third close inside the lower Containment Zone. In the next nine sessions, the GBP|USD moved both up and down; however, despite the price volatility, the pound remained inside the lower Containment Zone. What the daily chart was telling us from March 16th on, was this: Because Cable was now trading inside the lower Containment Zone, expectations were no longer aligned for a mind-numbing greenback rally (pound selloff), as witnessed in the previous seven months. Fact is, while many traders were scratching their heads wondering what the heck was about to happen next, savvy Macro to Micro traders were seeking just a little more confirmation of a larger bounce likely in the cards, specifically prompted by price trading back inside the lower Containment Zone... All of which was indicating most others were likely scratching their heads... Do you see it? Expectations were no longer aligned, and thus, traders without the incredible insights of Macro to Micro were scratching their heads wondering what was going on, but the ironic (and incredible) edge Macro to Micro traders had, was the mere fact that other traders were indeed scratching their heads in the first place. Expectations were no longer aligned and uncertainty had taken over, which savvy Macro to Micro traders know is precisely when prices revert to the mean. Again, as we learned in Chapter 2

and 6, when uncertainty rears up, the most likely place prices are headed... Drum roll please... Is the mean... One more time, when the smart money magicians on Wall Street are scratching their noggins, prices are likely already on the way back to the long-term mean... What's even more astounding is the fact that on March 3oth, the GBP|USD (in the midst of a four-session sell-off), witnessed a low that stalled exactly at the lower monthly Containment Zone, which I made sure to draw on every single one of the above GBP|USD charts as a black horizontal line. The instance of price stalling right on the monthly lower Containment Zone was even more proof that expectations were no longer aligned for a lower GBP|USD and with uncertainty kicking up; a big trade opportunity was likely at hand, as prices reverted to the mean. Do you see how remaining cognizant of the monthly lower Containment Zone on the daily chart gave us significant insight into the larger trend, while the weekly aided slightly too. In this specific example of the GBP|USD though, the best information came from the daily chart, which provided valuable insights into trader and market sentiment turning from expectations aligned into uncertainty unfolding......giving Macro to Micro traders an edge the rest of the crowd was likely without. Sure, we could have just looked at the monthly, weekly, or daily charts on their own, but had we done so, we might have missed key information NOT on the one chart we chose. Thus, in our macro down analysis, we must FIGHT the urge to be lazy and only look at one, or two time frames. Rather, we must always remember that only by examining ALL periods (monthly, weekly, daily, 4-hour, hourly, 30-minute, 15-minute,

5-minute, and sometimes 1-minute) will we truly understand whether expectations are aligned for trending, or uncertainty is prevalent and prices are headed back towards the mean. (I know we have yet to drill into the shorter-term time frames yet, but stick with me a little more and we will.) While the weekly chart did not provide the most valuable information of the three, it was necessary to examine, as most often, the most important clues will come from the one period we least expect, just FYI. By the way, I've seen traders kick some huge butt in markets once they learn Macro to Micro (which really isn't that complicated, but requires re-evaluating the simple, though amazingly powerful information most have hastily discarded); however, as is the nature of human beings, for too many, their fresh string of victories gave/gives way to ego, which then gave/gives way to laziness, which then gave/gives way to big losses. Please don't let the aforementioned happen to you.

Besides, there's nothing worse than a trader who is suddenly Mr. Expert "let me tell you about my wins" because someone else helped him learn some new things just a short while ago. What I'm saying is ego and profitability are natural magnets when one first starts winning; however, the more the former grows, the more the latter will likely turn from Jekyll to Hyde. Keep it humble - keep it profitable. I've found helping another person is always good medicine for trading-ego, which (unfortunately) eventually rears up in all of us. We've covered quite a bit of ground thus far, however, we're still missing some important details, which hopefully, readers are wondering about... Our previous analysis of the monthly, weekly, and daily charts gave us extraordinary insight into the potential pending movement ahead in the GBP|USD (in reference to early April of 2009); however, we are still lacking the most critical of details... Precisely... when and where to enter? In traditional Macro to Micro, we drill down from the monthly to the weekly, to the daily, to the 4-hour, to the hourly, to the 30-minute, to the 15-minute, to the 5-minute, and sometimes even into the 1-minute chart to unearth a possible trading opportunity. Avoiding writing a War & Peace length work here, in Chapter 7, I'm only going to drill down into the 4-hour chart during this chapter. Feel short-changed? Don't worry, there's a reason behind stopping at the 4-hour... And the reason is: I'm sick of writing about the pound. Just kidding... Well, sort-of anyway. I'm going to save the hourly, 30-minute, 15-minute and 5-minute periods for the Chapter 9, as we're really talking about a whole other animal... While all things are truly one in markets, meaning no matter how we hash and rehash information (even different timeframes), it's all the same thing...as in, data is data, there's a huge theoretical concept separating the short and long-term time series that we must still cover. What I'm saying is this... Before just spilling into the shorter-term periods, we must first take another step back from the larger situation at hand, and consider the theoretical reasoning of why what's happening...is happening in the first place. Though the larger concepts in Macro to Micro (like the mean being the outlier on a longer-term basis) remain mostly same throughout all time-series (including our intraday charts), we do need to make an important Reality Adjustment in our perceptions and understanding of the longer-term periods, versus that of the shortterm intraday charts... We are about to cover the Reality Adjustment in just a few pages, for now though...while we wrap up Chapter 7, please perhaps just tuck the concept of Reality Adjustment away in the back of your mind...

I know I've given readers absolutely no idea about what I specifically mean by Reality Adjustment, but just understand it's by design. When we get to Chapters 8 and 9, it should all come clear... In Figure 7.5 (below), readers will please take a moment to note the trading action of the GBP|USD on the 4-hour chart. The picture is clear at this level... Working from left to right, we first note the already mentioned test (and bounce off) of the lower monthly 200-period Containment Zone, drawn as a horizontal line on the chart. We also know Cable had traded back inside the lower daily 200-period Containment Zone, indicating expectations were no longer aligned for a continued downward move. The test of the lower monthly 200period Containment Zone simply confirmed what we already surmised; expectations previously aligned for downward action in the GBP|USD were shifting to that of uncertainty. Moreover, just about three sessions after the test of the lower monthly Containment Zone, the pound was trading above the 4-hour mean, which it bounced off, only to retrace again on April 27. Traders should have been asking, "If the mean is the outlier of uncertainty, and we're back at the 4-hour mean, does this indicate uncertainty within the GBP|USD?" The answer is: "Yes!" Fact is, after a big rally, or dump, one of the most difficult things for almost all traders to get their minds around when a deeper, steeper pullback ensues...is the fact that the trend has changed, if even just on a short-term basis... Figure 7.5 | GBPUSD 4-Hour Chart | Opportunity Apparent

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In the case of Cable here, many traders were likely taking short positions in the pullback (trading with the larger downward trend), only to be stopped out as the pair continually edged higher. I would expect those same traders were likely kicking the pooch at the time, griping about that dumb old cliché, the trend is your friend, after all, in the case of the GBP|USD, what looked like temporary pullback, likely smoked a ton of ‘with the trend’ bears. Here's the thing, when the GBP|USD came back to the mean in late April, uncertainty was prevalent. However, what we're talking about here is a much shorterterm period measured (over the weekly and monthly charts), and thus, requires a little common sense analysis. What I'm really saying is the further out the time frame measured, the more dangerous the uncertainty-related price, market, and probability

volatility at the mean. While 4-hour chart mean is quite often a place of uncertainty, because it is also a much shorter-term time frame than the monthly chart, the overall bars will likely have lesser total range, meaning pips or points from highs to lows. Thus, players are often able to take positions at points of greater uncertainty in shorter-term periods (like the 4-hour chart here), which they might not be able to on longer-term periods, like the monthly chart. What I mean by the aforementioned is as a general rule of thumb, the cutoff levels (stops) of shorter-term time frames (theoretically) often hold less risk than their longer-term counterparts. It's always great when we can pick off a perfectly risk averse position that lines up a high risk, low reward entry on both a 5-minute and monthly chart, but it's rare... Thus, we must retain common sense when trading Macro to Micro, remembering to separate expectations, risk and probability presented within longer-term time frames, from that of the shorter-term time-series. Given that the GBP|USD had just previously bounced off the lower monthly Containment Zone, while also having re-entered the lower daily Containment Zone in the previous month, the uncertainty of the 4-hour mean was relative to traders questioning how steep and deep the pullback would be, rather than whether the previous sell off was still in effect. Do you see what I'm saying? The relativity of uncertainty at the 4-hour mean is/was different - on a theoretical basis - from that of the monthly mean. Thus, given that the 4-hour mean held almost exactly, the instance was telling us, "those with the money, motivation, and resources to really drive prices downward on a longer-term basis, no longer saw the value in doing so." Moreover, when prices cross over the mean, the instance often indicates a paradigm shift in the expectations of the players with the money, motivation, and resources to really push prices around. If I were to rank the weight of the paradigm shift points of expectation pivots in markets, I would probably put more weight on the Containment Zones, over the mean though. Why? Uncertainty at the mean is about managing risk, over movements breaching outside of the Containment Zones, which are more about expectations of/for future growth or deterioration of value, earnings, interest income, etc... When expectations are aligned, participants will likely take on more risk, versus when uncertainty is at its highest. Thus, in the 4-hour chart of the GBP|USD, the stall at the mean indicated participants with serious money, motivation, and resources to push markets around, perceived more risk than reward to short the GBP|USD, despite the previous 2008 -

2009 downward spiral. Moreover, sometimes at a shorter-term distribution mid-points (the mean), we can sometimes use volume to help determine the mindset of larger players. But we're not looking for a volume increase... We're looking for volume decrease. When volume declines at a short-term mean, the instance is often indicative of an event where even though price is trading near the mean, those who are already holding positions are - at least - not bailing like lemmings over a cliff. When prices are jumping all over the mean on high volume, it may be a good idea for risk-reluctant traders to just sit out, as clearly, the dust has not settled - and there's likely an institutional tugof-war taking place. Though Figure 7.5 does not show it, volume in the GBP|USD (measured in total ticks) actually decreased during the test of the mean on the 27th, over the test of the mean on April 22. When the GBP|USD began to rally off the mean again after the April 27-28 tests, Macro to Micro traders would have been served well thinking, "The GBP|USD just tested a macro point of uncertainty and expectations pivot, and held... Right now - at least in the super short-term - I should align myself with current plausible expectations, which are for a deeper, steeper pullback to come, as expectations are no longer showing clear alignment for another leg down in the pound." Price confirmed such by continuing its upward voyage, subsequently testing the upper 1.25 standard deviation Containment Zone of the 4-hour chart on April 30th. However, price also failed the Containment Zone dropping back nearly 200-pips in just two sessions. Then, on May 1st, Cable tried to bump back above the upper Containment Zone again, which was short-lived in the final 4-hour bar of April 1st, and the first four 4-hour bars of April 4th. Traders who took positions above the Containment Zone thinking, "expectations are aligned" both times price first jumped above the benchmark standard deviation, were likely smoked when price did not hold ground above the Containment Zone. In all, the larger upsy-downsy range atop the Containment Zone (from May 1st thru the 4th) was about 80-pips, which is about three to four times a reasonable stop for most traders... I really hope readers are asking, 'What gives Whistler?’ Throughout the book I've mentioned price above, or below the Containment Zones indicates expectations aligned, or are in the process of aligning... with great possibility of trending ahead... However, in the case of the GBP|USD here, traders who tried to buy the pair in the two previously mentioned pops above the Containment Zone most

likely lost. Super savvy Macro to Micro traders would have likely never entered the longs in the first place, as price initially tagged the upper Containment Zone though... Can you guess why? The answer is right there in Figure 7.5 (above), can you spot it? I did already point out what we're looking for in Chapter 2... Give up? As Figure 7.6 (below) shows, the upper Containment Zone was moving laterally when price initially tested the 1.25 standard deviation. The aforementioned was a huge red flag telling us price volatility would be high, as price initially tried to move above the Containment Zone. Just in case, let me rephrase what I've just mentioned... When price first attacked the upper Containment Zone, the Containment Zone (1.25 standard deviation probability volatility) was showing lateral slope. Again, the Containment Zone was not showing SLOPE, indicating price volatility likely ahead at the key pivot. Figure 7.6 | GBPUSD 4-Hour Chart | Probability Volatility Slope

MACRO

TO

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Upper volatility (1.25 standard deviation), Containment Zone is moving laterally - not upward...

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Coming off the massive downside trend from August 2008 to May 2009, m: any traders were still unsure of the bounce and were likely still trying to take short positions (based on risk aversion at the time), thinking they were actually trading with the longer-term trend. Moreover, they might have been right, given that upper probability volatility had not begun moving higher... Ultimately, though, bears could not force the pound back underneath the critical expectation pivot (the upper Containment Zone) and by the time shorts figured it out, the jig was up, and they were likely already hemorrhaging a pile of cash.

So here's a few little lessons to write down and put in your trading toolbox: 1. In an ascending trend, if upper probability volatility has not confirmed bullishness, overall price volatility will most likely be very high - at key

2. Ina descending trend, if lower probability volatility has not confirmed bearishness, overall price volatility will most likely be very high - at key pivots. 3. We never have to chase price, so even if we miss (or just chose to sit out) of the first, or second test of a Containment Zone (either from inside, or outside the 1.25 standard deviations), if we're patient, we will almost always have an opportunity to take a position when price pulls back to the Containment Zone, shortly after. 4. If price pulls back to a Containment Zone shortly after breaking OUTSIDE of, and if we have solid reason to believe (meaning we've done our fundamental homework) expectations are still aligned (and relevant in terms of the time frame we are deriving the entry signal from versus the longer-term periods), we can then consider the serious possibility of buying the pullback into the Containment Zone with a stop, just slightly on the opposite side. 5. If the mean or upper/lower containment zone are NOT showing SLOPE, and is traveling laterally, when price attacks the upper or lower containment zone, price volatility will be high. Moreover, when the mean is not showing slope and price is trading near the upper, or lower containment zone, the instance could very likely be quickly followed by mean reversion... For expectations to clearly align, not only must price be trading above or below the 1.2 standard deviation Containment Zone, but the mean and upper/lower containment zones must be showing slope as well. On a common sense basis, a lateral mean tells us short-term price volatility is likely running amuck. 6. Missed money is always better than lost money, and half of the game of being a profitable trader is knowing when NOT to trade, and then having the discipline NOT to trade. Trading just so we feel like we're not missing something, when we're not totally certain about what's happening, is just downright goofy. When price is trading near the upper/lower containment

zone and we do not see slope in the mean or Containment Zone...and there is no readily apparent catalyst surfacing (or about to surface) in markets like news or a fundamental event...it may be a good time to just sit on the sidelines until greater guidance becomes clear. When the distribution is showing lateral slope (and probability volatility is collapsing) we can attempt a mean reversion position, but if price moves outside of the containment zone again, we must have the discipline to close the loser. We're going to wrap up Chapter 7 now, even though there's still plenty left to question in terms of timing shorter-term entries. Before we get to the short-term charts though, we will briefly touch on the concept of Reality Adjustment, which is really two things in one.

The Reality Adjustment is: 1. Actual empirical points of shifting relative period expectations visible on our charts. 2. Probability/paradigm pivot points, which traders must be able to recognize without bias, in order to effectively move with and profit from, expectations aligning and/or uncertainty kicking up, in both shorter and longer-term periods. With all that we've covered thus far, we will now move into Chapter 8, where we will discuss the concept of Reality Adjustment in detail, before then moving on to our actual discussion on short-term entries in Chapter 9.

Chapter 8 |

The Reality Adjustment

In Chapter 8, we will begin to pull the longer-term theoretical underpinning of Macro to Micro into shorter-term periods, by clarifying what the Reality Adjustment is within trading. First, we must again touch on the concept of longer-term expectations (which we have already thoroughly covered) in relation to that of the shorter-term intraday periods, which we will use to time our positions. So far we have seen how a movement back to the longer-term mean, directly alludes to uncertainty within markets, as participants cannot align with a congruent set of expectations to drive an individual stock, index, or larger market higher, or lower...as in a sustained trend. When prices are trading towards the long-term mean (like the 50-period, or 200period means on the monthly chart, for example) market volatility is likely higher than normal. Again, the VIX (market volatility index) shows just what I'm saying, as witnessed in option writers demanding more premium (seen through a higher VIX) for taking on more risk. We have also covered why and how when longer-term uncertainty prevails, price retreats to the mean, where price volatility kicks up, while mean-period volatility moves sideways. However, the longer prices trade near the mean, the greater the likelihood probability volatility eventually collapse, thus allowing the distributions to shed mass, setting up for trending when expectations once again align. Again, in terms of market volatility, the long-term price mean is the outlier, not the exterior of the distribution, where expectations are aligned and volatility (fear) low. What we must understand is that while the overall concept of expectations holds true across the larger Macro to Micro strategy, we must boldly note that short-term expectations are only relative to that of the larger picture. Again, aforementioned may initially seem a little confusing at first, but please keep reading and things should come considerably clearer throughout Chapters 8 and 9.

What traders must remain cognizant of is much like short-term distributions will display greater fluctuation within the larger range (mean period volatility), the occurrence is that of short-term perceptions fluctuating within longer-term aligned expectations, or uncertainty. What we are talking about is a short-term prospect, within a larger set of expectations. Let me clarify even more through a brief example... Let's just imagine for a moment that you like coffee as much as I do... When you often think of coffee, you think of an aromatic, soothing hot drink, with your mind's eye picturing a large pot freshly brewing in the morning. Overall, my longer-term thoughts of coffee are that of great present and future pleasure and satisfaction (expectations). With the aforementioned in mind, now please imagine you're with me one morning...say, last Wednesday perhaps... While on the way to my desk to start the morning we stop by the kitchen, where I pour a couple cups of piping hot Joe. We sit down, fire up the monitors, and as usual begin checking headlines... Mug of fresh steaming coffee to the right of the keyboard, I pick up my Joe and take the first sip of the day... Pssshbbbbtttttt! I spit the coffee out all over my monitor. Headlines dripping, I look at my coffee with a twisted face and then hold the mug up to my nose... Sniffing with a sour expression, I look at you and say, "Be careful, I think there was dish soap still in the bottom of my mug; yours might be bad too." What we are talking about here is a short-term experience within a set of larger expectations. Overall, I will still love coffee, however, over the next few days; I might drink a little less, with the taste of soapy-Joe still in my memory. Even if I do drink as much as I usually do (about eight cups a day), when I pour my first cup in the morning, I'm likely to take my first sip a little more cautiously, even if I checked the mug for soap before pouring. One crappy sip changed my short-term perceptions and execution of the process of drinking coffee, though overall, I still love the stuff. Do you see what I'm saying? Longer-term expectations may be aligned; however, a short-term event can trigger caution in the near-term perception and execution (the reality of now) within longer-term expectations. I expect continued pleasure from coffee over the long haul; and thus, despite the gross soap surprise, will carry on consuming the beverage. However, for a few days after the event, the dish-soap surprise will likely turn on a caution light in my mind when taking the first sip of coffee in the morning. As the weeks wear on, I will likely begin to forget about the java-soap experience, with the caution light growing dimmer and dimmer each morning.

Certainly, I will remember the experience overall, and will likely come back to it in my mind again in the future, however, one bad cup isn't enough to completely change my larger expectations of continued future enjoyment. Say though, each time I take a sip of coffee morning, day, and night for the next two weeks, somehow something gross continually makes its way into my Joe, my overall taste for the beverage might begin to shift... I might begin looking for something else to drink, like a sealed can of soda presenting less unforeseen ‘something else in my cup' risk. Either way, short-term expectations are only relative to my memory of the bad experience...in relation to my personal longer-term expectations still intact, or not. My previous rhetoric is precisely why trading is so difficult... Most retail traders cannot seem (or do not know) to separate short-term perceptions from longer-term expectations within markets, despite the fact that they know there's a difference between a daily and 15-minute chart. I hate to say it, but often, many retail traders have never even looked at a monthly chart at all... In their mind, they think, "Why should I? My trade is on the short-term chart." Right, but the problem is the trade on the short-term chart is also directly relative to the long-term trend, or lack of. What readers are hopefully seeing at this point is precisely why Macro to Micro is so darn effective and important. By enacting macro down analysis, we are actually making a conscious, coherent effort to recognize, and separate longer-term expectations from that of the short-run within trading. Case in point, if you're a short-term trader, and you buy stock in International Business Machines (NYSE: IBM), because you think the stock is going higher over the next week, or so... 1. Do you really care about the long-term value of IBM? 2. Will the long-term value of IBM's stock really have any impact on your larger wealth?

