large, chartists were about as welcome as your unemployed brother-in-lawuntil the early 1980s.This crowd gleaned over chart formations, found patterns, and gave them names like wedges, head andshoulders, pennants, flags, triangles, W bottoms and M tops, and 1-2-3 formations. These patterns weresupposed to represent the battle of supply and demand. Some patterns indicated selling, others professionalaccumulation. Fascinating stuff, but wrong-headed. These same precise patterns can be found in charts ofthings that do not have a supply/demand factor.
Figure 1.2 A flip of the coin heads and tails on accumulative basis.
13
Figure 1.3 A stock? No, daily temperature; high for the day; low for the day; last reading.
Figure 1.2 shows a chart of the 150 flips of an old silver dollar that graphs out to look much like a chartof Pork Bellies. Next, Figure 1.3 is a chart or graph of temperature extremes, or is it Soybeans? Whoknows? What we do know is that plotted data of nonmarket or economically driven information charts outjust like data for stocks and commodities, producing the same patterns that are supposed to reflect buyersand sellers. I caution you against confusing chart forms with intelligence.Chartists became "technical analysts," severing their ties from Ouija boards and charts in favor ofcomputers. Computers made chartists look and sound more respectable, like scientists. In fact, many bookscame out with titles like The New Science of... or Scientific Approaches to ... Is there science to thismadness?By and large, I think not.Prices do not dance to the beat of some mystical, magical drum that hides deep in the recesses of aplush room in New York City, and has a rhythm only a few insiders recognize. Prices jump all over theplace, and our charts become erratic because human emotions are influenced by news and brokers' hot tipsof immediate boom or gloom.
14
The Nonrandom MarketFor the most part, commodity prices are like a drunken sailor, wandering down the street without anyknowledge of where he is going, or where he has been. Mathematicians would say there is no correlationbetween past price activity and future trends.About that, they would be wrong: there is some correlation. Although that drunken sailor does swagger,stagger, and seemingly move in a nonrandom fashion, there is method to his madness. He is trying to gosomeplace, and we can usually find out where.While price action involves a large degree of randomness, it is far from a totally random game. If Icannot prove that point, right now, early on in this book, the remaining chapters should be devoted tolearning how to throw darts. In a random game, the dart thrower will outperform the experts.Start with a given-if we flip a coin 100 times, it will come up heads 50 times and tails 50 times. Eachtime it comes up heads, on the next flip we will have 50 percent heads and 50 percent tails. If heads has nowappeared two times in a row and we flip again, the results continue to be 50/50 that a head will appear on thenext flip. As you have probably heard, the coin, dice, or roulette wheel has no memory. The odds are fixed,as this is a random game.If that were true of the market and prices close higher 50 percent of the time, then after each up closewe would expect to see another up close 50 percent of the time, and following that up close again 50 percentodds of another up close. The same thing should apply to a down close: 50 percent of the time following onedown close, we should see a repeat; and again 50 percent of the time following two in a row, a third downclose should appear. In our real world of trading, it does not turn out that way, which can only mean priceaction is not totally random!
15Table 1.2Commodity Number of Times after One-Down Close Percent;Number of Times after Two-Down Close PercentNumber of Times after
Number of Times after
One Down % Up
Two Down % UP
ClosePercent
Next day
Closes
Next day
Percent
Table 1.1 shows the percentage of time that prices closed higher in a wide variety of markets. Therewere no criteria; the computer just bought on the open each day and exited on the close. Instead of having a50/50 result we have a slight skewing in that 53.2 percent of the time price closed higher than the opening.This shouldn't be.Well, if this "shouldn't be," how about buying on the opening following a down close? In theory, weshould see the same percent of up closes shown in Table 1. 1. The problem is (for college professors andother academics who are long on theory and short on market knowledge) that it does not turn out this way.Table 1.2 shows the number of times price closed higher following a number of down closes.This is not earth-shaking news to a trader; we know market declines set up rallies. The exactpercentages were not known in the past, and I would never use these tables to take or stay in a trade. That isnot the point: the point is we should have seen an average up close of 53.2 percent following the one minusclose as well as two consecutive minus closes. The fact we did not suggests the market is not random;patterns do "predict" and now we can proceed, sans darts.