If you're a short-term trader, the honest answers to the above are: No and no. But there are people who do care about the long-term value, like employees of the company, executives with fat stock option plans, retirees who've loaded their account with the stock, fund managers who believe in the company, and even, The Dow Jones Company, given that IBM is one of the most expensive stocks in the price-weighted

index. All of the above participants would like to see IBM travel upward over the long haul. However, should IBM disappoint during earnings, show reduced future earnings expectations (did I just say expectations? You bet!), or fall victim to a larger sector or market metaphoric earthquake, the longer-term participants might discard some, or all of their positions - an act of caution, AKA risk management - even though their longerterm expectations are still hopeful of higher ground. Really though, their short-term expectations (perception) of current events have triggered caution in the larger range. Moreover, short-term traders - without any care, insight to, and/or cognizance of long-term expectations move in and out of the stock with the slightest change in news or trading action. Moreover, those who have no concept or perception of longer-term expectations within the stock, likely often take counter-trend positions, only to be stopped out when those with the money, resources, and motivations (institutions, AKA, those who really care) resume buying or selling. I'm not going to beat a dead horse here... It is just very important for traders to understand...if you cannot perceive, comprehend, and separate short-term expectations (both literally and theoretically) from the longer-term counter parts within markets, you will likely lose over the long haul... Not only that, but the longer-term destructive cycle in your account is likely going to be painful too, as you constantly pound your fist on the table wondering why every time you take a position, the stock, currency, or whatever, always seems to move in the opposite direction. Traders who cannot process the fact that short-term timing requires a Reality Adjustment to effectively move positions in and out of longer-term expectations, will most likely lose. Stepping back for a moment, what we really must understand here is that the concept of Reality Adjustment is more than just a noun; we are talking about a sort of paradigm shift pivot of paradigms...a critical concept within trading itself. Understanding the concept of the Reality Adjustment is a critical underpinning in comprehending why market, price, and probability volatility exist in markets in the first place. The Reality Adjustment is a rusty hinge in markets where traders are forced to separate themselves from the unrealistic expectations of short-term ego-hungry, greedy blind, hypsters, retail public, pundits, and media, while also putting the rubber to the road from an institutional mindset, understanding risk management is the most effective larger trading plan at hand. See, retail traders are guys who coming off one or two winning trades have the

largest ego of the lot (I've seen it a thousand times), but unfortunately, these same megalo-traders really never understood the larger game from the start and so in essence, their winning streak of late is really likely nothing more than just luck. When conditions turn, the megalo-trader is left standing there scratching his head, blaming his losses on "volatility." However, the real problem is/was NOT volatility itself, but rather, the change in market, mean-period, and probability volatility in relation to short and long-term expectations, or uncertainty within markets, something that will always persist, as long as markets remain free trading. Thus, traders must be able to read when and where market, mean-period, and most importantly, probability volatility are changing, while being able to discern what type of volatility is shifting, which in and of itself, boils down to the Reality Adjustment. More specifically, the Reality Adjustment is the exact place where traders are able to clearly identify (on an accurate, relative, real time basis) where short-term perceptions are providing opportunity or risk, in relation to longer-term expectations, or uncertainty. Again, we must understand that the Reality Adjustment is the dividing point where we separate short-term opportunity or risk, from that of long-term expectations, or uncertainty. When expectations align on both a short and long-term basis, we can expect solid trending. However, when expectations are aligned on a longer-term basis, and yet, short-term risk prevails, we can expect a technical pullback, or return to the intraday mean (or even through the mean, if we are viewing a very short-term distribution, like 14-periods), while price and probability volatility kicks up. Moreover, when long-term expectations are uncertain, we can expect market, price, probability, and mean-period volatility to all uptick too. Within the uncertainty, short-term expectations may align, causing a jagged move to the upper, or lower 1.25 standard deviation Containment Zone of the longer-term distribution(s); however, without longer-term expectations aligned, the instance would likely be quickly followed by mean reversion in the lateral slope range. In the previous example, we're talking about sideways chop trading that would likely have most traders scratching their heads, cursing "volatility." In the end, everything really comes down to the Reality Adjustment where longer-term expectations or uncertainty collide with shorter-term perceptions of opportunity, or risk.

Reality Adjustment 1. The paradigm of short-term perceptions of opportunity or risk crash against longer-term aligned expectations or uncertainty... where adept traders are able to separate longer-term fear, or sang-froid prospects of a future increase in value (market volatility), over short-term perceptions of opportunity or risk (probability volatility) on hand within markets. 2. The action of seeking out an understanding of both short and long-term expectations, and then adjusting one's outlook (trading plan) to fit the proper short-term reality (probability, price, and mean-period volatility), within the context of longer-term expectations aligned, or uncertainty (market volatility) prevalent within current trading conditions. 3. The precise area within a trader's timeframe analysis, where one is able to separate and identify short-term opportunity or risk over longer-term expectations, or uncertainty, thus clearly identifying high reward, low risk trades working in tandem on short and long term periods. (More on this in Chapter 9.) At this point, traders should have a clear understanding of the theory that the Reality Adjustment is both an actual zone of physical pivot within trending, or not, while also a required point of mindset shift (from long-term expectations, to that of the short-run) where traders are able to perceive opportunity or risk within the larger range. Please take some time to mull over the above theoretical underpinning of markets and trading... Those who truly take time to understand why and how the instance of longer-term expectations aligned can be identified with price trading outside of the 1.2 standard deviation Containment Zone, while uncertainty can be spotted with prices moving back to the mean, have a chunk of knowledge in their pockets most traders never obtain. We MUST understand though, that price-moving back to the mean on a 5-minute chart does NOT indicate long-term expectations are no longer aligned. Rather, short-term perceptions of RISK are merely outweighing longer-term expectations prevalent within the moment. Longer-term expectations aligned or uncertainty unfolding are vastly DIFFERENT than shorter-term perceptions of

technical/fundamental opportunity and/or risk. Again, what we are talking about is the REALITY ADJUSTMENT of short-term perceptions of opportunity or risk within the larger paradigm of expectations or uncertainty. Finally, when we truly understand the larger paradigm behind the Reality Adjustment required to actively trade markets, we are able to identify longer-term expectations or uncertainty, and then check the relativity of such against shorter-term opportunity or risk, thus identifying what type of volatility is currently dogging markets. At the end of the day, by understanding the Reality Adjustment, we open the door to finding short-term, high probability, low risk trades within longer-term expectations aligned or uncertainty, finally seeing volatility for what it truly is: the collision of short and long-term greed and fear.

Chapter 9 | Short-Term Timing in the Larger Picture

In the beginning of Chapter 9, Short-Term Timing in the Larger Picture, please note we will shift gears away from our previous GBP|USD discussion in Chapter 7. As we begin examining shorter-term timeframes, I will dissect the Dow Jones Industrial Average (INDU) and specifically, International Business Machines (NYSE: IBM), one of the most expensive stocks pulling and pushing the Dow daily. Specifically, we are shifting from the GBP|USD to the DJIA and IBM so traders can see the concepts here work in all markets. I will quickly cover the macro charts just to ensure we're on the same page, before drilling into the shorter-term timeframes where we will identify specific trades. Though we have already covered macro charts, please do not skim over our initial longer-term analysis, as we must fully understand the bigger picture and the short-run reality present to truly digest the importance of the Reality Adjustment. Long-story short, into early summer 2010 the Dow Jones Industrial Average (INDU) is/was in a place of uncertainty, with Wall Street and retail investors both significantly unsure of what the future holds for the economy and markets... Bottom line, given all of the insane events of the Financial Crisis, recent earthquakes, Gulf oil leak debacle, political turmoil (think Greece, immigration in Arizona, and even unrest in Bangkok, Thailand) coupled with elevated national debt amid rising unemployment, uncertainty does indeed persist in markets, at least as of the time of this printing. What's important here is that readers note I did not just point my finger at a particular political party, Corporation, sector, or group; rather, I'm simply noting the events at hand causing fear, while also hindering clarity within markets. Xenophobic political pundits care about ego, opinions, and blame; as a trader though, I care about analyzing data - without bias - affecting markets. Right now, the data shows uncertainty, which the major indices are confirming, as seen in trading action currently

taking place directly at most longer-term means. To find profitability within trading, we must be able to see every situation with clear eyes, separating ourselves from religious, social, and political dogma which almost always, is created by, further manifests, and in the sad end, upholds opinions, not facts. (As a brief side note on politics... Do we all have political opinions and outlooks? You bet. However, we MUST be able to separate ourselves from our political opinions within markets to make money. Several years before the Financial Crisis, I personally thoughtI saw it coming, along with hefty political strife on the horizon... Consequently, I decided not to pen my thoughts in a non-fiction book, but instead wove my outlook into a re-write of George Orwell's 1984, titled 2034, The Corporation - Post 2012. I released the novel in March of 2009, though the manuscript was really completed into late summer of 2008. The point here is even I must separate my political outlook (relaying such previously in novel format), attempting to keep my own beliefs separate from my real-time market outlook. While I am deeply concerned about the constant degradation of information, media and society on the whole (and thus, our personal and political futures, and financial markets on the whole) even I have to separate such on a daily basis, in order to see markets clearly in the here and now.) Stepping off my soapbox, let's now dive into our macro down analysis, ultimately seeing how to use the Reality Adjustment to defeat all types of volatility, allowing us to locate high-reward, low risk trades.

Monthly Chart - Dow Jones Industrial Average Please take a look at Figure 9.1, which shows the same Dow Jones Industrial Average chart we already witnessed in Chapter 6. What we know is tax cuts historically bring the Dow into bullish territory, as the occurrence is/are something all people can understand, helping to align mass expectations of greater prosperity and wealth to

come. Taking a brief moment to list other possible market-positive events all of the greater population can easily understand, such would include:

MACRO

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VOLATILITY

TRADING

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What the above tells us is this: Because expectations are not aligned, expect markets to jump all over the place, until greater guidance (where expectations can again align) surfaces within markets. Just FYI, in July of 2009, I wrote in Volatility Illuminated exactly why and how the Dow Jones Industrial Average had a "sudden" almost miraculous value change in the previous month of June. I also stated thatI believed the “artificial revaluing" of the index (which the public was never

MACRO

TO

MICRO

made aware of by mainstream media) paved the way for a rally into 11,300 to 11,600, fair value at the time. Into summer of 2010, the Dow hit a recent high of 11,257 on May 26 and has now fallen into 10,000 as of these words. I'm not writing this to stroke my own ego... Rather, to show readers that we CAN foresee market movements, even in uncertainty, with common sense fundamentals, a level head and a deep understanding of why what's happening is happening. Moving forward, I expect the 10,300 to 10,800 area to serve as a type of mid-point while the index bounces above and below into summer of 2011, when global accounting standards are to be put into place. The 11,300 to 11,600 area serves as "fair valuation" and as we constantly covered in Macro to Micro, during periods of uncertainty, market, price and probability volatility will prompt swings all around the mean. Thus, when the Dow drops into 5,860 to 8,500, participants who see a "fair valuation" opportunity (based on current and historical valuation - not future perceptions during a time of total uncertainty) will likely buy the index as opportunity surfaces. When the Dow rallies back up to 11,300 and possibly even into the 14,800 area, those who understand the true valuation NOW, while also understanding the index cannot continually move higher without some sort of clear guidance helping to align mass expectations, will sell. What we are talking about is good old market volatility - and jagged volatility at that - which will likely persist for the next 13-months until some of the smoke has cleared and expectations can once again align. As a courtesy, I have reprinted the aforementioned chapter from Volatility Illuminated in this work, which you will find in AppendixA at the end of the book.) Looking at Figure 9.2, we see another amazing coincidence... During the week of May 2, 2010, the Dow rapidly fell, due to a "computer error." My thought is... Really, someone just wanted out...quick. Regardless, the Dow exhibited some noteworthy behavior that Macro to Micro traders should look at and perhaps say to themselves: "That's more than just a coincidence. In the Dow's rapid ‘computer error' drop, the index stalled precisely at the 50-week mean." Seriously. Anyway, the occurrence reaffirms what I am presenting in these pages needs to be taken seriously. Moreover, as we've already seen, in our previous macro analysis of the DJIA on the monthly chart, we know that the Dow is trading just above the 50-period

VOLATILITY

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weekly mean, as uncertainty remains prevalent within the economy and market. Moreover, in Figure 9.1, we also just witnessed the amazing occurrence of the index sitting right at the monthly mean as well. Figure 9.2 | Dow Weekly Chart EDR ce HDILXDI,Weekly

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Again, the weekly chart is telling us slightly shorter term expectations, as measured by the weekly chart, over the monthly chart, are just as uncertain. Moreover, in the current environment, a breach of the weekly mean would present a situation where traders could begin quickly discarding more positions, as uncertainty and risk prevails. A decline into the lower Containment Zone would likely quickly follow... Because we MUST consider both sides of the coin - ALWAYS - we must also note the upper Containment Zone just atop the current weekly bar. Should the Dow move back above the upper Containment Zone, expectations are likely aligning (at least on a

weekly basis) for higher ground. (In the current environment, like right now, I have to tell readers, I just don't see it... The current Administration just doesn't really appear to have the desire, or ability to cut tax rates right now (with the national debt ceiling at all time highs), interest rates really can't go any lower, while at the same time, any proposal of increased stimulus spending would likely be met with serious resistance on Capitol Hill. Seriously, think about it... The type of mass-understandable catalyst that has historically driven markets higher just aren’t really available options right now... Perhaps in the weeks and months after these words things will change, but as of right now (late May, early June 2010) it just doesn't seem feasible that Government even has the wiggle room to provide the type of catalyst required to cause market volatility to taper and hence expectations to align, prompting a sustained bull trend anytime soon. However, we must take a moment to note the slope within Containment Zone probability volatility and the mean, which are both showing upward movement. Given the horrendous dead-cat bounce the DJIA (and broader markets) has seen since lows

in March of 2009, it would make sense that probability volatility and slope volatility are all still traveling upward, even though price has stalled, based on valuation and common sense telling participants uncertainty still resides. The problem is this, as uncertainty resides and market volatility remains slightly elevated, the Dow will likely just experience great price volatility bouncing above and below the mean. However, because Containment Zone probability volatility and the mean are still headed upward, should prices fall through the mean, the instance would cause probability volatility to open up even more. I hope you see that probability volatility can open in two separate situations... 1. Probability volatility can open up when prices initially cease trending and begin reverting to the mean. 2. Probability volatility can open up when prices commence a breakout or breakdown after a sustained period of lateral trading (consolidation) and eventual volatility compression. Should the Dow fall below 10,000, traders would want to look for a probability volatility expansion, thus confirming that we're in for some jagged lateral chop action for some period to come. Hopefully with all of the above in mind, readers are seeing how we are trying to NOT have a direct opinion, but rather, are trying to merely map out the macro possibilities at hand, in an attempt to establish how participants could be digesting the

current economic and market-related events at hand. In the end, our macro analysis will likely serve as a guiding light for near-term trading action, assuming we are diligent, disciplined and opened minded enough to consider both sides of the coin. Honestly, I don't care whether markets go up or down, I just want to ensure I remain open minded enough to enact whatever necessary Reality Adjustment is required if and when needed, substantiated through unbiased analysis of markets. We will now step away from the Dow Jones Industrial Average, looking at an individual stock (IBM), while keeping the Index's larger macro picture in mind. One main point to remember is as our macro analysis has now shown, uncertainty DOES persist in markets - and when uncertainty is prevalent, the first place prices naturally retreat to (and trades erratically around)... is the long-term mean. Taking a look at Figure 9.3, we see International Business Machines (NYSE: IBM) on a monthly basis. While our previous charts of the Dow Jones Industrial Average (INDU) show price trading near the monthly and weekly means (uncertainty within the major indices), IBM is actually showing something quite different...

Monthly Chart - International Business Machines Figure 9.3 | IBM Monthly Chart

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Foremost, from August 2009 onward, the stock rallied boldly above the upper 50month Containment Zone, and stayed there throughout the end of the year and first half of 2010. Why? In many cases, technology is often under-reported within GDP growth, which savvy investors know. Secondly, during times of mass unemployment, one of the greatest solutions to paying for those annoying expensive people (a very sad state of affairs) is to increase productivity, through technology...think increased revenue and earnings expectations. In addition, with massive public and media anticipation of, and company campaign supporting the release of the iPad in April 2010, Apple (Nasdaq: AAPL) has actually helped drive much of the technology sector upward, despite the reality of

economic hardship at hand for most individuals. Finally, with economic conditions having shown troublesome conditions for more companies throughout 2008 and 2009, many significantly stepped up technology spending (both internal technology and Internet advertising), thus prompting first quarter 2010 double digit earnings growth for many tech companies. However, the massive surge in technology spending by public companies may now be slowing, something investors should be aware of.

Breaking it down While we know the larger DJIA is facing trouble on a monthly and weekly basis, the monthly chart of IBM (and larger technology sector) are showing reasonable resilience to greater economic hardship. However, the crazy-train mass increase in technology related spending by many companies may now be slowing slightly, which would taper technology earnings in the third and fourth quarters of 2010. Regardless, with IBM still above the upper 50-month Containment Zone, we cannot take a position short just yet, even though the DJIA is presenting a separate picture of uncertainty and mean-related price volatility still to come. At the same time, with IBM still above the 50-period monthly Containment Zone (while the DJIA and major indices are showing another picture), we cannot take a long position either, even though it appears technology is benefiting during the current economic crisis. Do you see what I'm saying here... We've added the major indices into the picture within our macro down analysis as an added filter thus keeping us from making a bum decision were we to only look at a specific stock, or sector. What I am saying here is Macro to Micro is more than just looking at the monthly, weekly daily (and so forth) charts of a specific stock, currency, commodity, but rather, also involves taking the time to look at the larger picture within markets as well.

Weekly Chart - International Business Machines Figure 9.4 | IBM Weekly Chart

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50-Week

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31 May 2009

20Sep 2009

10 Jan 2010

59,60

Figure 9.4 shows a weekly chart of IBM. I would like readers to note that I have added in a few things... Foremost, I have clearly noted the 50-week mean and 1.25 upper and lower Containment Zones as we've already seen, however, I have also added in the 2.2 and 3.2 standard deviations (probability volatility) as well. Furthermore, I have also added in the 200-week mean and 1.25 standard deviation upper and lower Containment Zones as well. Why? In Macro to Micro, we are not only looking at macro markets (think back to our DJIA analysis), but we are also looking at the relevant macro distributions on both short and long-term time frames too. What's more, by keeping an eye on both the 50-

period and 200-period distributions (on all time frames) we are also watching the most common distributions, which larger players are also likely noting as well... With such in mind, I always watch the 50 and 200-period distributions, taking note of the means and Containment Zones. I want to know where price is in relation to the 200-period mean AND the 50-period mean at all times, on all time frames... Readers will immediately notice a few huge events taking place on the weekly chart, which the monthly did not show... Foremost, while IBM showed price action above the upper 50-period monthly Containment Zone since August of 2009, the stock failed the upper 50-period weekly Containment Zone in the third week of January 2010. Moreover, in the week of April 11 and the week of April 18, IBM attempted to push back above (outside) the 50-period weekly Containment Zone, but failed... Red flag?

You had better damn well believe it... Furthermore, IBM tested support of the 200-period weekly upper Containment Zone in the final week of January 2010 and held ground... With the aforementioned in mind, it is important to understand that while the weekly 50-period was showing weakness, monthly 50-period and weekly 200-period expectations were still aligned, thus indicating technology buffs were not ready to concede any uncertainty from the broader economy might be leaking into the technology sector. Hence, IBM remained above the 200-period weekly upper Containment Zone, confirming what we already saw on the monthly chart, where longer-term technology expectations had not buckled, despite the DJIA trading near the 50-period weekly and monthly means. Regardless, the fact that IBM is now trading below the 50-week upper Containment Zone (at the time of this printing) indicates shorter-term weekly expectations (over massive monthly counterparts) are beginning to shift from alignment to uncertainty. Unless some type of bullish fundamental, or news related catalyst large shows up within markets to buoy potentially overly optimistic technology expectations against the larger riptide of uncertainly still prevalent within the economy and major indices, another test of the 200-period weekly Containment Zone could soon be in the cards for IBM...

Finally, I have noted something else quite critical in Figure 9.5... While the weekly and monthly Containment Zones and means are clearly showing ascending slope, the 3.2 and 2.2 standard deviations (probability volatility) are showing something amazingly different... Fact is, 50-week volatility is collapsing, alluding to lateral volatility-risk upticking, alerting us to the fact that uncertainty may be finally making its way into technology, and thus, indicating longer-term mean reversion could very

likely be on the way...

Breaking it Down We must not only watch macro indices when enacting our analysis, but must also keep a close eye on multiple larger distributions within both broader markets and individual instruments we seek to trade, as well. Figure 9.5 | IBM Weekly Chart Noted Rl

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ag 129.57 131.97 128,71 128.86

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IBM has fallen below 50-week upper Containment Zone... Uncertainty starting to show...

Volatility

collapsing... Trending unlikely...

In addition, as the weekly chart of IBM shows (contradicting the monthly picture), expectations are beginning to stumble, as noted in the stock failing the upper 50-week Containment Zone. However, without failure of the 200-period weekly Containment Zone, or the 50-period monthly Containment Zone, we can NOT blindly take a short, or long position just yet. However, should IBM fail the upper 200-period weekly Containment Zone too, the 50-period weekly mean (and even the 200-period weekly mean) could quickly come into play. Moreover, as we observed through 2.2 and 3.2 probability volatility, the standard deviations are collapsing, thus indicating lateral price volatility likely on the way near the upper Containment Zones, before a volatility spike leading price action lower into the long-term means. Again, as we learned in Chapter 4, collapsing probability volatility is often indicative of lateral choppy conditions, or consolidation - after a period of expanding volatility. In addition, we know probability volatility expands both when an established trend begins to shift and when a trends commences, or continues after a period of consolidation. Conversely, probability volatility eventually collapses in the in final stages of BOTH trending and lateral trading, a point we must be able to discern. Finally and this is VERY IMPORTANT, notice what I'm not doing is looking at the 2.2 and 3.2 standard deviations as REVERSAL POINTS, which is what is traditionally taught in markets... In fact, I'm doing something completely different; I'm using the standard deviations as an indication of probability volatility, AKA trending, or potential lateral action to come.

Daily Chart | International Business Machines Should IBM fail the 200-week upper Containment Zone, the 50-week mean (and then the 200-week mean) could quickly follow, especially if macro indices confirm the move, which would likely be noted through a Dow failure of 10,000.

TM 8Feb 2009

on AT 31 May 2009 20Sep 2009

10 Jan 2010

59,60

As a quick inside note, in our IBM example here, the daily chart is where the bulk of the information we've covered thus far, becomes pretty exciting... However, this does not mean that we can ignore the weekly or monthly charts in our analysis... It's really just random luck that in our example of IBM, the real meat of the situation is resting within the daily chart. In other instances, the information we're looking for might reside in the weekly, monthly, 4-hour (and so on) periods, and thus, we cannot afford to overlook any of the aforementioned. Looking at the daily chart (Figure 9.6), traders will notice significantly new information, which was not available on the weekly and monthly charts...

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MICRO

At this point... we're far enough along that readers should be starting to understand the larger paradigm behind Macro to Micro Volatility Trading, and should have solid understanding of the main concepts at hand... With such in mind, I'm going to cut some of the chit-chat and just number off what we're seeing on the charts, though I will provide detailed explanations as needed...

Figure 9.6 | IBM Daily Chart be #IBM,Daily 130.42 130.60 128.84 128.86

} 143.20

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-Day mean would

3 Mar 2010

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19 Apr 2010

The daily chart shows: 1. 200-period probability volatility as seen through the upper and lower Containment Zones is compressing, indicating lateral trading likely still on the way. Readers should note that while the chart does not show it, the downturn in upper 200-day Containment Zone probability volatility is the first of such that appeared in IBM since June of 2009. What I'm saying is

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TRADING

that by watching probability volatility slope of the Containment Zones, we would have noticed bullish chutzpah began fading all the way back in January of 2010. Again, compressing probability volatility indicates an uptick in uncertainty (or elevated risk in the absence of collective expectations aligned), thus prompting lateral trading. 2. On a daily chart basis, when IBM ceased trending upward in January and February of 2010, shorter-term probability volatility (50-period probability volatility, versus 200-period) spiked outward, confirming what we already know... Just like short-term probability volatility spikes when trending commences after consolidation, short-term probability volatility also initially spikes when a trend comes to an end. As IBM failed to put in new highs while trading underneath the 200-period upper Containment Zone (lateral action), probability volatility has concurrently been collapsing. Again, IBM failed the upper 200-period Containment Zone in April of 2010, while also showing consecutive closes below the 50-period mean in January 2010, for the first time since July of 2009. 3. Compressing probability volatility, confirmed lateral trading action. the same time, price tagging the lower 50-period Containment Zone in January of 2010, confirmed that while monthly expectations remained aligned (as noted in price remaining above the 50-period monthly Containment Zone), shorter-term expectations (measured through the chart) were no longer aligned and participants with less concern about longer-term value of the stock, were likely capitalizing on shorter-term volatility.

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daily the price

110,00

4. If we had been watching the stock back in January and February of 2010, a breach of the 200-period daily mean on the downside would have been proof of the pudding that expectations were becoming even less aligned, and prices would likely revert to the longer-term 200-period daily mean. 5. As an important note, a breach of the 50-period daily mean (and even the 200-period daily mean) does NOT mean all-out expectations have COMPLETELY CHANGED for a massive bear reversal. However, we can assume shorter-term expectations are uncertain and thus we begin looking

MACRO

for longer-term mean reversion to ensue... HOWEVER,

TO

MICRO

ONLY WHEN

PRICES FINALLY FALL BELOW THE UPPER CONTAINMENT ZONE (in the case of an ascending trend and vice versa for bearish action) OF THE WEEKLY AND MONTHLY CHARTS, CAN WE FINALLY INFER EXPECTATIONS ARE NO LONGER ALIGNED AND UNCERTAINTY RELATED MEAN REVERSION IS SIGNIFICANTLY PROBABLE. 6. The closer price comes to longer-term daily, weekly and monthly means, the more we can infer expectations are shifting from alignment to uncertainty. 7. Should prices break back above the upper 200-period and 50-period Containment Zones we must be prepared to - AND WILLING TO - change our bias to move with markets... We must be able to honestly ask ourselves if expectations have changed within markets? However, the occurrence of price showing consecutive closes above the 50-period and 200-period upper Containment Zone, would likely be supported with some sort of EASILY UNDERSTANDABLE, CLEAR economic, industry, or stock specific news surfacing. If the INDU were to recover back above the upper 50-period Containment Zone on the weekly chart (Figure 9.3. above), we must attempt

to discern whether the occurrence is a short-term price volatility spike, or an all-out larger shift in market-volatility expectations? Again, if expectations were aligning for higher ground, we would likely be able to easily spot such through some sort of large-scale news event that the masses can understand, like a tax cut, lower interest rates, or some other big whopper stock-specific news, the bulk of the people can comprehend. 8. If price were to tag the upper or lower 3.2 standard deviation (probability volatility), we would NOT instantly believe a reversal is imminent. Rather, we would think, "Perhaps the subset distribution really wants to move up, or down?" We would then search for readily identifiable news prompting such, while also looking for a quick retracement back to the exterior side of the upper or lower Containment Zone, which if held, would indicate expectations were indeed aligned for a shorter-term trend to continue. Without any clear news or fundamental guidance prompting higher or lower ground, a tag of the 3.2 standard deviation on the upper or lower end, would likely indicate price volatility within the larger probability volatility range,

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TRADING

and thus we could think about taking a mean-reversion reversal position. However, such would also likely be confirmed with the 200-period daily mean showing flat slope. g. In the case of our example here, because IBM is presently trading above the 50-period daily mean, while also above the 200-period weekly Containment Zone, we cannot just blindly dive into a long or short-position just yet, at least, not with the information we have thus far...

Breaking it Down The weekly and daily charts are starting to show some potentially leading information over the monthly chart... However, we have not found any concrete information leading us into a long, or short position just yet. Furthermore, by blindly jumping into a position without truly completing our macro down analysis, we are merely ‘hoping’ instead of really laying out a proper plan (ahead of time) with very clear money management (risk management) parameters to ensure our long-term success. However, the daily chart is now providing greater information indicating long-term expectations may be falling apart, at least in the near-term, alluding to uncertainty potentially surfacing in the technology sector. Again though, should price fail the weekly 200-period upper Containment Zone, we would have greater confirmation that expectations are truly shifting from alignment to uncertainty. However, we would NOT infer such as an all out reversal, but instead, high-probability of long-term mean reversion, at least UNTIL more data surfaces giving us greater guidance into the situation. Regardless, we can make money on longer-term mean reversion, if we are truly able to separate ourselves from the common market dogma that there are really only two directions to chose from within trading: up or down. Markets are NOT an all or none game, meaning a stock, currency, commodity, or index must always be moving up or down - sideways is just as real. Currencies, stocks and indices pullback when uncertainty becomes muddled, which really indicates risk management en route by savvy players. A pullback is a pullback is a pullback... Finally and most important - We must take time to examine BOTH SIDES OF THE COIN. Only looking at one set of options and then developing rigid, ego-attached, directional opinions (so we can prove to our investing-club we're the best stock pickers

in the room) is just plain stupid. Remember, do you want to be the smartest person in the room, or the most profitable person in the room? If we want to be the most profitable person in the room, we must be able to change our outlook, as the data changes. If you want to be the smartest person in the room (though 99.9% of the time the LEAST profitable), get a few MBA's, and perhaps a PhD or two, and become a media anchor, or an economist.

4-Hour Chart | International Business Machines Our 4-hour chart has some even more amazing information that I hope traders find as useful as I do. By the way, as I write this, I did not 'cherry pick' these charts from a large group of other charts... Rather, I just basically randomly picked one stock; which I believe shows Macro to Micro Volatility Trading is the real deal in multiple markets... Anyway, a quick note, I would like to reiterate one more time that part of being a good trader is knowing when to trade and when to sit out. For so many traders, they feel like if they are not in a trade, they are wasting potential opportunity, but the truth of the situation is...sitting on the sidelines until awesome opportunity is abundant is the best trading plan of all. Missed money is ALWAYS better then lost money, which is why we do Macro to Micro Volatility Analysis - always - ahead of time...so we're not just ‘chasing indicators' and markets like the rest of the herd... We want to make sure we have a solid trading plan in place before events occur... Even then, if we miss an opportunity, we must have the discipline to sit on the sidelines until another moment of high reward probability / low risk entry surfaces. We NEVER chase price, or markets. Doing so is the worst of all trading plans, as we will not only continually feel exhausted and tired from fighting losing positions in pullbacks, but also fighting off the negative emotions that come with continued losses as well. A well-executed small loss is a must better than a sloppy, chase your tail win, as continued reliance on the latter is really just setting ourselves up for big losses in the future, when the position isn't quite as lucky. By exiting markets for a wellexecuted small loss, at least we are sticking to the larger trading plan and thus setting ourselves up for a profitable future, when big winners do

surface. If we chase markets without a plan, occasionally pulling out sloppy ‘get lucky' little wins, when markets really move against us - and heaven forbid - we break our rules, the resulting damage will likely be a whopper. Remember, little loss, little loss, little loss, HUGE win. When we trade with probability, we are probing markets with an understanding of multiple types of volatility, not because we have hunch, or opinion, or signal from a bogus non-empirically justifiable indicator that we cannot break from, all too often because our ego is attached. The above said, we now move into the 4-hour chart, where we will really begin to drill into whether opportunity is at hand, or not. Furthermore, in the hourly chart (next) we will specifically see where the REALITY ADJUSTMENT comes into play. While the Reality Adjustment is evident on longer-term time frames, I have not really covered it in detail... My reasoning for doing so is I simply don't want to inundate you with too much (already) mindboggling information, which directly challenges many flawed paradigms we've been taught as truth within markets, all these years... Furthermore, I would like for traders to first see where the Reality Adjustment is most transparent, before potentially incorrectly assuming such within longer-term time frames. Again though, until we get to the hourly chart, please keep the concept of the Reality Adjustment in the back of your mind, especially as we pour through the 4-hour chart. Here's what the 4-hour chart in Figure 9.7 (below) is showing:

1. On January 20 2010, IBM failed the upper 200-period 4-hour Containment Zone, indicating shorter-term expectations than that of the daily, weekly and monthly charts (within the recent bull market) were also starting to shift to uncertainty. 2. Since late January, price traded down towards the lower Containment Zone of the 200-period 4-hour distribution, but did NOT trade below, indicating that expectations had NOT yet fully aligned negatively in 2010. The instance let us know uncertainty (on a 4-hour basis) had begun to significantly surface, which was noted with price running downward though the 200-period 4-hour mean to the lower Containment Zone... Again though, the instance of price attacking the lower Containment Zone, but not falling through, indicated 4-hour expectations had shifted to uncertainty,

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but not all the way to expectations aligned for lower ground (a larger pullback) within IBM, or technology. After falling through the mean (one more time: indicating uncertainty) in the final days or January, IBM stabilized above the lower 200 1.25 Containment Zone. In addition, at the same time, IBM had fallen below both the 50-period 4-hour distribution lower Containment Zone, and 2.2-probability volatility, but did NOT trade at, or outside the 3.2 standard deviation (probability volatility). Moreover, as we already know...because the mean is mobile and side effect of squaring the deviations in the formula for standard deviation (dynamic distributions where we not only see period-mean volatility, but probability volatility, as well), price rarely breaches the third standard deviation. Furthermore, when price does breach the 3.2 standard deviation (probability volatility), the occurrence is generally only for a short period... Finally, when prices make jagged moves across the distribution, the instance creates a probability volatility expansionary environment, which in and of itself is why shorterterm probability volatility spikes both when a sustained trend ends, and when a new trend (coming out of consolidation) begins. The initial occurrence of price traversing the entire short-term distribution, after a sustained trend, pushes the standard deviations (read: wingspan of the distribution, or probability volatility) further apart, not closer together. 3. When price touched 4-hour 3.2 lower standard deviation in late January, early February, it would have been foolish of us to assume an all out bullreversal looming, just because price touched the lower 3.2 standard deviation. However, in this particular case, we would have wanted to remind ourselves that prices were in an overall uptrend and thus, had been checking to see if the lower 200-period 4-hour Containment Zone held prices...

We would have done so in an effort to seek out greater information letting us know longer-term 200-period 4-hour expectations had not just shifted from bullish expectations aligned to uncertainty, but rather, all the way to shorter-term bearish expectations aligned for a deeper, steeper pullback. Our question was whether the downward pop was really risk-management related price-volatility triggered by short-term profit taking, or an indication of larger sentiment shifting within IBM and technology.

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Figure 9.7 | IBM 4-Hour Chart #IBM,H4

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17 Dec 20:30

13 Jan 20:30

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24Nov 2009

period 4-hour and 50-period 4-hour

2 Mar 16:30

Furthermore, when lower 50-period 4-hour Containment Zone did not hold price OUTSIDE the expectations benchmark (at the same time as 50-period 4-hour 3.2 probability-volatility clearly began collapsing), we would have said to ourselves, "The spike in 50-period probability-volatility (on both sides of the distribution) was because the longer-term trend has likely paused, or come to an end, and thus, now prices will likely trade erratically around the mean, while 4-hour probability collapses, thus ultimately shedding mass of the distribution, setting up for an eventual spike in probability-volatility, which would in-turn, signal prices are about to begin trending. As price traded back above the lower 50-period 4-hour Containment Zone, while also directly - and clearly - doing so just as probability volatility began collapsing, shorter-term traders could have inferred mean reversion related price-volatility (lateral trading) was about

119.95

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to ensue and then taken advantage of the situation. 4. Right on the money, mean reversion did show, as priced tagged the 50period 4-hour mean on February 17. When prices tagged the 50-period mean, we might have wanted to consider the possibility of a swing-trade short entry (with the assumption that the bounce was a 'reload' opportunity) to trade with the relevant short-term down trend. However, given the larger longer-term trend was still up, while price was still trading above the 200period weekly Containment Zone, the information contradicted, and thus, the best action would have been to sit out, if no other guidance was available. However, we did have a few other insights... By the time IBM traded above the 4-hour 50-period mean, the stock had also moved back above the 200-period 4-hour mean as well, likely tempting bulls back into the stock again. However, as we have learned, expectations are not aligned until price trades outside the 1.25 standard deviation Containment Zones, relative to a common sense sniff test of all the time frames we've set out to analyze in Macro to Micro. So why not just consider the bull market still in place? Because volatility of the 50-period distribution was still collapsing... However, bulls would have wanted to take note of the fact that by the time price made it back above the 50-period and 200-period 4-hour means, both distributions means and Containment Zones were still showing ascending slope, thus telling us that while 50-period 3.2 probability volatility was still collapsing, the event was likely the distribution shedding mass, in order to attempt another push higher, should prices actually trade back above the 200-period 4-hour upper Containment Zone. Until price was solidly above the upper 200-period 4-hour Containment Zone WITH the mean and/or Containment Zone still showing ascending slope, the situation was really telling us the upward rebound was really just a technical snapback within a larger consolidation range. Moreover, when considering the COMMON SENSE fact that the major indices had been on a tear upward since bottoming in March of 2009, savvy traders (who trade up, down or sideways) would have asked themselves whether risk-averse bulls who'd been along for the larger ride since 2008, were likely thinking about

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taking profit? Specifically, the Dow rallied upward from a low of 6,470 in March of 2009 to almost 10,700 in January of 2010. We're talking about a nearly 61% rally from the low, in just about nine months... Nothing goes straight up, or down in markets, and given the massive deadcat bounce within the major indices since March 2009 (while the economy still struggles, mind you) was indicating that a pause (AKA technical pullback) was likely long overdue. Then, the Dow Jones Industrial Average tagged the upper 50-period 4-hour Containment Zone on February 22, quickly pulling back to the mean, and then subsequently rallying back above the 50-period 4-hour 1.25 standard deviation upper Containment Zone, which it walked up all the way into the high of 11,257 on April 26. When price traded above the upper 200-period 4-hour Containment Zone (while still above the 50-period 4-hour Containment Zone) around March 16, 2010, the instance would have been a clear signal to traders that expectations were clearly aligned for higher ground in the index.

Figure 9.8 | Dow Jones Industrial Average 4-Hour Chart

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#DILXDI,H4

TO

MICRO

Dow walks up upper 50-period

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Zone all the way to highs...

Notice

| f| Dow trades through mean, tags upper Containment Zone, falls back to mean, jumps back up to Containment Zone, before heading higher.

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Price moves above 50-period AND 200period 4-hour upper

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Containment Zones Expectations re-align temporarily...

9546.00 9368.40

However, IBM was not showing the same situation at all, with price still below the 4-hour 200-period upper Containment Zone, while the Dow was doing something quite different. Savvy traders would have known to just sit out of IBM, until the stock showed significant consecutive closes above the upper 200-period 1.25 standard deviation probability volatility Containment Zone, while the bullish trend in the Dow remained intact. Low and behold, the Dow finally exhausted the indexes last relative hurrah, failing the upper 50-period Containment Zone and retracing the mean in late April. During the same period, while IBM did attempt a volatility pop above the 50-period upper Containment Zone, the 200-period mean NEVER showed the same ascending slope as the Dow, a HUGE red flag for technology bulls. And then of course, the ever-mysterious institutional "mistake" showed up creating a nearly 1,000 point downward range within ONE session...

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Of important note, take a look how the mysterious "computer error" within the Dow that triggered a massive dive on May 6, 2010 also coincidently shows up just five 4-hour bars after the index falls below the 50-period 4-hour mean and the 200-period 4-hour upper Containment Zone...for the first time since late February and mid-March respectively.

10780,68 10780,68 10556,60 10582.60

Dow fails upper Containment Zone and mean, heading towards 200-period lower Containment, but holds ground... Expectations not aligned for lower prices in larger index...

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Whether the accidental extra zeros atop an order for 5,000, 50,000, or however many shares were keyed in, were accidental or not, the data shows an institutional trader was placing a program sell only a few bars after the Dow breached significant points of statistical probability. 5. Working back into the 4-hour chart of IBM, traders will want to notice that in the current moment (as of this writing anyway) the stock and major indices were still in a bull trend, however, the situation looks to be changing... What I'm saying is this (as of the time of this writing), even though the Dow is technically still in a bull trend, we can assume that should the index's or IBM's 200-period 4-hour mean give way, a pop down into the lower 200-period 4-hour Containment Zone will likely surface quickly than a Wall Street banker on a free lunch (which is pretty darn quick!) Moreover, a failure of the 200-period mean would also show clear failure of the lower 50-period 4-hour Containment Zone in both IBM and the Dow, which would reaffirm uncertainty prevalent in the current environment.

6. Probability volatility, as noted in IBM's 200-period Containment Zone, is traveling laterally, and is trading near a ‘breaking point' where price and the upper 200-period 4-hour Containment Zone really needs to move above the $131 area, for the distribution to continue upward with ease. However, given price is not above the upper 200-period 4-hour Containment Zone, how likely is a price volatility breakout right now? Not very likely. 7. We still must remain cognizant of the upper 200-period and 4-hour

Containment Zones, as should some sort of catalyst within markets surface (meaning a larger legislative change, tax cut, or interest rate slash, which aren't likely going to happen anytime soon) pushing IBM and Dow above the aforementioned Containment Zones, we would need to both take notice, and seriously consider higher ground to come for the stock in the near-term. I would also like to note that earnings can have a great upward effect on stocks, as you likely know... However, sometimes the earnings rally can come from another highly correlated competitor... Like if another Tier I tech Large Cap stock reported huge earnings or sudden - out of the blue - big news (think mergers, and secondary offering, split announcement, dividend increase, or something similar), other stocks in the sector can move in sympathy. The aforementioned is called Macro to Micro Tier Trading and is another book, but I think you probably get the general idea... One thing though, this isn't earnings season... In the recent round of first quarter announcements, many companies and analysts were skeptical of continued blistering technology spending by the public and private sector. One final note, in my above note about mergers and/or secondary offerings, the instance of such generally translates to cash outlay by the company effecting the offering, or acquirer in a merger, which translate to near-term bearish action until the merger is complete and is showing cash flow... Or the offering is closed and the company is showing solid earnings growth, exceeding the increased saturation of EPS (Earnings Per Share) due to more shares outstanding overall.

Breaking it Down IBM is resting atop a critical pivot (the 200-period 4-hour mean) while the larger major indices have been showing strength lately (assuming 'pre-computer error prices’ that triggered the nearly 1,000 point downward range in the Dow on May 6.) All food for thought, while we also note that a breach of the 4-hour 200-period mean would bring IBM underneath the 50-period 4-hour lower Containment Zone as well, indicating shorter-term expectations are exceeding uncertainty, and may actually be aligning for a deeper, steeper pullback than most are currently considering. Moreover, because IBM is one of the most expensive stocks in the price weighted Dow Jones Industrial Average; a breach of the lower 50-period 4-hour Containment Zone

and 200-period 4-hour mean (and possible subsequent selling) would weigh heavily on the aforementioned index, which in turn would weigh on IBM. Do you see the vicious cycle? One last point... Even with the Dow just atop 10,000, the index is still up over 60 percent in the past 14-months, which is one heck of a haul for just over a year's worth of trading action. Moreover, in the same period, IBM was up nearly 50 percent... Given the uncertainty currently prevalent within markets...if you were up nearly 50% over the past year and suddenly things looked a little shaky within markets, would you start considering whether it's time to take profit? Yea, some other peeps probably are too. Just a little more food for thought. Hang around trading floors for too many years and you eventually pick up a pile of seemingly worthless market clichés from the older guys... The thing though, is clichés generally exist for a reason... While they seem trite, they're often true. Anyway, one such cliché that just came to mind is: People don't have to buy, but they do have to sell sometime... Even more reason why market and price volatility kick up when things turn south, over when the masses are content... When the thought of losing principal or giving back gains hits most investor's hearts, they often hit their sell buttons equally as fast.