Understanding Market StructureWhereas chartists have strange names for most every market wiggle and waggle, they have seemingly missed themajor point of the market, which is that price (as represented by daily bars, where the top of the bar is the highest
16
point prices traded on that day and the bottom of the bar the lowest price traded) move in a well-defined andamazingly mechanical fashion. It is similar to learning to read a new alphabet-once you understand thecharacters, you can read the words, and once you know the words you can read the story.The first letter to master tells you what market activity causes the formation of a short-term high orlow. If you learn this basic point, the meaning of all market structure will begin to fall into place.I can define a short-term market low with this simple formula: any time there is a daily low with higherlows on both sides of it, that low will he a short-term low. We know this because a study of market actionwill show that prices descended in the low day, then failed to make a new low, thus turned up, marking thatultimate low as a short-term point.A short-term market high is just the opposite. Here we will see a high with lower highs on both sides ofit. What this says is that prices rallied up to the zenith of that middle day, then began to move back down,and in the process formed a short-term high.I initially called these short-term changes "ringed" highs and lows in deference to the work done in the1930s by Henry Wheeler Chase. In the days before computers, we kept notebooks of prices, and to identifysuch termination of a move, we simply circled or "ringed" these points in our workbooks so we could seethem more easily.Figure 1.4 shows several short-term highs and lows. Take a minute now to see what this pattern is allabout.If you understand this concept, we can begin the building process of putting these elements together.You may have already figured out the sequence; the market swings from short-term highs to short-termlows. This is exciting; we can actually measure market movement in a mechanical and automatic way. Thereis no need for complex chartist talk, nor will we be as inclined to fall into the illusory world of the chartist ortechnician.Two specific types of trading days can cause confusion with our basic definition. First, there is what wecall an inside day. It is so named because all the trading on this day took place inside the previous day'srange. These days are identified by having a lower daily high and a higher daily low. In a study of ninemajor commodities covering 50,692 trading sessions, I noted 3,892 inside days, suggesting we will see thesedays appear about 7.6 percent of the time.For our purposes in identifying short-term swing points, we will simply ignore inside days and thepossible short-term points they produce. An inside day means the market has entered congestion, the currentswing did not go further, but then again it did not reverse, thus until this condition is resolved, we must waitand not use the inside day in our identification process.Next we have outside days. These days are easy to spot because they have both a higher high than the
17
Figure 1.4 British Pound (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services: 800 808 3282).
prior day and a lower low! When these days occur (and they do so about 3 percent of the time), we will haveto study the flow of prices during that day by looking at the way price moved from the opening of the day tothe close of that same day. In that study of 50,692 trading sessions cited earlier, there were 3,487 outsidedays, suggesting they are not as frequent as inside days, yet account for almost 7 percent of all days.With the preceding information in mind, turn your attention to Figure 1.5, which illustrates these insideand outside days. Remember, what we are out to do is identify the short-term swings as traders move pricefrom one terminus to another.By now you should understand the basic concept, and be able to see how prices move in swings. OnFigure 1.6 I have marked off these terminal points and connected a straight line from point to point to showthe swing patterns.
Defining Intermediate Highs and LowsNow the fun begins! Consider this, if we can identify a short-term high as any day with lower highs (not countinginside days) on both sides, we can take a gigantic step forward and identify an intermediate term high as anyshort-term high with lower short-term highs on both sides of it. Hold on to your seat belts because we can take yetanother step and say any intermediate term high with lower intermediate-term highs on both sides is-you've got it-along-term high.