Hourly Chart | International Business Machines At this point, we've covered the broader market through the Dow Jones Industrial Average, while also take a look at the monthly, weekly, daily, and 4-hour charts of IBM. It is here - at the hourly chart - where we hit a critical juncture in our analysis, where we will find greater guidance into potential trades looming within IBM, while also literally seeing the Reality Adjustment in action. I cannot reiterate enough, how important really taking some time to think about the larger paradigm behind the Reality Adjustment is, as what I'm about to describe is literally one of the main hurdles most traders are never able to overcome, partially because while they may intuitively sense the Reality Adjustment exists, most have never really identified such within the time frames they trade from. In understanding the Reality Adjustment we must be clear that we cannot simply flip through one set of time frames (like just 15-minute charts), expecting to find all necessary information about a potential trade on the horizon. I cannot tell you how many Webinars I've given where a participant asks/states,

"Hey, can you look at a trade I just made that didn't work out... So I was in the Yada Yada and everything you talk about was aligned on the 15-minute chart... The Yada Yada was trading above the Containment Zone, WAVE PM (Whistler Active Volatility Energy Price Mass - Volatility Illuminated) was showing mass compressed, while at the same time, I was on the right side of the longer-term mean, indicating thatI was trading with the trend... And still, I lost?!" And I inquire, "Where was the currency/stock within the monthly, weekly, daily, 4-hour, and hourly time frames?" At this point, there's generally always a pause, before the participant replies, "I was just trading off the 15-minute chart; it was a short-term trade, not a longer-term position." Seriously, what I've just mentioned drives me absolutely nuts... If you want to get my goat, show up in one of my Webinars and run through the above string... The thing is though, it feels like no matter how much I pound the table a Macro to Micro analysis and the importance of, there's always a lazy trader out there, who just refuses to take five extra minutes to really do their homework... At the point where the above question/statement surfaces, I usually ask just for kicks, "What was the economic news released that day?" If the guy was too lazy to look through four extra charts, you know for sure he sure as hell didn't bother to take three minutes to look at an economic calendar. But for some reason, time and time again, the same question/statement comes up. I'm through pondering why retail traders are so damn lazy, however, whatI do want to state about the whole mess is this: If we do not take the time to walk down through Macro to Micro time frames, not only are we just plain foolish, but sadly, we will never know how empowering it feels to finally be able to not only comprehend, but also spot the specific point on our charts, where we say, "That's it! That's the Reality Adjustment pivot." The amazing thing about the Reality Adjustment is that without a proper understanding of whether expectations are aligned, or uncertainty resides within longer-term timeframes, we really have no true 'reference point' to identify what type of volatility is currently dogging markets. While we can pick out possible important pivots, just by looking at a 15-minute chart, without going through macro down, the pivots we've identified are really only relative to our ability to manage risk in situations where we only have about a quarter of the information other, more

dedicated traders, have taken the time to uncover. See what I'm saying here? While we certainly can find trades on a 15-minute chart alone, we're likely missing a significant portion of the information the guy taking the other side of our trade, has taken the time to unearth. By the way, the same goes for sheer laziness and/or common sense in our lack of understanding why and how fundamentals are so important... I don't want to get too far off track, but just please make a mental note that if we truly hope to understand volatility, and then profit from such within markets, we must do ALL of our homework. If you were learning to fly a plane, it sure wouldn't be a good idea to only learn how to take off, while skipping over the ‘landing part' right? I'm serious, this is exactly what retail traders do every day... Moving on... Looking at the below hourly chart of IBM, we see some very new and interesting information unfolding. Foremost, traders will notice that on an hourly basis, the stock was trading just underneath the 200-period hourly mean. Moreover, IBM recently failed the 50-period hour mean, and is now resting just underneath (outside) the 50-period hourly lower Containment Zone as well. Are you thinking subset distribution potentially on the move, as short-term (intraday) expectations are shifting from uncertainty to alignment for lower future ground? Perhaps you shouldn't be, at least not yet anyway... Here's why, when we take a step back from the situation, yes the micro charts are showing weakness, however, IBM has not fully failed the upper 1.25 Containment Zone on the monthly chart yet... The fact that the stock is still trading above the upper monthly Containment Zone is a huge red flag for presumptuous traders who often jump in and out of currencies and stocks, based on nothing more than a 15-minute chart. See, by having done our macro analysis, we know that while the stock DOES look week on the daily, 4-hour, and hourly charts (and is perhaps on the eve of a large pullback) we MUST continue to keep the monthly chart in mind. Why? Because at the end of the day, if we were to blindly short IBM here, we would be taking a position against the larger trend. So what do we do? We do nothing yet... Except continue on with our analysis to see if we can unearth more guidance. Figure 9.9 | IBM Hourly Chart

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TO

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I want to make one point very clear though... We are not sitting on the sidelines just because the monthly chart is still bull confirmed... We're sitting on the sidelines, because the hourly chart is showing compressing probability volatility (as seen through the 50-period hourly 3.2 standard deviations (probability volatility) headed inward towards the mean). In addition, one quick glance at the 200-period hourly 1.25 Containment Zones (and mean) shows the distribution traveling exactly sideways. Sideways means uncertainty means price volatility means sideways means likely consolidation means expectations not aligned means sideways. Dig? Until the hourly chart is clearly showing slope within the 50-hour, or 200-hour distributions, taking a position at - or near - the mean(s), while both means are

traveling sideways is basically the same as flipping a coin. To provide more guidance, what I've done is drawn two areas of noteworthy importance, which I have labeled the Reality Adjustment Pivots. Upon first glance, it might look as if I placed the Reality Adjustments right at the

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TRADING

third standard deviations... First glances are deceiving though... In reality, the lower Reality Adjustment Pivot is right about where the upper 50period weekly and monthly Containment Zones are presently resting... Strange coincidence how 50-period hourly 3.2 probability volatility lines up almost exactly in the same place... See, traders who don't understand the movement of subset distributions in markets would likely only look at the hourly chart - and not the monthly and weekly charts and thus, if price tagged the lower 3.2 standard deviation, the not-so-volatility-savuvy trader would be thinking, "Ah! My reversal entry!" However, I would be thinking, great IBM just tagged the lower 3.2 standard deviation, indicating the distribution is trying move downward below major pivot points on the weekly and monthly charts... I know there is a 99.7% probability that all the data will lie within three standard deviations of the mean and thus, price will likely travel sideways for a few more bars, or bounce ever so slightly, allowing lower probability volatility a chance to open up, so prices can then really get moving to the downside. I would also be thinking, retail traders are looking for a reversal here, so they're buying long... Their short-term orderflow will trigger just the precise sideways or slight bounce I need, to fill at a slightly higher price short, to minimize risk... In essence, I'm selling into their "reversal mentality" orderflow. Here's another look at the hourly chart, though with just a few changes in my notes...

Figure 9.10 | IBM Hourly Chart

Collapsing volatility DIRECTLY trading to come... The more volatility compresses, the larger the likely move to ensue... UNCERTAINTY creates collapsing volatility and MEAN reversion...

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Guess what market makers and institutional guys do to? Right-o. Sadly, retail traders who do not take time to learn about volatility within markets are almost always just pawns in a larger game... No more though right? Right-o-again. Thus, a slight bounce off the lower 3.2 standard deviation would likely provide a great short-entry just below the lower 50-hour Containment Zone, which would also be a great risk-averse entry point, as if price were to move back above the lower 50-hour Containment Zone, I could infer expectations are not yet aligned for a larger move downward and cut my position with the slightest loss, while waiting for more guidance in the situation. However, I would likely also have confirmation of whether the distribution is really about to move, as the 3.2 standard deviations would be spiking outward, foreshadowing a larger price move about to occur, while the 50-hour 1.25 distribution would likely be showing the first signs of downward slope. If the distribution were to remain totally sideways, I would too, changing my bias from 'expectations aligning’ to 'uncertainty prevails' and then just put on my ‘trade

lateral mean reversion’ all day long. The thing is though, when price hits the Reality Adjustment, I better be with it. What I mean is, if I'm sitting there trading lateral chop mean reversion and price strikes the Reality Adjustment, I'd be living in Ia la land if I did not take notice that expectations were potentially aligning and start to alter my perceptions too. One area where traders TOTALLY get killed in markets - is right after they've just kicked butt in a steamy up, or down trend... Just off the win-high, they're ready to keep trading... But the thing is when prices fall back into the Containment Zone - and if slope starts to fade - those same traders likely keep trying to do the same thing they just were - which worked out pretty well. However, they likely haven't located the Reality Adjustment pivot on their charts referenced to both long and short-term timeframes - and thus, while they were just winning right and left, and are now thinking they've ‘got it the game licked, what we're talking about is just about the precise time where they start getting killed, taking position after position against the trend (even though they really believe they are trading with the trend), while also getting stopped out at precise highs and lows. Fact is, when we cannot locate Macro to Micro Reality Adjustment pivots - THROUGH UNBIASED ANALYSIS - we're just chasing breakouts, breakdowns, indicators and basically high and low prints, because really, we're trying to trade to trade, without having a true understanding of the greater volatility conditions within markets and/or whether expectations are aligned, or uncertainty prevails on both long and short-term time frames.

30-Minute & 15-Minute Charts | International Business Machines Alright - taking a look at the 30-minute and 15-minute charts, we're basically confirming what we already know, but we're going to take notice of just a few extra items, just to be sure... One point I REALLY want traders to SPECIFICALLY BOOKMARK in their minds... Have you noticed that by doing Macro to Micro, by the time we get down into the 30 and 15-minute charts, we already have a good idea of what we're about to see? It's

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MICRO

pretty amazing considering so many often spend hours looking at a 15-minute chart, not really being able to see anything at all. Fact is, when we're cognizant of the larger picture within markets, the short-term time frames (and market, price, mean-period, and probability volatility) become much less fearsome, seemingly erratic, and intimidating. How many people would really think to go out further in time, to clarify short-term volatility conditions? Figure 9.11 | IBM 30-Minute Chart

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30 Apr 16:30

I hate to say it, but most things in markets are just backward... When we're looking for answers, we often have to look in some pretty bizarre places, which we wouldn't normally do in other areas of our professional lives. Generally, when I come up against a massive problem in markets that doesn't seem to have a rational explanation, I turn

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my research inside out, and start looking at the opposite side of what I'm truly seeking... Just food for thought. Anyway, the 30-minute chart shows IBM trading on the lower end of the range, though still in a lateral trend for the most part... A move below the 200-period 30minute lower Containment Zone would be bearish indeed. CAREFUL THOUGH should prices just wiggle around at - or near - the lower 200-period 30-minute Containment Zone, without showing successive closes into bearish territory, because the both the 200 and 50-period distributions are STILL traveling sideways, we MUST be careful not to get caught in a head fake... Which again is why we NEVER chase price breaking outside of a Containment Zone and instead wait for a pullback, before entering. Figure 9.12, the below 15-minute chart is further proof of the pudding that IBM is starting to show signs of weakness in the Intraday lateral trend, however, while the 15minute and 30-minute distributions appear sideways-bound, we must be very careful not to get too presumptuous, just because we're afraid to miss an opportunity. Most traders, by the way, enter their positions excessively early, because when price even gets remotely near the territory where they previously were eyeing a position, the suspense and worry that the opportunity will pass them by is just too much to overcome, and thus, they pounce. Just moments after, price travels precisely to their previously planned entry, but instead of getting an awesome fill, they now have to sit through a pile of pain. If you really trade, you know what I'm talking about. All I can say is... Patience pays in the end... Figure 9.12 | IBM 15-Minute Chart

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26 Apr 2010

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In the above chart, traders will notice I have basically zeroed into the relevant possibilities within the 15-minute chart... Please note that I just wrote 'possibilities', as in plural... See, even though I've been really covering IBM from the perspective of weakness likely to surface, I must always consider the other side of the coin - no matter what. Shortly after what readers are seeing on Figure 9.11, IBM fell through the floor, taking out the lower Reality Adjustment, taking out even lower ground when the mysterious "computer error" hit the Dow Jones Industrial Average on May 6. The point is traders could have enacted a quick mean reversion position when IBM was trading near the lower end of the lateral range... However, as much of our macro down analysis was showing, weakness prevailed, and thus when IBM tagged the lower Reality Adjustment pivot, we would have needed to change our bias from lateral mean reversion uncertainty persisting, to lower ground with expectations aligning, on the way. Bottom line, if I'm not looking at all possibilities within markets, I am really setting

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myself up for failure. What it comes down to is making sure that from the perspective of a Reality Adjustment, I understand that there are at least two Reality Adjustments in every picture of markets we study. At times, we will find even more Reality Adjustment paradigm pivots, as markets can move up, down and sideways. Hardly any new traders EVER actually look for pivots where lateral trading could ensue, as we're conditioned to only seek out major pivots of trending, or reversal. However, those who've been in the trenches with real money know... Lateral trading and volatility are MAJOR factors that must be considered to navigate markets profitably.

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Chapter 10 | Pulling it All Together

Good 'ol Chapter 10 - the place where we cover all of our basis, in quick bullet point format, to ensure we've captured the whole picture of Macro to Micro... Starting our list of what traders should have learned throughout the book: On a longer-term basis, we are taught outliers are near the exterior of a distribution; however, as history repeatedly shows, the real outliers within markets are events that push prices back towards the mean. When prices are trading away from the mean... Above, or below the 1.25 standard deviation Containment Zones and often at, or beyond the 2nd and 3rd standard deviations, the occurrences do NOT directly tell us a reversal is about to ensue. Rather, data near the exterior of the distribution indicates expectations are aligned for greater, or less value, growth, earnings/income, or wealth. If we assume markets and distributions are static (like most Expert Advisor systems, which are curve fitted to historical finite data), we will CONSTANTLY fall victim to real outliers where prices move out to 7th, 8th, 15th, or more, standard deviations, as prices are organic and dynamic, just like time. We must understand that the mean and distributions are mobile and organic, ultimately following price wherever it goes. It is critical to understand that within day-to-day trading really only four types of volatility exist: The four types of volatility that affect common trading and markets are:

VOLATILITY

1. 2. 3. 4.

TRADING

Market Volatility Probability Volatility Mean-period Volatility Price Volatility

(For more information, please see Chapter 4.) We must understand that because of the squaring of the deviations in standard deviations, the standard deviations are also organic and dynamic expanding and contracting with price volatility, regardless of the larger trend. Think Jelly Fish in the sea. Standard deviations are more than just points of probability measuring the wingspan of a distribution; rather, standard deviations are measurements of volatility as well, thus serving multiple key functions: a. Alerting us to points where data could stall, slow, or reverse; but also often also directly indicate traders are seeking higher, or lower ground, as noted in the fact that prices must ultimately lean on one side of the distribution, or another, in order for prices to trend. Prices cannot trend if they are sitting comfortably at the mean.

b. Alerting us to volatility decreasing after a sustained period of trending, denoting that more trending is likely to come, as contrarians are now out of the picture. c. Alerting us to volatility decreasing after an initial spike in uncertainty, or expectations aligning, ultimately indicating lateral trading action, otherwise known as cooling, or consolidation, or a pullback to come within markets. d. Alerting us to price spikes on the horizon when after consolidation has taken place, the distribution has shed weight (compressed) and thus, is primed for a volatility breakout up, or down. (For more information, please see Appendix B.)

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e. Alerting us to the fact that a trend has come to an end, through a sudden collapse of short-term volatility underneath long-term volatility. (For more information, please see Appendix B.) Prices moving above, or below a Containment Zone key us into the fact that expectations may be aligning, and trending could be on the way. For prices to truly continue trading outside of the Containment Zone, volatility must initially spike, helping open up the standard deviations, so price can move within the distribution...and ultimately move the distribution, as well. By watching the 1.2 standard deviations, we are able to track whether expectations are beginning to align, or are aligned, as prices above (or below) the Containment Zone allude to trending to come... However when prices fall back underneath (or above) the Containment Zone, we must be able to recognize that expectations are shifting to uncertainty. All understanding of distributions, the mean, standard deviations and volatility in markets is lost if we do not approach our analysis from a macro down perspective. Macro down means, starting with the monthly chart, then moving to the weekly chart, daily chart, 4-hour chart, hourly, 30-minute, 15-minute, and even 5-minute and 1-minute, if need be. We want to use simple parameters (like the 200 and 50-period distributions) when doing macro down analysis. We are measuring volatility and standard deviations as they apply to relevant distributions. Trying to "fancy pants" up the concepts, may work for some, but for most, keeping things simple is the best bet. We're talking about statistics and volatility - empirically justifiable occurrences within markets. Most unjustifiable technical junk is markets are made to look fancy for a reason... The underlying theory and math is not empirically justifiable, and thus, it's really just junk altogether. By keeping things simple, we stray from the typical junk trader' mentality of most. When effecting macro down analysis, we do NOT want to skip any of the time frames, as each specifically relays information that is not likely to be

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seen on the other timeframes... at least not at first, anyway. If prices are trading above the Containment Zone on our macro charts, we must resist the temptation to take a position against the trend, no matter how good the short-term charts look. Patience pays. If a reversal is truly at hand, a failure of the Containment Zone (with prices headed back towards the mean) will surface shortly. When prices begin to break outside of the Containment Zone, indicating expectations aligning, we must fight the urge to chase price. Almost always, we will have a chance to enter on a pullback to the Containment Zone, thus minimizing risk, while identifying a clear stop exit (taking as little of a loss as possible) point. We must remember to watch the slope of volatility itself, as if the slope of the standard deviations and mean are not trending, prices trading near the Containment Zones could really just be volatility in the range. In terms of the Reality Adjustment, if we cannot perceive, comprehend, and separate short-term expectations (both literally and theoretically) from longer-term counter parts, we will likely lose over the long haul... The longer the lateral range persists, the higher the probability volatility will collapse. Prices will not trade laterally forever, and thus, volatility cannot collapse into the mean endlessly... In an ascending trend, if upper volatility has not confirmed bullishness, overall volatility will most likely be very high - at key pivots. Ina descending trend, if lower volatility has not confirmed bearishness, overall volatility will most likely be very high - at key pivots. We never have to chase price, so even if we miss (or just chose to sit out) of the first, or second test of a Containment Zone (either from inside, or

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outside the 1.25 standard deviations), if we're patient, we will almost always have an opportunity to take a position when price pulls back to the Containment Zone, shortly after.

If price pulls back to a Containment Zone shortly after breaking OUTSIDE of, and if we have solid reason to believe (meaning we've done our fundamental homework) expectations are still aligned (and relevant in terms of the time frame we are deriving the entry signal from versus the longer-term periods), we can then consider the serious possibility of buying the pullback into the Containment Zone with a stop, just slightly on the opposite side. Ijust want to mention that missed money is always better than lost money, and half of the game of being a profitable trader is knowing when NOT to trade, and then having the discipline NOT to trade. Trading just so we feel like we're not missing something, when we're not totally certain about what's happening, is just downright goofy. Actual empirical points of shifting relative period expectations visible on our charts. Probability/paradigm pivot points, which traders must be able to recognize without bias, in order to effectively move with and profit from, expectations aligning and/or uncertainty kicking up, in both shorter and longer-term periods. The paradigm where short-term expectations meet long-term expectations, where traders are able to read overlay the longer-term paradigm of fear, or sang-froid prospects over the short-term expectant opportunity truly within markets. The action of studying both short and long term expectations and then adjusting one's outlook, or mindset to fit the proper short-term reality of any given situation, within the context of longer-term expectations aligned, or uncertainty prevalent within current trading conditions.

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For macro market to sustain ground above the longer-term Containment Zone, the masses must have some sort of easily understandable information available such as: 1. Tax cuts. 2. Lower interest rates.

3. Factors helping to ease unemployment. 4. Technology advances that allow individuals greater opportunity to make money (think Internet and the invention of the computer.) 5. The abolition of unfair political bias and or practices. 6. Changes in legislation that allow for greater personal gain (lowering capital gains taxes, rightful regulatory changes, and legislative easing on barriers to entry of and for small business.) 7. Political shifts that enable any of the above. 8. Huge jumps in Corporate earnings that the masses can participate in through dividends, or capital appreciation.

Appendix A | Commodity Channel Index and the Containment Zone Updated from Volatility Illuminated

Within Forex, there's a term professional traders often throw around when referencing the retail community... Retail traders are frequently cited as doing nothing more than just ‘chasing indicators’. Sadly, so many retail traders fall victim to the mindset of believing trading is easy and/or if they just uncover the ‘secret technical code’, they will never lose again. With this mindset, it's no wonder these same traders unfortunately constantly find themselves on the wrong side of the eight ball when volatility kicks in. However, there is another way to trade... Throughout Chapter Six, traders will gain more insight into how and why ‘chasing indicators’ is such a losing game, while also seeing how traders can begin putting indicators back on their side...to overcome destructive volatility that overwhelms most of the retail community daily. However, it is important to remember that while the Quad CCI strategy you're about to read about is something I personally use, I overlap the strategy with volatility and probability, WVAV and WAVE « Price Mass to gain greater insight into whether the signals produced are real- or are really just ‘those old charts fibbing’ - yet again. While the discussion on multiple CCI time periods is certainly not a "one stop shop" to completely navigate the larger universe of trading stocks, futures, commodities and Forex, using the indicator correctly can definitely assist traders when markets are offering little guidance. Take a look at the below hourly chart of the EUR/USD, which shows the basic ‘feel’ of the Commodity Channel Index (CCI), at least, as most traders use the indicator. For the most part, the indicator is used to look for reversal (or trend re-entry points) when the indicators falls from above +100 back under +100, or up from underneath -100, above -100.

What you will hopefully notice right away is the first 'sell' signal (from the left) and the third buy (also from the left, which I've marked with an asterisk '*'), both provided false signals. To understand why the false signals could be appearing, we need to take a closer look at the indicator overall... According to one major Website, (I'm keeping their name anonymous to not drag them through the dirt, though I have notified them of the error):

"An oscillator used in technical analysis to help determine when an investment vehicle has been overbought and oversold. The Commodity Channel Index, first developed by Donald Lambert, quantifies the relationship between the asset's price, a moving average (MA) of the asset's price, and normal deviations (D) from that average. It is computed with the following formula..." Here's where we begin to split hairs on a few important matters that I believe you should be aware of... Figure 6.1 EURUSO,H1

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The Website is correct that CCI was developed by Lambert (in 1980, FYI), moreover, the definition is also correct in that the indicator is meant to help determine

overbought and oversold areas... However, CCI was also developed to help find 'cyclical swings’ of commodities and other financial instruments. What's more, in the above definition, you will notice the use of the terminology ‘normal deviation.' Just to make sure I'm not completely losing my mind, I checked about twenty different Websites for definitions of CCI while writing this and what I've found is that almost all mislead readers using the same, or similar terminology...setting you up for failure. The problem is likely because most of the information on trading-related Websites is written by people who do not really trade in real life...they do not even know the crap they are regurgitating is wrong. I like to think of the situation similar to a rocket scientist explaining how to smooth drywall mud almost perfectly...in one shot. Though the task might seem easy overall, unless you've actually done it a few times, there's no way you'd know the tricks of the trade, or the ‘inner workings’ of how drywall mud sets on the wall and in the bucket, or even how important the ‘art' of trowelling is. (I learned the hard way renovating a house many years ago... For the rest of my life, I will have an immense amount of respect for drywallers. And let me tell you, I mean it too!) Back on subject, in most definitions of CCI, we often hear terms like normal deviation, or standard deviation as part of the equation. However, what we must understand...is that CCI is calculated using mean deviation (or mean absolute deviation MAD), not standard deviation. Moreover, there is no such thing as 'normal deviation.’ (Just to split hairs, Roget's 21st Century Thesaurus, Third Edition lists 'normal deviation’ as a synonym for standard deviation; however, standard deviation is not used calculating CCI, mean deviation is.)

Furthermore, what the heck is 'overbought' and oversold’? As you're about to see, when CCl is trading above +100 and below -100, whatever financial instrument the indicator is measuring may not be overbought, or oversold at all... I'm not kidding, the stuff most Websites feed traders is just plain damaging... To dig into the specifics, CCI is calculated using the 'mean deviation’, which is thought of as providing greater accuracy to non-normal (non-Gaussian) data than the more commonly used standard deviation. In the paper Revisiting a 90-year-old debate: the advantages of the mean deviation by Stephen Gorard (presented at the British Educational Research Association Annual Conference, University of Manchester in 2004), the author asserts,

"In those rare situations in which we obtain full response from a random sample with no measurement error and wish to estimate, using the dispersion in our sample, the dispersion in a perfect Gaussian population, then the standard deviation has been shown to be a more stable indicator of its equivalent in the population than the mean deviation has. Note that we can only calculate this via simulation, since in real-life research we would not know the actual population figure, else we would not be trying to estimate it via a sample."[43] I understand why mean deviation is used in CCI (to attempt to compensate for nonsynthetic, non-perfect Gaussian price data); however, after spending much of the past

year mapping distributions in markets, I would argue the shorter the timeframe measured, the more 'bell shaped’ data becomes. What I'm saying is perhaps the formula for CCI is more accurate with a greater set

of data and that perhaps on shorter timeframes, the formula needs to be changed to utilize standard deviation, over mean deviation. Don't worry though, we will change the formula on your charts, without actually changing the formula at all! I'll explain in just a moment... Below is the formula for CCI, please don't worry about digging too deep into the math... You don't have to crunch the numbers, I would just like for you to understand 'the philosophy’ of what's happening, over the mathematical fun-o-rama calculator super-time. The philosophy is what matters...

CCI is calculated as: CCI = (TPn - TPSMAn) / (MD * 0.015)

Where TPn = Typical Price (High + Low + Close)/3 TPMA = SMAn

= [(TP1 + TP2 + TP3...+TPn)/n]

Then... CCI = [[TP current period — TPMA current period] Divided by [((TP1 — TPMA1) + (TP2 — TPMA2)

+...TPn + TPMAn))/n]]

All multiplied by 0.015 Okay, so where the heck is all of this going? The whole point of CCT is really to

measure the major ‘Containment Zone’ of a commodity, stock, currency, or whatever... However, like everything else in the market that's totally misleading and confusing, for whatever reason, the whole point of CCI (as explained by 90% of the Websites out there) is as well.

Definitions of CCI state 'cyclical swings', major price movements and reversals, deviation this and that...yada, yada, yada... Here's what's really happening... Imagine a teeter-totter that has values from -500 to +500, with zero being the middle. I place a bowling ball on the teeter-totter and get it rolling to the right side towards +500 and then tell you that you can't let the ball roll off the end, but the rules are you can only touch the teeter-totter and not the ball itself, what would you do? Most likely, you would move to our left and push down on the opposite side of the teeter-totter (the -500 side) to get the back rolling back towards the middle. Now, imagine that I had drawn -100 and +100 on either side of 0 (zero), which right now, because you are pushing down on -500, the ball is rolling back towards. It would make sense that when the ball is rolling back from the +500 area and crosses over +100 towards the 0 (zero), the ball would likely cross zero right? Right? Right... However, please also imagine that I've drawn the numbers slightly awkward to common sense in that the further from zero we get (conversely, the closer to -500 and +500 the ball is), the more densely I've drawn the numbers grouped together... As a picture tells a thousand words, I've drawn the teeter-totter for you, as a bit of a visual aid. Please see Figure 6.1 on the following page and please excuse my art... The main point is via Chebyshevu's Theorem in statistics, we know that (on average), about 75% all data will rest within two standard deviations of the mean. (In the case of a normal Gaussian curve, the probability is actually nearly 95% of all data should sit within two standard deviations of the mean, thus, in using mean deviation, Lambert was likely attempting to accommodate for non-perfect bell-shaped data. Moreover, Lambert was likely also using Chebyshev's Theorem to justify the containment range of -100 and +100, implying 70% to 80% of all data, which is why he used 0.015 as the multiplier...) Just FYI Chebyshev's Theorem states: "The proportion of any set of data lying with K standard deviation of the mean is

always at least 1-1/K?, where K is any positive number greater than 1. For K=2 and K=3, we get the following results: At least 3/4 (or 75%) of all values lie within 2 standard deviations of the mean." At least 8/9 (or 89%) of all values lie within 3 standard deviations of the mean."44] CCI Attempts to measure 70% to 80% of ‘containment area’ for potential values...

Figure 6.2 Also, please note that in the real CCI, -500 and +500 are NOT the final outlier values the indicator can move to... Theoretically, CCI could travel infinitely up, or down... However, on a common sense basis, people don't have to buy, but they do have to sell sometime, and thus, prices (and CCI) will never travel infinitely in one direction. The basic point here is that when CCI crosses back under +100, or back above -100, the theoretical underpinning is prices are moving back into a more ‘normal range’ after just having witnessed a period of extreme activity. What we're looking at is really just a measurement of 70% to 80% of the expected statistical range for whatever time period we're gauging ... You might not be jumping up and down in your seat right now, but wait a moment

and I think you could be... Though the above sounds both simple and boring, I like to think of anything that could make hordes of money...as the exact opposite... Amazingly, I would bet (without too much trouble) about 99% of all retail Forex traders don't ever even know what they're looking at when they use CCI to trade with... No wonder they have such a difficult time making money with the indicator! Honestly, by simply reading these pages, you're in the 1% elite who actually know what they're looking at when they are trading with CCI... So what we know is this... CCI is NOT indicating overbought or oversold whenever the indicator moves above +100, or below -100. Instead, CCI attempts to measure illuminate the 'Containment Zone’, which is intended to be 70% to 80% of the total range. Again, the ‘Containment Zone’ is denoted as the area in-between -100 and +100 in the oscillator. What I'm saying is this... In theory, when CCI is trading above +100, the stock, currency, commodity, or whatever is thought to be trading towards the top of the larger, typical range, and is in essence trading at, or above +1.2 to +1.5 standard deviations (using the term ‘standard deviations' merely for the sake of example, knowing standard deviations are not used in the actual calculation of the indicator.) Conversely, when CCI is trading below -100, we generally assume the financial instrument measured is trading below the larger, normal range and is experiencing a period of volatility, denoted most often a sharp or prolonged selloff. Here's where CCI gets very interesting for those who understand what they're looking at... On the below chart (and I apologize for the likely confusion- it was difficult to get everything in the image even remotely clearly) you will notice what looks like fire, or hair, or something growing from the left side... What the mysterious wig-looking thing truly is showing...is an actual representation of the ‘distribution’ of the price data on the chart. What we've done is literally —visually mapped- the distribution of data, so that you can see the distribution unfolding through trading action... Here's what readers MUST make clear in their minds: What we are really looking at when we look at any price data...is data. The data is forming one HUGE distribution over all time, or a series of subset distributions, on smaller timeframes. Notice there is outlying data towards the upper and lower end of the distributions; however, the bulk of the data rests in the middle; hence the higher mountain-like ‘peak

thing' towards the center. What you are seeing is —in essence- the Central Limit Theorem in action. In statistics, the Central Limit Theorem says if we pluck a small subset of data out of a larger skewed range (the totality of market data is skewed, since time is skewwy skewness in itself), the subset should —for the most part- retain a Gaussian bell curve like shape. What you are seeing on the chart is exactly the case in point. For those who are interested, in the final chapter of Volatility Illuminated, I will discuss subset distributions further (which are really amazing and complex animals in markets); reserving the ‘'mega-in-depth-conversation' for last. (In reference to my own ‘best for last' hype just deployed — and if you remember Chapter One, I have a 50,000 megafog-mouth-lamp too! Just kidding. Anyway, the topic of distributions exposes Poisson distributions as a regular facet of markets as well, thus, given the extent of the discussion required, I will hold off on the exchange until the end of the book.) Now, taking a look at Figure 6.3 (below) we are able to easily identify that —indeedtrading action is -data- forming distributions. It is vital to 'show’ the distributions, so that readers visually perceive why understanding (at the very least) the conceptual framework behind descriptive statistics is so important in day-to-day trading. The bottom line is to be better traders -on the whole- we absolutely must

understand that the action unfolding on our charts is really nothing more than: data. Moreover, as you're also about to see, CCI is really an attempt a measuring 'mean reversion’ and or 'divergence' of the data. Divergence is fancy-talk for ‘trending’, ‘moving outside of the Containment Zone’, or ‘geez Zeb, lookie that thang go.' I personally think the below chart (Figure 6.3) has many, many implications within trading and volatility; which I hope you will see too. Figure 6.3

Bollinger Bands come preloaded in every charting package (on the face of the planet) at two standard deviations, which most traders never even think to change... However, in my humble opinion, to truly unlock the ‘power' of standard deviations as a measurement of volatility and probability within trading, we must change the preloaded default variables. Again, I will discuss standard deviations in great length in Part Two of Volatility Mluminated. Anyway, I have changed the values in the below bands to 1.25 standard deviations... By doing so, I'm measuring just over 70% of the total potential 'containment' of the distribution at hand. Does what I've just mentioned sound a little like CCI? It should, because we're basically looking at the same thing; however, CCI is

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Please take a moment to notice the lower oscillator, where I have attempted to draw lines from the actual CCI 'convergence points' at +100 and -100 (meaning the points at +100 and -100 where CCI breaks from outside the containment area...back within) up to corresponding squiggly lines in the actual chart itself. Though my connecting lines (from CCI to the squiggly lines on the chart) are not straight, a vertical line drawn on each would connect the points. What you will notice is the CCI convergence points line up almost precisely with the USD/JPY turning back into the middle range (upwards and downwards) of the channel looking thing that is overlapping the actual price action on the chart. What are the channel lines on the actual chart and how is it that they (almost precisely!) identify the same mean-convergence type of action as CCI? Believe it or not [insert dramatic pause] I've drawn Bollinger Bands on the USD/JPY chart as well. However, what so many traders don't understand is that Bollinger Bands are really a visual display of potential distribution wingspan- as denoted through the visual mapping of standard deviations. (I will discuss Bollinger Bands -in detail- in the second half of the book, just FYI.) Sadly though, so many retail traders never even change the input variables (standard deviations) within Bollinger Bands, while also failing to even understand what the indicator is presenting in the first place.

Bollinger Bands are calculated using standard deviation. Overall, yes I had to tweak the standard deviations setting in the Bollinger Bands slightly, but really, the two are one in the same. (FYI, just in case you might be wondering, the Bollinger Bands are measuring 14-periods of data, just as CCI in the chart.)

Here's where the story gets even better... Have you ever had trouble identifying trend? Or, have you taken a position that suddenly goes against you and you look back and say, ‘ohmygosh that was ridiculous, what was I thinking?!" If you've ever been in this place of brilliant trading euphoria, what I'm about to present could help tons. See, when looking at shorter-term periods, many traders often mistakenly take a position against the longer-term trend, based on CCI reversing down from the +100oscillator level, or up from below the -100-oscillator level and thus, find themselves in big trouble when the longer-term trend resumes. And really, I understand why they're making this mistake...after all, if they had learned about CCI through just about any trading related Website on the Internet, they would think above +100 is overbought and below -100 is oversold. Really though, above +100 means "moving above the first standard deviation", while -100 means "moving below the first standard deviation", both of which mean, "subset distribution on the move — alert — alert!" So how do we defeat this potentially misleading and costly pitfall? We identify trend.

Note: Please always remember to walk down through all your chart timeframes (Macro to Micro) from monthly to weekly to daily to 4-hour to hourly to 30-minute to 15-minute to 10-minute and so forth... Walking down through the charts is the best way to identify trend from the wide-scope monthly all the way into the minutia of the minute. Again, we're simply attempting to keep the forest in our mind's eye, when we're in the trees. Specifically to identify trend though, please look at the below 4-hour chart of the EUR/USD... I have added in 1.25 14-period standard deviations with 14-period CCI. What I think you will notice is that when the EUR/USD is traveling near the mean, we're basically 'trendless." What's more, in those peculiar times when CCI is actually trading below -100, it is precisely when the EUR/USD was staging the largest and fiercest moves downward... Conversely, just when the EUR/USD traded to the top of the 'Containment Zone’, we failed. But how many people would have known to associate the move downward below -100 in CCI as ‘big selloff to come’? What's more, how many would have known to spot the ‘failure coming' at the top of the range as well? I can assure you not many... Here's why... Because we're taught to use CCI as a tool for reversals, or trend-reentry (still a type of reversal) we're not taught that the breach of -100 and +100 actually means the stock, currency, commodity, or whatever is really headed towards, or trading outside of the first standard deviation, which is exactly when and where the steepest moves occur. We're taught to look for CCI to cross back into the middle from above +100, or below -100. Figure 6.4

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I would like to argue; however, some of the best trades are waiting for CCI to slip outside of +100 and__ -100 and then trading ‘with the trend’ or with momentum, or ‘volatility’. Then, when CCI comes back across +100 and -100 (after we've participated in the trending move), we can close our positions and let the 'containment-area trendless chop-chop traders' slug it out. On the top of the range, you will also quickly notice that we were already in a descending trend when the EUR/USD was trying to make new highs... But really, the EUR/USD wasn't making new highs, it was just trading to the top of the Containment Zone in an already descending trend, which is also likely, one of the best places to enter ‘with the trend’ for those who just love those mean reversion 'CCI crossing back towards zero' types of positions. I don't know about you though, but there's nothing I hate more than getting stuck in choppy lateral trading, so often, I look for the breakout, or breakdown from the Containment Zone, after (and only after) an identifiable trend is in place. Okay... we should now see that at times, identifying trend (with CCI) can be almost as easy as simply adding 1.25 standard deviations to our charts and keeping an eye on BOTH CCI and the Containment Zone. Why is it called the Containment Zone? Because I just made it up... Okay, other than that, unless you have some insight

into fundamentals, volatility/probability, order flow, or hopefully another technical analysis magic trick, the only thing you get in the Containment Zone is a date with Mean Joe Green...

Where the 'mean' sits...is where the ‘bulk’ of our data should sit. The ‘Containment Zone' is statistically (if we're measuring 1-standard deviation on either side of the mean) where approximately 70% of the data should reside. And in the case of the 1.25 standard deviations, we're really looking at a little over 70%. What I'm saying is if you're taking a position at, or near the mean, without having a really good reason for doing so, the position could just as easily reverse against you, as work in your favor. If you're on the mean, you're on the hill, on the top of the triangle, at 50/50...tossing your cash into the mystical Forex roulette wheel. As I just stepped away from my screens for a cup of coffee... I realized that perhaps I'm being a little harsh on old Mean Joe Green... There are actually times when we can take a step into Green's house and walk away unscathed... With the aforementioned in mind, we'll take a few moments to show how we can take positions into the mean, while also using CCI in a more traditional fashion. However, we're not going to use the ‘same old' one-line in an oscillator trick... Just to be on the safe-side, we're going to overlay four CCI's to form the 'Quad CCI Strategy.’ Does this thing sound like a wicked new Bic® Razor, or what?

Standard Stan And The Four Horsemen When I first started writing about CCI a few years ago, there were only two... Somehow, the dynamic duo grew into 'The Four Horsemen’. I guess if I write a sequel to this book in a few years, there might be two more... Maybe I'll call it: Six Pack CCI. Yes, I know I'm not funny. Jumping right in, over the following pages we will learn how to use CCI to identify trend...to defeat false CCI signals, while also attempting to time positions ‘with the trend' both inside and outside the Containment Zone. All of which can be efficiently accomplished by applying #we four CCI time periods to the same chart. Our goals in doing so are to: 1. Prevent ourselves from taking positions against momentum.

2. Time our trades with short-term 'wrist-rocket' thrust from the larger market momentum.

3. Clearly determine whether the trend is up, down, or sideways. Foremost please note that I have provided MetaTrader code for 'two CCI’ at the end of the book, while having also made the code available on www. WallStreetRockStar.com and fxVolatility.com. After the code is copied into your MetaEditor / MetaTrader Indicator folder, you should be ready to go. Also, please note that if you do not have MetaTrader, you can simply stack four CCI indicators windows in one of the various free charting applications available on the Internet. For those who use the popular charting Website Stockcharts.com, you will only be able to stack three CCI's one above and below the chart and one in the actual chart window... I've already prepared a chart for you and you can access it at:

http://stockcharts.com/h-sc/ui?s=$INDU&p=D&b=5&g=0&id=p53666348969 Moreover the code I am supplying only contains two CCI's in one widow, this is to prevent excess confusion with too much happening in one oscillator window. To see all four CCI, please stack two windows, as you will see I have done in the examples throughout the remainder of the chapter. If you are using MetaTrader (it's free, by the way... [and] some brokerage demo accounts [United World Capital, for example] will allow tracking stocks and commodities too), after you have placed the files in your Indicator folder, please close and reopen MetaTrader. Assuming the file is in the right folder, the indicator will show up in your "custom indicator" drop down menu within your charts. Add the Two CCI code twice, using 14 and 100-periods in the top window and 50 and 200-periods in the lower window. Standard Sam, the Deviation Man will help keep The Four Horsemen in check as the trading day rolls out... To see Standard Sam, please add a 50-period Bollinger Band(s) to your chart, set at 1.25 Standard Deviations (STD). Also, as you have likely noticed, I am using a chart of IBM (NYSE: IBM) to show that the quad CCI strategy (really though, all of the strategies in this book) work with stocks, futures, and commodities too.

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jump at every volatility spike, like it's a massive trend change... However, it takes more than a quick 'spike' to move the 200 and 100-CCI's above, or below the zero line, thus the resilience of the indicator within itself, can be a great buffer for 'on edge’ nerves, analysis paralysis, and/or muddled vision for whatever reason. Looking at the below chart...

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50 Period SMA 1.125 Std Dev

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We can take a position -with the trend- on the mean,

90-period and

and 14-period CCI

All three longer-term CCI are ‘bull confirmed!"

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Figure 6.5 As you can see, in the lower two windows, the 100-period and 200-period CCI's are denoted in the thicker black lines, while the 14-period and the 50-period are shown as the thinner in each window. Then, on the actual price portion of the chart, you can see that I have drawn the 50period moving average and two 1.125 standard deviations, which as you likely already have guessed, should line up with the 50-period CCI as the casing of the ‘Containment Zone’. Readers should immediately notice that the two thicker CCI lines (the 200-period CCI and the 100-period CCI in separate windows) are both traveling above zero. Plain Jane, the trend is up. Here's what I would like to mention for newer traders... If you're having trouble deciphering trend direction, drop in quad CCI on an hourly or 4-hour chart .... If the two longer-term CCI's (200 and 100) are above 0, common sense tells us there's more bullish momentum, than bearish and vice versa for below zero. While this may sound simple, don't scoff, I know plenty of 'tenured traders' who

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Figure 6.6 Traders seeking to take a position ‘with the trend’ can attempt to purchase pullbacks on the mean (yes, Mean Joe Green) if: 1. Longer-term CCI (at least the 200 and 100) are above zero...

2. The 50-period CCI is not below -100. 3. Common sense seems inline.

What you will notice is that if we time our long-entry when the 14-period travels back up from underneath the -100 area, we are using the shorter-term indicator as a sort of 'wrist rocket’ to step in just at the right time when momentum is seemingly in our favor. What's more, by taking a position on the mean (while timing such with a 14-

period pop back up into the Containment Zone) we have also given ourselves a clear stop loss point, should the market fall apart. We simply place our stops on the opposite side of the mean, knowing a breach will likely have the pair testing the lower end of the Containment Zone... Please remember: The first loss is always the best loss. Anyway, should the pair pop up into Standard Stan in the 1.25 area... Once we're above the standard deviation level, we can actually use the visual identification of the ‘Containment Zone' as our stop to ensure we exit the trade profitably... Thus, savvy traders needed to only wait until the 14-period CCI ‘reloaded’ and crossed (the trigger) back above the -100 oscillator containment area reentry point to implement long positions. What we 're really doing here is using The Four Horsemen to guide our way... The 100 and 200 are like big burly swordsmen, which are hard to budge without significant force. The 50-period CCI is more like the guy who's fast on his feet, but still tough enough to take on the big dudes... And the 14-period is similar to the scout of the party...

The fastest of the bunch, but also the first to turn-tail at any sign of danger... Basically, when we see the 100 and 200-CCI stay above the o-line, we can infer there really isn't any reason for them to move out of their range... The 50-period CCI will sometimes venture over the o-line, before the hefty battlers, just mentioned... However, the 14-period will often venture (quickly) way out into the yonder...and he will always return to tell his pals what he's found. Crossing back over the 100-line, traders can take 'rocket trend reentry’ positions (usually on the median); however, we still want to keep an eye on the flighty 14-period CCI character... If he crosses back over the +100 or -100 level he was just scouting, it means the larger weighted CCI lines could soon to follow too, as the whole bunch runs from larger momentum on the way... Just to mention the opposite situation from what we covered in the two charts above, if the 100-Period CCI were trading below the o-baseline (meaning the stock, currency, or commodity is likely trading right at the 100-period moving average (mean) as well) and the 14-period CCI spiked above the +100 oscillator level, the trader

could look for a short entry, assuming the 100-period CCI had not begun ascending (attacking the o-baseline), at the same time.

I'd like to mention that aggressively taking positions before CCI lines actually breach the +100/-100 oscillator levels, falling/rising from upper/lower extreme ends of the indicator window is extremely dangerous. Moreover, as the 1.25 standard deviation representation on the chart shows,

movement above +100 and below -100 could indicate a larger spike of momentum to come. With all of the aforementioned in mind, we will now look at one final aspect of CCI — Identifying Reversals.

Identifying POTENTIAL Reversals with CCI Identifying reversals can be slightly trickier than timing 'with the trend’ positions, as anytime we are trying to take a larger contrarian position against the trend, we are truly fighting momentum. However, as in life, all things eventually -always- come to an end, trends eventually...stall...and cease too. Thus, to spot potential larger reversals at hand, we will use the +100 and -100 Containment Zone lines (again), applying the 100-period CCI; however, unlike the above example, we will only use one CCI window, instead of two. What's more, in our single CCI window, we will also draw a 21-period CCI, leaving the 50-period and 200period CCI's out. I'm making this change for two reasons:

1. At times, we must 'switch up' the lens, which we're observing the market from, especially if we are not seeing markets clearly.

2. Using the 21-period gives us a sort of ‘middle ground’ between the jittery trading action of the 14-period CCI and the more stagnate movement of the 50-period CCI. Basically, if your intuition is telling you a reversal might be on the horizon, but you are not able to see any real signs of such within news, fundamentals, or your charts...you can always switch-up timeframes, or indicators (if even momentarily) just to turn over a few more stones.

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then fails, we might assume that simple mean-bound choppy trading is really the name of the game in the near-term, instead of looking for a larger reversal. (That is, unless some sort of fundamental news has surfaced making us think differently.) What I'm saying is if CCI is just "flirting" with the topside +100 oscillator level, but did not make a pronounced move above, implementing a short position would likely be more presumptuous than calculated. To hammer home my point, when the 100-period CCI line is trading ‘almost at’, or above the -100-Containment Zone line, taking a short position is a low probability scenario, as jittery trading could easily bring about a pop up into the mean, or higher, where we would likely be stopped out. High probability (overbought) reversals [meaning the bull-trend could be nearing an end and a sharp downside move could potentially be pending] generally surface when both short-term (21-period) and long-term (100-period) CCI lines are trading well above the +100 oscillator level, or below the -100 oscillator level. When the short and long-term CCI lines are at the extreme ends of the oscillator, ‘trend capitulation’ or ‘the fifth wave,' in terms of Elliot Wave Theory could be in 243.389

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We're actually going to use the longer-term 100-period CCI line as inference and confirmation of a potential downside and/or upside reversal, even though the line may be trading above (in the case of a bull trend), or below (in the case of a bear trend) the +100 or -100 Containment Zone lines...