18
Figure 1.5 Pork Bellies (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
Figure 1.6 Pork Bellies (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
19
In just one paragraph, we have been able to define the three dominant swings in a market, going fromshort term to intermediate to long. The identification of market lows is done in just the same fashion: firstfind a day with higher lows on both sides; that is a short-term low. Then find a short-term low with highershort-term lows on both sides and you have an intermediate term low. Locating a long-term low is simple:it is any intermediate-term low with higher intermediate-term lows on both sides.It is time for a picture of what this all looks like. In Figure 1.7, I have marked off all short-termswings, then located the intermediate-term points, and finally gone to the next level and marked off thelonger term points. This chart tells all; it is really all there in a simple format. If you look at it now, youwill understand market structure and will see that we can create order out of much of the chaos.With the preceding in mind, 1 have moved from a sample chart to a real one of the Swiss Franc andCoffee (see Figures 1.8 and 1.9). My first step was to circle or ring all short-term swings; then I began theoverlaying pattern of higher/lower short-term points. After that, I identified the next layer of higher/lowerintermediate-term points to arrive at the long-term points. While words are great, until you study thesecharts, it will be difficult for you to get the picture. Go study.
(MISSING PICTURE)
Figure 1.7 Charting creates order out of chaos.
20
Figure 1.8 Swiss Franc (daily bars). Graphed by the Navigator(Genesis Financial Data Services).
Figure 1.9 Coffee (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
21
Why This Is ImportantOnce you have this basic understanding of market structure you can identify, very early on, thesemarket turns. You will always know that a short-term low has been made when you rally above the high ofa day with a lower low than the prior day. By the very nature of this penetration, we know the short-termdown swing has terminated. By the same token, whenever price declines below the low of a day with ahigher high than the prior day, a short-term high has been formed. This means we can know, during thetrading session, when these points are established.As short-term traders, we also can tell when intermediate-term highs and lows are made. How?Simple, if the formation of a short-term high will confirm an intermediate-term high, which in turnconfirms a long-term high, we can get in at some optimal turning points.Figure 1.10 shows how this can all be combined. By going above the high of the day marked at (A),we have formed a short-term low that is in turn higher than the prior short-term low. This means the low at(B) is a longterm low and we can be buying at the start of an up leg in what is some type of long-termmove.
Figure 1.10 Pork Bellies (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
22
It is really all about nesting swings together, fitting the pieces of the puzzle into their proper place,to give us an understanding of the structure of market activity. The beauty is you can now identify, atall times and for all markets, whether the trend (based on price structure) is up or down and pick yourpoints to get in and out.For years, I made a pretty good living using just the formation of these points as buy and sellentries. These points are the only valid support and resistance levels I have ever found. They are highlysignificant and the violation of these price points provides important information of trend and trendchange. Thus I can use them for my stop-loss protection and entry techniques.
Chapter 2It's a Question of Price and Time
Like a circle being squared,Going round and roundA wheel within a wheelSpinning a syncopated soundCreating cycles that we find Will o'wisps of our mind.
All You Will Ever Need to Know about CyclesOur charts are a record of price activity over time the horizontal scale being time, the vertical scalerepresenting price. An entire technical school is; devoted to the study of time, the cycle watchers. Thesegood-thinking people count the number of minutes, hours, days, weeks, months, and years between highand low points in search for some master time cycle to tell us price will behave in the future as it has in thepast. Being a somewhat sleek learner (an even slower unlearner), I spent almost 15 vears of my life tryingto figure out these time cycles.I am still convinced there are cycles in the market. in fact three of them, but they are not time circles.The root of the problem of time cycle is, that there always seems to be a current, or dominant. cycle, thatwe cansee on our chart right now. The rub is, another cycle is Always about to become dominant, overpoweringthe one we have just located and invested in.
23
24Although our first problem is cycle dominance, if there are such cycles, they change direction moreoften than a politician looking for votes. In the 1960s and early 1970s, the hope was that a combination ofhigh-powered math and high-powered computers would solve this master cycle problem. It has yet to bedone. Just what the heck cycle we should lay our bets down on at any given time is impossible to tell. But aneven greater problem is the one of magnitude.The cycle crowd deals exclusively with time. But I have yet to find a banker who allows me to depositdays, weeks, or months! By that, I mean that a cyclist might ferret out a market low, say an 18 -year low, butprice does not respond much to the upside, climbing up that vertical scale of dollars, the reward mechanismof the game. In theory, calling a major cycle low or high, if you could do it, would produce a move of somemagnitude. But in the real world where I live and trade, that has seldom been the case; instead the cyclequickly faded. Sure, price stopped there-in time-and bobbled along for a few days or weeks, but there wasnot enough price magnitude for a profit.I will prove my point with an actual study of past price activity. Figure 2.1 shows the result of a test ofa timing system for Soybeans. I fired up my computer, asking it to buy when a short-term moving average ofprice crossed above a longer term moving average. This is standard technical stuff. The only variable wastime, the number of days in the moving average. Thus it is cycle impacted. A moving average is simply theaverage closing price for the last "X" number of days. There are no other variables, only time.