I'm not really a big fan of using the 100-period (or even the 50-period) as an indicator of reversal, through mean convergence from outside the Containment Zone... However, because what I'm now discussing is the 'usual' way CCI is used, I must mention it. Please keep in mind, that when we attempt to identify a reversal with the 100-period appearing as if it is about to cross from above or below the Containment Zone —back into the Containment Zone- the seconds of data that transpire while the 100-period is actually crossing back into the Containment Zone can be (what feels like) and eternity of volatility with the currency, or stock bouncing around, before finally making the move back into the mean. The bottom line is that if we are going to trade reversals back into (and through) the Containment Zone we must be prepared for excess volatility as traders duke out uncertainty. Anyway, the main rule is this: The 100-period CCI line must be trading well above the +100-line level, or below the -100-line, before even thinking about a reversal. If the 100-period CCI has just barely traded above the +100-containment line, and

effect. I would like to mention that several years ago, it seemed like CCI rarely moved above, or below -250, or +250 in the CCI oscillator; however, with more volatility these days...the occurrence is commonplace. Thus, when I mention 'extreme values' within the CCI oscillator window, we must remember to use common sense, while also making sure to note that increased volatility in the current market, could push values further above and below +100 and -100 than in recent years. You may have noticed in Figure 6.7, when the 100-period CCI-line was trading well above the +100 Containment Zone line (not "just flirting" with it), the occurrence foreshadowed a large reversal looming on the hourly chart, which could have provided significant opportunity for savvy traders. What's more, you will also notice that the 21-Period CCI line crossed BELOW 100period CCI - while STILL above the +100-Containment Zone line...and then subsequently fell under +100 towards the mean. In essence, we are basically saying "I want to see longer-term momentum rapped out with short-term momentum also overheated, before even considering the possibility of looking for a reversal." It's important to note traders will need to configure their CCI time periods to best fit each chart timeframe traded; however, 21-period and 100-period CCI can be used as initial benchmarks.

Moreover, never ever, ever, ever, ever, ever, ever, ever trade without a stop and

never take on more risk your account can handle. When in doubt stay out. While there a many more examples of CCI trading, everything we've discussed here are a great starting point for traders who are not only seeking to help identify trend —in an effort to increase discipline— but may also help one identify reversals and most importantly...give additional guidance when to 'stay out' of the game, should choppy mean-bound trading seem in the cards over the near term. With everything that we've covered in Part One of Volatility Illuminated, I'm thrilled to now move into Part Two, where we will really dive into volatility and probability, while also taking a very close look at VWAP and of course WVAV and WAVE ¢ PM.

The information you are about to read —as far as I know -is nowhere else readily available for traders within markets, or on the Internet. I have spent hundreds of hours preparing the following pages of Volatility Illuminated. The information you're about to read is NOT a

re-hash of someone else's stuff...

What you are about to read came from REAL trading, which I discovered only after countless hours grinding it out in markets with real money. Some of the gems you're going to read about were discovered through massive losses (always a great place to discover incredible insights) and some were unearthed through long strings of wins- carving out similar data (outside of traditional market knowledge) to refine and shape in preparation of the information here. What I'm saying is even if at the end of the book, you're like, ‘geez, that sucked, I'd like my money back,' I'm okay with it, because I plan on using the information here in my own trading —because it was derived from REAL trading— not sitting behind a ‘strategy tester' looking back into history. What you're about to read was uncovered with real cash, in real time and I will continue to use the information with my own real cash, long after you have put the book down. I hope you are able to take a ton away from the following pages, though as always, please remember to trade safely and of course: common sense is king. Even though we're not even close to the end of the book yet, I would like to mention that I appreciate you having purchased this book... Should I swing for the fences [again] trading -when I'm like, 60; you sleep easy knowing you helped pay for my room at the old folks home. I will do my best to convert the Bingo hall into a trading floor though. Anyway, I hope you enjoy Part Two, and please remember to keep an open mind...

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Appendix B | The Movement of Subset Distributions Updated from Volatility Illuminated

Over the following pages, we will focus on volatility and probability in terms of Forex markets; however, the concepts also apply to equities, options, commodities, and futures. As many traders are already aware, Forex markets often display significant volatility catching many participants by surprise. However, with a simple understanding of descriptive statistics, traders could soon find themselves ahead of the curve. Many traders — both new and experienced — often find themselves at a loss attempting to understand why Forex markets tend to experience extended volatility both intraday and over the long haul. In simple terms, much of the seemingly erratic moves are really the product of institutional order flow, causing larger movements within markets. While the aforementioned explanation is almost infuriating simple, we must understand, large money is really the pure catalyst behind extended movements within markets, not at-home investors. We can argue that by acting in unison on common technical signals, the mass army retail investors do indeed add to the issue of erratic volatility within markets.

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However, volatility caused by the retail community is 'quick-shot' volatility at, or near areas where commonplace technical signals (like Stochastics rolling under +80, for example) unfold. What's more, quick-shot volatility caused by retail traders is not ‘trend sustaining’, as really, institutions are the only entities with enough buying or selling power to prolong trends. Moreover, the individual investor is often set up for failure from the start, because not only is he or she not able to 'see’ institutional order flow (imagine the ticker tape in equities), but because traditional 'pre-loaded' information he/she is receiving is often ‘missing’ critical components, all at the same time. The question is then, how can traders transcend failing technicals, increasing volatility and jaded information- to achieve greater insights and perceptions into serrated movements within Forex. In other words, "How can we perceive volatility before it occurs?" Over the following pages, I will attempt to explain how using principals of descriptive statistics can help identify trending and reversals, while also foreseeing volatility within almost any charting timeframe. In the end, we will break through market volatility with a greater understanding of the dynamic movements of subset distributions, while also utilizing common

probability within descriptive statistics to capitalize on market action. It is important to note that even when traders embody substantial technical and fundamental knowledge, risk prevails without the proper understanding of the larger probability and volatility paradigm behind currency trading. Here, traders are encouraged to boldly challenge typical pre-conceived notions of technical and fundamental analysis, in an effort to see beyond 'the accepted standard’ retail traders are told to believe daily. The ‘accepted standard’ does not presently uphold volatility and probability as important aspects of markets or trading. However, given that 80% to 95% (depending on who you talk to) of retail Forex traders lose, while many retail brokerages actually take the opposite side of their trades, perhaps the at-home trader isn't supposed to know anything more than the ‘accepted standard’. Traders who understand descriptive statistics though, will find greater clarity and perception of volatility within intraday and longer-term movements unfolding in markets.

Words of Caution Within Forex and trading, there is no holy grail; thus, please do not read the following with the firm belief that you will never again be faced with confusion, unclear volatility, or losses in markets. What you are about to learn is an incredibly effective guidance tool helping identify trending, volatility and at times, reversals; however, even the concepts here must be used with prudence and common sense. You are about to read about descriptive statistics, which within itself has many different approaches, methodologies and studies. I will not delve into the justification of math underneath most statistical concepts here. Instead, I am presenting descriptive statistics from a simple, conceptual framework. However, there are many resources available to explain the empiricism of descriptive statistics on the Internet and in your local library; you will also find plenty of recommended reading in the bibliography at the end of the book, should you chose to learn more about the subject (which I highly recommend). Never forget that economics and fundamentals rule all...at least, over the

long haul. Traders who do not take the time to properly uncover the true economic paradigm within markets —and the future possibilities of such— will likely often find themselves on the wrong side of the trade, especially those who hold positions during longer timeframes. While the concepts presented within Volatility IIluminated are designed to specifically help traders navigate intraday movements and volatility within markets, we have no excuse to ever slack on our fundamental research. Please take time to do the proper research every day.

Transcending Markets through Volatility and Probability Almost all financial markets display relentless volatility for traders attempting to capitalize on trading within shorter-term timeframes. Intraday uncertainty and volatility are just facts of trading in virtually every market. Moreover, because of the inherent underlying "volatility paradigms", many traders (both new and seasoned) stand significant risk of unforeseen losses, almost any moment their positions move against intraday order flow. In Addition, while order flow may not seem like a reasonably transparent variable in Forex, in reality, through descriptive statistics, we may not only predict when and where institutional order flow could commence, but where such could end as well. In the end, through volatility and probability, savvy traders will learn to 'ride the waves' of order flow within markets. At some level, I expect these concepts to be met with resistance, as traders find difficulty in leaving ‘the old notions' of technical analysis behind. By this I mean, often when 'the accepted standards' are challenged, some have trouble letting go of the information that has been taught as reliable for so long, even if it's flawed. However, reiterating Henrik Ibsen's famous statement from the 1882 play An Enemy of the People, "the majority is always wrong." From the shoes of most retail traders, many likely find themselves continually frustrated from confusing signals from technicals, which often seem completely misleading in real time. For some strange reason though, many retail traders continue to believe (or perhaps want to believe) in their ‘accepted standard’ technicals, despite

the losses surfacing in their accounts. Really, technical analysis is only true so long as enough people are acting on the same information at the same time. However, as we saw in Chapter Two, technicals are actually failing in today's market, because too many people are acting on the same ‘pre-set’ information at the same time. Here's where we start to level the playing field... We will no longer look at charts as instruments of trading... producing 'signals', which we trade from. Instead, we will look beyond the charts. We will firmly resolve to understand that by looking at charts whatsoever, we are viewing data and the translation of data and nothing more... We will also resolve to stop looking at magical technical patterns (hyped, invalidated signals some schmooze is selling), or bogus Expert Advisors (EAs) that do not and/or cannot justify their existence from some sort of understandable framework of descriptive statistics, mathematics, or physics. In addition, while I've already touched on the aspect of price action as 'data', the concept must be revisited briefly... just in case. The 'data' is so much more than just price action, in reality, 'the data’ is screaming aloud...begging us to stop and pay attention for a moment... The data is saying, "If you take a moment to truly look into me, I provide significant insights into probability/volatility, order flow and fundamental underpinnings...truly moving markets." Fact is, technicals generally only perceive events in the past... Traditional technical analysis' predictive worth is nothing more than hope that an event that occurred yesterday, will happen again. Really, technical analysis is the study of information that has already occurred, without any regard to the probability of what could truly transpire in the future. In essence, technical analysis is nothing more than the study repetitive pretty pictures throughout history. Unfortunately, there are those who will continue to argue traditional technical analysis can predict tomorrow... Concerning most signals though, when the technician is asked ‘why will it work again?’ most explanations will sound like: "Because it worked in the past." Humbug. Technicals work perfectly when studying historical charts; I agree. Nevertheless, the one time you attempt to trade from one of the supposed super-signals... I can

assure you, will be the one time the signal will fail. Have you ever entered a trade based on an overbought or oversold indicator, only to see the trade move against you? In the amazing book Technical Analysis for the Trading Professional, author Constance Brown inquires, "How did I miss such an obvious signal like that one?"

"How did I miss such

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that one?" Chances are that you did not miss the signal; it may not have been presented when you oe kees ets Celoetetme ele trade. Many traders

Itsy erie adores toa eT may have appeared differently to them in ere oreo Cay really take the time to explore the character of the indicators from which they trade.”

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How many traders ever really look into 'why' what is happening - is actually happening? Repeatedly, I see traders completely disregard the philosophical, fundamental, mathematical and common sense reasoning behind the charts they trade from. I can assure you that when these traders and investors grow sick and tired of losing money, they will finally either quit, or start putting in the time. Regardless, trading purely off technicals is similar to looking for Braille in the drive-thru.

Using our earlier discussion of CCI as an example, when (for example) the EUR/USD begins ascending and CCI travels above +100, traders who have based their understanding of the indicator on 99% of the commonplace explanations available, one would believe the EUR/USD is now entering ‘overbought territory. In reality, the EUR/USD (on whatever timeframe being viewed) is simply moving outside of (about) the ist standard deviation mark, which really means ‘distribution

potentially on the move.' The same logic applies to expectant fundamentals, in that enough people have to believe in a future outcome for the actual trading action to mimic the "perceived outcome" by those same people. Even more devastating though, when fundamentals shift, often, many technical traders are not even aware that the occurrence has taken place and thus, find themselves not only on the wrong side of the trade, but often stopped out at exact high and low prints of the relative range too. When we begin to understand descriptive statistics not only allow us to intuitively ‘feel' the true fundamental sentiment behind the market, while also overstepping the simple ‘hope’, which too often comes from traditional charting, we begin to see that both technical and fundamental perceptions of the larger market are actually transparent within the data unfolding before our very eyes. Descriptive statistics are defined as:[45] [1]Descriptive Statistics are used to describe the basic features of the data gathered from an experimental study in various ways. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. It is necessary to be familiar with primary methods of describing data in order to understand phenomena and make intelligent decisions. Various techniques that are commonly used are classified as: ¢ Graphical displays of the data in which graphs summarize the data or facilitate comparisons. ¢ Tabular description in which tables of numbers summarize the data. ¢ Summary statistics (single numbers) which summarize the data. I ask, "How can anyone make an intelligent decision in markets, without understanding the data at hand?"

See, so many traders use technical analysis to decipher their entry and exits without truly evaluating the logical validity of the technical signals surfacing. What's more, tack on complete oversight of the common sense economic fundamentals and truly, it's no wonder retail traders are often stopped out at the top, or bottom...or just plain wrong altogether. The aforementioned aside, let's discuss how technical analysis also completely overlooks vital "real data" traders need most. Principally, we know technical analysis is a "lagging event," meaning the action unfolding on charts can only exist insofar as another event has already occurred. In short, an equity, index, option, commodity, futures contract, or even currency must have already witnessed a trade 'print', before the data ever even shows up on a chart. Which came first, the chicken, or the egg? In the world of trading, a transaction MUST always take place before the instance shows up on a chart. (To split hairs, one might argue the two events are simultaneous. however, a transaction can take place without the existence of a price chart, but a chart cannot exist without a transaction, or series of.) Really, the true wealth in technical analysis is the identifiable and/or representative display of empirical data unfolding, otherwise known as descriptive statistics. When we change the way we think about what we are seeing on charts, we change our total understanding of technical analysis, especially ‘hocus pocus hope indicators' and/or methodologies built for failure from the start. When we begin to understand that by viewing and analyzing the data displayed on charts, from a stance of descriptive statistics, we will begin to see that we can completely discard the mainstream technical crap foolishly embraced as the ‘accepted standard, ' and instead start measuring and utilizing volatility and probability as tools for profitability. I hope that readers understand why I'm beating a dead horse... We must understand that the accepted standards of trading tools presented to the retail crowd... are flawed. So how do we overcome the issue through volatility and probability? First, we must also understand that all data, all price action we empirically see on our charts is:

1. Data coming forward tick by tick. 2. When summed, creates a series of smaller subset distributions, within larger

distributions. Within descriptive and inferential statistics, we will use a random normal

3. No different, or more complex, than any other data measured by statisticians. How is it we can predict weather patterns, and yet, mainstream media and general populace investors can't see the Dow tumbling downward through 10,000 one week in advance? Anyway, in our discussion of descriptive statistics, we really only going to focus on the concept of 'normal' (bell shaped) distributions... The application of normal distributions applies to trading insomuch as the data we are measuring is constantly moving with the periods we are studying. What I'm saying is because a subset distribution's mean (seen through a moving average, for example) is constantly in motion, as prices rise and fall, the 'data' (prices) will never stay skewed on one side of the mean, or another. When studying smaller subset distributions, eventually the data will cross back over the mean (moving average); because within markets, our means are never totally stagnate. The ‘traveling factor’ behind a moving mean proves that the data we are measuring will eventually return to the mean, perhaps even crossing above, or below, possibly even extending significantly in the opposite direction. It is the concept of the mobile mean (where old data is replaced — i.e. the newest day of a 50-period Simple Moving Average forcing the 51st day to drop off) that justifies measuring volatility through standard deviations under the premise of a normal Gaussian curve. (Really, we're talking about the Central Limit Theorem.) What's more, as John Bollinger points out in his book Bollinger on Bollinger Bands, the Central Limit Theorem tells us that even when long-term data is not normally distributed (as is the case with virtually every financial instrument, including Forex), "a random sampling will produce a normally distributed subset for which the statistical rules will hold."161

In short, smaller samples within the market will not produce the kurtosis of the larger data set.07]

Gaussian Curve Revisited

distribution (Gaussian Curve) to measure volatility via standard deviations.

In terms

of the Gaussian Curve, we are looking at a normalized distribution, where the sum of all values of x, are meant to equal 1. The previously mentioned means if we are measuring price movement within 21periods on a 5-minute chart (for example), the result should equal a probability of 1. In other words, the sum of all values that have transpired in the measured period should present a probability of 1 (an empirical absolute) that the events have happened. What we're referring to here is a probably of 1 that all of the data presented will rest within our chart, regardless of where the data is within the chart. As long as the data exists, we're in business on a probability basis.

Figure 8.3 Random Normal Distnbution

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Moreover, by using historically measured occurrences of data, we can statistically infer the probability of future events. I need to clarify a question a few readers might have lurking in their heads... In my previous attack on traditional technical analysis, I pointed out that if one were to ask a technician why most traditional technical signals were valid, he or she, would often be stuck without a proper response, other than that of the signal having shown to work in the past. So why, for heaven's sake, is this statistical junk any better? As I already mentioned, I'm not going to write a novel showing the mathematical justification for volatility/probability; however, I believe some explanation is required for peace of mind. In statistics, the concept of authenticating data is known as classical test theory,

predictive, or concurrent validity.[18] Concurrent Validity is, "An index of criterion-related validity used to predict performance in a real-life situation given at about the same time as the test or procedure; the extent to which the index from one test correlates with that of a nonidentical test or index; e.g., how well a score on an aptitude test correlates with the

Gaussian Distribution Function Gaussian or "normal" distribution

score on an intelligence test."1191 What we're looking for is the theoretical outcome (on paper) to return a high correlation value (correlation coefficient) to the real world information presented

daily. How will we do all of this? As you are likely already aware, data within a Gaussian Curve is measured via ‘standard deviations' from the mean. The arithmetic mean, of course, is the average of all prices recorded in the period we are studying. Thus, by being able to calculate the average prices for a particular period, we can also measure probability of movement away from the mean through standard deviations (confidence intervals, otherwise known as "probability volatility") and thus, the probability of all data truly resting within our distribution, validating our previous concept that all of our data (in the timeframe measured) should total 1.

If you remember your old statistics days, you may also recollect that the majority of all data occurrence probability falls within three standard deviations of either side of the mean. What we know is that 49.86% of all the data should rest within three standard deviations of each side of the mean. In translation, measuring three standard deviations (probability volatility) on both sides of the mean, equates to 99.72% of the data within the period measured. In other words, there is a 99.72% probability that all of the data will fall within three standard deviations of the mean. Breaking the standard deviations down, there is a 34.13% probability our data should rest on one side (above, or below) of the mean. Moving out a little further, there is a 47.72% probably that all of the data will sit within two standard deviations of one side of the mean, and a 49.86% probability that all of the data will rest within three standard deviations of one side of the mean.

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However, while there is one standard deviation to the left of the mean (above a moving average, in the case of trading charts), there is also one standard deviation to the right (below), and thus, we know to multiply all of our probabilities by two, compensating for data on both sides of the mean, not just one. There is a 68.26% probability all of the data will sit within one standard deviation of either side of the mean, a 95.44% probability all of the data will rest within two standard deviations, and a 99.72% probability of all data in our period measured will reside within three standard deviations of either side of the mean. In terms of measuring the 'mean' for this chapter, we will only be using Simple Moving Averages and not Exponential Moving Averages (which put more weight on near-term price data, over the latter.) It's true Exponential Moving Averages track price action more closely, however, we're keeping things simple here... I think John Bollinger presents a great argument for using Simple Moving Averages in his book Bollinger on Bollinger Bands, asserting that we are simply adding one more variable to an already complicated scenario, by using Exponential Moving Averages. I'm not saying that you shouldn't use EMA's, but for these pages (at least for now), we're going to keep things clean and simple. As many likely already know, a Simple Moving Average (SMA) is created by summing the data (close prices, for example) for any given period and then dividing by

MACRO

TO

MICRO

the total number of periods aggregated. For example, on a five-minute chart, a 20-period SMA will present the arithmetic mean (average) of all closing prices for the past twenty 5-minute bars at the location of the mean, on the current bar

VOLATILITY

TRADING

Moreover, we are also mapping the actual distributions to be able to answer the question of why implementing probability not only accurately reflects the past, but is also incredibly useful in attempting to perceive the future, over standard technicals. In essence, the distribution is really a sort of teeter-totter (unfolding around the mean), where we are able to actually see how the data is unfolding on our chart.

Figure 8.5 EURUSD,H4

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The Simple Moving Average, is thus, the ‘mobile mean’, which we are basing our standard deviations (confidence intervals) from, attempting to measure total ‘probability’ of breadth, or 'wingspan' of the data unfolding on a chart. As Figure 8.5 shows that we are seeing ‘more than just a moving average’, rather, we are seeing a visual representation of the "mean data range" over the past 20periods, where we will uncover key statistical data to volatility and probability. In essence, we are able visually map the middle point of our data curve, by charting a Simple Moving Average. With the mean identified, we must now identify benchmarks (confidence intervals), in an effort to validate our probability distributions, as accurate and usable within trading. We're really talking about mapping the ‘distribution’ of data through standard deviations.

1.39024 1.39096 1.38854 1.38861

We are visually seeing a distribution of data on our chart...

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Figure 8.6 In terms of inferential statistics, we also know there should be a 99.7% probability that all of the data (in the period measured) will rest within three standard deviations of the mean... As the below chart shows, other than two small blips outside of the lower 3rd standard deviation, almost all (99.7%) of the data does indeed rest within 3-standard deviations of the mean.

Figure 8.7

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TO

MICRO

VOLATILITY

TRADING

longer-term distribution. As the shorter-term distribution moves from right to left on the larger teeter-totter, the result should be for the larger teeter-totter to fall to the left, as the bulk of data begins to add pressure, thus denoting trending... Think of it like this... Suppose we're standing in a part looking at a big teeter-totter called the '50-period seesaw’, which is for the most part, well balanced. Now imagine I walk up to it with a box of bricks labeled '20-period stones' and dump the load on the far left side of the teeter-totter, what would happen to the larger 50-period seesaw?

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Just incase Figure 8.7 was a little confusing, Figure 8.8 (on the following page) shows a clearer picture of Gaussian Curve concept on an actual chart. Please note that I used a 20-period Simple Moving Average (SMA) to measure the Gaussian Curve in Figure 8.7. Looking at Figure 8.8, traders will notice that when we simply overlay three representative curves (denoting the 1st, 2nd an 3rd standard deviations) with the mean being the 20-SMA, we see how the Gaussian Curve looks when mapping actual data. Now though, let us think of the Gaussian Curve in another format... Let us imagine the curve as an actual "teeter-totter" like scale, which will shift higher and lower on the left and right sides, as the data moves from side to side. The question is how we will measure, or map the data shifting from side to side on the teeter-totter? To provide any insight into ascending, or descending prices, we must have some way to map distribution data shifting, thus prompting the teeter-totter to lean in one direction, over the other. As a solution, we will simply map a shorter-term subset distribution within a

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It's a no-brainer of course, the larger teeter-totter would lean to the left... With a little shameless marketing in mind, I'm going to call the teeter-totter the Whistler Trending Scale (WTS). Moreover, just to make sure the point of moving subset distributions is absolutely clear, please again imagine a larger teeter-totter sitting idle in a park, with both sides suspended in mid-air equally... We can generally assume all things are relatively calm (or normal) in that one side is not dramatically higher, or lower than the other. However, if I were to set my neighbor's super big chunky ice-cream kid on the left side of the totter, what would happen? Everything you own in a box to the left. Now, if you imagine the larger teeter-totter is 50-periods of data, what happens when I slide 20-periods of data to the left? The key point to note in the WTS, is simply that when the shorter-term data (measured by a 20-SMA) moves to the left of the 50-SMA (longer-term mean), the

scale should tip down to the left, thus denoting a bull trend.

Instead, we will be using distributions (and the outliers of distribution width) to

1. Data to the left: bull. 2. Data to the right: bear. 3. Data significantly flanking either side, but headed back to the center: Mean reversion. Figure 8.9 Whistler Trending Scale

(WTS)

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measure potential future volatility probability and current ‘acceleration’ volatility/probability within the short and long-term mass (distributions), we are measuring. Moreover, we will use the outliers of distribution width (total mass width), by means of standard deviations, to identify trend entries and exits for trending and/or potential consolidation to come. (Note: The areas shaded more darkly in Figure 8.9 are simply to point out the second and third standard deviation outliers of the 50-period distribution.) If you are having trouble with the larger concept of sliding distributions, please take a look at Figure 8.10, where I have rotated the WTS 90 degrees to the left. The image again shows how when a shorter-term distribution slides to the left of the mean, whatever instrument we are measuring...is taking on a ‘bullish bias’, and vice versa for distributions sliding to the right. Again, we are not looking for 'moving average crossovers' here at all, we simply taking note of the occurrence of sliding distributions. Moreover, instead of eying the precise traveling of the moving averages, our trading clues will come from the expansion and contraction of the standard deviations framing the various distributions. Figure 8.10

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Historically, the instance of a short-term distribution sliding from one side of the totter to the other is known as a "moving average crossover". In effect, Moving Average Convergence Divergence (MACD), comprised of 12 and 26-day moving averages is an example of a short-term distribution mean sliding back and forth across a longer-term average. However, it is important to note that what we are not doing here is simply looking for instances of a short-term distribution crossing over a longer-term mean for a trade signal. Again, what we are not doing in our study of volatility and probability is simply using mean (moving average) crossovers for signals. Not only would the signal be lagging, but likely constantly setting us up for failure as our traders were stopped out on pullbacks surfacing almost immediately after taking a position.

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How do we map standard deviations? As you may have already guessed from Figure 8.7, we can simply apply Bollinger Bands (the actual application of standard deviations on a chart); however, we will not be using the typical settings commonly pre-loaded within charting packages. Again, if you are not aware, Bollinger Bands are really the ‘application’ of the standard deviation formula to charting. I sometimes think many retail traders never truly understand how powerful Bollinger Bands really are, simply because the name is slightly misleading... Don't get me wrong, John Bollinger is a genius and one of my greatest market inspirations, however, what we're really seeing is a probability distribution measured through the actual expansion and contraction of standard deviations. For the sake of keeping our terms clear, over the following pages, we will call Bollinger Bands ‘probability volatility’ or ‘volatility bands'. Again though I believe Mr. Bollinger is nothing less than a market genius, like so much information surfacing in markets today, many investors are missing the power of indicator (based within probability theory and statistics) because most mainstream explanations of the tool are incredibly misleading. Here's an example: One major Website defines Bollinger Bands as: "A technical analysis technique in which lines are plotted two standard deviations above and below a moving average, and at the moving average itself. Because standard deviation measures volatility, these bands will be wider during increased volatility and narrower during decreased volatility. Some technical analysts consider a market which approaches the upper band to be overbought, and a market which approaches the lower band to be oversold."/22]

There we are with our two favorite words again: overbought and oversold. Whoever wrote the above definition has obviously never traded with real money... Foremost, Bollinger Bands (volatility bands) expand and contract because of the squaring of the deviations in the formula, which causes a 'multiplier effect' in the bands. What's more, even remotely inferring that when price touches the upper, or lower bands means ‘overbought’ or 'oversold' is fairly incorrect, at least as presented to average JoeQ-investing public. Obviously, a 7-period distribution will have significantly less mass than a 50-period distribution; thus, usually when price touches the second standard deviation of a 7-period distribution, price is likely just starting to near the ist-standard deviation of a 50-period distribution. Do you see what I'm saying? The concept of 'overbought' and ‘oversold’ is relative

to the period measured... I hate to pick on details, but the little snippet of information the mainstream investment education Website left out...is pretty darn important. Geesh... I'm not kidding, this stuff keeps me up at night... With the previously mentioned in mind, I would like to note that the WTS is literally a scale of common sense, showing that when we add weight to one side, the occurrence creates motion whereby the scale shifts. What I'm saying is the tag of the second standard deviation on a short-term distribution does not mean price is oversold, the occurrence likely means the lesser distribution (in terms of periods measured) is shifting on the scale and price is moving... To clarify the actual movement of a distribution, as seen in price action, in Figure 8.11, you will notice I have drawn a 20-period moving average and a 50-period moving average, the former with volatility bands at 3.2 standard deviations, and the latter at 1.2 standard deviations. (It is important to note that when drawing volatility bands on charts, we must always set our standard deviations slightly above where we normally would, to account for data loss in the electronic application of the formula. By this I mean, we want to set the 1st-standard deviation at 1.2, the second at 2.2 and so on.) In terms of the WTS concept, we immediately see the 20-period distribution is now above the 50-period mean, obviously denoting the teeter-totter has leaned to the left, or in other words, the EUR/USD is experiencing bullish trading action. There's more to the story though... Remember the previous discussion of data probability (in relation to the mean), measured through standard deviations? Figure 8.11

Moreover, we will also know when the probability of continued trending has ceased, thus telling us to close our directional positions and switch gears to lateral trading strategies... It's all probability and volatility, seen through the expansion and compression of standard deviations and distributions on our charts...

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The Expansion and Compression of Subset Distributions

Looking at Figure 8.11, we know through probability 99.7% of all our data should theoretically rest within 3-standard deviations of the mean. In the case of our 20period distribution, if a sharp selloff were to occur in the next bar, where do you think the selling action would likely pause, at least temporarily? Probability tells us...likely on the lower 3rd-standard deviation of the 20-period distribution, which is coincidentally sitting almost precisely at the mean of the 50period distribution. However, as witnessed in the chart, just a short while ago, price actually tagged the upper 3rd-standard deviation of the shorter-term distribution, before commencing the present consolidation... The event of price hitting the third standard deviation did not mean the EUR/USD was oversold, rather, the occurrence just meant fierce buying was attempting to push the shorter-term distribution into a new range... Now, with the 20period lower third standard deviation still above the 50-period mean, the occurrence of consolidation could simply be allowing both distributions a moment to ‘breathe’ and potentially store energy for another move higher... The aforementioned is just a quick example of the great power measuring standard deviations (and the movement of distributions) within markets...

You will soon see

that we can also identify when trending is about to begin, when 'true trending’ is in effect and also when trending has ceased and consolidation is about to commence. Using concepts of descriptive and inferential statistics, and specifically standard deviations, we can time our entries to capitalize on volatility induced breakouts and breakdowns, taking positions when probability of order flow is truly in our favor.

Jumping right in, Figure 8.12 (below) shows the EUR/USD on a 4-hour chart... We are again looking at a 50-SMA, with our volatility bands set at 1.2-standard deviations. We've also included a 20-SMA with volatility bands set at 3.2-standard deviations. Within the longer-term distribution (the 50-period), we are using utilizing the 1ststandard deviation to derive the smallest portion of probability (in terms of descriptive statistics), that would infer potential follow through of price away from the mean (divergence).

We know there is a 68.4% probability that all of the data should rest within 1-standard deviation of the mean. It's also important to note that when measuring 1-standard deviation from the mean, the occurrence of price moving away from the mean, through the 1st-standard deviation would line up with 50-period CCI traveling above +100, or below -100. Again, while the rest of the retail herd is thinking the event is showing the currency as ‘overbought’ or 'oversold', because you are more educated and much more savvy now, you will know that really, the instance just means the distribution is ‘on the move.’ Don't you just feel bad for all those other traders attempting to navigate markets with flawed information. They've been set up for failure, unlike you... What the above is telling us is when a currency, stock, or other trading instrument moves above, or below a longer-term 1ist-standard deviation, the instance is NOT screaming overbought, or oversold, rather, the instance may instead be saying three things: 1. A fundamental event or coincidental news occurrence has prompted traders to

take action, and is creating a move away from the mean. In essence, there is a reason the bricks (prices) are sliding to one side of the teeter-totter. 2. If the move is real (not just a ‘quick pop' anomaly), the short-term distribution should 'confirm' the fundamental mindset shift, by sliding over the mean of the longerterm distribution and then hold reasonable ground during the initial pullback(s), after the breakout. 3. Short term volatility (measured through standard deviation) will lead price, an inherent luxury of squaring of the deviations in the standard deviation formula, when electronically applied to price action on charts. (I will explain this in greater detail in just a moment.) Figure 8.12 Mad

If all things were equal, meaning normal and calm in markets...and we were measuring 50-periods of data and 20-periods of data...and we were measuring total ‘width’ (mass) of both distributions...and we were doing so by mapping the 3rdstandard deviations of each, would the 20-period 3rd-standard deviations be inside, or outside of the 3rd-standard deviations, of the 50-period distribution? Think about it for a moment... If all things were equal, meaning prices were in a 'normal state’, like not trending, just calmly traveling sideways, would the total mass (width) of the 20period distribution be greater, or lesser than that of the 50-period distribution? Clearly, the 20-period mass would be lesser (narrower) than the 50-period distribution. Why? We're simply measuring less data. In short, there is greater probability of higher and lower prices in 50-periods of data, than 20-periods of data, when ‘all things are equal', or prices are in a ‘normal state’. However, when volatility increases, because we're measuring less data in the 20period distribution, the distribution mass would expand quicker than the 50-period distribution. The rule holds true when measuring 50-periods at 2.2-standard deviations and 20periods at 3.2 standard deviations as well, though there is slightly more room for trader error. Figure 8.13

If short-term volatility, as denoted by the 20-SMA 3rd-standard deviation spikes above long-term volatility, as denoted by the 50-SMA 1st-standard deviation, the breakout is "confirmed." Why? What we are seeing is an instance of short-term volatility leading price... Please allow me a moment to pause and explain a very, very important concept... If we were simply measuring total distribution mass through probability (standard deviations), conceptually, we're talking about the total ‘wingspan’ of the distribution...or in other words, how ‘wide’ the distribution is...

MACRO

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Short-term volatility spikes outside of long-term volatility Short-term volatility collapses below long-term

volatility

Really though, what we're talking about is the empirical state where short-term distribution width will always expand quicker than longer-term distribution width, based on the fact that the shorter-term distribution has less mass overall. Remember our previous discussion of Newtonian Motion and the equation of force? Force = Mass * Acceleration. The lesser the mass, the greater the possibility of acceleration and thus, more force to move our distributions. As the Figure 8.13 shows, when short-term volatility spiked outside of long-term volatility, a significant downside move in the GBP/USD occurred on the hourly chart Figure 8.14

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There is even more to the story here though... Back on our original chart of the EUR/USD (Figure 8.14), readers will notice when the EUR/USD touched the topside 3rd-standard deviation of the 20-SMA, the pair quickly fell back to the 50-SMA mean, before resuming the ascending trend. The longer the period measured, the greater the probability prices will fail at the 3rd-standard deviation. What's more, in shorter-term periods, prices tagging the 3rdstandard deviation denotes three events occurring: 1. Short-term volatility has accelerated to a point where prices are tagging statistical ‘outlier’ variables. 2. Trending will likely ensue. 3. Before trending ensues, a pullback to the mean, or 1st standard deviation is highly probable. See, we know statistically, there is a 99.7% probability that all of the data will sit within 3-standard deviations of the mean. Thus, on a short-term basis, if we have bought a currency pair in a bull-trend and the pair strikes the shorter-term 3rd standard deviation (on the topside volatility band), we may want to consider taking profits. It is important to note that price touching the 3rd-standard deviation does not

mean the trend is over, rather, simply that a pullback to the mean may perhaps, be in the cards... Then, we can attempt to re-buy the pair on the mean, if we believe the trend is still intact. Moreover, if price has just hit the third standard deviation and short-term volatility is collapsing back below long-term volatility, we can rest assured price is indeed about to consolidate, or reverse. Why? Simply put, price cannot move up, or down if short-term volatility is compressing. Because of the squaring of the deviations in the standard deviation formula, the application of standard deviations to price data creates a 'multiplier effect' in the lines representing the standard deviations. What I have just mentioned is the reason volatility bands expand and contract, providing us with key information regarding the release of energy within price action and thus, the expansion and compression of distributions. It doesn't matter if you're looking at a 5-minute chart or a weekly chart, if shortterm volatility is expanding, prices are trending in the period measured. Moreover, if short-term probability volatility is compressing, prices are consolidating. Figure 8.15 shows precisely what I'm talking about... As you can see, when short-term volatility (measured through the 20-period 3.2 standard deviation) compresses, in every occurrence, prices travel laterally until shortterm probability volatility begins expanding again. Moreover, as you will also notice, both times when short-term topside volatility attacked long-term volatility and compressed back below, the occurrences were almost precisely where the EUR/USD stalled on the upside. Why? Again, price simply cannot continue trending if short-term volatility is compressing. Short-term distribution width (mass) as measured through the 3.2standard deviation, otherwise known as topside volatility, must be traveling upward for bullish action to continue. Why? If short-term volatility is compressing, the occurrence means the distribution is ‘compressing’ and a distribution must release energy to move, not vice versa. Short-term distribution width (mass) as measured through the 3.2 standard deviation, otherwise known as volatility, must be expanding for directional action to continue. See, price movement in markets is about order flow, which is really about the expansion and compression of energy in subset distributions. Logically, short-term distributions must release energy first, thus stimulating the same occurrence in longerterm distributions.

Figure 8.15 EURUSD,H4

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You will read more about the 'release of energy’ in the following chapters so if you're having trouble fully comprehending, or believing, this 'energy expansion and compression’ thing, don't worry... You will see the exact same occurrence in Figure 8.16, with short-term probability volatility expanding outside of long-term volatility on the downside, prior to the EUR/USD plummeting on the 4-hour chart. As short-term volatility remains outside of long-term volatility, trending continues. Then, just like clockwork, when short-term probability volatility compresses back below long-term volatility, the move is over and lateral trading ensues. One more time, when short-term probability volatility is outside of long-term volatility, we will likely want to use trending strategies. Conversely, when short-term probability volatility collapses below long-term volatility, we will likely want to start using short-term term channel trading strategies, while continually eyeing a possible longer-term "trade with the trend" entry. If we were able to close a position when short-term probability volatility collapses below long-term volatility and then open the position back up when short-term volatility begins to expand, we are -in essence- putting significant probability in our

MACRO

TO

MICRO

favor- to ride a larger wave of market action, otherwise known as energy expansion, or trending.

VOLATILITY

TRADING

begin (or continue) trending, or...

2. You can use the indicator I have developed and supplied with this book titled: Whistler Active Volatility Energy « Price Mass (WAVE « PM).

Figure 8.16 bMS EURUSO,H4

1.4606 1.4627 1.4601

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For now, another problem likely persists for traders... How to denote trending?

1612

Volatility contraction indicates channeling

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movement down, coupled with shortterm voltility spiking

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Of note, when traders begin to apply all of the aforementioned to their own charts, there will be instances of short-term volatility expanding (with price moving), however, just at the time short-term volatility actually moves outside of longer-term volatility, the price reverses. Why? Because the longer-term distribution had too much mass to start with. Please remember: Force = Mass * Acceleration. It doesn't matter how hard you try... You (the person reading this book right now) cannot push a dump truck full of lead up a mountain. The dump truck has too much mass. So how do we measure mass in terms of trading and price distributions? We can measure mass two ways:

1. Visually, you can look at the total width of the longer-term distribution and gauge (with common sense) whether the distribution is too wide to

In the Figure 8.17, I have inserted a 50-SMA 1.2-STD, a 50-SMA 3.2-STD, and a 20-

period 3.2-STD, while also adding two 3-period SMA's (the darker two black lines closely tracking price), one measuring highs and one measuring lows. Working from left to right in Figure 8.17, we see trending was confirmed when short-term volatility spiked outside of long-term volatility, as denoted with the 20-SMA 3.2-STD spiking outside of the 50-SMA 3.2-STD. What's more, the EUR/USD also fell below the 50-period 1.2-standard deviation, indicating the larger distribution was on the move. The instance of the EUR/USD falling below the 50-period 1.2-standard deviation would have shown up on most CCI windows with the 50-period CCI falling below -100, which clearly did not mean ‘oversold’, rather, the occurrence was shouting ‘distribution on the move’. As the chart shows, the downside move stalled when the 20-SMA 3.2-STD collapsed back below the 50-SMA 3.2-STD. For those who were thinking there was more downside to come (at least immediately, anyway), volatility/probability traders knew the move was over and mean-regression (at the very least) was in effect. What I've just mentioned was clearly noted not only when short-term volatility collapsed back below long-term volatility, but also when the 3-SMA lows crossed back over the 50-SMA 1.2 STD, implying downside order flow was no longer in effect and shorts would be squeezed. Using short-term highs and lows of the 3-SMA (traders who prefer a little more ‘wiggle room' can use 10-period SMA's off highs and lows) we are able to quickly identify when the highs and lows of the immediate range fail support and resistance. What's more, because we are measuring 3-periods of data, it will take more than one freak candle of upside, or downside volatility to truly change our directional

bias. By using the 3-SMA for confirmation, we are able to further isolate head-fake moves, from true trending. You will notice that while the EUR/USD was channeling, both the 3-SMA highs and lows never moved above, or below the 50-SMA 1.2-STD. Again though, the actual

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MICRO

instance of trending was not confirmed until short-term volatility spiked outside of long-term volatility. What's more, as the chart shows, the WTS would have moved violently to the right, thus inferring trending to come, as the short-term (20-period) distribution moved to the right side of the long-term (50-period) teeter-totter. Shown in Figure 8.17, recognizing volatility spikes can yield big moves, as noted in the almost 300-PIP drop

in the EUR/USD.

Figure 8.17 "& EURUSD,H4

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It's important to note that in the framework of the information here —as alwayscommon sense is required. As you may have noticed, in the above chart, short-term volatility did not constantly stay outside of long-term volatility... The instances of short-term volatility compressing are marked with price moving back to the 20-SMA, but did not fully reverse. Every extended move in markets will see pullbacks as volatility reloads for another move... In the above chart, we also know the EUR/USD never violated descending resistance of the relative range, until the trend completed itself. Using the 3-SMA (lows); however, would have absolutely closed the trade before descending resistance

VOLATILITY

TRADING

was violated, thus perhaps saving a few PIPs of profit. What's more, in one instance (a little over halfway through the move) the EUR/USD struck the 3rd-standard deviation of the 20-period distribution. Savvy traders may have inferred it was time to take profits; thus setting themselves to re-enter -with the trend- at the 20-SMA mean (which was precisely at descending resistance as well.) In addition, as previously noted, because the 3-SMA lows never moved above the 1.2-standard deviation of the 50-period distribution, taking a long-position any time during the decline would have been foolish. Regardless, it is important to note that when trending ensues, we should always attempt to understand why the move is occurring in the first place, on a fundamental basis. However, with a little common sense understanding of descriptive and inferential statistics, distributions and probability, all of the information here should help traders identify trending, channeling and reversals. Again though, common sense is king, and if you're not sure why what's happening is happening...as a rule of thumb, "when in doubt, get out." We have just covered volatility and probability (in terms of mobile subset distributions), based on 'price action’ alone. It's important to note what we did not cover is volume/dollar volatility/probability as well. Price action is reliable when we understand that it is... ...purely price action. Looking towards volume/dollar trading action as well, we will now jump into Chapter Nine, were we will discuss VWAP (Volume Weighted Average Price), thus adding another variable for aggressive intraday traders seeking to capitalize on volatility... After our introduction of VWAP, we will enter into a more detailed discussion of ‘institutional order flow’ and then tie everything together through Whistle Active Energy Volatility » Price Mass (WAVE « PM).

Appendix C | Information and the Volatility Paradigm Updated from Volatility Illuminated "Believe nothing, no matter where you read it, or who said it, no matter if I have said it, unless it agrees with your own reason and your own common sense." ~ Hindu Prince | Gautama Siddharta

What if? What if some of the information we believe is truth...is not... Rather, please allow me a moment to rephrase... What if the information we believe -as truth- is valid in some regard, but to us -as traders- is not providing an accurate picture of reality within markets? In Chapter Three, we already covered how our own memories can play tricks on us, how media can literally recreate our perception of past events (shown in the poison tree example), we even saw how our emotions can be cyclically altered through headlines and media. What we have to understand...is that not all information is incorrect and not information that we have might believe is false...actually is. In reality, there is another variable happening here, which we did not touch on in Chapter Three. See, the entire paradigm of how we (and markets) receive information has changed dramatically over the past 10, 30, 50 and 100 years. To understand Volatility Illuminated, we have to look into information —itself- in raw form, as we take it in daily. I know the aforementioned sounds slightly cryptic, however, as you're about to see, information has changed. Moreover, the 'speed of information’ has also

accelerated, causing the critical mass of 'noteworthy distributions' to alter in look, shape, size, smell, and impact, as well. As you are about to see, credulous public acceptance of 21st Century information and dangerous historical benchmarks are not only causing greater potential fallibility and volatility within Government and markets, but in our reception, digestion and action upon much of the information we receive daily, as well. In the end, we will never truly understand market volatility if we do not come to a solid understanding that present-day mass-market and public ignorance of the contemporary ‘altered information' paradigm, has created an ‘explosive exponent’ within trading, politics, economics, and even sociology. To illuminate the problematic circumstances I have just mentioned, throughout Chapter Seven, we will see how even the major indices may be presenting unclear information, how and why the larger information paradigm has changed, and how all of the aforesaid is impacting markets, trading and volatility overall. After Chapter Seven, you should: 1. See why we must question the larger paradigm of the distribution and reception of information by market participants, affiliates and media overall. 2. Understand that often, extensive market volatility is the product of the masses not understanding the true reality of the information they are presented, misleading packaging of the information from the start, and overall, a significant —unrecognizedchange in the larger light-speed information paradigm...over the past 100 years. 3. See how information and markets have changed in recent years, thus, clearly recognize why understanding volatility and probability within today's trading environment is absolutely critical to navigating markets profitably. 4. Come away with firm clarity and understanding that those who trade purely from technicals and/or fundamentals (retail, or institutional), without attempting to understand the volatility paradigm surfacing in modern markets, are setting themselves up for eventual failure. See, populations, investors and even politicians often move in herds, sometimes completely disregarding new and challenging information, should the information require a change in belief structure and/or defy comfortability, career, wealth, or socio-

economic standing and status. What I've just mentioned means when there is a dirty truth hanging over markets, the masses usually ignore the occurrence for as long as possible. However, when the larger potentially damaging situation can no longer be ignored, the masses will again move ‘en masse’, with fierce vigor. What we're talking about is panic... To separate ourselves from the crowd in life and trading, we must take the time to understand why and how the information paradigm has changed. In the end, it is not enough to simply understand volatility through traditional methods such as the Market Volatility Index VIX (a measurement of market fear through option premiums); we must also understand the theoretical and philosophical underpinnings of how mass market volatility is created from the start.