Figure 2.1 Test of a timing system.
Our first test was on Soybean prices from 4/29/75 through 1/1/87, and it looked at all possiblecombinations of short-term averages from 5 to 50 days against the longer term or second average from 10 to60 days.
25The best result, during this time period shown here used a 5-day average against a 2 5-day average. Thistime-based "system" made $40,075 with 54 profitable trades out of a total of 153. Wow, have wediscovered a money machine',Figure 2.2 shows what would have taken place had we traded this system from 1/1/87 through4/23/98. The results are not promising. Whereas our accuracy at 31 percent winners on 163 trades hasimproved, we actually lost money, $9,100 to be specific and along the way suffered a drawdown (howmuch the system went against you before getting back in the black) of $2 8,612. Putting up $2 8,612 to lose$9, 100 is hardly a good wager, The average profit per trade was $-55. What happened to the originalcyclical or time influence? Beats me!Reversing the process, I checked to see what two moving averages worked best in the second timeperiod from 1/1/87 into April 23, 1998 (see Figure 2.3). The best combination was a 25-day movingaverage against a 30-day. This made $34,900 with a nice 59 percent accuracy. This system made S234 pertrade and had a drawdown of $13,962. This too, is not a good bet.Applying the best case results back on the earlier data, out of sample, produced a loss of $28,725, asshown in Figure 2.4. Forward, backward., the time or length or cycle of the moving average that worked inone time period does not work in another time period."Perhaps," you query, "The problem is not that time does not work but that Soybeans do not trendenough."What appears next is the best case study of a moving average crossover system on the British Pound, avery trending market. From 1975 to 1987, the best such crossover system was a 5-day average versus a45-day, making a most impressive $135 443
Figure 2.2 What might have happened.
26
Figure 2.3 Testing another time periodDataPOUNDCalc Dates01/01/98
BRITISH67/9901/01/87 -
Num. Conv. P. Value CommSlippageMarginFormatDrive:\Path\FileName-------------------------- --------------------------------------------------264 $ 6.250 $ 50$0$3,000CSI C:\GD\BACK67\FO03.DTAALL TRADES - Test 1Total net profit$45,287.50Gross profit$134,175.00Gross loss$-88,887.50Total # of trades104Percent profitable31%Number winning trades 33Number losing trades 71Largest winning trade $17,262.50Largest losing trade$-4,575.00Average winning trade $4,065.91Average losing trade$-1,251.94Ratio avg win/avg loss 3.24Avg trade (win & loss)$435.46Max consecutive winners3Maxconsecutive losers12Avg # bars in winners 54Avg # bare in losers 13Max closed-out drawdown$-29,100.00Maxintra-day drawdown$-29,450.00Profit factor 1.50Max # of contracts held1Account size required $32,450.00Return on account139%
Figure 2.4 Applying best case resultsThe next time period, 1987 through 1997, the same system made money all right, $45,287 as Figure2.5 shows, but suffered a $29,100 losing spell! Not such a hot bet. The best crossover to use on this currentset of 10year data was a 20/40, which cleaned up making $121,700, the problem is it only made $26,025 onthe first time period and got tagged with a $30,000 drawdown. Sorry, the problem was not beans or thepound, the problem is that time based studies simply do not hold up. Using time exclusively as aconsideration in speculation is one of the surer ways I know of getting a free pass to the poorhouse.
27.