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First in our discussion, I would like to take a moment to introduce you to what you likely already know as a reliable benchmark of markets: The Dow Jones Industrial Average (DJIA). As most are likely aware, the Dow is comprised of thirty of the largest major public companies within the United States. In essence, the DJIA is an 'average' of thirty stocks, all with ‘large cap' weighting, meaning their respective markets caps generally exceed $5 to $10 billion. As the Figure 7.1 shows, the DJIA is comprised of large name companies most would recognize in a heartbeat. On first glance, it makes sense to trust the Dow Jones Industrial Average as a valid benchmark to track the U.S. economy and the sentiment of investors through the daily buying and selling of the index. Moreover, it is also likely fair to say that then the Dow moves up, investors feel good about the economy and when the Dow falls, investors feel the economy is in trouble.

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Not so fast though, there's something else happening here that savvy traders will likely want to be aware of. Some argue that measuring the ‘average’ of thirty stocks is not a true snapshot of the whole economy and thus, does not accurately measure the present-day health of all sectors and business within the United States. However, in our never-ending pursuit of truth - and the true underpinnings of volatility - we brush off the typical arguments both for and against the DJIA in exchange for greater understanding of a perhaps larger (hushed) problem looming under the surface. The problematic issue with the DJIA actually has nothing to do with the ‘total amount' of companies that make up the index... Rather, the knotty concern with the DJIA is really in the way the index calculated from the start. Did you know there are basically five methods used to calculate indices?!21] They are:

1. 2. 3. 4. 5.

Market Capitalization Weighted Indices Modified Market Capitalization Weighted Indices Price Weighted Indices Fundamentally Based Stock Indices Attribute Weighted Indices

Can you guess which of the previous five methods is used to calculate the Dow Jones Industrial Average? If you guessed ‘price weighted’, you guessed correctly. Traditionally, price weighted indexes are calculated by summing the prices of all components and then dividing by the total.

components. There's more to the story though... The real meat to this tale is in the math behind how the Dow is calculated... Let's take an example where we have 10 companies in an index: Looking at the Figure 7.2, we see that the total of all prices equals $355. Moreover, by taking the sum and then dividing by the total (10), we end up with an average price of $35.5, which would be the ‘index value’. But let me ask you a question, what happens one of the stocks split in the index? Would it change the index value? You bet it would. Moreover, given that the more expensive a stock becomes, the more likely it is to split, unless of course, it's Berkshire Hathaway.

Figure 7.2

Figure 7.3

Company1

$30

Company 2

$20

Company 1

$30

Company 2

$20

Company3

$40

Company4

$50

Company 3

$40

Company5

$100

Company 4

$50

Company 6

$30

Company 5 (spilt)

$50

Company 7

$20

Company 6

$30

Company &

$30

Company 7

$20

Company 9

$20

Company 8

$30

Company 10

$15

Company 9

$20

Company 10

$15

Sum

$355

Divided by total

$10

Index Value

35.5

When the Dow kicked off way back in 1896, there were only 12 initial components. Charles Dow added up the prices of all the companies and then divided by 12. That's it, nothing to it! Over the years, 18 more companies were added to develop the present total of thirty

Sum

$305

For the sake of example, let us assume the most expensive stock in our list, Company 5, splits. Instead of calculating the index value with the previous $100, if we made no other adjustments, we would use the post split price of $50 in our sum of all the prices.

The new sum of $305 divided by the total (10), would give us a new index value of 30.5, versus the pre-split index value of 35.5. Because of the split, the index value is now 14% lower than it was before the split, however, nothing fundamentally changed in the stock and really, not a single share was sold in the whole process. To overcome the loss in index value should a split occur, the Dow Jones Company instituted the use of a divisor. Figure 7.4

Company 1

$30

Company 2

$20

Company 3

$40

Company 4

$50

Company 5 (spilt)

$50

Company 6

$30

Company 7

$20

Company 8

$30

Company 9

$20

Company 10

$15

Sum Divided by total

Index Value Divisor Correct Value

$305 $10

30.5 0.87 $350.00

The divisor is calculated by taking the post spilt total of the prices and then dividing by the original value.

For example, if we had 10 $100 stocks and one component split, the divisor calculation would be simply to divide $950 by the original $1000, which would give us a divisor of 0.95. Then, to calculate the actual index, the post split sum of all stock prices is divided by the divisor, which provides a more accurate price of the index. In the case of our previous example, if we utilize a divisor we would divide the postsplit sum of prices ($305) by the new divisor (0.87), bringing us back to original total of $350. Then dividing by 10, just like in our pre-split example, we would end up with a correct index value of 35.5. Thus, the divisor brings the sum of all prices (reflecting splits) back to the correct pre-split summed value of prices...for us to then divide by the total components in the index. The methodology I have just shown is a Price Weighted Index. Sounds reasonable correct? Not so fast. There is an inherent issue with this method. Foremost, as time passes and more companies within the index split, the divisor becomes smaller and smaller... Normally small numbers aren't too scary right? Wrong. Every time a stock splits in the Dow, the divisor becomes more minute, thus creating even more volatility within the index. Did I just use the word volatility? Yes I did. Volatility is in fact created within the Dow Jones Industrial Average because of the way it is calculated. Let me explain... As of June 9, 2009, the Dow Jones Company announced:!22] The Dow Jones Industrial Average will be calculated with a new divisor prior to the open of trading on Monday, June 8, 2009. The divisor for the Dow Jones Industrial Average changes to 0.132319125 (from 0.125552709) as a result of the following actions:

- General Motors Corp. (Pink Sheet: GUGMQ) is to be deleted from the Dow Jones Industrial Average. - Cisco Systems Inc. (NASDAQ: CSCO) is to be added to the Dow Jones

Industrial Average. - Citigroup Inc. (NYSE: C) is to be deleted from the Dow Jones Industrial Average. - Travelers Cos. Inc. (NYSE: TRV) is to be added to the Dow Jones

Industrial Average.

After 113 years, so many stocks have split in the Dow Jones Industrial Average, the divisor is now at an extremely low 0.132319125. What does this mean to you? Think about it for a moment, if a stock moves up $3 in a single day, the occurrence would add 22.7 points to the DJIA closing value for that day. ($3 / 0.132319125 = 22.7) Let's take a moment and step back from the situation though...to consider the ‘common sense’ behind what's happening. If you have a $10 stock that moves $3 in one day, the stock would have individually gained 30% in the session. However, if you have a $100 stock that moved $3 in one day, the stock would have gained 3% in one session. Which is more likely- a stock gaining 30% in one session, or a stock gaining 3% in one session? The common sense answer is obviously that it is more likely that a $100 stock will move $3, over a $10 stock. Thus, because of the way the Dow Jones Industrial Average is calculated, higher price stocks within in the index have greater influence in the total value of the index. What's more, the higher stocks move (within the index), the more rapidly the index value climbs...or falls. To gain the upper hand on the Dow, one could theoretically track the top ten most expensive stocks on a daily basis for greater guidance into the indexes’ potential movement, versus just tracking the index by itself. Why? Think about it for a moment... If higher price stocks have greater probability of larger price swings, and prices really have the greatest impact on the index value, it would makes sense that we would more closely watch the stocks that see greater dayto-day price swings, over the lesser price components. I will explain this in detail in a moment, first though, please note that as of June 18, 2009, the components (and respective prices) of the DJIA were:

Components of Dow Jones Industrial Average Company Name

Ticker Price

3M Co MMM Alcoa Inc AA American Express Co AXP AT&T Inc i Bank of America Corp BAC Boeing Co BA Caterpillar Inc CAT Chevron Corp CVX Cisco Systems Inc CSCO Coca-Cola Co KO E.1. DuPont DD Exxon Mobil Corp XOM = General Electric Co GE Hewlett-Packard Co HPQ Home Depot Inc HD Intel Corp INTC Int] Business Machines IBM Johnson & Johnson JNJ JPMorgan Chase & Co JPM Kraft Foods Inc. ClA KFT McDonald's Corp MCD Merck & Co. Inc. MRK Microsoft Corp MSFT Pfizer Inc PFE Procter & Gamble Co PG Travelers Cos. Inc TRV UTX United Technologies Corp Verizon Communications Wal-Mart Stores Inc Walt Disney Co

VZ WMT DIS

Index Value on June 19, 2009

59.37 11 24.64 24.04 13.22 48.44 33.65 68.06 18.92 48.81 24.97 71.05 12.1 38.35 23.52 16.01 105.89 56.09 35 25.41 58.17 25.91 24.07 15 50.64 42.08 54.2 29.66 48.17 23:53

8,539.73

Now, in Figure 7.6, please note the ‘total impact on index value’ of a one-day 1.5% gain in the top 10 most expensive stocks, over the top 20 least expensive components (Figure 7.7).

Figure 7.5

Figure 7.6 Top 10 (Most Expensive) Dow Stocks

MACRO

Company

Stock

Intl Business Machines

Price 105.89

Ghieyron Corp:

68.06

Exxon Mobil Corp.

One-Day

1.5% Gain 1.58835

71.05

TO

MICRO

Pt. Impact

on Index 12.00

VOLATILITY

TRADING

Bottom 20 (Least Expensive) Dow Stocks

1.06575

8.05 772

Wal-Mart Stores

4.0209

One-Day

Pt. Impact

48.17

1.5% Gain

0.72255

on Index

Stock

Company

Price

5.46

3M Co.

59.37

0.89055

6.73

Travelers Cos.

42.08

0.6312

477

McDonald's Corp.

58.17

0.87255

6.59

Hewlett-Packard

38.35

0.57525

4.35

Johnson & Johnson

56.09

0.84135

6.36

JPMorgan

Chase

35.00

0.525

397

54.2

0.813

6.14

50.64

0.7596

5.74

=

ae

United Technologies

Procter & Gamble Co.

Coca-Cola Co.

48.81

0.73215

5.53

Boeing Co.

48.44

0.7266

5.49 Total

-

a

Verizon

All things being equal, a 1.5% move up in every stock in the Dow Jones Industrial

3.36

25.91

0.38865

2.94

Kraft Foods

25.41

0.38115

2.88

EI. DuPont

24.97

0.37455

2.83

AmericanExp

24.64

0.3696

2.79

Merck &Co. Inc.

_+70.37

29.66 0.4449

_

Average would equate to a +128-point gain in the total index closing value. Of the +128-point gain, the top 10 most expensive stocks would have contributed to

MicrosoftCorp.

24.07

0.36105

2.73

AT&T Inc.

24.04

0.3606

2.73

54.93% of the gain, while the bottom 20 would have contributed to 45.07% of the gain.

Walt Disney Co.

23.53

0.35295

267

Ironically, the top 10 stocks moved the exact same percentage as the bottom 20 and yet have greater ‘weight’ on the total index value.

Home Depot Inc. 23.52 Cisco Systems 1892

0.3528 0.2838

267 214

.

Figure 7.7

Intel Corp.

16.01

0.24015

1.81

Pfizer Inc.

15.0

0.225

1.70

Bank ofAmerica

13.22

0.1983

1.50

General Elec.

12.1

0.1815

1.37

Alcoa Inc.

11.0

0.165

1.25

Total

+57.73

What we're talking about here is an index that is preloaded for volatility, especially if old market adage of higher prices over the long haul holds true. Fact is, as the prices of the individual components in the DJIA climb, the more volatile the index becomes, given the present price/divisor paradigm. With the aforementioned in mind, we can assume that the higher the DJIA moves: 1. In terms of point swings, the daily moves will be greater. 2. Media hype will only increase in years to come, as 200, 300, 400 and

600 point swings begin to surface -daily- in the index. 3. The index has been ‘artificially pre-loaded' to gain-ground. Point number three in my last statement hopefully caused a few readers to raise their eyebrows... I did -in fact- say 'artificially pre-loaded to gain ground.'

ever pay close attention to the index again, if Citigroup and General Motors stocks are likely to stay under $10 for a long, long time? What do you think the solution is? General Motors and Citigroup were ‘black eyes' for the Dow, and considering it is much, much tougher for a $40 stock to add $10, versus a $100 stock, both stocks were poised to remain as major drags on the index for years. What I'm saying is if a few of the stocks in the index have been beat up considerably (like fallen under $10), wouldn't the occurrence make it much harder for the Dow to

Please allow me a moment to clarify... In the fall of 2007, the Dow Jones Industrial Average hit an all time high of 14,198.10, obviously before U.S. mortgage markets fell through the floor, and before the entire Financial Crisis really unfolded. By June of 2009, the DJIA was down about 40% from the 2007 high. Funny thing though...if you remember the divisor announcement a few pages ago, we also know in June of 2009, two stocks were removed from the index, and were replaced by two new companies. The announcement read: ¢ General Motors Corp. (Pink Sheet: GUGMQ) is to be deleted from the Dow Jones Industrial Average. * Cisco Systems Inc. (NASDAQ: CSCO) is to be added to the Dow Jones Industrial Average. * Citigroup Inc. (NYSE: C) is to be deleted from the Dow Jones Industrial Average. ¢ Travelers Cos. Inc. (NYSE: TRV) is to be added to the Dow Jones Industrial Average. Let's take a moment to consider the reality of the situation here... In October of 2007, General Motors posted a relative high of $41.93, while Citigroup tagged a high of $45.09 in the same month... By June of 2009, General Motor's stock was trading at $1.75, while Citigroup was trading at $3.17. In all, from October of 2007 to June of 2009, General Motor's stock lost 40 points (-95.8%), while Citigroup had peeled off 42 points (-93%). Here's the thing though, if you remember our discussion a moment ago about the DJIA being ‘price weighted’, using the pre-June 9, 2009 divisor of 0.125552709, the total point declines of General Motors and Citigroup contributed to 653 points of the 5,658.37 points lost in the DJIA, since the October 2007 highs. So how would the Dow ever climb back to a level where media and investors would

ever post new highs again, with the cheaper stocks weighing heavily on the total index value? You know it. Dump the losers and replace them with stocks that have a better chance of gaining ground. The higher the new stocks climb, the more points they will add to the total index value. Bingo! General Motors and Citigroup were kicked to the curb and replaced by Cisco and Travelers. Fact is, as of June 9, 2009, because Citigroup and General Motors were booted from the Dow, the index now stands a greater chance of recouping losses (and eventually making new highs) than if the flailing bank and auto manufacturer were still a part of the benchmark index. When the Dow eventually climbs back to new highs, media will likely tout ‘the bull is back’ or something like 'you can't keep the Dow down’ (or whatever), however, media will likely forget to mention the mega-money center bank that was replaced by a property & casualty insurance company and the ‘American as apple pie’ auto-manufacturer that was replaced by a network and communication devices company. What's more, when considering that there's no longer any auto-manufacturers in the Dow now, it seems awkward to assume the index truly reflects a fair breadth of products used in the calculation of consumption and GDP growth — in America. At the end of the day, if every time the Dow pulls back in an economic hiccup, ‘beatdown' stocks are replaced by others that hold greater potential to ascend, the event really means the index will likely make new highs —again- in the years to come, but not truly reflect the American economy, as it is said too. Analysts will continue to tout 'the investor who bought Dow stocks and held them over the past fifty years has seen his portfolio continually climb, even with the crash of 1987, the dot.com bomb and the Financial Crisis.’ But what they won't tell you is part of the reason the Dow is able to keep making

new highs —decade after decade- is the lesser performing components are dumped like a bad date, any time the kitchen gets hot. Eventually, the Dow will hit new highs and day-to-day volatility (in terms of price swings) will be greater than ever. Really, we're talking about preloading volatility into markets. In a few years, when the Dow is back at highs... Citigroup and General Motors will likely still be in the gutter trying to recover. There's even more to the story though...the volatility story, I mean... As I've just pointed out, the way the DJIA is calculated —and preloaded- is all geared to help the index constantly make new highs over time. New highs mean new press and new press means more exposure. It's about maintaining "benchmark status’, while keeping investors interested in...investing. However, much like our memories are easily influenced by media (in recollection of historical events) the Dow example is another instance of ‘important information’ completely passing by the general public. Given the larger Financial Crisis, political events of the day and everything else one must constantly worry about, why would the calculation of an index ever even be important to take note of? What we're really talking about is the total mass of information required for investors to truly stop and mark as important. In the 21st Century information paradigm, unless an event has enough 'sauce' to spark significant fear

‘information delivery and reception’ paradigm I keep mentioning, we will now cover ‘why' and 'how' the aforementioned has changed and what it all means to markets and volatility. Over the next section, we're going examine Einstein's Theory of Special Relativity to see how information has changed and at the same time, how such impacts markets. However, please do not feel that you need to work through the math; really, the most important part is to simply just make sure to take the time to understand the philosophical and theoretical underpinnings (as pertaining to markets and volatility) behind the physics. Break in Chapter Continued... Moving on, to the below table of major declines in the Dow Jones Industrial Average (shown again), I would like readers to take serious note of final column on the right labeled ‘Avg Time’.

(loss) or exuberance (greed) within individuals, the information passes like a shooting

only lasted about a quarter of the time that they did in the past 47 years.* What we can assume -over all- is bear markets, though totaling ‘about the same’ in terms of percentage loss, are accelerating in duration. Why? Because fundamentals improve quicker today, over 75 years ago? No... Duration of sell offs has decreased in total time, because through the acceleration of information, collectively, investors have a much narrower memory of yesterday, with media constantly pumping exuberance through television, Internet and radio the second the economy shows signs of recovery. What's more, take into consideration the fact that the Dow Jones Industrial Average has now been 'reloaded' for volatility, the second the index fires over 10,000 again; the occurrence will become headline news... Guaranteed. Will you buy into the volatility? Information has accelerated, and thus our memory of yesterday has declined, while at the same time, the collective public's ability (and desire/effort) to take notice of important micro-information (that could have major impact on the future) has

star on a cloudy night. Eventually, the DJIA will likely hit new highs and media will again gawk and squawk about the event, thus sucking investors into the poison tree —yet again- and of course, eventually the economic cycle will take a downward turn and those same investors will see their wealth decrease, as major indices take another nosedive. I'm not saying that we shouldn't invest...after all, now that the DJIA has been ‘reloaded’ for possible upside, the present day actually appears to be a decent time to buy...at least if the U.S. Dollar doesn't plummet in the near future, due to the loss of credit quality of U.S. Treasuries, based on excessive spending by the Government...but that's another conversation. The point, however, is we can certainly invest in markets and indices, so long as we are fully cognizant of the volatility paradigm. Moreover, we must also recognize that the larger problematic issues behind how the delivery and reception of 'information' has changed in the 21st Century, while actively seeking out information that could present future volatility — ahead of time. As many readers may still be a little unclear about the larger shift in the

What I would like to point out is that the average duration of bear markets has decreased by nearly 60% since the 1901 to 1929 market. What's more, if we take out the larger total bear market from 1999 to 2002, bear markets in the past 20 years have

decreased. What I'm saying is... Not only have markets become more volatile (as noted in the increase in total time of DJIA bear markets); but speed and volatility of intraday and longer-term market changes are only going to increase even more in the years to come... $ -Note- The historical average PE ratio for the Dow Jones Industrial Average is 15.5 (since 1929). In early 2002, despite dot.com bomb selling, the index still had a PE of 25, thus overvaluation contributed to the longer than usual bear market. Index changes in 2009 instantly lowered DJIA trailing PE from 21.21 to 11.41, while also dropping forward PE from 18.06 to 12.80, thus setting the index for a future run into (approx) 11,700, or a 15.5 historical PE. The fact that 99% of the investing public has no idea of events like the DJIA having been re-loaded for gains is proof of the pudding of the general public's microinformation mass ignorance, with your old pal 'media' doing nothing to bring you the real truth behind the events of today... At least, until some sort of underlying hum within markets can no longer be ignored, and the major indices are crushed once again... Perhaps something like the United States' National Debt of $11 trillion (FYI, foreign investors owned $3.47 trillion of public debt, at the time of printing), the potential pending deterioration of credit quality in U.S. Treasury notes...and the implosion of the greenback.

Endnotes [1] Mean. Definition. BusinessDictionary.com. Accessed: May 3, 2010. http: //www.businessdictionary.com/definition/mean.html [2] Mean. Definition. BusinessDictionary.com. Accessed: May 3, 2010. http: //www.businessdictionary.com/definition/mean.html

[3] "outlier." Encyclopedia Britannica. 2010. Encyclopedia Britannica Online. Accessed: 22 Apr. 2010 . [4] Bollinger, John. Endnote 6, Page 53.

Bollinger on Bollinger Bands. 2001. McGraw-Hill, New York.

[5] Hillebrand, Eric. A Mean-Reversion Theory of Stock-Market Crashes. CeVis — Center for Complex Systems and Visualization, University at Bremen; Department of Mathematics, Stanford University, Stanford, CA. March 30, 2003 [6] [Black, F. (1988). An Equilibrium Model of the C rash. NBER Macroeconomics

Annual: 269-275.

[7] Standard deviation diagram based an original graph by Jeremy Kemp, in 2005-02-09 [http://pbeirne.com/Programming/gaussian.ps]. This figure was started in R using: x