Figure 2.5 Using this system on the next time period.I have duplicated this study at various times, on wildly different sets of data and have yet to see the bestcyclical-based approach to trading be even close to being the best in the next test on out-of-sample data.My advice is to forget cycles of time, they are the will-o'-the-wisp of Wall Street.There are cycles (maybe it is a pattern) to the way price moves that you can quickly see on any chart,any time frame, any market, any country in the world where I have traded. Once you understand thesepatterns, you will be better able to align yourself with where prices will most likely go.Over the years, I have codified and identified three cycles and now refer to them as (1) smallrange/large range, (2) moving closes within ranges, and (3) closes opposite openings.It is time for your first lesson in chart reading; we will begin with a study of changing ranges. When Irefer to ranges what I am talking about is the total distance traveled by a stock or commodity in a day, week,month, year, it could even be in one minute. Think of range as the price distance traveled in whatever timeperiod you are using. For all three cycles you will learn, the rules work equally well in any time frame. Therules I have uncovered are universal to markets as well as time frame references.
The Natural Cycle of Range ChangeOn any given day, the range of price in a commodity can do anything. That is what causes so muchchartist confusion. But over any time period you want to study, you will notice a clear-cut, precise cadenceto range activity.
28At all times, all markets, ranges fluctuate from-and this is critical-a series of small ranges to a cluster oflarge ranges.The cycle continues repeating itself year in and year out; small ranges are followed by large ranges,large ranges are followed by small ranges. This is clockwork, this is the basic key to profitable short-termtrading.This seemingly obvious cycle is so powerful and important to us because speculators must have pricechange to make money. The greater the change, the greater the potential for profit. If there is no, or little,price change, a speculator is simply stuck in the mud as price fails to trend.That is why short-term traders need explosive price moves over a few hours or days. Without this, wewill wither on the vine. Got it? I hope so, because here comes the fascinating part. What usually attracts thepublic or uninformed to a market is large price change. They, usually incorrectly, think the current largechange will continue.You now know better.Large ranges give way, most often, to small ranges. Your objective is to establish a position in advanceof large price change. It is a classic sucker play to see a market that has been hot, with large ranges for a dayor two, pull in the public just before a sideways or congestion move. Most short-term traders are losers. Thereason they are is that they go from one hot market to the next because they have no understanding of howthe drunken sailor swaggers, how prices move across the great wasteland of their chart books.On the other hand, we who are the knowledgeable few, play just the opposite game. We look formarkets that have been volatile in the past and are known for large daily ranges, but have recently producedsmall daily ranges because we know a large-range day is out there not too far away!You can eliminate the madness of charts by laying low on the sidelines, carefully waiting until rangeshave dwindled, dried up. Once that part of the natural cycle is about over, it is time for short-term fireworks.By the same token, large-range days tell us we may soon get stuck in the mud of small ranges where wecannot make money. This is certainly no time to overstay our welcome. Let me prove this point with somecharts. Figure 2.6 shows gold in the September 1997 to January 1998 time frame.Do yourself a big favor. Mark off all the large-range days you see in this time period. Then study thesize of the ranges just prior to these explosive up-and-down days. See what I see? We were given amplewarning of virtually every large-range day by the shrinkage of ranges a few days earlier.Voila! We are on the edge of a major market discovery here. I know-I have not yet told you how to tellin which direction these ranges will take off, but don't get ahead of the teacher. For now study every chartyou can so that you can imprint on your brain, your very speculative spirit, the first undeniable short-termtruth of the market:Small ranges beget large ranges. Large ranges beget small ranges.
29
Figure 2.6 Comex Gold (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).Look at Figure 2.7, which shows the always volatile S&P 500 from October 1991 through January1992. Grab a pencil, mark off the days with smallest ranges on the chart and then note what happenedshortly thereafter ... a large-range day or two or even three, then a contraction of ranges, small to big, big tosmall-on and on it goes as it always has. Always will.Our next study in speculative technique is that of Coffee (Figure 2.8), a fast-paced market, ripe withopportunity for the trader with an understanding of the truth. Again, mark off the small-range days, thenobserve what follows: large-range days when we can make money while the public gets all lathered up aboutthese days hopping aboard only to lose patience as the ranges contract into decaffeinated days and thesupposed opportunity evaporates. Just about the time most of the public has been bored out of their positions... zingo! ... away go prices, switching gears back to large ranges.Finally, I'd like you to take a close and hard look at Figures 2.9 and 2.10, which are for markets nottraded in the United States, the Australian Dollar and the Nikkei (the Japanese answer to the Dow JonesIndustrial average).If you are still not convinced that we have uncovered a major cycle to price movement-a cycle withouttime-I am next presenting three charts of
30
Figure 2.7 S&P 500 Index (daily bars). Graphed by the Navigator(Genesis Financial Data Services).
Figure 2.8 Coffee (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
31
Figure 2.9 Australian Dollar (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
Figure 2.10 Nikkei Stock Index (daily bars). Graphed by the "Navigator"(Genesis Financial Data Services).
32
Figure 2.11 S&P 500 Index (5-minute bars). Graphed by the "Navigator"(Genesis Financial Data Services).
Figure 2.12 S&P 500 Index (30-minute bars). Graphed by the "Navigator"(Genesis Financial Data Services).
33
Figure 2.13 S&P 500 Index (60-minute bars). Graphed by the "Navigator"(Genesis Financial Data Services).the S&P 500 (see Figures 2.11, 2.12, and 2.13). In Figure 2.11, each bar reflects the high, low, and close ofevery 5-minute time period for two days, chosen at random. As you can see in almost a glance, the large barsare preceded by smaller bars. Figure 2.12 shows the use of a 30-minute bar to capture the markets swingsfor a full week. Again the facts speak for themselves, virtually every long-range bar, the only place weshort-term traders make our money, has been set up by one or a series of small ranges. Figure 2.13 is basedon hourly bars and again the phenomenon is present. It takes no tea reader or mumbo jumbo spin-doctor tohype or stretch these facts. What's there is there, always has been and always will be-we are continuallyalerted to those moneymaking, large-range bars by the early warning of small ranges.
The Importance of the Open to Low or High of the DayHere is the second absolute truism about large-range days, those big blast-off days we short-termerssimply must have to come out ahead; large-range up close days usually open close to the low and close onthe high. Large-range down close days open around the high of the day and close near the low.This means you must take two things into consideration in your trading. The first is that if we are"aboard" a day that we think will be a
34large-range up day do not look for a buying point very far below the open. As I said, large-range up daysseldom trade very much below the opening price of the day. This means you must not look for much of abuying opportunity below the opening price.By the same token, if you think you have a tiger by the tail-the possibility of a large-range day-andprice dips very much below the opening the probability of a large-range up close is greatly reduced.This is a major insight into profitable short-term trading. Do not blow it off. Here are several studies to provethe validity of this concept. Figure 2.14 shows on the horizontal scale the distribution of the difference fromthe opening to the close of all days in Treasury Bonds from 1970 to 1998.The vertical scale reflects the net change for all days, that is, the open minus the close. The fewer price pointsbelow the open (the zero horizontal line) and the closer those price points are to the zero line, the more daysthere are with positive-and large-open-to-close patterns. As you read the scale moving to the right, the fartherbelow the zero line the price points are. The fewer positive price points we see above the zero line.Looking on the left side of this chart we see that large-range profitablecloses seldom have large open minus close values. This trend is clear as the mass of data slants from left toright, that is, the profitable side of trading.
Figure 2.14 Distribution of dollar value of open to close versus (open-low)as percentage of yesterday's range-T-Bonds.
35The large opens to closes are pulled down by large opens to lows. This is also convincing proofthe markets are not random. If they were, the distributions of highs minus opens, would be the same asopens minus the lows. This data, as simple as it might seem, reveals a powerul fundamental truth ofbecoming a successful speculator.Figure 2.15 shows three different lines; the top one represents the probability that the close will begreater than the open, dependent on the bottom scale of the open minus the low. At the point I havemarked, the data tell us that about 87 percent of the time we will close above the open if the dip fromthe open to the low is less than 20